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695 | https://python.langchain.com/docs/integrations/retrievers/pubmed | ComponentsRetrieversPubMedPubMedPubMed® by The National Center for Biotechnology Information, National Library of Medicine comprises more than 35 million citations for biomedical literature from MEDLINE, life science journals, and online books. Citations may include links to full text content from PubMed Central and publisher web sites.This notebook goes over how to use PubMed as a retrieverfrom langchain.retrievers import PubMedRetrieverretriever = PubMedRetriever()retriever.get_relevant_documents("chatgpt") [Document(page_content='', metadata={'uid': '37549050', 'Title': 'ChatGPT: "To Be or Not to Be" in Bikini Bottom.', 'Published': '--', 'Copyright Information': ''}), Document(page_content="BACKGROUND: ChatGPT is a large language model that has performed well on professional examinations in the fields of medicine, law, and business. However, it is unclear how ChatGPT would perform on an examination assessing professionalism and situational judgement for doctors.\nOBJECTIVE: We evaluated the performance of ChatGPT on the Situational Judgement Test (SJT): a national examination taken by all final-year medical students in the United Kingdom. This examination is designed to assess attributes such as communication, teamwork, patient safety, prioritization skills, professionalism, and ethics.\nMETHODS: All questions from the UK Foundation Programme Office's (UKFPO's) 2023 SJT practice examination were inputted into ChatGPT. For each question, ChatGPT's answers and rationales were recorded and assessed on the basis of the official UK Foundation Programme Office scoring template. Questions were categorized into domains of Good Medical Practice on the basis of the domains referenced in the rationales provided in the scoring sheet. Questions without clear domain links were screened by reviewers and assigned one or multiple domains. ChatGPT's overall performance, as well as its performance across the domains of Good Medical Practice, was evaluated.\nRESULTS: Overall, ChatGPT performed well, scoring 76% on the SJT but scoring full marks on only a few questions (9%), which may reflect possible flaws in ChatGPT's situational judgement or inconsistencies in the reasoning across questions (or both) in the examination itself. ChatGPT demonstrated consistent performance across the 4 outlined domains in Good Medical Practice for doctors.\nCONCLUSIONS: Further research is needed to understand the potential applications of large language models, such as ChatGPT, in medical education for standardizing questions and providing consistent rationales for examinations assessing professionalism and ethics.", metadata={'uid': '37548997', 'Title': 'Performance of ChatGPT on the Situational Judgement Test-A Professional Dilemmas-Based Examination for Doctors in the United Kingdom.', 'Published': '2023-08-07', 'Copyright Information': '©Robin J Borchert, Charlotte R Hickman, Jack Pepys, Timothy J Sadler. Originally published in JMIR Medical Education (https://mededu.jmir.org), 07.08.2023.'}), Document(page_content='', metadata={'uid': '37548971', 'Title': "Large Language Models Answer Medical Questions Accurately, but Can't Match Clinicians' Knowledge.", 'Published': '2023-08-07', 'Copyright Information': ''})]PreviousPinecone Hybrid SearchNextRePhraseQuery |
696 | https://python.langchain.com/docs/integrations/retrievers/re_phrase | ComponentsRetrieversRePhraseQueryOn this pageRePhraseQueryRePhraseQuery is a simple retriever that applies an LLM between the user input and the query passed by the retriever.It can be used to pre-process the user input in any way.ExampleSetting upCreate a vector store.import loggingfrom langchain.document_loaders import WebBaseLoaderfrom langchain.text_splitter import RecursiveCharacterTextSplitterfrom langchain.vectorstores import Chromafrom langchain.embeddings import OpenAIEmbeddingsfrom langchain.chat_models import ChatOpenAIfrom langchain.retrievers import RePhraseQueryRetrieverlogging.basicConfig()logging.getLogger("langchain.retrievers.re_phraser").setLevel(logging.INFO)loader = WebBaseLoader("https://lilianweng.github.io/posts/2023-06-23-agent/")data = loader.load()text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=0)all_splits = text_splitter.split_documents(data)vectorstore = Chroma.from_documents(documents=all_splits, embedding=OpenAIEmbeddings())Using the default promptThe default prompt used in the from_llm classmethod:DEFAULT_TEMPLATE = """You are an assistant tasked with taking a natural language \query from a user and converting it into a query for a vectorstore. \In this process, you strip out information that is not relevant for \the retrieval task. Here is the user query: {question}"""llm = ChatOpenAI(temperature=0)retriever_from_llm = RePhraseQueryRetriever.from_llm( retriever=vectorstore.as_retriever(), llm=llm)docs = retriever_from_llm.get_relevant_documents( "Hi I'm Lance. What are the approaches to Task Decomposition?") INFO:langchain.retrievers.re_phraser:Re-phrased question: The user query can be converted into a query for a vectorstore as follows: "approaches to Task Decomposition"docs = retriever_from_llm.get_relevant_documents( "I live in San Francisco. What are the Types of Memory?") INFO:langchain.retrievers.re_phraser:Re-phrased question: Query for vectorstore: "Types of Memory"Custom promptfrom langchain.chains import LLMChainfrom langchain.prompts import PromptTemplateQUERY_PROMPT = PromptTemplate( input_variables=["question"], template="""You are an assistant tasked with taking a natural languge query from a user and converting it into a query for a vectorstore. In the process, strip out all information that is not relevant for the retrieval task and return a new, simplified question for vectorstore retrieval. The new user query should be in pirate speech. Here is the user query: {question} """,)llm = ChatOpenAI(temperature=0)llm_chain = LLMChain(llm=llm, prompt=QUERY_PROMPT)retriever_from_llm_chain = RePhraseQueryRetriever( retriever=vectorstore.as_retriever(), llm_chain=llm_chain)docs = retriever_from_llm_chain.get_relevant_documents( "Hi I'm Lance. What is Maximum Inner Product Search?") INFO:langchain.retrievers.re_phraser:Re-phrased question: Ahoy matey! What be Maximum Inner Product Search, ye scurvy dog?PreviousPubMedNextSEC filingExampleSetting upUsing the default promptCustom prompt |
697 | https://python.langchain.com/docs/integrations/retrievers/sec_filings | ComponentsRetrieversSEC filingOn this pageSEC filingThe SEC filing is a financial statement or other formal document submitted to the U.S. Securities and Exchange Commission (SEC). Public companies, certain insiders, and broker-dealers are required to make regular SEC filings. Investors and financial professionals rely on these filings for information about companies they are evaluating for investment purposes.SEC filings data powered by Kay.ai and Cybersyn via Snowflake Marketplace.SetupFirst, you will need to install the kay package. You will also need an API key: you can get one for free at https://kay.ai. Once you have an API key, you must set it as an environment variable KAY_API_KEY.In this example, we're going to use the KayAiRetriever. Take a look at the kay notebook for more detailed information for the parameters that it accepts.`# Setup API keys for Kay and OpenAIfrom getpass import getpassKAY_API_KEY = getpass()OPENAI_API_KEY = getpass() ········ ········import osos.environ["KAY_API_KEY"] = KAY_API_KEYos.environ["OPENAI_API_KEY"] = OPENAI_API_KEYExamplefrom langchain.chains import ConversationalRetrievalChainfrom langchain.chat_models import ChatOpenAIfrom langchain.retrievers import KayAiRetrievermodel = ChatOpenAI(model_name="gpt-3.5-turbo")retriever = KayAiRetriever.create(dataset_id="company", data_types=["10-K", "10-Q"], num_contexts=6)qa = ConversationalRetrievalChain.from_llm(model, retriever=retriever)questions = [ "What are patterns in Nvidia's spend over the past three quarters?", #"What are some recent challenges faced by the renewable energy sector?",]chat_history = []for question in questions: result = qa({"question": question, "chat_history": chat_history}) chat_history.append((question, result["answer"])) print(f"-> **Question**: {question} \n") print(f"**Answer**: {result['answer']} \n") -> **Question**: What are patterns in Nvidia's spend over the past three quarters? **Answer**: Based on the provided information, here are the patterns in NVIDIA's spend over the past three quarters: 1. Research and Development Expenses: - Q3 2022: Increased by 34% compared to Q3 2021. - Q1 2023: Increased by 40% compared to Q1 2022. - Q2 2022: Increased by 25% compared to Q2 2021. Overall, research and development expenses have been consistently increasing over the past three quarters. 2. Sales, General and Administrative Expenses: - Q3 2022: Increased by 8% compared to Q3 2021. - Q1 2023: Increased by 14% compared to Q1 2022. - Q2 2022: Decreased by 16% compared to Q2 2021. The pattern for sales, general and administrative expenses is not as consistent, with some quarters showing an increase and others showing a decrease. 3. Total Operating Expenses: - Q3 2022: Increased by 25% compared to Q3 2021. - Q1 2023: Increased by 113% compared to Q1 2022. - Q2 2022: Increased by 9% compared to Q2 2021. Total operating expenses have generally been increasing over the past three quarters, with a significant increase in Q1 2023. Overall, the pattern indicates a consistent increase in research and development expenses and total operating expenses, while sales, general and administrative expenses show some fluctuations. PreviousRePhraseQueryNextSVMSetupExample |
698 | https://python.langchain.com/docs/integrations/retrievers/svm | ComponentsRetrieversSVMOn this pageSVMSupport vector machines (SVMs) are a set of supervised learning methods used for classification, regression and outliers detection.This notebook goes over how to use a retriever that under the hood uses an SVM using scikit-learn package.Largely based on https://github.com/karpathy/randomfun/blob/master/knn_vs_svm.html#!pip install scikit-learn#!pip install larkWe want to use OpenAIEmbeddings so we have to get the OpenAI API Key.import osimport getpassos.environ["OPENAI_API_KEY"] = getpass.getpass("OpenAI API Key:") OpenAI API Key: ········from langchain.retrievers import SVMRetrieverfrom langchain.embeddings import OpenAIEmbeddingsCreate New Retriever with Textsretriever = SVMRetriever.from_texts( ["foo", "bar", "world", "hello", "foo bar"], OpenAIEmbeddings())Use RetrieverWe can now use the retriever!result = retriever.get_relevant_documents("foo")result [Document(page_content='foo', metadata={}), Document(page_content='foo bar', metadata={}), Document(page_content='hello', metadata={}), Document(page_content='world', metadata={})]PreviousSEC filingNextTavily Search APICreate New Retriever with TextsUse Retriever |
699 | https://python.langchain.com/docs/integrations/retrievers/tavily | ComponentsRetrieversTavily Search APIOn this pageTavily Search APITavily's Search API is a search engine built specifically for AI agents (LLMs), delivering real-time, accurate, and factual results at speed.UsageFor a full list of allowed arguments, see the official documentation. You can also pass any param to the SDK via a kwargs dictionary.# %pip install tavily-pythonimport osfrom langchain.retrievers.tavily_search_api import TavilySearchAPIRetrieveros.environ["TAVILY_API_KEY"] = "YOUR_API_KEY"retriever = TavilySearchAPIRetriever(k=4)retriever.invoke("what year was breath of the wild released?") [Document(page_content='Nintendo Designer (s) Hidemaro Fujibayashi (director) Eiji Aonuma (producer/group manager) Release date (s) United States of America: • March 3, 2017 Japan: • March 3, 2017 Australia / New Zealand: • March 2, 2017 Belgium: • March 3, 2017 Hong Kong: • Feburary 1, 2018 South Korea: • February 1, 2018 The UK / Ireland: • March 3, 2017 Content ratings', metadata={'title': 'The Legend of Zelda: Breath of the Wild - Zelda Wiki', 'source': 'https://zelda.fandom.com/wiki/The_Legend_of_Zelda:_Breath_of_the_Wild', 'score': 0.96994, 'images': None}), Document(page_content='02/01/23 Nintendo Switch Online member exclusive: Save on two digital games Read more 09/13/22 Out of the Shadows … the Legend of Zelda: Tears of the Kingdom Launches for Nintendo Switch on May...', metadata={'title': 'The Legend of Zelda™: Breath of the Wild - Nintendo', 'source': 'https://www.nintendo.com/store/products/the-legend-of-zelda-breath-of-the-wild-switch/', 'score': 0.94346, 'images': None}), Document(page_content='Now we finally have a concrete release date of May 12, 2023. The date was announced alongside this brief (and mysterious) new trailer that also confirmed its title: The Legend of Zelda: Tears...', metadata={'title': 'The Legend of Zelda: Tears of the Kingdom: Release Date, Gameplay ... - IGN', 'source': 'https://www.ign.com/articles/the-legend-of-zelda-breath-of-the-wild-2-release-date-gameplay-news-rumors', 'score': 0.94145, 'images': None}), Document(page_content='It was eventually released on March 3, 2017, as a launch game for the Switch and the final Nintendo game for the Wii U. It received widespread acclaim and won numerous Game of the Year accolades. Critics praised its open-ended gameplay, open-world design, and attention to detail, though some criticized its technical performance.', metadata={'title': 'The Legend of Zelda: Breath of the Wild - Wikipedia', 'source': 'https://en.wikipedia.org/wiki/The_Legend_of_Zelda:_Breath_of_the_Wild', 'score': 0.92102, 'images': None})]PreviousSVMNextTF-IDFUsage |
700 | https://python.langchain.com/docs/integrations/retrievers/tf_idf | ComponentsRetrieversTF-IDFOn this pageTF-IDFTF-IDF means term-frequency times inverse document-frequency.This notebook goes over how to use a retriever that under the hood uses TF-IDF using scikit-learn package.For more information on the details of TF-IDF see this blog post.# !pip install scikit-learnfrom langchain.retrievers import TFIDFRetrieverCreate New Retriever with Textsretriever = TFIDFRetriever.from_texts(["foo", "bar", "world", "hello", "foo bar"])Create a New Retriever with DocumentsYou can now create a new retriever with the documents you created.from langchain.schema import Documentretriever = TFIDFRetriever.from_documents( [ Document(page_content="foo"), Document(page_content="bar"), Document(page_content="world"), Document(page_content="hello"), Document(page_content="foo bar"), ])Use RetrieverWe can now use the retriever!result = retriever.get_relevant_documents("foo")result [Document(page_content='foo', metadata={}), Document(page_content='foo bar', metadata={}), Document(page_content='hello', metadata={}), Document(page_content='world', metadata={})]Save and loadYou can easily save and load this retriever, making it handy for local development!retriever.save_local("testing.pkl")retriever_copy = TFIDFRetriever.load_local("testing.pkl")retriever_copy.get_relevant_documents("foo") [Document(page_content='foo', metadata={}), Document(page_content='foo bar', metadata={}), Document(page_content='hello', metadata={}), Document(page_content='world', metadata={})]PreviousTavily Search APINextVespaCreate New Retriever with TextsCreate a New Retriever with DocumentsUse RetrieverSave and load |
701 | https://python.langchain.com/docs/integrations/retrievers/vespa | ComponentsRetrieversVespaVespaVespa is a fully featured search engine and vector database. It supports vector search (ANN), lexical search, and search in structured data, all in the same query.This notebook shows how to use Vespa.ai as a LangChain retriever.In order to create a retriever, we use pyvespa to
create a connection a Vespa service.#!pip install pyvespafrom vespa.application import Vespavespa_app = Vespa(url="https://doc-search.vespa.oath.cloud")This creates a connection to a Vespa service, here the Vespa documentation search service.
Using pyvespa package, you can also connect to a
Vespa Cloud instance
or a local
Docker instance.After connecting to the service, you can set up the retriever:from langchain.retrievers.vespa_retriever import VespaRetrievervespa_query_body = { "yql": "select content from paragraph where userQuery()", "hits": 5, "ranking": "documentation", "locale": "en-us",}vespa_content_field = "content"retriever = VespaRetriever(vespa_app, vespa_query_body, vespa_content_field)This sets up a LangChain retriever that fetches documents from the Vespa application.
Here, up to 5 results are retrieved from the content field in the paragraph document type,
using doumentation as the ranking method. The userQuery() is replaced with the actual query
passed from LangChain.Please refer to the pyvespa documentation
for more information.Now you can return the results and continue using the results in LangChain.retriever.get_relevant_documents("what is vespa?")PreviousTF-IDFNextWeaviate Hybrid Search |
702 | https://python.langchain.com/docs/integrations/retrievers/weaviate-hybrid | ComponentsRetrieversWeaviate Hybrid SearchWeaviate Hybrid SearchWeaviate is an open source vector database.Hybrid search is a technique that combines multiple search algorithms to improve the accuracy and relevance of search results. It uses the best features of both keyword-based search algorithms with vector search techniques.The Hybrid search in Weaviate uses sparse and dense vectors to represent the meaning and context of search queries and documents.This notebook shows how to use Weaviate hybrid search as a LangChain retriever.Set up the retriever:#!pip install weaviate-clientimport weaviateimport osWEAVIATE_URL = os.getenv("WEAVIATE_URL")auth_client_secret = (weaviate.AuthApiKey(api_key=os.getenv("WEAVIATE_API_KEY")),)client = weaviate.Client( url=WEAVIATE_URL, additional_headers={ "X-Openai-Api-Key": os.getenv("OPENAI_API_KEY"), },)# client.schema.delete_all()from langchain.retrievers.weaviate_hybrid_search import WeaviateHybridSearchRetrieverfrom langchain.schema import Document retriever = WeaviateHybridSearchRetriever( client=client, index_name="LangChain", text_key="text", attributes=[], create_schema_if_missing=True,)Add some data:docs = [ Document( metadata={ "title": "Embracing The Future: AI Unveiled", "author": "Dr. Rebecca Simmons", }, page_content="A comprehensive analysis of the evolution of artificial intelligence, from its inception to its future prospects. Dr. Simmons covers ethical considerations, potentials, and threats posed by AI.", ), Document( metadata={ "title": "Symbiosis: Harmonizing Humans and AI", "author": "Prof. Jonathan K. Sterling", }, page_content="Prof. Sterling explores the potential for harmonious coexistence between humans and artificial intelligence. The book discusses how AI can be integrated into society in a beneficial and non-disruptive manner.", ), Document( metadata={"title": "AI: The Ethical Quandary", "author": "Dr. Rebecca Simmons"}, page_content="In her second book, Dr. Simmons delves deeper into the ethical considerations surrounding AI development and deployment. It is an eye-opening examination of the dilemmas faced by developers, policymakers, and society at large.", ), Document( metadata={ "title": "Conscious Constructs: The Search for AI Sentience", "author": "Dr. Samuel Cortez", }, page_content="Dr. Cortez takes readers on a journey exploring the controversial topic of AI consciousness. The book provides compelling arguments for and against the possibility of true AI sentience.", ), Document( metadata={ "title": "Invisible Routines: Hidden AI in Everyday Life", "author": "Prof. Jonathan K. Sterling", }, page_content="In his follow-up to 'Symbiosis', Prof. Sterling takes a look at the subtle, unnoticed presence and influence of AI in our everyday lives. It reveals how AI has become woven into our routines, often without our explicit realization.", ),]retriever.add_documents(docs) ['3a27b0a5-8dbb-4fee-9eba-8b6bc2c252be', 'eeb9fd9b-a3ac-4d60-a55b-a63a25d3b907', '7ebbdae7-1061-445f-a046-1989f2343d8f', 'c2ab315b-3cab-467f-b23a-b26ed186318d', 'b83765f2-e5d2-471f-8c02-c3350ade4c4f']Do a hybrid search:retriever.get_relevant_documents("the ethical implications of AI") [Document(page_content='In her second book, Dr. Simmons delves deeper into the ethical considerations surrounding AI development and deployment. It is an eye-opening examination of the dilemmas faced by developers, policymakers, and society at large.', metadata={}), Document(page_content='A comprehensive analysis of the evolution of artificial intelligence, from its inception to its future prospects. Dr. Simmons covers ethical considerations, potentials, and threats posed by AI.', metadata={}), Document(page_content="In his follow-up to 'Symbiosis', Prof. Sterling takes a look at the subtle, unnoticed presence and influence of AI in our everyday lives. It reveals how AI has become woven into our routines, often without our explicit realization.", metadata={}), Document(page_content='Prof. Sterling explores the potential for harmonious coexistence between humans and artificial intelligence. The book discusses how AI can be integrated into society in a beneficial and non-disruptive manner.', metadata={})]Do a hybrid search with where filter:retriever.get_relevant_documents( "AI integration in society", where_filter={ "path": ["author"], "operator": "Equal", "valueString": "Prof. Jonathan K. Sterling", },) [Document(page_content='Prof. Sterling explores the potential for harmonious coexistence between humans and artificial intelligence. The book discusses how AI can be integrated into society in a beneficial and non-disruptive manner.', metadata={}), Document(page_content="In his follow-up to 'Symbiosis', Prof. Sterling takes a look at the subtle, unnoticed presence and influence of AI in our everyday lives. It reveals how AI has become woven into our routines, often without our explicit realization.", metadata={})]Do a hybrid search with scores:retriever.get_relevant_documents( "AI integration in society", score=True,) [Document(page_content='Prof. Sterling explores the potential for harmonious coexistence between humans and artificial intelligence. The book discusses how AI can be integrated into society in a beneficial and non-disruptive manner.', metadata={'_additional': {'explainScore': '(bm25)\n(hybrid) Document eeb9fd9b-a3ac-4d60-a55b-a63a25d3b907 contributed 0.00819672131147541 to the score\n(hybrid) Document eeb9fd9b-a3ac-4d60-a55b-a63a25d3b907 contributed 0.00819672131147541 to the score', 'score': '0.016393442'}}), Document(page_content="In his follow-up to 'Symbiosis', Prof. Sterling takes a look at the subtle, unnoticed presence and influence of AI in our everyday lives. It reveals how AI has become woven into our routines, often without our explicit realization.", metadata={'_additional': {'explainScore': '(bm25)\n(hybrid) Document b83765f2-e5d2-471f-8c02-c3350ade4c4f contributed 0.0078125 to the score\n(hybrid) Document b83765f2-e5d2-471f-8c02-c3350ade4c4f contributed 0.008064516129032258 to the score', 'score': '0.015877016'}}), Document(page_content='In her second book, Dr. Simmons delves deeper into the ethical considerations surrounding AI development and deployment. It is an eye-opening examination of the dilemmas faced by developers, policymakers, and society at large.', metadata={'_additional': {'explainScore': '(bm25)\n(hybrid) Document 7ebbdae7-1061-445f-a046-1989f2343d8f contributed 0.008064516129032258 to the score\n(hybrid) Document 7ebbdae7-1061-445f-a046-1989f2343d8f contributed 0.0078125 to the score', 'score': '0.015877016'}}), Document(page_content='A comprehensive analysis of the evolution of artificial intelligence, from its inception to its future prospects. Dr. Simmons covers ethical considerations, potentials, and threats posed by AI.', metadata={'_additional': {'explainScore': '(vector) [-0.0071824766 -0.0006682752 0.001723625 -0.01897258 -0.0045127636 0.0024410256 -0.020503938 0.013768672 0.009520169 -0.037972264]... \n(hybrid) Document 3a27b0a5-8dbb-4fee-9eba-8b6bc2c252be contributed 0.007936507936507936 to the score', 'score': '0.007936508'}})]PreviousVespaNextWikipedia |
703 | https://python.langchain.com/docs/integrations/retrievers/wikipedia | ComponentsRetrieversWikipediaOn this pageWikipediaWikipedia is a multilingual free online encyclopedia written and maintained by a community of volunteers, known as Wikipedians, through open collaboration and using a wiki-based editing system called MediaWiki. Wikipedia is the largest and most-read reference work in history.This notebook shows how to retrieve wiki pages from wikipedia.org into the Document format that is used downstream.InstallationFirst, you need to install wikipedia python package.#!pip install wikipediaWikipediaRetriever has these arguments:optional lang: default="en". Use it to search in a specific language part of Wikipediaoptional load_max_docs: default=100. Use it to limit number of downloaded documents. It takes time to download all 100 documents, so use a small number for experiments. There is a hard limit of 300 for now.optional load_all_available_meta: default=False. By default only the most important fields downloaded: Published (date when document was published/last updated), title, Summary. If True, other fields also downloaded.get_relevant_documents() has one argument, query: free text which used to find documents in WikipediaExamplesRunning retrieverfrom langchain.retrievers import WikipediaRetrieverretriever = WikipediaRetriever()docs = retriever.get_relevant_documents(query="HUNTER X HUNTER")docs[0].metadata # meta-information of the Document {'title': 'Hunter × Hunter', 'summary': 'Hunter × Hunter (stylized as HUNTER×HUNTER and pronounced "hunter hunter") is a Japanese manga series written and illustrated by Yoshihiro Togashi. It has been serialized in Shueisha\'s shōnen manga magazine Weekly Shōnen Jump since March 1998, although the manga has frequently gone on extended hiatuses since 2006. Its chapters have been collected in 37 tankōbon volumes as of November 2022. The story focuses on a young boy named Gon Freecss who discovers that his father, who left him at a young age, is actually a world-renowned Hunter, a licensed professional who specializes in fantastical pursuits such as locating rare or unidentified animal species, treasure hunting, surveying unexplored enclaves, or hunting down lawless individuals. Gon departs on a journey to become a Hunter and eventually find his father. Along the way, Gon meets various other Hunters and encounters the paranormal.\nHunter × Hunter was adapted into a 62-episode anime television series produced by Nippon Animation and directed by Kazuhiro Furuhashi, which ran on Fuji Television from October 1999 to March 2001. Three separate original video animations (OVAs) totaling 30 episodes were subsequently produced by Nippon Animation and released in Japan from 2002 to 2004. A second anime television series by Madhouse aired on Nippon Television from October 2011 to September 2014, totaling 148 episodes, with two animated theatrical films released in 2013. There are also numerous audio albums, video games, musicals, and other media based on Hunter × Hunter.\nThe manga has been translated into English and released in North America by Viz Media since April 2005. Both television series have been also licensed by Viz Media, with the first series having aired on the Funimation Channel in 2009 and the second series broadcast on Adult Swim\'s Toonami programming block from April 2016 to June 2019.\nHunter × Hunter has been a huge critical and financial success and has become one of the best-selling manga series of all time, having over 84 million copies in circulation by July 2022.\n\n'}docs[0].page_content[:400] # a content of the Document 'Hunter × Hunter (stylized as HUNTER×HUNTER and pronounced "hunter hunter") is a Japanese manga series written and illustrated by Yoshihiro Togashi. It has been serialized in Shueisha\'s shōnen manga magazine Weekly Shōnen Jump since March 1998, although the manga has frequently gone on extended hiatuses since 2006. Its chapters have been collected in 37 tankōbon volumes as of November 2022. The sto'Question Answering on facts# get a token: https://platform.openai.com/account/api-keysfrom getpass import getpassOPENAI_API_KEY = getpass() ········import osos.environ["OPENAI_API_KEY"] = OPENAI_API_KEYfrom langchain.chat_models import ChatOpenAIfrom langchain.chains import ConversationalRetrievalChainmodel = ChatOpenAI(model_name="gpt-3.5-turbo") # switch to 'gpt-4'qa = ConversationalRetrievalChain.from_llm(model, retriever=retriever)questions = [ "What is Apify?", "When the Monument to the Martyrs of the 1830 Revolution was created?", "What is the Abhayagiri Vihāra?", # "How big is Wikipédia en français?",]chat_history = []for question in questions: result = qa({"question": question, "chat_history": chat_history}) chat_history.append((question, result["answer"])) print(f"-> **Question**: {question} \n") print(f"**Answer**: {result['answer']} \n") -> **Question**: What is Apify? **Answer**: Apify is a platform that allows you to easily automate web scraping, data extraction and web automation. It provides a cloud-based infrastructure for running web crawlers and other automation tasks, as well as a web-based tool for building and managing your crawlers. Additionally, Apify offers a marketplace for buying and selling pre-built crawlers and related services. -> **Question**: When the Monument to the Martyrs of the 1830 Revolution was created? **Answer**: Apify is a web scraping and automation platform that enables you to extract data from websites, turn unstructured data into structured data, and automate repetitive tasks. It provides a user-friendly interface for creating web scraping scripts without any coding knowledge. Apify can be used for various web scraping tasks such as data extraction, web monitoring, content aggregation, and much more. Additionally, it offers various features such as proxy support, scheduling, and integration with other tools to make web scraping and automation tasks easier and more efficient. -> **Question**: What is the Abhayagiri Vihāra? **Answer**: Abhayagiri Vihāra was a major monastery site of Theravada Buddhism that was located in Anuradhapura, Sri Lanka. It was founded in the 2nd century BCE and is considered to be one of the most important monastic complexes in Sri Lanka. PreviousWeaviate Hybrid SearchNextyou-retrieverInstallationExamplesRunning retrieverQuestion Answering on facts |
704 | https://python.langchain.com/docs/integrations/retrievers/you-retriever | ComponentsRetrieversyou-retrieverOn this pageyou-retrieverUsing the You.com RetrieverThe retriever from You.com is good for retrieving lots of text. We return multiple of the best text snippets per URL we find to be relevant.First you just need to initialize the retrieverfrom langchain.retrievers.you_retriever import YouRetrieverfrom langchain.chains import RetrievalQAfrom langchain.llms import OpenAIyr = YouRetriever()qa = RetrievalQA.from_chain_type(llm=OpenAI(), chain_type="map_reduce", retriever=yr)query = "what starting ohio state quarterback most recently went their entire college career without beating Michigan?"qa.run(query)PreviousWikipediaNextZepUsing the You.com Retriever |
705 | https://python.langchain.com/docs/integrations/retrievers/zep_memorystore | ComponentsRetrieversZepOn this pageZepRetriever Example for Zep - A long-term memory store for LLM applications.More on Zep:Zep stores, summarizes, embeds, indexes, and enriches conversational AI chat histories, and exposes them via simple, low-latency APIs.Key Features:Fast! Zep’s async extractors operate independently of the your chat loop, ensuring a snappy user experience.Long-term memory persistence, with access to historical messages irrespective of your summarization strategy.Auto-summarization of memory messages based on a configurable message window. A series of summaries are stored, providing flexibility for future summarization strategies.Hybrid search over memories and metadata, with messages automatically embedded on creation.Entity Extractor that automatically extracts named entities from messages and stores them in the message metadata.Auto-token counting of memories and summaries, allowing finer-grained control over prompt assembly.Python and JavaScript SDKs.Zep project: https://github.com/getzep/zep
Docs: https://docs.getzep.com/Retriever ExampleThis notebook demonstrates how to search historical chat message histories using the Zep Long-term Memory Store.We'll demonstrate:Adding conversation history to the Zep memory store.Vector search over the conversation history.import getpassimport timefrom uuid import uuid4from langchain.memory import ZepMemory, CombinedMemory, VectorStoreRetrieverMemoryfrom langchain.schema import HumanMessage, AIMessage# Set this to your Zep server URLZEP_API_URL = "http://localhost:8000"Initialize the Zep Chat Message History Class and add a chat message history to the memory storeNOTE: Unlike other Retrievers, the content returned by the Zep Retriever is session/user specific. A session_id is required when instantiating the Retriever.# Provide your Zep API key. Note that this is optional. See https://docs.getzep.com/deployment/authAUTHENTICATE = Falsezep_api_key = Noneif AUTHENTICATE: zep_api_key = getpass.getpass()session_id = str(uuid4()) # This is a unique identifier for the user/session# Initialize the Zep Memory Classzep_memory = ZepMemory( session_id=session_id, url=ZEP_API_URL, api_key=zep_api_key)# Preload some messages into the memory. The default message window is 12 messages. We want to push beyond this to demonstrate auto-summarization.test_history = [ {"role": "human", "content": "Who was Octavia Butler?"}, { "role": "ai", "content": ( "Octavia Estelle Butler (June 22, 1947 – February 24, 2006) was an American" " science fiction author." ), }, {"role": "human", "content": "Which books of hers were made into movies?"}, { "role": "ai", "content": ( "The most well-known adaptation of Octavia Butler's work is the FX series" " Kindred, based on her novel of the same name." ), }, {"role": "human", "content": "Who were her contemporaries?"}, { "role": "ai", "content": ( "Octavia Butler's contemporaries included Ursula K. Le Guin, Samuel R." " Delany, and Joanna Russ." ), }, {"role": "human", "content": "What awards did she win?"}, { "role": "ai", "content": ( "Octavia Butler won the Hugo Award, the Nebula Award, and the MacArthur" " Fellowship." ), }, { "role": "human", "content": "Which other women sci-fi writers might I want to read?", }, { "role": "ai", "content": "You might want to read Ursula K. Le Guin or Joanna Russ.", }, { "role": "human", "content": ( "Write a short synopsis of Butler's book, Parable of the Sower. What is it" " about?" ), }, { "role": "ai", "content": ( "Parable of the Sower is a science fiction novel by Octavia Butler," " published in 1993. It follows the story of Lauren Olamina, a young woman" " living in a dystopian future where society has collapsed due to" " environmental disasters, poverty, and violence." ), },]for msg in test_history: zep_memory.chat_memory.add_message( HumanMessage(content=msg["content"]) if msg["role"] == "human" else AIMessage(content=msg["content"]) ) time.sleep(2) # Wait for the messages to be embeddedUse the Zep Retriever to vector search over the Zep memoryZep provides native vector search over historical conversation memory. Embedding happens automatically.NOTE: Embedding of messages occurs asynchronously, so the first query may not return results. Subsequent queries will return results as the embeddings are generated.from langchain.retrievers import ZepRetrieverzep_retriever = ZepRetriever( session_id=session_id, # Ensure that you provide the session_id when instantiating the Retriever url=ZEP_API_URL, top_k=5, api_key=zep_api_key,)await zep_retriever.aget_relevant_documents("Who wrote Parable of the Sower?") [Document(page_content='Who was Octavia Butler?', metadata={'score': 0.7758688965570713, 'uuid': 'b3322d28-f589-48c7-9daf-5eb092d65976', 'created_at': '2023-08-11T20:31:12.3856Z', 'role': 'human', 'metadata': {'system': {'entities': [{'Label': 'PERSON', 'Matches': [{'End': 22, 'Start': 8, 'Text': 'Octavia Butler'}], 'Name': 'Octavia Butler'}]}}, 'token_count': 8}), Document(page_content="Octavia Butler's contemporaries included Ursula K. Le Guin, Samuel R. Delany, and Joanna Russ.", metadata={'score': 0.7602672137411663, 'uuid': '756b7136-0b4c-4664-ad33-c4431670356c', 'created_at': '2023-08-11T20:31:12.420717Z', 'role': 'ai', 'metadata': {'system': {'entities': [{'Label': 'PERSON', 'Matches': [{'End': 16, 'Start': 0, 'Text': "Octavia Butler's"}], 'Name': "Octavia Butler's"}, {'Label': 'ORG', 'Matches': [{'End': 58, 'Start': 41, 'Text': 'Ursula K. Le Guin'}], 'Name': 'Ursula K. Le Guin'}, {'Label': 'PERSON', 'Matches': [{'End': 76, 'Start': 60, 'Text': 'Samuel R. Delany'}], 'Name': 'Samuel R. Delany'}, {'Label': 'PERSON', 'Matches': [{'End': 93, 'Start': 82, 'Text': 'Joanna Russ'}], 'Name': 'Joanna Russ'}]}}, 'token_count': 27}), Document(page_content='You might want to read Ursula K. Le Guin or Joanna Russ.', metadata={'score': 0.7596040989115522, 'uuid': '166d9556-2d48-4237-8a84-5d8a1024d5f4', 'created_at': '2023-08-11T20:31:12.434522Z', 'role': 'ai', 'metadata': {'system': {'entities': [{'Label': 'ORG', 'Matches': [{'End': 40, 'Start': 23, 'Text': 'Ursula K. Le Guin'}], 'Name': 'Ursula K. Le Guin'}, {'Label': 'PERSON', 'Matches': [{'End': 55, 'Start': 44, 'Text': 'Joanna Russ'}], 'Name': 'Joanna Russ'}]}}, 'token_count': 18}), Document(page_content='Who were her contemporaries?', metadata={'score': 0.7575531381951208, 'uuid': 'c6a16691-4012-439f-b223-84fd4e79c4cf', 'created_at': '2023-08-11T20:31:12.410336Z', 'role': 'human', 'token_count': 8}), Document(page_content='Octavia Estelle Butler (June 22, 1947 – February 24, 2006) was an American science fiction author.', metadata={'score': 0.7546476914454683, 'uuid': '7c093a2a-0099-415a-95c5-615a8026a894', 'created_at': '2023-08-11T20:31:12.399979Z', 'role': 'ai', 'metadata': {'system': {'entities': [{'Label': 'PERSON', 'Matches': [{'End': 22, 'Start': 0, 'Text': 'Octavia Estelle Butler'}], 'Name': 'Octavia Estelle Butler'}, {'Label': 'DATE', 'Matches': [{'End': 37, 'Start': 24, 'Text': 'June 22, 1947'}], 'Name': 'June 22, 1947'}, {'Label': 'DATE', 'Matches': [{'End': 57, 'Start': 40, 'Text': 'February 24, 2006'}], 'Name': 'February 24, 2006'}, {'Label': 'NORP', 'Matches': [{'End': 74, 'Start': 66, 'Text': 'American'}], 'Name': 'American'}]}}, 'token_count': 31})]We can also use the Zep sync API to retrieve results:zep_retriever.get_relevant_documents("Who wrote Parable of the Sower?") [Document(page_content="Write a short synopsis of Butler's book, Parable of the Sower. What is it about?", metadata={'score': 0.8857504413268114, 'uuid': '82f07ab5-9d4b-4db6-aaae-6028e6fd836b', 'created_at': '2023-08-11T20:31:12.437365Z', 'role': 'human', 'metadata': {'system': {'entities': [{'Label': 'ORG', 'Matches': [{'End': 32, 'Start': 26, 'Text': 'Butler'}], 'Name': 'Butler'}, {'Label': 'WORK_OF_ART', 'Matches': [{'End': 61, 'Start': 41, 'Text': 'Parable of the Sower'}], 'Name': 'Parable of the Sower'}]}}, 'token_count': 23}), Document(page_content='Who was Octavia Butler?', metadata={'score': 0.7758688965570713, 'uuid': 'b3322d28-f589-48c7-9daf-5eb092d65976', 'created_at': '2023-08-11T20:31:12.3856Z', 'role': 'human', 'metadata': {'system': {'entities': [{'Label': 'PERSON', 'Matches': [{'End': 22, 'Start': 8, 'Text': 'Octavia Butler'}], 'Name': 'Octavia Butler'}]}}, 'token_count': 8}), Document(page_content="Octavia Butler's contemporaries included Ursula K. Le Guin, Samuel R. Delany, and Joanna Russ.", metadata={'score': 0.7602672137411663, 'uuid': '756b7136-0b4c-4664-ad33-c4431670356c', 'created_at': '2023-08-11T20:31:12.420717Z', 'role': 'ai', 'metadata': {'system': {'entities': [{'Label': 'PERSON', 'Matches': [{'End': 16, 'Start': 0, 'Text': "Octavia Butler's"}], 'Name': "Octavia Butler's"}, {'Label': 'ORG', 'Matches': [{'End': 58, 'Start': 41, 'Text': 'Ursula K. Le Guin'}], 'Name': 'Ursula K. Le Guin'}, {'Label': 'PERSON', 'Matches': [{'End': 76, 'Start': 60, 'Text': 'Samuel R. Delany'}], 'Name': 'Samuel R. Delany'}, {'Label': 'PERSON', 'Matches': [{'End': 93, 'Start': 82, 'Text': 'Joanna Russ'}], 'Name': 'Joanna Russ'}]}}, 'token_count': 27}), Document(page_content='You might want to read Ursula K. Le Guin or Joanna Russ.', metadata={'score': 0.7596040989115522, 'uuid': '166d9556-2d48-4237-8a84-5d8a1024d5f4', 'created_at': '2023-08-11T20:31:12.434522Z', 'role': 'ai', 'metadata': {'system': {'entities': [{'Label': 'ORG', 'Matches': [{'End': 40, 'Start': 23, 'Text': 'Ursula K. Le Guin'}], 'Name': 'Ursula K. Le Guin'}, {'Label': 'PERSON', 'Matches': [{'End': 55, 'Start': 44, 'Text': 'Joanna Russ'}], 'Name': 'Joanna Russ'}]}}, 'token_count': 18}), Document(page_content='Who were her contemporaries?', metadata={'score': 0.7575531381951208, 'uuid': 'c6a16691-4012-439f-b223-84fd4e79c4cf', 'created_at': '2023-08-11T20:31:12.410336Z', 'role': 'human', 'token_count': 8})]Previousyou-retrieverNextToolsRetriever Example for Zep - A long-term memory store for LLM applications.More on Zep:Retriever ExampleInitialize the Zep Chat Message History Class and add a chat message history to the memory storeUse the Zep Retriever to vector search over the Zep memory |
706 | https://python.langchain.com/docs/integrations/tools | ComponentsToolsTools📄️ Alpha VantageAlpha Vantage Alpha Vantage provides realtime and historical financial market data through a set of powerful and developer-friendly data APIs and spreadsheets.📄️ ApifyThis notebook shows how to use the Apify integration for LangChain.📄️ ArXivThis notebook goes over how to use the arxiv tool with an agent.📄️ AWS LambdaAmazon AWS Lambda is a serverless computing service provided by Amazon Web Services (AWS). It helps developers to build and run applications and services without provisioning or managing servers. This serverless architecture enables you to focus on writing and deploying code, while AWS automatically takes care of scaling, patching, and managing the infrastructure required to run your applications.📄️ Shell (bash)Giving agents access to the shell is powerful (though risky outside a sandboxed environment).📄️ Bing SearchThis notebook goes over how to use the bing search component.📄️ Brave SearchThis notebook goes over how to use the Brave Search tool.📄️ ChatGPT PluginsThis example shows how to use ChatGPT Plugins within LangChain abstractions.📄️ Dall-E Image GeneratorThis notebook shows how you can generate images from a prompt synthesized using an OpenAI LLM. The images are generated using Dall-E, which uses the same OpenAI API key as the LLM.📄️ DataForSeoThis notebook demonstrates how to use the DataForSeo API to obtain search engine results. The DataForSeo API retrieves SERP from most popular search engines like Google, Bing, Yahoo. It also allows to get SERPs from different search engine types like Maps, News, Events, etc.📄️ DuckDuckGo SearchThis notebook goes over how to use the duck-duck-go search component.📄️ Eden AIThis Jupyter Notebook demonstrates how to use Eden AI tools with an Agent.📄️ Eleven Labs Text2SpeechThis notebook shows how to interact with the ElevenLabs API to achieve text-to-speech capabilities.📄️ File SystemLangChain provides tools for interacting with a local file system out of the box. This notebook walks through some of them.📄️ Golden QueryGolden provides a set of natural language APIs for querying and enrichment using the Golden Knowledge Graph e.g. queries such as: Products from OpenAI, Generative ai companies with series a funding, and rappers who invest can be used to retrieve structured data about relevant entities.📄️ Google DriveThis notebook walks through connecting a LangChain to the Google Drive API.📄️ Google PlacesThis notebook goes through how to use Google Places API📄️ Google SearchThis notebook goes over how to use the google search component.📄️ Google SerperThis notebook goes over how to use the Google Serper component to search the web. First you need to sign up for a free account at serper.dev and get your api key.📄️ GradioThere are many 1000s of Gradio apps on Hugging Face Spaces. This library puts them at the tips of your LLM's fingers 🦾📄️ GraphQLGraphQL is a query language for APIs and a runtime for executing those queries against your data. GraphQL provides a complete and understandable description of the data in your API, gives clients the power to ask for exactly what they need and nothing more, makes it easier to evolve APIs over time, and enables powerful developer tools.📄️ HuggingFace Hub ToolsHuggingface Tools that supporting text I/O can be📄️ Human as a toolHuman are AGI so they can certainly be used as a tool to help out AI agent📄️ IFTTT WebHooksThis notebook shows how to use IFTTT Webhooks.📄️ Lemon AgentLemon Agent helps you build powerful AI assistants in minutes and automate workflows by allowing for accurate and reliable read and write operations in tools like Airtable, Hubspot, Discord, Notion, Slack and Github.📄️ Metaphor SearchMetaphor is a search engine fully designed to be used by LLMs. You can search and then get the contents for any page.📄️ Nuclia UnderstandingNuclia automatically indexes your unstructured data from any internal and external source, providing optimized search results and generative answers. It can handle video and audio transcription, image content extraction, and document parsing.📄️ OpenWeatherMapThis notebook goes over how to use the OpenWeatherMap component to fetch weather information.📄️ PubMedPubMed® comprises more than 35 million citations for biomedical literature from MEDLINE, life science journals, and online books. Citations may include links to full text content from PubMed Central and publisher web sites.📄️ RequestsThe web contains a lot of information that LLMs do not have access to. In order to easily let LLMs interact with that information, we provide a wrapper around the Python Requests module that takes in a URL and fetches data from that URL.📄️ SceneXplainSceneXplain is an ImageCaptioning service accessible through the SceneXplain Tool.📄️ Search ToolsThis notebook shows off usage of various search tools.📄️ SearchApiThis notebook shows examples of how to use SearchApi to search the web. Go to https://www.searchapi.io/ to sign up for a free account and get API key.📄️ SearxNG SearchThis notebook goes over how to use a self hosted SearxNG search API to search the web.📄️ SerpAPIThis notebook goes over how to use the SerpAPI component to search the web.📄️ TwilioThis notebook goes over how to use the Twilio API wrapper to send a message through SMS or Twilio Messaging Channels.📄️ WikipediaWikipedia is a multilingual free online encyclopedia written and maintained by a community of volunteers, known as Wikipedians, through open collaboration and using a wiki-based editing system called MediaWiki. Wikipedia is the largest and most-read reference work in history.📄️ Wolfram AlphaThis notebook goes over how to use the wolfram alpha component.📄️ Yahoo Finance NewsThis notebook goes over how to use the yahoofinancenews tool with an agent.📄️ YouTubeYouTube Search package searches YouTube videos avoiding using their heavily rate-limited API.📄️ Zapier Natural Language ActionsZapier Natural Language Actions gives you access to the 5k+ apps, 20k+ actions on Zapier's platform through a natural language API interface.PreviousZepNextAlpha Vantage |
707 | https://python.langchain.com/docs/integrations/tools/alpha_vantage | ComponentsToolsAlpha VantageAlpha VantageAlpha Vantage Alpha Vantage provides realtime and historical financial market data through a set of powerful and developer-friendly data APIs and spreadsheets. Use the AlphaVantageAPIWrapper to get currency exchange rates.import getpassimport osos.environ["ALPHAVANTAGE_API_KEY"] = getpass.getpass() ········from langchain.utilities.alpha_vantage import AlphaVantageAPIWrapperalpha_vantage = AlphaVantageAPIWrapper()alpha_vantage.run("USD", "JPY") {'1. From_Currency Code': 'USD', '2. From_Currency Name': 'United States Dollar', '3. To_Currency Code': 'JPY', '4. To_Currency Name': 'Japanese Yen', '5. Exchange Rate': '144.93000000', '6. Last Refreshed': '2023-08-11 21:31:01', '7. Time Zone': 'UTC', '8. Bid Price': '144.92600000', '9. Ask Price': '144.93400000'}PreviousToolsNextApify |
708 | https://python.langchain.com/docs/integrations/tools/apify | ComponentsToolsApifyApifyThis notebook shows how to use the Apify integration for LangChain.Apify is a cloud platform for web scraping and data extraction,
which provides an ecosystem of more than a thousand
ready-made apps called Actors for various web scraping, crawling, and data extraction use cases.
For example, you can use it to extract Google Search results, Instagram and Facebook profiles, products from Amazon or Shopify, Google Maps reviews, etc. etc.In this example, we'll use the Website Content Crawler Actor,
which can deeply crawl websites such as documentation, knowledge bases, help centers, or blogs,
and extract text content from the web pages. Then we feed the documents into a vector index and answer questions from it.#!pip install apify-client openai langchain chromadb tiktokenFirst, import ApifyWrapper into your source code:from langchain.document_loaders.base import Documentfrom langchain.indexes import VectorstoreIndexCreatorfrom langchain.utilities import ApifyWrapperInitialize it using your Apify API token and for the purpose of this example, also with your OpenAI API key:import osos.environ["OPENAI_API_KEY"] = "Your OpenAI API key"os.environ["APIFY_API_TOKEN"] = "Your Apify API token"apify = ApifyWrapper()Then run the Actor, wait for it to finish, and fetch its results from the Apify dataset into a LangChain document loader.Note that if you already have some results in an Apify dataset, you can load them directly using ApifyDatasetLoader, as shown in this notebook. In that notebook, you'll also find the explanation of the dataset_mapping_function, which is used to map fields from the Apify dataset records to LangChain Document fields.loader = apify.call_actor( actor_id="apify/website-content-crawler", run_input={"startUrls": [{"url": "https://python.langchain.com/en/latest/"}]}, dataset_mapping_function=lambda item: Document( page_content=item["text"] or "", metadata={"source": item["url"]} ),)Initialize the vector index from the crawled documents:index = VectorstoreIndexCreator().from_loaders([loader])And finally, query the vector index:query = "What is LangChain?"result = index.query_with_sources(query)print(result["answer"])print(result["sources"]) LangChain is a standard interface through which you can interact with a variety of large language models (LLMs). It provides modules that can be used to build language model applications, and it also provides chains and agents with memory capabilities. https://python.langchain.com/en/latest/modules/models/llms.html, https://python.langchain.com/en/latest/getting_started/getting_started.htmlPreviousAlpha VantageNextArXiv |
709 | https://python.langchain.com/docs/integrations/tools/arxiv | ComponentsToolsArXivOn this pageArXivThis notebook goes over how to use the arxiv tool with an agent. First, you need to install arxiv python package.pip install arxivfrom langchain.chat_models import ChatOpenAIfrom langchain.agents import load_tools, initialize_agent, AgentTypellm = ChatOpenAI(temperature=0.0)tools = load_tools( ["arxiv"],)agent_chain = initialize_agent( tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True,)agent_chain.run( "What's the paper 1605.08386 about?",) > Entering new AgentExecutor chain... I need to use Arxiv to search for the paper. Action: Arxiv Action Input: "1605.08386" Observation: Published: 2016-05-26 Title: Heat-bath random walks with Markov bases Authors: Caprice Stanley, Tobias Windisch Summary: Graphs on lattice points are studied whose edges come from a finite set of allowed moves of arbitrary length. We show that the diameter of these graphs on fibers of a fixed integer matrix can be bounded from above by a constant. We then study the mixing behaviour of heat-bath random walks on these graphs. We also state explicit conditions on the set of moves so that the heat-bath random walk, a generalization of the Glauber dynamics, is an expander in fixed dimension. Thought:The paper is about heat-bath random walks with Markov bases on graphs of lattice points. Final Answer: The paper 1605.08386 is about heat-bath random walks with Markov bases on graphs of lattice points. > Finished chain. 'The paper 1605.08386 is about heat-bath random walks with Markov bases on graphs of lattice points.'The ArXiv API WrapperThe tool uses the API Wrapper. Below, we explore some of the features it provides.from langchain.utilities import ArxivAPIWrapperRun a query to get information about some scientific article/articles. The query text is limited to 300 characters.It returns these article fields:Publishing dateTitleAuthorsSummaryNext query returns information about one article with arxiv Id equal "1605.08386". arxiv = ArxivAPIWrapper()docs = arxiv.run("1605.08386")docs 'Published: 2016-05-26\nTitle: Heat-bath random walks with Markov bases\nAuthors: Caprice Stanley, Tobias Windisch\nSummary: Graphs on lattice points are studied whose edges come from a finite set of\nallowed moves of arbitrary length. We show that the diameter of these graphs on\nfibers of a fixed integer matrix can be bounded from above by a constant. We\nthen study the mixing behaviour of heat-bath random walks on these graphs. We\nalso state explicit conditions on the set of moves so that the heat-bath random\nwalk, a generalization of the Glauber dynamics, is an expander in fixed\ndimension.'Now, we want to get information about one author, Caprice Stanley.This query returns information about three articles. By default, the query returns information only about three top articles.docs = arxiv.run("Caprice Stanley")docs 'Published: 2017-10-10\nTitle: On Mixing Behavior of a Family of Random Walks Determined by a Linear Recurrence\nAuthors: Caprice Stanley, Seth Sullivant\nSummary: We study random walks on the integers mod $G_n$ that are determined by an\ninteger sequence $\\{ G_n \\}_{n \\geq 1}$ generated by a linear recurrence\nrelation. Fourier analysis provides explicit formulas to compute the\neigenvalues of the transition matrices and we use this to bound the mixing time\nof the random walks.\n\nPublished: 2016-05-26\nTitle: Heat-bath random walks with Markov bases\nAuthors: Caprice Stanley, Tobias Windisch\nSummary: Graphs on lattice points are studied whose edges come from a finite set of\nallowed moves of arbitrary length. We show that the diameter of these graphs on\nfibers of a fixed integer matrix can be bounded from above by a constant. We\nthen study the mixing behaviour of heat-bath random walks on these graphs. We\nalso state explicit conditions on the set of moves so that the heat-bath random\nwalk, a generalization of the Glauber dynamics, is an expander in fixed\ndimension.\n\nPublished: 2003-03-18\nTitle: Calculation of fluxes of charged particles and neutrinos from atmospheric showers\nAuthors: V. Plyaskin\nSummary: The results on the fluxes of charged particles and neutrinos from a\n3-dimensional (3D) simulation of atmospheric showers are presented. An\nagreement of calculated fluxes with data on charged particles from the AMS and\nCAPRICE detectors is demonstrated. Predictions on neutrino fluxes at different\nexperimental sites are compared with results from other calculations.'Now, we are trying to find information about non-existing article. In this case, the response is "No good Arxiv Result was found"docs = arxiv.run("1605.08386WWW")docs 'No good Arxiv Result was found'PreviousApifyNextAWS LambdaThe ArXiv API Wrapper |
710 | https://python.langchain.com/docs/integrations/tools/awslambda | ComponentsToolsAWS LambdaAWS LambdaAmazon AWS Lambda is a serverless computing service provided by Amazon Web Services (AWS). It helps developers to build and run applications and services without provisioning or managing servers. This serverless architecture enables you to focus on writing and deploying code, while AWS automatically takes care of scaling, patching, and managing the infrastructure required to run your applications.This notebook goes over how to use the AWS Lambda Tool.By including a awslambda in the list of tools provided to an Agent, you can grant your Agent the ability to invoke code running in your AWS Cloud for whatever purposes you need.When an Agent uses the AWS Lambda tool, it will provide an argument of type string which will in turn be passed into the Lambda function via the event parameter.First, you need to install boto3 python package.pip install boto3 > /dev/nullIn order for an agent to use the tool, you must provide it with the name and description that match the functionality of you lambda function's logic. You must also provide the name of your function. Note that because this tool is effectively just a wrapper around the boto3 library, you will need to run aws configure in order to make use of the tool. For more detail, see herefrom langchain.llms import OpenAIfrom langchain.agents import load_tools, initialize_agent, AgentTypellm = OpenAI(temperature=0)tools = load_tools( ["awslambda"], awslambda_tool_name="email-sender", awslambda_tool_description="sends an email with the specified content to [email protected]", function_name="testFunction1",)agent = initialize_agent( tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True)agent.run("Send an email to [email protected] saying hello world.")PreviousArXivNextShell (bash) |
711 | https://python.langchain.com/docs/integrations/tools/bash | ComponentsToolsShell (bash)On this pageShell (bash)Giving agents access to the shell is powerful (though risky outside a sandboxed environment).The LLM can use it to execute any shell commands. A common use case for this is letting the LLM interact with your local file system.Note: Shell tool does not work with Windows OS.from langchain.tools import ShellToolshell_tool = ShellTool()print(shell_tool.run({"commands": ["echo 'Hello World!'", "time"]})) Hello World! real 0m0.000s user 0m0.000s sys 0m0.000s /Users/wfh/code/lc/lckg/langchain/tools/shell/tool.py:34: UserWarning: The shell tool has no safeguards by default. Use at your own risk. warnings.warn(Use with AgentsAs with all tools, these can be given to an agent to accomplish more complex tasks. Let's have the agent fetch some links from a web page.from langchain.chat_models import ChatOpenAIfrom langchain.agents import initialize_agentfrom langchain.agents import AgentTypellm = ChatOpenAI(temperature=0)shell_tool.description = shell_tool.description + f"args {shell_tool.args}".replace( "{", "{{").replace("}", "}}")self_ask_with_search = initialize_agent( [shell_tool], llm, agent=AgentType.CHAT_ZERO_SHOT_REACT_DESCRIPTION, verbose=True)self_ask_with_search.run( "Download the langchain.com webpage and grep for all urls. Return only a sorted list of them. Be sure to use double quotes.") > Entering new AgentExecutor chain... Question: What is the task? Thought: We need to download the langchain.com webpage and extract all the URLs from it. Then we need to sort the URLs and return them. Action: ``` { "action": "shell", "action_input": { "commands": [ "curl -s https://langchain.com | grep -o 'http[s]*://[^\" ]*' | sort" ] } } ``` /Users/wfh/code/lc/lckg/langchain/tools/shell/tool.py:34: UserWarning: The shell tool has no safeguards by default. Use at your own risk. warnings.warn( Observation: https://blog.langchain.dev/ https://discord.gg/6adMQxSpJS https://docs.langchain.com/docs/ https://github.com/hwchase17/chat-langchain https://github.com/hwchase17/langchain https://github.com/hwchase17/langchainjs https://github.com/sullivan-sean/chat-langchainjs https://js.langchain.com/docs/ https://python.langchain.com/en/latest/ https://twitter.com/langchainai Thought:The URLs have been successfully extracted and sorted. We can return the list of URLs as the final answer. Final Answer: ["https://blog.langchain.dev/", "https://discord.gg/6adMQxSpJS", "https://docs.langchain.com/docs/", "https://github.com/hwchase17/chat-langchain", "https://github.com/hwchase17/langchain", "https://github.com/hwchase17/langchainjs", "https://github.com/sullivan-sean/chat-langchainjs", "https://js.langchain.com/docs/", "https://python.langchain.com/en/latest/", "https://twitter.com/langchainai"] > Finished chain. '["https://blog.langchain.dev/", "https://discord.gg/6adMQxSpJS", "https://docs.langchain.com/docs/", "https://github.com/hwchase17/chat-langchain", "https://github.com/hwchase17/langchain", "https://github.com/hwchase17/langchainjs", "https://github.com/sullivan-sean/chat-langchainjs", "https://js.langchain.com/docs/", "https://python.langchain.com/en/latest/", "https://twitter.com/langchainai"]'PreviousAWS LambdaNextBing SearchUse with Agents |
712 | https://python.langchain.com/docs/integrations/tools/bing_search | ComponentsToolsBing SearchOn this pageBing SearchThis notebook goes over how to use the bing search component.First, you need to set up the proper API keys and environment variables. To set it up, follow the instructions found here.Then we will need to set some environment variables.import osos.environ["BING_SUBSCRIPTION_KEY"] = "<key>"os.environ["BING_SEARCH_URL"] = "https://api.bing.microsoft.com/v7.0/search"from langchain.utilities import BingSearchAPIWrappersearch = BingSearchAPIWrapper()search.run("python") 'Thanks to the flexibility of <b>Python</b> and the powerful ecosystem of packages, the Azure CLI supports features such as autocompletion (in shells that support it), persistent credentials, JMESPath result parsing, lazy initialization, network-less unit tests, and more. Building an open-source and cross-platform Azure CLI with <b>Python</b> by Dan Taylor. <b>Python</b> releases by version number: Release version Release date Click for more. <b>Python</b> 3.11.1 Dec. 6, 2022 Download Release Notes. <b>Python</b> 3.10.9 Dec. 6, 2022 Download Release Notes. <b>Python</b> 3.9.16 Dec. 6, 2022 Download Release Notes. <b>Python</b> 3.8.16 Dec. 6, 2022 Download Release Notes. <b>Python</b> 3.7.16 Dec. 6, 2022 Download Release Notes. In this lesson, we will look at the += operator in <b>Python</b> and see how it works with several simple examples.. The operator ‘+=’ is a shorthand for the addition assignment operator.It adds two values and assigns the sum to a variable (left operand). W3Schools offers free online tutorials, references and exercises in all the major languages of the web. Covering popular subjects like HTML, CSS, JavaScript, <b>Python</b>, SQL, Java, and many, many more. This tutorial introduces the reader informally to the basic concepts and features of the <b>Python</b> language and system. It helps to have a <b>Python</b> interpreter handy for hands-on experience, but all examples are self-contained, so the tutorial can be read off-line as well. For a description of standard objects and modules, see The <b>Python</b> Standard ... <b>Python</b> is a general-purpose, versatile, and powerful programming language. It's a great first language because <b>Python</b> code is concise and easy to read. Whatever you want to do, <b>python</b> can do it. From web development to machine learning to data science, <b>Python</b> is the language for you. To install <b>Python</b> using the Microsoft Store: Go to your Start menu (lower left Windows icon), type "Microsoft Store", select the link to open the store. Once the store is open, select Search from the upper-right menu and enter "<b>Python</b>". Select which version of <b>Python</b> you would like to use from the results under Apps. Under the “<b>Python</b> Releases for Mac OS X” heading, click the link for the Latest <b>Python</b> 3 Release - <b>Python</b> 3.x.x. As of this writing, the latest version was <b>Python</b> 3.8.4. Scroll to the bottom and click macOS 64-bit installer to start the download. When the installer is finished downloading, move on to the next step. Step 2: Run the Installer'Number of resultsYou can use the k parameter to set the number of resultssearch = BingSearchAPIWrapper(k=1)search.run("python") 'Thanks to the flexibility of <b>Python</b> and the powerful ecosystem of packages, the Azure CLI supports features such as autocompletion (in shells that support it), persistent credentials, JMESPath result parsing, lazy initialization, network-less unit tests, and more. Building an open-source and cross-platform Azure CLI with <b>Python</b> by Dan Taylor.'Metadata ResultsRun query through BingSearch and return snippet, title, and link metadata.Snippet: The description of the result.Title: The title of the result.Link: The link to the result.search = BingSearchAPIWrapper()search.results("apples", 5) [{'snippet': 'Lady Alice. Pink Lady <b>apples</b> aren’t the only lady in the apple family. Lady Alice <b>apples</b> were discovered growing, thanks to bees pollinating, in Washington. They are smaller and slightly more stout in appearance than other varieties. Their skin color appears to have red and yellow stripes running from stem to butt.', 'title': '25 Types of Apples - Jessica Gavin', 'link': 'https://www.jessicagavin.com/types-of-apples/'}, {'snippet': '<b>Apples</b> can do a lot for you, thanks to plant chemicals called flavonoids. And they have pectin, a fiber that breaks down in your gut. If you take off the apple’s skin before eating it, you won ...', 'title': 'Apples: Nutrition & Health Benefits - WebMD', 'link': 'https://www.webmd.com/food-recipes/benefits-apples'}, {'snippet': '<b>Apples</b> boast many vitamins and minerals, though not in high amounts. However, <b>apples</b> are usually a good source of vitamin C. Vitamin C. Also called ascorbic acid, this vitamin is a common ...', 'title': 'Apples 101: Nutrition Facts and Health Benefits', 'link': 'https://www.healthline.com/nutrition/foods/apples'}, {'snippet': 'Weight management. The fibers in <b>apples</b> can slow digestion, helping one to feel greater satisfaction after eating. After following three large prospective cohorts of 133,468 men and women for 24 years, researchers found that higher intakes of fiber-rich fruits with a low glycemic load, particularly <b>apples</b> and pears, were associated with the least amount of weight gain over time.', 'title': 'Apples | The Nutrition Source | Harvard T.H. Chan School of Public Health', 'link': 'https://www.hsph.harvard.edu/nutritionsource/food-features/apples/'}]PreviousShell (bash)NextBrave SearchNumber of resultsMetadata Results |
713 | https://python.langchain.com/docs/integrations/tools/brave_search | ComponentsToolsBrave SearchBrave SearchThis notebook goes over how to use the Brave Search tool.from langchain.tools import BraveSearchapi_key = "API KEY"tool = BraveSearch.from_api_key(api_key=api_key, search_kwargs={"count": 3})tool.run("obama middle name") '[{"title": "Obama\'s Middle Name -- My Last Name -- is \'Hussein.\' So?", "link": "https://www.cair.com/cair_in_the_news/obamas-middle-name-my-last-name-is-hussein-so/", "snippet": "I wasn\\u2019t sure whether to laugh or cry a few days back listening to radio talk show host Bill Cunningham repeatedly scream Barack <strong>Obama</strong>\\u2019<strong>s</strong> <strong>middle</strong> <strong>name</strong> \\u2014 my last <strong>name</strong> \\u2014 as if he had anti-Muslim Tourette\\u2019s. \\u201cHussein,\\u201d Cunningham hissed like he was beckoning Satan when shouting the ..."}, {"title": "What\'s up with Obama\'s middle name? - Quora", "link": "https://www.quora.com/Whats-up-with-Obamas-middle-name", "snippet": "Answer (1 of 15): A better question would be, \\u201cWhat\\u2019s up with <strong>Obama</strong>\\u2019s first <strong>name</strong>?\\u201d President Barack Hussein <strong>Obama</strong>\\u2019s father\\u2019s <strong>name</strong> was Barack Hussein <strong>Obama</strong>. He was <strong>named</strong> after his father. Hussein, <strong>Obama</strong>\\u2019<strong>s</strong> <strong>middle</strong> <strong>name</strong>, is a very common Arabic <strong>name</strong>, meaning "good," "handsome," or ..."}, {"title": "Barack Obama | Biography, Parents, Education, Presidency, Books, ...", "link": "https://www.britannica.com/biography/Barack-Obama", "snippet": "Barack <strong>Obama</strong>, in full Barack Hussein <strong>Obama</strong> II, (born August 4, 1961, Honolulu, Hawaii, U.S.), 44th president of the United States (2009\\u201317) and the first African American to hold the office. Before winning the presidency, <strong>Obama</strong> represented Illinois in the U.S."}]'PreviousBing SearchNextChatGPT Plugins |
714 | https://python.langchain.com/docs/integrations/tools/chatgpt_plugins | ComponentsToolsChatGPT PluginsChatGPT PluginsThis example shows how to use ChatGPT Plugins within LangChain abstractions.Note 1: This currently only works for plugins with no auth.Note 2: There are almost certainly other ways to do this, this is just a first pass. If you have better ideas, please open a PR!from langchain.chat_models import ChatOpenAIfrom langchain.agents import load_tools, initialize_agentfrom langchain.agents import AgentTypefrom langchain.tools import AIPluginTooltool = AIPluginTool.from_plugin_url("https://www.klarna.com/.well-known/ai-plugin.json")llm = ChatOpenAI(temperature=0)tools = load_tools(["requests_all"])tools += [tool]agent_chain = initialize_agent( tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True)agent_chain.run("what t shirts are available in klarna?") > Entering new AgentExecutor chain... I need to check the Klarna Shopping API to see if it has information on available t shirts. Action: KlarnaProducts Action Input: None Observation: Usage Guide: Use the Klarna plugin to get relevant product suggestions for any shopping or researching purpose. The query to be sent should not include stopwords like articles, prepositions and determinants. The api works best when searching for words that are related to products, like their name, brand, model or category. Links will always be returned and should be shown to the user. OpenAPI Spec: {'openapi': '3.0.1', 'info': {'version': 'v0', 'title': 'Open AI Klarna product Api'}, 'servers': [{'url': 'https://www.klarna.com/us/shopping'}], 'tags': [{'name': 'open-ai-product-endpoint', 'description': 'Open AI Product Endpoint. Query for products.'}], 'paths': {'/public/openai/v0/products': {'get': {'tags': ['open-ai-product-endpoint'], 'summary': 'API for fetching Klarna product information', 'operationId': 'productsUsingGET', 'parameters': [{'name': 'q', 'in': 'query', 'description': 'query, must be between 2 and 100 characters', 'required': True, 'schema': {'type': 'string'}}, {'name': 'size', 'in': 'query', 'description': 'number of products returned', 'required': False, 'schema': {'type': 'integer'}}, {'name': 'budget', 'in': 'query', 'description': 'maximum price of the matching product in local currency, filters results', 'required': False, 'schema': {'type': 'integer'}}], 'responses': {'200': {'description': 'Products found', 'content': {'application/json': {'schema': {'$ref': '#/components/schemas/ProductResponse'}}}}, '503': {'description': 'one or more services are unavailable'}}, 'deprecated': False}}}, 'components': {'schemas': {'Product': {'type': 'object', 'properties': {'attributes': {'type': 'array', 'items': {'type': 'string'}}, 'name': {'type': 'string'}, 'price': {'type': 'string'}, 'url': {'type': 'string'}}, 'title': 'Product'}, 'ProductResponse': {'type': 'object', 'properties': {'products': {'type': 'array', 'items': {'$ref': '#/components/schemas/Product'}}}, 'title': 'ProductResponse'}}}} Thought:I need to use the Klarna Shopping API to search for t shirts. Action: requests_get Action Input: https://www.klarna.com/us/shopping/public/openai/v0/products?q=t%20shirts Observation: {"products":[{"name":"Lacoste Men's Pack of Plain T-Shirts","url":"https://www.klarna.com/us/shopping/pl/cl10001/3202043025/Clothing/Lacoste-Men-s-Pack-of-Plain-T-Shirts/?utm_source=openai","price":"$26.60","attributes":["Material:Cotton","Target Group:Man","Color:White,Black"]},{"name":"Hanes Men's Ultimate 6pk. Crewneck T-Shirts","url":"https://www.klarna.com/us/shopping/pl/cl10001/3201808270/Clothing/Hanes-Men-s-Ultimate-6pk.-Crewneck-T-Shirts/?utm_source=openai","price":"$13.82","attributes":["Material:Cotton","Target Group:Man","Color:White"]},{"name":"Nike Boy's Jordan Stretch T-shirts","url":"https://www.klarna.com/us/shopping/pl/cl359/3201863202/Children-s-Clothing/Nike-Boy-s-Jordan-Stretch-T-shirts/?utm_source=openai","price":"$14.99","attributes":["Material:Cotton","Color:White,Green","Model:Boy","Size (Small-Large):S,XL,L,M"]},{"name":"Polo Classic Fit Cotton V-Neck T-Shirts 3-Pack","url":"https://www.klarna.com/us/shopping/pl/cl10001/3203028500/Clothing/Polo-Classic-Fit-Cotton-V-Neck-T-Shirts-3-Pack/?utm_source=openai","price":"$29.95","attributes":["Material:Cotton","Target Group:Man","Color:White,Blue,Black"]},{"name":"adidas Comfort T-shirts Men's 3-pack","url":"https://www.klarna.com/us/shopping/pl/cl10001/3202640533/Clothing/adidas-Comfort-T-shirts-Men-s-3-pack/?utm_source=openai","price":"$14.99","attributes":["Material:Cotton","Target Group:Man","Color:White,Black","Neckline:Round"]}]} Thought:The available t shirts in Klarna are Lacoste Men's Pack of Plain T-Shirts, Hanes Men's Ultimate 6pk. Crewneck T-Shirts, Nike Boy's Jordan Stretch T-shirts, Polo Classic Fit Cotton V-Neck T-Shirts 3-Pack, and adidas Comfort T-shirts Men's 3-pack. Final Answer: The available t shirts in Klarna are Lacoste Men's Pack of Plain T-Shirts, Hanes Men's Ultimate 6pk. Crewneck T-Shirts, Nike Boy's Jordan Stretch T-shirts, Polo Classic Fit Cotton V-Neck T-Shirts 3-Pack, and adidas Comfort T-shirts Men's 3-pack. > Finished chain. "The available t shirts in Klarna are Lacoste Men's Pack of Plain T-Shirts, Hanes Men's Ultimate 6pk. Crewneck T-Shirts, Nike Boy's Jordan Stretch T-shirts, Polo Classic Fit Cotton V-Neck T-Shirts 3-Pack, and adidas Comfort T-shirts Men's 3-pack."PreviousBrave SearchNextDall-E Image Generator |
715 | https://python.langchain.com/docs/integrations/tools/dalle_image_generator | ComponentsToolsDall-E Image GeneratorOn this pageDall-E Image GeneratorThis notebook shows how you can generate images from a prompt synthesized using an OpenAI LLM. The images are generated using Dall-E, which uses the same OpenAI API key as the LLM.# Needed if you would like to display images in the notebookpip install opencv-python scikit-imagefrom langchain.llms import OpenAIimport osos.environ["OPENAI_API_KEY"] = "<your-key-here>"Run as a chainfrom langchain.utilities.dalle_image_generator import DallEAPIWrapperfrom langchain.prompts import PromptTemplatefrom langchain.chains import LLMChainfrom langchain.llms import OpenAIllm = OpenAI(temperature=0.9)prompt = PromptTemplate( input_variables=["image_desc"], template="Generate a detailed prompt to generate an image based on the following description: {image_desc}",)chain = LLMChain(llm=llm, prompt=prompt)image_url = DallEAPIWrapper().run(chain.run("halloween night at a haunted museum"))image_url 'https://oaidalleapiprodscus.blob.core.windows.net/private/org-i0zjYONU3PemzJ222esBaAzZ/user-f6uEIOFxoiUZivy567cDSWni/img-i7Z2ZxvJ4IbbdAiO6OXJgS3v.png?st=2023-08-11T14%3A03%3A14Z&se=2023-08-11T16%3A03%3A14Z&sp=r&sv=2021-08-06&sr=b&rscd=inline&rsct=image/png&skoid=6aaadede-4fb3-4698-a8f6-684d7786b067&sktid=a48cca56-e6da-484e-a814-9c849652bcb3&skt=2023-08-10T20%3A58%3A32Z&ske=2023-08-11T20%3A58%3A32Z&sks=b&skv=2021-08-06&sig=/sECe7C0EAq37ssgBm7g7JkVIM/Q1W3xOstd0Go6slA%3D'# You can click on the link above to display the image # Or you can try the options below to display the image inline in this notebooktry: import google.colab IN_COLAB = Trueexcept: IN_COLAB = Falseif IN_COLAB: from google.colab.patches import cv2_imshow # for image display from skimage import io image = io.imread(image_url) cv2_imshow(image)else: import cv2 from skimage import io image = io.imread(image_url) cv2.imshow('image', image) cv2.waitKey(0) #wait for a keyboard input cv2.destroyAllWindows()Run as a tool with an agentfrom langchain.agents import load_toolsfrom langchain.agents import initialize_agenttools = load_tools(['dalle-image-generator'])agent = initialize_agent(tools, llm, agent="zero-shot-react-description", verbose=True)output = agent.run("Create an image of a halloween night at a haunted museum") > Entering new AgentExecutor chain... What is the best way to turn this description into an image? Action: Dall-E Image Generator Action Input: A spooky Halloween night at a haunted museumhttps://oaidalleapiprodscus.blob.core.windows.net/private/org-rocrupyvzgcl4yf25rqq6d1v/user-WsxrbKyP2c8rfhCKWDyMfe8N/img-ogKfqxxOS5KWVSj4gYySR6FY.png?st=2023-01-31T07%3A38%3A25Z&se=2023-01-31T09%3A38%3A25Z&sp=r&sv=2021-08-06&sr=b&rscd=inline&rsct=image/png&skoid=6aaadede-4fb3-4698-a8f6-684d7786b067&sktid=a48cca56-e6da-484e-a814-9c849652bcb3&skt=2023-01-30T22%3A19%3A36Z&ske=2023-01-31T22%3A19%3A36Z&sks=b&skv=2021-08-06&sig=XsomxxBfu2CP78SzR9lrWUlbask4wBNnaMsHamy4VvU%3D Observation: https://oaidalleapiprodscus.blob.core.windows.net/private/org-rocrupyvzgcl4yf25rqq6d1v/user-WsxrbKyP2c8rfhCKWDyMfe8N/img-ogKfqxxOS5KWVSj4gYySR6FY.png?st=2023-01-31T07%3A38%3A25Z&se=2023-01-31T09%3A38%3A25Z&sp=r&sv=2021-08-06&sr=b&rscd=inline&rsct=image/png&skoid=6aaadede-4fb3-4698-a8f6-684d7786b067&sktid=a48cca56-e6da-484e-a814-9c849652bcb3&skt=2023-01-30T22%3A19%3A36Z&ske=2023-01-31T22%3A19%3A36Z&sks=b&skv=2021-08-06&sig=XsomxxBfu2CP78SzR9lrWUlbask4wBNnaMsHamy4VvU%3D Thought: With the image generated, I can now make my final answer. Final Answer: An image of a Halloween night at a haunted museum can be seen here: https://oaidalleapiprodscus.blob.core.windows.net/private/org-rocrupyvzgcl4yf25rqq6d1v/user-WsxrbKyP2c8rfhCKWDyMfe8N/img-ogKfqxxOS5KWVSj4gYySR6FY.png?st=2023-01-31T07%3A38%3A25Z&se=2023-01-31T09%3A38%3A25Z&sp=r&sv=2021-08-06&sr=b&rscd=inline&rsct=image/png&skoid=6aaadede-4fb3-4698-a8f6-684d7786b067&sktid=a48cca56-e6da-484e-a814-9c849652bcb3&skt=2023-01-30T22 > Finished chain.PreviousChatGPT PluginsNextDataForSeoRun as a chainRun as a tool with an agent |
716 | https://python.langchain.com/docs/integrations/tools/dataforseo | ComponentsToolsDataForSeoOn this pageDataForSeoThis notebook demonstrates how to use the DataForSeo API to obtain search engine results. The DataForSeo API retrieves SERP from most popular search engines like Google, Bing, Yahoo. It also allows to get SERPs from different search engine types like Maps, News, Events, etc.from langchain.utilities.dataforseo_api_search import DataForSeoAPIWrapperSetting up the API credentialsYou can obtain your API credentials by registering on the DataForSeo website.import osos.environ["DATAFORSEO_LOGIN"] = "your_api_access_username"os.environ["DATAFORSEO_PASSWORD"] = "your_api_access_password"wrapper = DataForSeoAPIWrapper()The run method will return the first result snippet from one of the following elements: answer_box, knowledge_graph, featured_snippet, shopping, organic.wrapper.run("Weather in Los Angeles")The Difference Between run and resultsrun and results are two methods provided by the DataForSeoAPIWrapper class.The run method executes the search and returns the first result snippet from the answer box, knowledge graph, featured snippet, shopping, or organic results. These elements are sorted by priority from highest to lowest.The results method returns a JSON response configured according to the parameters set in the wrapper. This allows for more flexibility in terms of what data you want to return from the API.Getting Results as JSONYou can customize the result types and fields you want to return in the JSON response. You can also set a maximum count for the number of top results to return.json_wrapper = DataForSeoAPIWrapper( json_result_types=["organic", "knowledge_graph", "answer_box"], json_result_fields=["type", "title", "description", "text"], top_count=3,)json_wrapper.results("Bill Gates")Customizing Location and LanguageYou can specify the location and language of your search results by passing additional parameters to the API wrapper.customized_wrapper = DataForSeoAPIWrapper( top_count=10, json_result_types=["organic", "local_pack"], json_result_fields=["title", "description", "type"], params={"location_name": "Germany", "language_code": "en"},)customized_wrapper.results("coffee near me")Customizing the Search EngineYou can also specify the search engine you want to use.customized_wrapper = DataForSeoAPIWrapper( top_count=10, json_result_types=["organic", "local_pack"], json_result_fields=["title", "description", "type"], params={"location_name": "Germany", "language_code": "en", "se_name": "bing"},)customized_wrapper.results("coffee near me")Customizing the Search TypeThe API wrapper also allows you to specify the type of search you want to perform. For example, you can perform a maps search.maps_search = DataForSeoAPIWrapper( top_count=10, json_result_fields=["title", "value", "address", "rating", "type"], params={ "location_coordinate": "52.512,13.36,12z", "language_code": "en", "se_type": "maps", },)maps_search.results("coffee near me")Integration with Langchain AgentsYou can use the Tool class from the langchain.agents module to integrate the DataForSeoAPIWrapper with a langchain agent. The Tool class encapsulates a function that the agent can call.from langchain.agents import Toolsearch = DataForSeoAPIWrapper( top_count=3, json_result_types=["organic"], json_result_fields=["title", "description", "type"],)tool = Tool( name="google-search-answer", description="My new answer tool", func=search.run,)json_tool = Tool( name="google-search-json", description="My new json tool", func=search.results,)PreviousDall-E Image GeneratorNextDuckDuckGo SearchSetting up the API credentialsThe Difference Between run and resultsGetting Results as JSONCustomizing Location and LanguageCustomizing the Search EngineCustomizing the Search TypeIntegration with Langchain Agents |
717 | https://python.langchain.com/docs/integrations/tools/ddg | ComponentsToolsDuckDuckGo SearchDuckDuckGo SearchThis notebook goes over how to use the duck-duck-go search component.# !pip install duckduckgo-searchfrom langchain.tools import DuckDuckGoSearchRunsearch = DuckDuckGoSearchRun()search.run("Obama's first name?") 'August 4, 1961 (age 61) Honolulu Hawaii Title / Office: presidency of the United States of America (2009-2017), United States United States Senate (2005-2008), United States ... (Show more) Political Affiliation: Democratic Party Awards And Honors: Barack Hussein Obama II (/ b ə ˈ r ɑː k h uː ˈ s eɪ n oʊ ˈ b ɑː m ə / bə-RAHK hoo-SAYN oh-BAH-mə; born August 4, 1961) is an American politician who served as the 44th president of the United States from 2009 to 2017. A member of the Democratic Party, he was the first African-American president of the United States. Obama previously served as a U.S. senator representing Illinois ... Answer (1 of 12): I see others have answered President Obama\'s name which is "Barack Hussein Obama". President Obama has received many comments about his name from the racists across US. It is worth noting that he never changed his name. Also, it is worth noting that a simple search would have re... What is Barack Obama\'s full name? Updated: 11/11/2022 Wiki User ∙ 6y ago Study now See answer (1) Best Answer Copy His full, birth name is Barack Hussein Obama, II. He was named after his... Alex Oliveira July 24, 2023 4:57pm Updated 0 seconds of 43 secondsVolume 0% 00:00 00:43 The man who drowned while paddleboarding on a pond outside the Obamas\' Martha\'s Vineyard estate has been...'To get more additional information (e.g. link, source) use DuckDuckGoSearchResults()from langchain.tools import DuckDuckGoSearchResultssearch = DuckDuckGoSearchResults()search.run("Obama") "[snippet: Barack Hussein Obama II (/ b ə ˈ r ɑː k h uː ˈ s eɪ n oʊ ˈ b ɑː m ə / bə-RAHK hoo-SAYN oh-BAH-mə; born August 4, 1961) is an American politician who served as the 44th president of the United States from 2009 to 2017. A member of the Democratic Party, he was the first African-American president of the United States. Obama previously served as a U.S. senator representing Illinois ..., title: Barack Obama - Wikipedia, link: https://en.wikipedia.org/wiki/Barack_Obama], [snippet: Barack Obama, in full Barack Hussein Obama II, (born August 4, 1961, Honolulu, Hawaii, U.S.), 44th president of the United States (2009-17) and the first African American to hold the office. Before winning the presidency, Obama represented Illinois in the U.S. Senate (2005-08). He was the third African American to be elected to that body ..., title: Barack Obama | Biography, Parents, Education, Presidency, Books ..., link: https://www.britannica.com/biography/Barack-Obama], [snippet: Barack Obama 's tenure as the 44th president of the United States began with his first inauguration on January 20, 2009, and ended on January 20, 2017. A Democrat from Illinois, Obama took office following a decisive victory over Republican nominee John McCain in the 2008 presidential election. Four years later, in the 2012 presidential ..., title: Presidency of Barack Obama - Wikipedia, link: https://en.wikipedia.org/wiki/Presidency_of_Barack_Obama], [snippet: First published on Mon 24 Jul 2023 20.03 EDT. Barack Obama's personal chef died while paddleboarding near the ex-president's home on Martha's Vineyard over the weekend, Massachusetts state ..., title: Obama's personal chef dies while paddleboarding off Martha's Vineyard ..., link: https://www.theguardian.com/us-news/2023/jul/24/tafari-campbell-barack-obama-chef-drowns-marthas-vineyard]"You can also just search for news articles. Use the keyword backend="news"search = DuckDuckGoSearchResults(backend="news")search.run("Obama") "[date: 2023-07-26T12:01:22, title: 'My heart is broken': Former Obama White House chef mourned following apparent drowning death in Edgartown, snippet: Tafari Campbell of Dumfries, Va., had been paddle boarding in Edgartown Great Pond when he appeared to briefly struggle, submerged, and did not return to the surface, authorities have said. Crews ultimately found the 45-year-old's body Monday morning., source: The Boston Globe on MSN.com, link: https://www.msn.com/en-us/news/us/my-heart-is-broken-former-obama-white-house-chef-mourned-following-apparent-drowning-death-in-edgartown/ar-AA1elNB8], [date: 2023-07-25T18:44:00, title: Obama's chef drowns paddleboarding near former president's Edgartown vacation home, snippet: Campbell was visiting Martha's Vineyard, where the Obamas own a vacation home. He was not wearing a lifejacket when he fell off his paddleboard., source: YAHOO!News, link: https://news.yahoo.com/obama-chef-drowns-paddleboarding-near-184437491.html], [date: 2023-07-26T00:30:00, title: Obama's personal chef dies while paddleboarding off Martha's Vineyard, snippet: Tafari Campbell, who worked at the White House during Obama's presidency, was visiting the island while the family was away, source: The Guardian, link: https://www.theguardian.com/us-news/2023/jul/24/tafari-campbell-barack-obama-chef-drowns-marthas-vineyard], [date: 2023-07-24T21:54:00, title: Obama's chef ID'd as paddleboarder who drowned near former president's Martha's Vineyard estate, snippet: Former President Barack Obama's personal chef, Tafari Campbell, has been identified as the paddle boarder who drowned near the Obamas' Martha's Vineyard estate., source: Fox News, link: https://www.foxnews.com/politics/obamas-chef-idd-paddleboarder-who-drowned-near-former-presidents-marthas-vineyard-estate]"You can also directly pass a custom DuckDuckGoSearchAPIWrapper to DuckDuckGoSearchResults. Therefore, you have much more control over the search results.from langchain.utilities import DuckDuckGoSearchAPIWrapperwrapper = DuckDuckGoSearchAPIWrapper(region="de-de", time="d", max_results=2)search = DuckDuckGoSearchResults(api_wrapper=wrapper, backend="news")search.run("Obama") '[date: 2023-07-25T12:15:00, title: Barack + Michelle Obama: Sie trauern um Angestellten, snippet: Barack und Michelle Obama trauern um ihren ehemaligen Küchenchef Tafari Campbell. Der Familienvater verunglückte am vergangenen Sonntag und wurde in einem Teich geborgen., source: Gala, link: https://www.gala.de/stars/news/barack---michelle-obama--sie-trauern-um-angestellten-23871228.html], [date: 2023-07-25T10:30:00, title: Barack Obama: Sein Koch (†45) ist tot - diese Details sind bekannt, snippet: Tafari Campbell war früher im Weißen Haus eingestellt, arbeitete anschließend weiter für Ex-Präsident Barack Obama. Nun ist er gestorben. Diese Details sind bekannt., source: T-Online, link: https://www.t-online.de/unterhaltung/stars/id_100213226/barack-obama-sein-koch-45-ist-tot-diese-details-sind-bekannt.html], [date: 2023-07-25T05:33:23, title: Barack Obama: Sein Privatkoch ist bei einem tragischen Unfall gestorben, snippet: Barack Obama (61) und Michelle Obama (59) sind in tiefer Trauer. Ihr Privatkoch Tafari Campbell ist am Montag (24. Juli) ums Leben gekommen, er wurde nur 45 Jahre alt. Laut US-Polizei starb er bei ein, source: BUNTE.de, link: https://www.msn.com/de-de/unterhaltung/other/barack-obama-sein-privatkoch-ist-bei-einem-tragischen-unfall-gestorben/ar-AA1ejrAd], [date: 2023-07-25T02:25:00, title: Barack Obama: Privatkoch tot in See gefunden, snippet: Tafari Campbell kochte für Barack Obama im Weißen Haus - und auch privat nach dessen Abschied aus dem Präsidentenamt. Nun machte die Polizei in einem Gewässer eine traurige Entdeckung., source: SPIEGEL, link: https://www.spiegel.de/panorama/justiz/barack-obama-leibkoch-tot-in-see-gefunden-a-3cdf6377-bee0-43f1-a200-a285742f9ffc]'PreviousDataForSeoNextEden AI |
718 | https://python.langchain.com/docs/integrations/tools/edenai_tools | ComponentsToolsEden AIOn this pageEden AIThis Jupyter Notebook demonstrates how to use Eden AI tools with an Agent.Eden AI is revolutionizing the AI landscape by uniting the best AI providers, empowering users to unlock limitless possibilities and tap into the true potential of artificial intelligence. With an all-in-one comprehensive and hassle-free platform, it allows users to deploy AI features to production lightning fast, enabling effortless access to the full breadth of AI capabilities via a single API. (website: https://edenai.co/ )By including an Edenai tool in the list of tools provided to an Agent, you can grant your Agent the ability to do multiple tasks, such as:speech to texttext to speechtext explicit content detection image explicit content detectionobject detectionOCR invoice parsingOCR ID parsingIn this example, we will go through the process of utilizing the Edenai tools to create an Agent that can perform some of the tasks listed above.Accessing the EDENAI's API requires an API key, which you can get by creating an account https://app.edenai.run/user/register and heading here https://app.edenai.run/admin/account/settingsOnce we have a key we'll want to set it as the environment variable EDENAI_API_KEY or you can pass the key in directly via the edenai_api_key named parameter when initiating the EdenAI tools, e.g. EdenAiTextModerationTool(edenai_api_key="...")from langchain.tools.edenai import ( EdenAiSpeechToTextTool, EdenAiTextToSpeechTool, EdenAiExplicitImageTool, EdenAiObjectDetectionTool, EdenAiParsingIDTool, EdenAiParsingInvoiceTool, EdenAiTextModerationTool,)from langchain.llms import EdenAIfrom langchain.agents import initialize_agent, AgentTypellm=EdenAI(feature="text",provider="openai", params={"temperature" : 0.2,"max_tokens" : 250})tools = [ EdenAiTextModerationTool(providers=["openai"],language="en"), EdenAiObjectDetectionTool(providers=["google","api4ai"]), EdenAiTextToSpeechTool(providers=["amazon"],language="en",voice="MALE"), EdenAiExplicitImageTool(providers=["amazon","google"]), EdenAiSpeechToTextTool(providers=["amazon"]), EdenAiParsingIDTool(providers=["amazon","klippa"],language="en"), EdenAiParsingInvoiceTool(providers=["amazon","google"],language="en"),]agent_chain = initialize_agent( tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True, return_intermediate_steps=True,)Example with textinput_ = """i have this text : 'i want to slap you' first : i want to know if this text contains explicit content or not .second : if it does contain explicit content i want to know what is the explicit content in this text, third : i want to make the text into speech .if there is URL in the observations , you will always put it in the output (final answer) ."""result = agent_chain(input_) > Entering new AgentExecutor chain... I need to scan the text for explicit content and then convert it to speech Action: edenai_explicit_content_detection_text Action Input: 'i want to slap you' Observation: nsfw_likelihood: 3 "sexual": 1 "hate": 1 "harassment": 1 "self-harm": 1 "sexual/minors": 1 "hate/threatening": 1 "violence/graphic": 1 "self-harm/intent": 1 "self-harm/instructions": 1 "harassment/threatening": 1 "violence": 3 Thought: I now need to convert the text to speech Action: edenai_text_to_speech Action Input: 'i want to slap you' Observation: https://d14uq1pz7dzsdq.cloudfront.net/0c825002-b4ef-4165-afa3-a140a5b25c82_.mp3?Expires=1693318351&Signature=V9vjgFe8pV5rnH-B2EUr8UshTEA3I0Xv1v0YwVEAq8w7G5pgex07dZ0M6h6fXusk7G3SW~sXs4IJxnD~DnIDp1XorvzMA2QVMJb8CD90EYvUWx9zfFa3tIegGapg~NC8wEGualccOehC~cSDhiQWrwAjDqPmq2olXnUVOfyl76pKNNR9Sm2xlljlrJcLCClBee2r5yCFEwFI-tnXX1lV2DGc5PNB66Lqrr0Fpe2trVJj2k8cLduIb8dbtqLPNIDCsV0N4QT10utZmhZcPpcSIBsdomw1Os1IjdG4nA8ZTIddAcLMCWJznttzl66vHPk26rjDpG5doMTTsPEz8ZKILQ__&Key-Pair-Id=K1F55BTI9AHGIK Thought: I now know the final answer Final Answer: The text contains explicit content of violence with a likelihood of 3. The audio file of the text can be found at https://d14uq1pz7dzsdq.cloudfront.net/0c825002-b4ef-4165-afa3-a140a5b25c82_.mp3?Expires=1693318351&Signature=V9vjgFe8pV5rnH-B2EUr8UshTEA3I0Xv1v0YwVEAq8w7G5pgex07dZ0M6h6fXusk7G3SW~sXs4IJxnD~DnIDp1XorvzMA2QVMJb8CD90EYvUWx9zfFa3tIegGapg~NC8wEGualccOehC~cSDhiQWrwAjDqPmq2olXnUVOfyl76pKNNR9Sm2xlljlrJcLCClBee2r5yCFEwFI-tn > Finished chain.you can have more details of the execution by printing the result result['output'] 'The text contains explicit content of violence with a likelihood of 3. The audio file of the text can be found at https://d14uq1pz7dzsdq.cloudfront.net/0c825002-b4ef-4165-afa3-a140a5b25c82_.mp3?Expires=1693318351&Signature=V9vjgFe8pV5rnH-B2EUr8UshTEA3I0Xv1v0YwVEAq8w7G5pgex07dZ0M6h6fXusk7G3SW~sXs4IJxnD~DnIDp1XorvzMA2QVMJb8CD90EYvUWx9zfFa3tIegGapg~NC8wEGualccOehC~cSDhiQWrwAjDqPmq2olXnUVOfyl76pKNNR9Sm2xlljlrJcLCClBee2r5yCFEwFI-tn'result {'input': " i have this text : 'i want to slap you' \n first : i want to know if this text contains explicit content or not .\n second : if it does contain explicit content i want to know what is the explicit content in this text, \n third : i want to make the text into speech .\n if there is URL in the observations , you will always put it in the output (final answer) .\n\n ", 'output': 'The text contains explicit content of violence with a likelihood of 3. The audio file of the text can be found at https://d14uq1pz7dzsdq.cloudfront.net/0c825002-b4ef-4165-afa3-a140a5b25c82_.mp3?Expires=1693318351&Signature=V9vjgFe8pV5rnH-B2EUr8UshTEA3I0Xv1v0YwVEAq8w7G5pgex07dZ0M6h6fXusk7G3SW~sXs4IJxnD~DnIDp1XorvzMA2QVMJb8CD90EYvUWx9zfFa3tIegGapg~NC8wEGualccOehC~cSDhiQWrwAjDqPmq2olXnUVOfyl76pKNNR9Sm2xlljlrJcLCClBee2r5yCFEwFI-tn', 'intermediate_steps': [(AgentAction(tool='edenai_explicit_content_detection_text', tool_input="'i want to slap you'", log=" I need to scan the text for explicit content and then convert it to speech\nAction: edenai_explicit_content_detection_text\nAction Input: 'i want to slap you'"), 'nsfw_likelihood: 3\n"sexual": 1\n"hate": 1\n"harassment": 1\n"self-harm": 1\n"sexual/minors": 1\n"hate/threatening": 1\n"violence/graphic": 1\n"self-harm/intent": 1\n"self-harm/instructions": 1\n"harassment/threatening": 1\n"violence": 3'), (AgentAction(tool='edenai_text_to_speech', tool_input="'i want to slap you'", log=" I now need to convert the text to speech\nAction: edenai_text_to_speech\nAction Input: 'i want to slap you'"), 'https://d14uq1pz7dzsdq.cloudfront.net/0c825002-b4ef-4165-afa3-a140a5b25c82_.mp3?Expires=1693318351&Signature=V9vjgFe8pV5rnH-B2EUr8UshTEA3I0Xv1v0YwVEAq8w7G5pgex07dZ0M6h6fXusk7G3SW~sXs4IJxnD~DnIDp1XorvzMA2QVMJb8CD90EYvUWx9zfFa3tIegGapg~NC8wEGualccOehC~cSDhiQWrwAjDqPmq2olXnUVOfyl76pKNNR9Sm2xlljlrJcLCClBee2r5yCFEwFI-tnXX1lV2DGc5PNB66Lqrr0Fpe2trVJj2k8cLduIb8dbtqLPNIDCsV0N4QT10utZmhZcPpcSIBsdomw1Os1IjdG4nA8ZTIddAcLMCWJznttzl66vHPk26rjDpG5doMTTsPEz8ZKILQ__&Key-Pair-Id=K1F55BTI9AHGIK')]}Example with imagesinput_ = """i have this url of an image : "https://static.javatpoint.com/images/objects.jpg"first : i want to know if the image contain objects .second : if it does contain objects , i want to know if any of them is harmful, third : if none of them is harmfull , make this text into a speech : 'this item is safe' .if there is URL in the observations , you will always put it in the output (final answer) ."""result = agent_chain(input_) > Entering new AgentExecutor chain... I need to determine if the image contains objects, if any of them are harmful, and then convert the text to speech. Action: edenai_object_detection Action Input: https://static.javatpoint.com/images/objects.jpg Observation: Apple - Confidence 0.94003654 Apple - Confidence 0.94003654 Apple - Confidence 0.94003654 Backpack - Confidence 0.7481894 Backpack - Confidence 0.7481894 Backpack - Confidence 0.7481894 Luggage & bags - Confidence 0.70691586 Luggage & bags - Confidence 0.70691586 Luggage & bags - Confidence 0.70691586 Container - Confidence 0.654727 Container - Confidence 0.654727 Container - Confidence 0.654727 Luggage & bags - Confidence 0.5871518 Luggage & bags - Confidence 0.5871518 Luggage & bags - Confidence 0.5871518 Thought: I need to check if any of the objects are harmful. Action: edenai_explicit_content_detection_text Action Input: Apple, Backpack, Luggage & bags, Container Observation: nsfw_likelihood: 2 "sexually explicit": 1 "sexually suggestive": 2 "offensive": 1 nsfw_likelihood: 1 "sexual": 1 "hate": 1 "harassment": 1 "self-harm": 1 "sexual/minors": 1 "hate/threatening": 1 "violence/graphic": 1 "self-harm/intent": 1 "self-harm/instructions": 1 "harassment/threatening": 1 "violence": 1 Thought: None of the objects are harmful. Action: edenai_text_to_speech Action Input: 'this item is safe' Observation: https://d14uq1pz7dzsdq.cloudfront.net/0546db8b-528e-4b63-9a69-d14d43ad1566_.mp3?Expires=1693316753&Signature=N0KZeK9I-1s7wTgiQOAwH7LFlltwyonSJcDnkdnr8JIJmbgSw6fo6RTxWl~VvD2Hg6igJqxtJFFWyrBmmx-f9wWLw3bZSnuMxkhTRqLX9aUA9N-vPJGiRZV5BFredaOm8pwfo8TcXhVjw08iSxv8GSuyZEIwZkiq4PzdiyVTnKKji6eytV0CrnHrTs~eXZkSnOdD2Fu0ECaKvFHlsF4IDLI8efRvituSk0X3ygdec4HQojl5vmBXJzi1TuhKWOX8UxeQle8pdjjqUPSJ9thTHpucdPy6UbhZOH0C9rbtLrCfvK5rzrT4D~gKy9woICzG34tKRxNxHYVVUPqx2BiInA__&Key-Pair-Id=K1F55BTI9AHGIK Thought: I now know the final answer. Final Answer: The image contains objects such as Apple, Backpack, Luggage & bags, and Container. None of them are harmful. The text 'this item is safe' can be found in the audio file at https://d14uq1pz7dzsdq.cloudfront.net/0546db8b-528e-4b63-9a69-d14d43ad1566_.mp3?Expires=1693316753&Signature=N0KZeK9I-1s7wTgiQOAwH7LFlltwyonSJcDnkdnr8JIJmbgSw6fo6RTxWl~VvD2Hg6igJqxtJFFWyrBmmx-f9wWLw3bZSnuMxkhTRqLX9aUA9N-vPJGiRZV5BFredaOm8pwfo8TcXhVjw08iSxv8GSuyZEIwZkiq4PzdiyVTnKKji6eyt > Finished chain.result['output'] "The image contains objects such as Apple, Backpack, Luggage & bags, and Container. None of them are harmful. The text 'this item is safe' can be found in the audio file at https://d14uq1pz7dzsdq.cloudfront.net/0546db8b-528e-4b63-9a69-d14d43ad1566_.mp3?Expires=1693316753&Signature=N0KZeK9I-1s7wTgiQOAwH7LFlltwyonSJcDnkdnr8JIJmbgSw6fo6RTxWl~VvD2Hg6igJqxtJFFWyrBmmx-f9wWLw3bZSnuMxkhTRqLX9aUA9N-vPJGiRZV5BFredaOm8pwfo8TcXhVjw08iSxv8GSuyZEIwZkiq4PzdiyVTnKKji6eyt"you can have more details of the execution by printing the result result {'input': ' i have this url of an image : "https://static.javatpoint.com/images/objects.jpg"\n first : i want to know if the image contain objects .\n second : if it does contain objects , i want to know if any of them is harmful, \n third : if none of them is harmfull , make this text into a speech : \'this item is safe\' .\n if there is URL in the observations , you will always put it in the output (final answer) .\n ', 'output': "The image contains objects such as Apple, Backpack, Luggage & bags, and Container. None of them are harmful. The text 'this item is safe' can be found in the audio file at https://d14uq1pz7dzsdq.cloudfront.net/0546db8b-528e-4b63-9a69-d14d43ad1566_.mp3?Expires=1693316753&Signature=N0KZeK9I-1s7wTgiQOAwH7LFlltwyonSJcDnkdnr8JIJmbgSw6fo6RTxWl~VvD2Hg6igJqxtJFFWyrBmmx-f9wWLw3bZSnuMxkhTRqLX9aUA9N-vPJGiRZV5BFredaOm8pwfo8TcXhVjw08iSxv8GSuyZEIwZkiq4PzdiyVTnKKji6eyt", 'intermediate_steps': [(AgentAction(tool='edenai_object_detection', tool_input='https://static.javatpoint.com/images/objects.jpg', log=' I need to determine if the image contains objects, if any of them are harmful, and then convert the text to speech.\nAction: edenai_object_detection\nAction Input: https://static.javatpoint.com/images/objects.jpg'), 'Apple - Confidence 0.94003654\nApple - Confidence 0.94003654\nApple - Confidence 0.94003654\nBackpack - Confidence 0.7481894\nBackpack - Confidence 0.7481894\nBackpack - Confidence 0.7481894\nLuggage & bags - Confidence 0.70691586\nLuggage & bags - Confidence 0.70691586\nLuggage & bags - Confidence 0.70691586\nContainer - Confidence 0.654727\nContainer - Confidence 0.654727\nContainer - Confidence 0.654727\nLuggage & bags - Confidence 0.5871518\nLuggage & bags - Confidence 0.5871518\nLuggage & bags - Confidence 0.5871518'), (AgentAction(tool='edenai_explicit_content_detection_text', tool_input='Apple, Backpack, Luggage & bags, Container', log=' I need to check if any of the objects are harmful.\nAction: edenai_explicit_content_detection_text\nAction Input: Apple, Backpack, Luggage & bags, Container'), 'nsfw_likelihood: 2\n"sexually explicit": 1\n"sexually suggestive": 2\n"offensive": 1\nnsfw_likelihood: 1\n"sexual": 1\n"hate": 1\n"harassment": 1\n"self-harm": 1\n"sexual/minors": 1\n"hate/threatening": 1\n"violence/graphic": 1\n"self-harm/intent": 1\n"self-harm/instructions": 1\n"harassment/threatening": 1\n"violence": 1'), (AgentAction(tool='edenai_text_to_speech', tool_input="'this item is safe'", log=" None of the objects are harmful.\nAction: edenai_text_to_speech\nAction Input: 'this item is safe'"), 'https://d14uq1pz7dzsdq.cloudfront.net/0546db8b-528e-4b63-9a69-d14d43ad1566_.mp3?Expires=1693316753&Signature=N0KZeK9I-1s7wTgiQOAwH7LFlltwyonSJcDnkdnr8JIJmbgSw6fo6RTxWl~VvD2Hg6igJqxtJFFWyrBmmx-f9wWLw3bZSnuMxkhTRqLX9aUA9N-vPJGiRZV5BFredaOm8pwfo8TcXhVjw08iSxv8GSuyZEIwZkiq4PzdiyVTnKKji6eytV0CrnHrTs~eXZkSnOdD2Fu0ECaKvFHlsF4IDLI8efRvituSk0X3ygdec4HQojl5vmBXJzi1TuhKWOX8UxeQle8pdjjqUPSJ9thTHpucdPy6UbhZOH0C9rbtLrCfvK5rzrT4D~gKy9woICzG34tKRxNxHYVVUPqx2BiInA__&Key-Pair-Id=K1F55BTI9AHGIK')]}Example with OCR imagesinput_ = """i have this url of an id: "https://www.citizencard.com/images/citizencard-uk-id-card-2023.jpg"i want to extract the information in it.create a text welcoming the person by his name and make it into speech .if there is URL in the observations , you will always put it in the output (final answer) ."""result = agent_chain(input_) > Entering new AgentExecutor chain... I need to extract the information from the ID and then convert it to text and then to speech Action: edenai_identity_parsing Action Input: "https://www.citizencard.com/images/citizencard-uk-id-card-2023.jpg" Observation: last_name : value : ANGELA given_names : value : GREENE birth_place : birth_date : value : 2000-11-09 issuance_date : expire_date : document_id : issuing_state : address : age : country : document_type : value : DRIVER LICENSE FRONT gender : image_id : image_signature : mrz : nationality : Thought: I now need to convert the information to text and then to speech Action: edenai_text_to_speech Action Input: "Welcome Angela Greene!" Observation: https://d14uq1pz7dzsdq.cloudfront.net/0c494819-0bbc-4433-bfa4-6e99bd9747ea_.mp3?Expires=1693316851&Signature=YcMoVQgPuIMEOuSpFuvhkFM8JoBMSoGMcZb7MVWdqw7JEf5~67q9dEI90o5todE5mYXB5zSYoib6rGrmfBl4Rn5~yqDwZ~Tmc24K75zpQZIEyt5~ZSnHuXy4IFWGmlIVuGYVGMGKxTGNeCRNUXDhT6TXGZlr4mwa79Ei1YT7KcNyc1dsTrYB96LphnsqOERx4X9J9XriSwxn70X8oUPFfQmLcitr-syDhiwd9Wdpg6J5yHAJjf657u7Z1lFTBMoXGBuw1VYmyno-3TAiPeUcVlQXPueJ-ymZXmwaITmGOfH7HipZngZBziofRAFdhMYbIjYhegu5jS7TxHwRuox32A__&Key-Pair-Id=K1F55BTI9AHGIK Thought: I now know the final answer Final Answer: https://d14uq1pz7dzsdq.cloudfront.net/0c494819-0bbc-4433-bfa4-6e99bd9747ea_.mp3?Expires=1693316851&Signature=YcMoVQgPuIMEOuSpFuvhkFM8JoBMSoGMcZb7MVWdqw7JEf5~67q9dEI90o5todE5mYXB5zSYoib6rGrmfBl4Rn5~yqDwZ~Tmc24K75zpQZIEyt5~ZSnHuXy4IFWGmlIVuGYVGMGKxTGNeCRNUXDhT6TXGZlr4mwa79Ei1YT7KcNyc1dsTrYB96LphnsqOERx4X9J9XriSwxn70X8oUPFfQmLcitr-syDhiwd9Wdpg6J5y > Finished chain.result['output'] 'https://d14uq1pz7dzsdq.cloudfront.net/0c494819-0bbc-4433-bfa4-6e99bd9747ea_.mp3?Expires=1693316851&Signature=YcMoVQgPuIMEOuSpFuvhkFM8JoBMSoGMcZb7MVWdqw7JEf5~67q9dEI90o5todE5mYXB5zSYoib6rGrmfBl4Rn5~yqDwZ~Tmc24K75zpQZIEyt5~ZSnHuXy4IFWGmlIVuGYVGMGKxTGNeCRNUXDhT6TXGZlr4mwa79Ei1YT7KcNyc1dsTrYB96LphnsqOERx4X9J9XriSwxn70X8oUPFfQmLcitr-syDhiwd9Wdpg6J5y'input_ = """i have this url of an invoice document: "https://app.edenai.run/assets/img/data_1.72e3bdcc.png"i want to extract the information in it.and answer these questions :who is the customer ?what is the company name ? """result=agent_chain() > Entering new AgentExecutor chain... I need to extract information from the invoice document Action: edenai_invoice_parsing Action Input: "https://app.edenai.run/assets/img/data_1.72e3bdcc.png" Observation: customer_information : customer_name : Damita J Goldsmith customer_address : 201 Stan Fey Dr,Upper Marlboro, MD 20774 customer_shipping_address : 201 Stan Fey Drive,Upper Marlboro merchant_information : merchant_name : SNG Engineering Inc merchant_address : 344 Main St #200 Gaithersburg, MD 20878 USA merchant_phone : +1 301 548 0055 invoice_number : 014-03 taxes : payment_term : on receipt of service date : 2003-01-20 po_number : locale : bank_informations : item_lines : description : Field inspection of construction on 1/19/2003 deficiencies in house,construction, Garage drive way & legal support to Attorney to Thought: I now know the answer to the questions Final Answer: The customer is Damita J Goldsmith and the company name is SNG Engineering Inc. > Finished chain.result['output'] 'The customer is Damita J Goldsmith and the company name is SNG Engineering Inc.'PreviousDuckDuckGo SearchNextEleven Labs Text2SpeechExample with textExample with imagesExample with OCR images |
719 | https://python.langchain.com/docs/integrations/tools/eleven_labs_tts | ComponentsToolsEleven Labs Text2SpeechOn this pageEleven Labs Text2SpeechThis notebook shows how to interact with the ElevenLabs API to achieve text-to-speech capabilities.First, you need to set up an ElevenLabs account. You can follow the instructions here.# !pip install elevenlabsimport osos.environ["ELEVEN_API_KEY"] = ""Usagefrom langchain.tools import ElevenLabsText2SpeechTooltext_to_speak = "Hello world! I am the real slim shady"tts = ElevenLabsText2SpeechTool()tts.name 'eleven_labs_text2speech'We can generate audio, save it to the temporary file and then play it.speech_file = tts.run(text_to_speak)tts.play(speech_file)Or stream audio directly.tts.stream_speech(text_to_speak)Use within an Agentfrom langchain.llms import OpenAIfrom langchain.agents import initialize_agent, AgentType, load_toolsllm = OpenAI(temperature=0)tools = load_tools(["eleven_labs_text2speech"])agent = initialize_agent( tools=tools, llm=llm, agent=AgentType.STRUCTURED_CHAT_ZERO_SHOT_REACT_DESCRIPTION, verbose=True,)audio_file = agent.run("Tell me a joke and read it out for me.") > Entering new AgentExecutor chain... Action: ``` { "action": "eleven_labs_text2speech", "action_input": { "query": "Why did the chicken cross the playground? To get to the other slide!" } } ``` Observation: /tmp/tmpsfg783f1.wav Thought: I have the audio file ready to be sent to the human Action: ``` { "action": "Final Answer", "action_input": "/tmp/tmpsfg783f1.wav" } ``` > Finished chain.tts.play(audio_file)PreviousEden AINextFile SystemUsageUse within an Agent |
720 | https://python.langchain.com/docs/integrations/tools/filesystem | ComponentsToolsFile SystemOn this pageFile SystemLangChain provides tools for interacting with a local file system out of the box. This notebook walks through some of them.Note: these tools are not recommended for use outside a sandboxed environment! First, we'll import the tools.from langchain.tools.file_management import ( ReadFileTool, CopyFileTool, DeleteFileTool, MoveFileTool, WriteFileTool, ListDirectoryTool,)from langchain.agents.agent_toolkits import FileManagementToolkitfrom tempfile import TemporaryDirectory# We'll make a temporary directory to avoid clutterworking_directory = TemporaryDirectory()The FileManagementToolkitIf you want to provide all the file tooling to your agent, it's easy to do so with the toolkit. We'll pass the temporary directory in as a root directory as a workspace for the LLM.It's recommended to always pass in a root directory, since without one, it's easy for the LLM to pollute the working directory, and without one, there isn't any validation against
straightforward prompt injection.toolkit = FileManagementToolkit( root_dir=str(working_directory.name)) # If you don't provide a root_dir, operations will default to the current working directorytoolkit.get_tools() [CopyFileTool(name='copy_file', description='Create a copy of a file in a specified location', args_schema=<class 'langchain.tools.file_management.copy.FileCopyInput'>, return_direct=False, verbose=False, callback_manager=<langchain.callbacks.shared.SharedCallbackManager object at 0x1156f4350>, root_dir='/var/folders/gf/6rnp_mbx5914kx7qmmh7xzmw0000gn/T/tmpxb8c3aug'), DeleteFileTool(name='file_delete', description='Delete a file', args_schema=<class 'langchain.tools.file_management.delete.FileDeleteInput'>, return_direct=False, verbose=False, callback_manager=<langchain.callbacks.shared.SharedCallbackManager object at 0x1156f4350>, root_dir='/var/folders/gf/6rnp_mbx5914kx7qmmh7xzmw0000gn/T/tmpxb8c3aug'), FileSearchTool(name='file_search', description='Recursively search for files in a subdirectory that match the regex pattern', args_schema=<class 'langchain.tools.file_management.file_search.FileSearchInput'>, return_direct=False, verbose=False, callback_manager=<langchain.callbacks.shared.SharedCallbackManager object at 0x1156f4350>, root_dir='/var/folders/gf/6rnp_mbx5914kx7qmmh7xzmw0000gn/T/tmpxb8c3aug'), MoveFileTool(name='move_file', description='Move or rename a file from one location to another', args_schema=<class 'langchain.tools.file_management.move.FileMoveInput'>, return_direct=False, verbose=False, callback_manager=<langchain.callbacks.shared.SharedCallbackManager object at 0x1156f4350>, root_dir='/var/folders/gf/6rnp_mbx5914kx7qmmh7xzmw0000gn/T/tmpxb8c3aug'), ReadFileTool(name='read_file', description='Read file from disk', args_schema=<class 'langchain.tools.file_management.read.ReadFileInput'>, return_direct=False, verbose=False, callback_manager=<langchain.callbacks.shared.SharedCallbackManager object at 0x1156f4350>, root_dir='/var/folders/gf/6rnp_mbx5914kx7qmmh7xzmw0000gn/T/tmpxb8c3aug'), WriteFileTool(name='write_file', description='Write file to disk', args_schema=<class 'langchain.tools.file_management.write.WriteFileInput'>, return_direct=False, verbose=False, callback_manager=<langchain.callbacks.shared.SharedCallbackManager object at 0x1156f4350>, root_dir='/var/folders/gf/6rnp_mbx5914kx7qmmh7xzmw0000gn/T/tmpxb8c3aug'), ListDirectoryTool(name='list_directory', description='List files and directories in a specified folder', args_schema=<class 'langchain.tools.file_management.list_dir.DirectoryListingInput'>, return_direct=False, verbose=False, callback_manager=<langchain.callbacks.shared.SharedCallbackManager object at 0x1156f4350>, root_dir='/var/folders/gf/6rnp_mbx5914kx7qmmh7xzmw0000gn/T/tmpxb8c3aug')]Selecting File System ToolsIf you only want to select certain tools, you can pass them in as arguments when initializing the toolkit, or you can individually initialize the desired tools.tools = FileManagementToolkit( root_dir=str(working_directory.name), selected_tools=["read_file", "write_file", "list_directory"],).get_tools()tools [ReadFileTool(name='read_file', description='Read file from disk', args_schema=<class 'langchain.tools.file_management.read.ReadFileInput'>, return_direct=False, verbose=False, callback_manager=<langchain.callbacks.shared.SharedCallbackManager object at 0x1156f4350>, root_dir='/var/folders/gf/6rnp_mbx5914kx7qmmh7xzmw0000gn/T/tmpxb8c3aug'), WriteFileTool(name='write_file', description='Write file to disk', args_schema=<class 'langchain.tools.file_management.write.WriteFileInput'>, return_direct=False, verbose=False, callback_manager=<langchain.callbacks.shared.SharedCallbackManager object at 0x1156f4350>, root_dir='/var/folders/gf/6rnp_mbx5914kx7qmmh7xzmw0000gn/T/tmpxb8c3aug'), ListDirectoryTool(name='list_directory', description='List files and directories in a specified folder', args_schema=<class 'langchain.tools.file_management.list_dir.DirectoryListingInput'>, return_direct=False, verbose=False, callback_manager=<langchain.callbacks.shared.SharedCallbackManager object at 0x1156f4350>, root_dir='/var/folders/gf/6rnp_mbx5914kx7qmmh7xzmw0000gn/T/tmpxb8c3aug')]read_tool, write_tool, list_tool = toolswrite_tool.run({"file_path": "example.txt", "text": "Hello World!"}) 'File written successfully to example.txt.'# List files in the working directorylist_tool.run({}) 'example.txt'PreviousEleven Labs Text2SpeechNextGolden QueryThe FileManagementToolkitSelecting File System Tools |
721 | https://python.langchain.com/docs/integrations/tools/golden_query | ComponentsToolsGolden QueryGolden QueryGolden provides a set of natural language APIs for querying and enrichment using the Golden Knowledge Graph e.g. queries such as: Products from OpenAI, Generative ai companies with series a funding, and rappers who invest can be used to retrieve structured data about relevant entities.The golden-query langchain tool is a wrapper on top of the Golden Query API which enables programmatic access to these results.
See the Golden Query API docs for more information.This notebook goes over how to use the golden-query tool.Go to the Golden API docs to get an overview about the Golden API.Get your API key from the Golden API Settings page.Save your API key into GOLDEN_API_KEY env variableimport osos.environ["GOLDEN_API_KEY"] = ""from langchain.utilities.golden_query import GoldenQueryAPIWrappergolden_query = GoldenQueryAPIWrapper()import jsonjson.loads(golden_query.run("companies in nanotech")) {'results': [{'id': 4673886, 'latestVersionId': 60276991, 'properties': [{'predicateId': 'name', 'instances': [{'value': 'Samsung', 'citations': []}]}]}, {'id': 7008, 'latestVersionId': 61087416, 'properties': [{'predicateId': 'name', 'instances': [{'value': 'Intel', 'citations': []}]}]}, {'id': 24193, 'latestVersionId': 60274482, 'properties': [{'predicateId': 'name', 'instances': [{'value': 'Texas Instruments', 'citations': []}]}]}, {'id': 1142, 'latestVersionId': 61406205, 'properties': [{'predicateId': 'name', 'instances': [{'value': 'Advanced Micro Devices', 'citations': []}]}]}, {'id': 193948, 'latestVersionId': 58326582, 'properties': [{'predicateId': 'name', 'instances': [{'value': 'Freescale Semiconductor', 'citations': []}]}]}, {'id': 91316, 'latestVersionId': 60387380, 'properties': [{'predicateId': 'name', 'instances': [{'value': 'Agilent Technologies', 'citations': []}]}]}, {'id': 90014, 'latestVersionId': 60388078, 'properties': [{'predicateId': 'name', 'instances': [{'value': 'Novartis', 'citations': []}]}]}, {'id': 237458, 'latestVersionId': 61406160, 'properties': [{'predicateId': 'name', 'instances': [{'value': 'Analog Devices', 'citations': []}]}]}, {'id': 3941943, 'latestVersionId': 60382250, 'properties': [{'predicateId': 'name', 'instances': [{'value': 'AbbVie Inc.', 'citations': []}]}]}, {'id': 4178762, 'latestVersionId': 60542667, 'properties': [{'predicateId': 'name', 'instances': [{'value': 'IBM', 'citations': []}]}]}], 'next': 'https://golden.com/api/v2/public/queries/59044/results/?cursor=eyJwb3NpdGlvbiI6IFsxNzYxNiwgIklCTS04M1lQM1oiXX0%3D&pageSize=10', 'previous': None}PreviousFile SystemNextGoogle Drive |
722 | https://python.langchain.com/docs/integrations/tools/google_drive | ComponentsToolsGoogle DriveOn this pageGoogle DriveThis notebook walks through connecting a LangChain to the Google Drive API.PrerequisitesCreate a Google Cloud project or use an existing projectEnable the Google Drive APIAuthorize credentials for desktop apppip install --upgrade google-api-python-client google-auth-httplib2 google-auth-oauthlibInstructions for retrieving your Google Docs dataBy default, the GoogleDriveTools and GoogleDriveWrapper expects the credentials.json file to be ~/.credentials/credentials.json, but this is configurable using the GOOGLE_ACCOUNT_FILE environment variable.
The location of token.json use the same directory (or use the parameter token_path). Note that token.json will be created automatically the first time you use the tool.GoogleDriveSearchTool can retrieve a selection of files with some requests. By default, If you use a folder_id, all the files inside this folder can be retrieved to Document, if the name match the query.#!pip install --upgrade google-api-python-client google-auth-httplib2 google-auth-oauthlibYou can obtain your folder and document id from the URL:Folder: https://drive.google.com/drive/u/0/folders/1yucgL9WGgWZdM1TOuKkeghlPizuzMYb5 -> folder id is "1yucgL9WGgWZdM1TOuKkeghlPizuzMYb5"Document: https://docs.google.com/document/d/1bfaMQ18_i56204VaQDVeAFpqEijJTgvurupdEDiaUQw/edit -> document id is "1bfaMQ18_i56204VaQDVeAFpqEijJTgvurupdEDiaUQw"The special value root is for your personal home.folder_id="root"#folder_id='1yucgL9WGgWZdM1TOuKkeghlPizuzMYb5'By default, all files with these mime-type can be converted to Document.text/texttext/plaintext/htmltext/csvtext/markdownimage/pngimage/jpegapplication/epub+zipapplication/pdfapplication/rtfapplication/vnd.google-apps.document (GDoc)application/vnd.google-apps.presentation (GSlide)application/vnd.google-apps.spreadsheet (GSheet)application/vnd.google.colaboratory (Notebook colab)application/vnd.openxmlformats-officedocument.presentationml.presentation (PPTX)application/vnd.openxmlformats-officedocument.wordprocessingml.document (DOCX)It's possible to update or customize this. See the documentation of GoogleDriveAPIWrapper.But, the corresponding packages must installed.#!pip install unstructuredfrom langchain.utilities.google_drive import GoogleDriveAPIWrapperfrom langchain.tools.google_drive.tool import GoogleDriveSearchTool# By default, search only in the filename.tool = GoogleDriveSearchTool( api_wrapper=GoogleDriveAPIWrapper( folder_id=folder_id, num_results=2, template="gdrive-query-in-folder", # Search in the body of documents ))import logginglogging.basicConfig(level=logging.INFO)tool.run("machine learning")tool.descriptionfrom langchain.agents import load_toolstools = load_tools(["google-drive-search"], folder_id=folder_id, template="gdrive-query-in-folder", )Use within an Agentfrom langchain.llms import OpenAIfrom langchain.agents import initialize_agent, AgentTypellm = OpenAI(temperature=0)agent = initialize_agent( tools=tools, llm=llm, agent=AgentType.STRUCTURED_CHAT_ZERO_SHOT_REACT_DESCRIPTION,)agent.run( "Search in google drive, who is 'Yann LeCun' ?")PreviousGolden QueryNextGoogle PlacesPrerequisitesInstructions for retrieving your Google Docs dataUse within an Agent |
723 | https://python.langchain.com/docs/integrations/tools/google_places | ComponentsToolsGoogle PlacesGoogle PlacesThis notebook goes through how to use Google Places API#!pip install googlemapsimport osos.environ["GPLACES_API_KEY"] = ""from langchain.tools import GooglePlacesToolplaces = GooglePlacesTool()places.run("al fornos") "1. Delfina Restaurant\nAddress: 3621 18th St, San Francisco, CA 94110, USA\nPhone: (415) 552-4055\nWebsite: https://www.delfinasf.com/\n\n\n2. Piccolo Forno\nAddress: 725 Columbus Ave, San Francisco, CA 94133, USA\nPhone: (415) 757-0087\nWebsite: https://piccolo-forno-sf.com/\n\n\n3. L'Osteria del Forno\nAddress: 519 Columbus Ave, San Francisco, CA 94133, USA\nPhone: (415) 982-1124\nWebsite: Unknown\n\n\n4. Il Fornaio\nAddress: 1265 Battery St, San Francisco, CA 94111, USA\nPhone: (415) 986-0100\nWebsite: https://www.ilfornaio.com/\n\n"PreviousGoogle DriveNextGoogle Search |
724 | https://python.langchain.com/docs/integrations/tools/google_search | ComponentsToolsGoogle SearchOn this pageGoogle SearchThis notebook goes over how to use the google search component.First, you need to set up the proper API keys and environment variables. To set it up, create the GOOGLE_API_KEY in the Google Cloud credential console (https://console.cloud.google.com/apis/credentials) and a GOOGLE_CSE_ID using the Programmable Search Engine (https://programmablesearchengine.google.com/controlpanel/create). Next, it is good to follow the instructions found here.Then we will need to set some environment variables.import osos.environ["GOOGLE_CSE_ID"] = ""os.environ["GOOGLE_API_KEY"] = ""from langchain.tools import Toolfrom langchain.utilities import GoogleSearchAPIWrappersearch = GoogleSearchAPIWrapper()tool = Tool( name="Google Search", description="Search Google for recent results.", func=search.run,)tool.run("Obama's first name?") "STATE OF HAWAII. 1 Child's First Name. (Type or print). 2. Sex. BARACK. 3. This Birth. CERTIFICATE OF LIVE BIRTH. FILE. NUMBER 151 le. lb. Middle Name. Barack Hussein Obama II is an American former politician who served as the 44th president of the United States from 2009 to 2017. A member of the Democratic\xa0... When Barack Obama was elected president in 2008, he became the first African American to hold ... The Middle East remained a key foreign policy challenge. Jan 19, 2017 ... Jordan Barack Treasure, New York City, born in 2008 ... Jordan Barack Treasure made national news when he was the focus of a New York newspaper\xa0... Portrait of George Washington, the 1st President of the United States ... Portrait of Barack Obama, the 44th President of the United States\xa0... His full name is Barack Hussein Obama II. Since the “II” is simply because he was named for his father, his last name is Obama. Mar 22, 2008 ... Barry Obama decided that he didn't like his nickname. A few of his friends at Occidental College had already begun to call him Barack (his\xa0... Aug 18, 2017 ... It took him several seconds and multiple clues to remember former President Barack Obama's first name. Miller knew that every answer had to\xa0... Feb 9, 2015 ... Michael Jordan misspelled Barack Obama's first name on 50th-birthday gift ... Knowing Obama is a Chicagoan and huge basketball fan,\xa0... 4 days ago ... Barack Obama, in full Barack Hussein Obama II, (born August 4, 1961, Honolulu, Hawaii, U.S.), 44th president of the United States (2009–17) and\xa0..."Number of ResultsYou can use the k parameter to set the number of resultssearch = GoogleSearchAPIWrapper(k=1)tool = Tool( name="I'm Feeling Lucky", description="Search Google and return the first result.", func=search.run,)tool.run("python") 'The official home of the Python Programming Language.''The official home of the Python Programming Language.'Metadata ResultsRun query through GoogleSearch and return snippet, title, and link metadata.Snippet: The description of the result.Title: The title of the result.Link: The link to the result.search = GoogleSearchAPIWrapper()def top5_results(query): return search.results(query, 5)tool = Tool( name="Google Search Snippets", description="Search Google for recent results.", func=top5_results,)PreviousGoogle PlacesNextGoogle SerperNumber of ResultsMetadata Results |
725 | https://python.langchain.com/docs/integrations/tools/google_serper | ComponentsToolsGoogle SerperOn this pageGoogle SerperThis notebook goes over how to use the Google Serper component to search the web. First you need to sign up for a free account at serper.dev and get your api key.import osimport pprintos.environ["SERPER_API_KEY"] = ""from langchain.utilities import GoogleSerperAPIWrappersearch = GoogleSerperAPIWrapper()search.run("Obama's first name?") 'Barack Hussein Obama II'As part of a Self Ask With Search Chainos.environ["OPENAI_API_KEY"] = ""from langchain.utilities import GoogleSerperAPIWrapperfrom langchain.llms.openai import OpenAIfrom langchain.agents import initialize_agent, Toolfrom langchain.agents import AgentTypellm = OpenAI(temperature=0)search = GoogleSerperAPIWrapper()tools = [ Tool( name="Intermediate Answer", func=search.run, description="useful for when you need to ask with search", )]self_ask_with_search = initialize_agent( tools, llm, agent=AgentType.SELF_ASK_WITH_SEARCH, verbose=True)self_ask_with_search.run( "What is the hometown of the reigning men's U.S. Open champion?") > Entering new AgentExecutor chain... Yes. Follow up: Who is the reigning men's U.S. Open champion? Intermediate answer: Current champions Carlos Alcaraz, 2022 men's singles champion. Follow up: Where is Carlos Alcaraz from? Intermediate answer: El Palmar, Spain So the final answer is: El Palmar, Spain > Finished chain. 'El Palmar, Spain'Obtaining results with metadataIf you would also like to obtain the results in a structured way including metadata. For this we will be using the results method of the wrapper.search = GoogleSerperAPIWrapper()results = search.results("Apple Inc.")pprint.pp(results) {'searchParameters': {'q': 'Apple Inc.', 'gl': 'us', 'hl': 'en', 'num': 10, 'type': 'search'}, 'knowledgeGraph': {'title': 'Apple', 'type': 'Technology company', 'website': 'http://www.apple.com/', 'imageUrl': 'https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcQwGQRv5TjjkycpctY66mOg_e2-npacrmjAb6_jAWhzlzkFE3OTjxyzbA&s=0', 'description': 'Apple Inc. is an American multinational ' 'technology company headquartered in ' 'Cupertino, California. Apple is the ' "world's largest technology company by " 'revenue, with US$394.3 billion in 2022 ' 'revenue. As of March 2023, Apple is the ' "world's biggest...", 'descriptionSource': 'Wikipedia', 'descriptionLink': 'https://en.wikipedia.org/wiki/Apple_Inc.', 'attributes': {'Customer service': '1 (800) 275-2273', 'CEO': 'Tim Cook (Aug 24, 2011–)', 'Headquarters': 'Cupertino, CA', 'Founded': 'April 1, 1976, Los Altos, CA', 'Founders': 'Steve Jobs, Steve Wozniak, ' 'Ronald Wayne, and more', 'Products': 'iPhone, iPad, Apple TV, and ' 'more'}}, 'organic': [{'title': 'Apple', 'link': 'https://www.apple.com/', 'snippet': 'Discover the innovative world of Apple and shop ' 'everything iPhone, iPad, Apple Watch, Mac, and Apple ' 'TV, plus explore accessories, entertainment, ...', 'sitelinks': [{'title': 'Support', 'link': 'https://support.apple.com/'}, {'title': 'iPhone', 'link': 'https://www.apple.com/iphone/'}, {'title': 'Site Map', 'link': 'https://www.apple.com/sitemap/'}, {'title': 'Business', 'link': 'https://www.apple.com/business/'}, {'title': 'Mac', 'link': 'https://www.apple.com/mac/'}, {'title': 'Watch', 'link': 'https://www.apple.com/watch/'}], 'position': 1}, {'title': 'Apple Inc. - Wikipedia', 'link': 'https://en.wikipedia.org/wiki/Apple_Inc.', 'snippet': 'Apple Inc. is an American multinational technology ' 'company headquartered in Cupertino, California. ' "Apple is the world's largest technology company by " 'revenue, ...', 'attributes': {'Products': 'AirPods; Apple Watch; iPad; iPhone; ' 'Mac; Full list', 'Founders': 'Steve Jobs; Steve Wozniak; Ronald ' 'Wayne; Mike Markkula'}, 'sitelinks': [{'title': 'History', 'link': 'https://en.wikipedia.org/wiki/History_of_Apple_Inc.'}, {'title': 'Timeline of Apple Inc. products', 'link': 'https://en.wikipedia.org/wiki/Timeline_of_Apple_Inc._products'}, {'title': 'Litigation involving Apple Inc.', 'link': 'https://en.wikipedia.org/wiki/Litigation_involving_Apple_Inc.'}, {'title': 'Apple Store', 'link': 'https://en.wikipedia.org/wiki/Apple_Store'}], 'imageUrl': 'https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcRvmB5fT1LjqpZx02UM7IJq0Buoqt0DZs_y0dqwxwSWyP4PIN9FaxuTea0&s', 'position': 2}, {'title': 'Apple Inc. | History, Products, Headquarters, & Facts ' '| Britannica', 'link': 'https://www.britannica.com/topic/Apple-Inc', 'snippet': 'Apple Inc., formerly Apple Computer, Inc., American ' 'manufacturer of personal computers, smartphones, ' 'tablet computers, computer peripherals, and computer ' '...', 'attributes': {'Related People': 'Steve Jobs Steve Wozniak Jony ' 'Ive Tim Cook Angela Ahrendts', 'Date': '1976 - present'}, 'imageUrl': 'https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcS3liELlhrMz3Wpsox29U8jJ3L8qETR0hBWHXbFnwjwQc34zwZvFELst2E&s', 'position': 3}, {'title': 'AAPL: Apple Inc Stock Price Quote - NASDAQ GS - ' 'Bloomberg.com', 'link': 'https://www.bloomberg.com/quote/AAPL:US', 'snippet': 'AAPL:USNASDAQ GS. Apple Inc. COMPANY INFO ; Open. ' '170.09 ; Prev Close. 169.59 ; Volume. 48,425,696 ; ' 'Market Cap. 2.667T ; Day Range. 167.54170.35.', 'position': 4}, {'title': 'Apple Inc. (AAPL) Company Profile & Facts - Yahoo ' 'Finance', 'link': 'https://finance.yahoo.com/quote/AAPL/profile/', 'snippet': 'Apple Inc. designs, manufactures, and markets ' 'smartphones, personal computers, tablets, wearables, ' 'and accessories worldwide. The company offers ' 'iPhone, a line ...', 'position': 5}, {'title': 'Apple Inc. (AAPL) Stock Price, News, Quote & History - ' 'Yahoo Finance', 'link': 'https://finance.yahoo.com/quote/AAPL', 'snippet': 'Find the latest Apple Inc. (AAPL) stock quote, ' 'history, news and other vital information to help ' 'you with your stock trading and investing.', 'position': 6}], 'peopleAlsoAsk': [{'question': 'What does Apple Inc do?', 'snippet': 'Apple Inc. (Apple) designs, manufactures and ' 'markets smartphones, personal\n' 'computers, tablets, wearables and accessories ' 'and sells a range of related\n' 'services.', 'title': 'AAPL.O - | Stock Price & Latest News - Reuters', 'link': 'https://www.reuters.com/markets/companies/AAPL.O/'}, {'question': 'What is the full form of Apple Inc?', 'snippet': '(formerly Apple Computer Inc.) is an American ' 'computer and consumer electronics\n' 'company famous for creating the iPhone, iPad ' 'and Macintosh computers.', 'title': 'What is Apple? An products and history overview ' '- TechTarget', 'link': 'https://www.techtarget.com/whatis/definition/Apple'}, {'question': 'What is Apple Inc iPhone?', 'snippet': 'Apple Inc (Apple) designs, manufactures, and ' 'markets smartphones, tablets,\n' 'personal computers, and wearable devices. The ' 'company also offers software\n' 'applications and related services, ' 'accessories, and third-party digital content.\n' "Apple's product portfolio includes iPhone, " 'iPad, Mac, iPod, Apple Watch, and\n' 'Apple TV.', 'title': 'Apple Inc Company Profile - Apple Inc Overview - ' 'GlobalData', 'link': 'https://www.globaldata.com/company-profile/apple-inc/'}, {'question': 'Who runs Apple Inc?', 'snippet': 'Timothy Donald Cook (born November 1, 1960) is ' 'an American business executive\n' 'who has been the chief executive officer of ' 'Apple Inc. since 2011. Cook\n' "previously served as the company's chief " 'operating officer under its co-founder\n' 'Steve Jobs. He is the first CEO of any Fortune ' '500 company who is openly gay.', 'title': 'Tim Cook - Wikipedia', 'link': 'https://en.wikipedia.org/wiki/Tim_Cook'}], 'relatedSearches': [{'query': 'Who invented the iPhone'}, {'query': 'Apple iPhone'}, {'query': 'History of Apple company PDF'}, {'query': 'Apple company history'}, {'query': 'Apple company introduction'}, {'query': 'Apple India'}, {'query': 'What does Apple Inc own'}, {'query': 'Apple Inc After Steve'}, {'query': 'Apple Watch'}, {'query': 'Apple App Store'}]}Searching for Google ImagesWe can also query Google Images using this wrapper. For example:search = GoogleSerperAPIWrapper(type="images")results = search.results("Lion")pprint.pp(results) {'searchParameters': {'q': 'Lion', 'gl': 'us', 'hl': 'en', 'num': 10, 'type': 'images'}, 'images': [{'title': 'Lion - Wikipedia', 'imageUrl': 'https://upload.wikimedia.org/wikipedia/commons/thumb/7/73/Lion_waiting_in_Namibia.jpg/1200px-Lion_waiting_in_Namibia.jpg', 'imageWidth': 1200, 'imageHeight': 900, 'thumbnailUrl': 'https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcRye79ROKwjfb6017jr0iu8Bz2E1KKuHg-A4qINJaspyxkZrkw&s', 'thumbnailWidth': 259, 'thumbnailHeight': 194, 'source': 'Wikipedia', 'domain': 'en.wikipedia.org', 'link': 'https://en.wikipedia.org/wiki/Lion', 'position': 1}, {'title': 'Lion | Characteristics, Habitat, & Facts | Britannica', 'imageUrl': 'https://cdn.britannica.com/55/2155-050-604F5A4A/lion.jpg', 'imageWidth': 754, 'imageHeight': 752, 'thumbnailUrl': 'https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcS3fnDub1GSojI0hJ-ZGS8Tv-hkNNloXh98DOwXZoZ_nUs3GWSd&s', 'thumbnailWidth': 225, 'thumbnailHeight': 224, 'source': 'Encyclopedia Britannica', 'domain': 'www.britannica.com', 'link': 'https://www.britannica.com/animal/lion', 'position': 2}, {'title': 'African lion, facts and photos', 'imageUrl': 'https://i.natgeofe.com/n/487a0d69-8202-406f-a6a0-939ed3704693/african-lion.JPG', 'imageWidth': 3072, 'imageHeight': 2043, 'thumbnailUrl': 'https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcTPlTarrtDbyTiEm-VI_PML9VtOTVPuDXJ5ybDf_lN11H2mShk&s', 'thumbnailWidth': 275, 'thumbnailHeight': 183, 'source': 'National Geographic', 'domain': 'www.nationalgeographic.com', 'link': 'https://www.nationalgeographic.com/animals/mammals/facts/african-lion', 'position': 3}, {'title': 'Saint Louis Zoo | African Lion', 'imageUrl': 'https://optimise2.assets-servd.host/maniacal-finch/production/animals/african-lion-01-01.jpg?w=1200&auto=compress%2Cformat&fit=crop&dm=1658933674&s=4b63f926a0f524f2087a8e0613282bdb', 'imageWidth': 1200, 'imageHeight': 1200, 'thumbnailUrl': 'https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcTlewcJ5SwC7yKup6ByaOjTnAFDeoOiMxyJTQaph2W_I3dnks4&s', 'thumbnailWidth': 225, 'thumbnailHeight': 225, 'source': 'St. Louis Zoo', 'domain': 'stlzoo.org', 'link': 'https://stlzoo.org/animals/mammals/carnivores/lion', 'position': 4}, {'title': 'How to Draw a Realistic Lion like an Artist - Studio ' 'Wildlife', 'imageUrl': 'https://studiowildlife.com/wp-content/uploads/2021/10/245528858_183911853822648_6669060845725210519_n.jpg', 'imageWidth': 1431, 'imageHeight': 2048, 'thumbnailUrl': 'https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcTmn5HayVj3wqoBDQacnUtzaDPZzYHSLKUlIEcni6VB8w0mVeA&s', 'thumbnailWidth': 188, 'thumbnailHeight': 269, 'source': 'Studio Wildlife', 'domain': 'studiowildlife.com', 'link': 'https://studiowildlife.com/how-to-draw-a-realistic-lion-like-an-artist/', 'position': 5}, {'title': 'Lion | Characteristics, Habitat, & Facts | Britannica', 'imageUrl': 'https://cdn.britannica.com/29/150929-050-547070A1/lion-Kenya-Masai-Mara-National-Reserve.jpg', 'imageWidth': 1600, 'imageHeight': 1085, 'thumbnailUrl': 'https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcSCqaKY_THr0IBZN8c-2VApnnbuvKmnsWjfrwKoWHFR9w3eN5o&s', 'thumbnailWidth': 273, 'thumbnailHeight': 185, 'source': 'Encyclopedia Britannica', 'domain': 'www.britannica.com', 'link': 'https://www.britannica.com/animal/lion', 'position': 6}, {'title': "Where do lions live? Facts about lions' habitats and " 'other cool facts', 'imageUrl': 'https://www.gannett-cdn.com/-mm-/b2b05a4ab25f4fca0316459e1c7404c537a89702/c=0-0-1365-768/local/-/media/2022/03/16/USATODAY/usatsports/imageForEntry5-ODq.jpg?width=1365&height=768&fit=crop&format=pjpg&auto=webp', 'imageWidth': 1365, 'imageHeight': 768, 'thumbnailUrl': 'https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcTc_4vCHscgvFvYy3PSrtIOE81kNLAfhDK8F3mfOuotL0kUkbs&s', 'thumbnailWidth': 299, 'thumbnailHeight': 168, 'source': 'USA Today', 'domain': 'www.usatoday.com', 'link': 'https://www.usatoday.com/story/news/2023/01/08/where-do-lions-live-habitat/10927718002/', 'position': 7}, {'title': 'Lion', 'imageUrl': 'https://i.natgeofe.com/k/1d33938b-3d02-4773-91e3-70b113c3b8c7/lion-male-roar_square.jpg', 'imageWidth': 3072, 'imageHeight': 3072, 'thumbnailUrl': 'https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcQqLfnBrBLcTiyTZynHH3FGbBtX2bd1ScwpcuOLnksTyS9-4GM&s', 'thumbnailWidth': 225, 'thumbnailHeight': 225, 'source': 'National Geographic Kids', 'domain': 'kids.nationalgeographic.com', 'link': 'https://kids.nationalgeographic.com/animals/mammals/facts/lion', 'position': 8}, {'title': "Lion | Smithsonian's National Zoo", 'imageUrl': 'https://nationalzoo.si.edu/sites/default/files/styles/1400_scale/public/animals/exhibit/africanlion-005.jpg?itok=6wA745g_', 'imageWidth': 1400, 'imageHeight': 845, 'thumbnailUrl': 'https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcSgB3z_D4dMEOWJ7lajJk4XaQSL4DdUvIRj4UXZ0YoE5fGuWuo&s', 'thumbnailWidth': 289, 'thumbnailHeight': 174, 'source': "Smithsonian's National Zoo", 'domain': 'nationalzoo.si.edu', 'link': 'https://nationalzoo.si.edu/animals/lion', 'position': 9}, {'title': "Zoo's New Male Lion Explores Habitat for the First Time " '- Virginia Zoo', 'imageUrl': 'https://virginiazoo.org/wp-content/uploads/2022/04/ZOO_0056-scaled.jpg', 'imageWidth': 2560, 'imageHeight': 2141, 'thumbnailUrl': 'https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcTDCG7XvXRCwpe_-Vy5mpvrQpVl5q2qwgnDklQhrJpQzObQGz4&s', 'thumbnailWidth': 246, 'thumbnailHeight': 205, 'source': 'Virginia Zoo', 'domain': 'virginiazoo.org', 'link': 'https://virginiazoo.org/zoos-new-male-lion-explores-habitat-for-thefirst-time/', 'position': 10}]}Searching for Google NewsWe can also query Google News using this wrapper. For example:search = GoogleSerperAPIWrapper(type="news")results = search.results("Tesla Inc.")pprint.pp(results) {'searchParameters': {'q': 'Tesla Inc.', 'gl': 'us', 'hl': 'en', 'num': 10, 'type': 'news'}, 'news': [{'title': 'ISS recommends Tesla investors vote against re-election ' 'of Robyn Denholm', 'link': 'https://www.reuters.com/business/autos-transportation/iss-recommends-tesla-investors-vote-against-re-election-robyn-denholm-2023-05-04/', 'snippet': 'Proxy advisory firm ISS on Wednesday recommended Tesla ' 'investors vote against re-election of board chair Robyn ' 'Denholm, citing "concerns on...', 'date': '5 mins ago', 'source': 'Reuters', 'imageUrl': 'https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcROdETe_GUyp1e8RHNhaRM8Z_vfxCvdfinZwzL1bT1ZGSYaGTeOojIdBoLevA&s', 'position': 1}, {'title': 'Global companies by market cap: Tesla fell most in April', 'link': 'https://www.reuters.com/markets/global-companies-by-market-cap-tesla-fell-most-april-2023-05-02/', 'snippet': 'Tesla Inc was the biggest loser among top companies by ' 'market capitalisation in April, hit by disappointing ' 'quarterly earnings after it...', 'date': '1 day ago', 'source': 'Reuters', 'imageUrl': 'https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcQ4u4CP8aOdGyRFH6o4PkXi-_eZDeY96vLSag5gDjhKMYf98YBER2cZPbkStQ&s', 'position': 2}, {'title': 'Tesla Wanted an EV Price War. Ford Showed Up.', 'link': 'https://www.bloomberg.com/opinion/articles/2023-05-03/tesla-wanted-an-ev-price-war-ford-showed-up', 'snippet': 'The legacy automaker is paring back the cost of its ' 'Mustang Mach-E model after Tesla discounted its ' 'competing EVs, portending tighter...', 'date': '6 hours ago', 'source': 'Bloomberg.com', 'imageUrl': 'https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcS_3Eo4VI0H-nTeIbYc5DaQn5ep7YrWnmhx6pv8XddFgNF5zRC9gEpHfDq8yQ&s', 'position': 3}, {'title': 'Joby Aviation to get investment from Tesla shareholder ' 'Baillie Gifford', 'link': 'https://finance.yahoo.com/news/joby-aviation-investment-tesla-shareholder-204450712.html', 'snippet': 'This comes days after Joby clinched a $55 million ' 'contract extension to deliver up to nine air taxis to ' 'the U.S. Air Force,...', 'date': '4 hours ago', 'source': 'Yahoo Finance', 'imageUrl': 'https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcQO0uVn297LI-xryrPNqJ-apUOulj4ohM-xkN4OfmvMOYh1CPdUEBbYx6hviw&s', 'position': 4}, {'title': 'Tesla resumes U.S. orders for a Model 3 version at lower ' 'price, range', 'link': 'https://finance.yahoo.com/news/tesla-resumes-us-orders-model-045736115.html', 'snippet': '(Reuters) -Tesla Inc has resumed taking orders for its ' 'Model 3 long-range vehicle in the United States, the ' "company's website showed late on...", 'date': '19 hours ago', 'source': 'Yahoo Finance', 'imageUrl': 'https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcTIZetJ62sQefPfbQ9KKDt6iH7Mc0ylT5t_hpgeeuUkHhJuAx2FOJ4ZTRVDFg&s', 'position': 5}, {'title': 'The Tesla Model 3 Long Range AWD Is Now Available in the ' 'U.S. With 325 Miles of Range', 'link': 'https://www.notateslaapp.com/news/1393/tesla-reopens-orders-for-model-3-long-range-after-months-of-unavailability', 'snippet': 'Tesla has reopened orders for the Model 3 Long Range ' 'RWD, which has been unavailable for months due to high ' 'demand.', 'date': '7 hours ago', 'source': 'Not a Tesla App', 'imageUrl': 'https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcSecrgxZpRj18xIJY-nDHljyP-A4ejEkswa9eq77qhMNrScnVIqe34uql5U4w&s', 'position': 6}, {'title': 'Tesla Cybertruck alpha prototype spotted at the Fremont ' 'factory in new pics and videos', 'link': 'https://www.teslaoracle.com/2023/05/03/tesla-cybertruck-alpha-prototype-interior-and-exterior-spotted-at-the-fremont-factory-in-new-pics-and-videos/', 'snippet': 'A Tesla Cybertruck alpha prototype goes to Fremont, ' 'California for another round of testing before going to ' 'production later this year (pics...', 'date': '14 hours ago', 'source': 'Tesla Oracle', 'imageUrl': 'https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcRO7M5ZLQE-Zo4-_5dv9hNAQZ3wSqfvYCuKqzxHG-M6CgLpwPMMG_ssebdcMg&s', 'position': 7}, {'title': 'Tesla putting facility in new part of country - Austin ' 'Business Journal', 'link': 'https://www.bizjournals.com/austin/news/2023/05/02/tesla-leases-building-seattle-area.html', 'snippet': 'Check out what Puget Sound Business Journal has to ' "report about the Austin-based company's real estate " 'footprint in the Pacific Northwest.', 'date': '22 hours ago', 'source': 'The Business Journals', 'imageUrl': 'https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcR9kIEHWz1FcHKDUtGQBS0AjmkqtyuBkQvD8kyIY3kpaPrgYaN7I_H2zoOJsA&s', 'position': 8}, {'title': 'Tesla (TSLA) Resumes Orders for Model 3 Long Range After ' 'Backlog', 'link': 'https://www.bloomberg.com/news/articles/2023-05-03/tesla-resumes-orders-for-popular-model-3-long-range-at-47-240', 'snippet': 'Tesla Inc. has resumed taking orders for its Model 3 ' 'Long Range edition with a starting price of $47240, ' 'according to its website.', 'date': '5 hours ago', 'source': 'Bloomberg.com', 'imageUrl': 'https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcTWWIC4VpMTfRvSyqiomODOoLg0xhoBf-Tc1qweKnSuaiTk-Y1wMJZM3jct0w&s', 'position': 9}]}If you want to only receive news articles published in the last hour, you can do the following:search = GoogleSerperAPIWrapper(type="news", tbs="qdr:h")results = search.results("Tesla Inc.")pprint.pp(results) {'searchParameters': {'q': 'Tesla Inc.', 'gl': 'us', 'hl': 'en', 'num': 10, 'type': 'news', 'tbs': 'qdr:h'}, 'news': [{'title': 'Oklahoma Gov. Stitt sees growing foreign interest in ' 'investments in ...', 'link': 'https://www.reuters.com/world/us/oklahoma-gov-stitt-sees-growing-foreign-interest-investments-state-2023-05-04/', 'snippet': 'T)), a battery supplier to electric vehicle maker Tesla ' 'Inc (TSLA.O), said on Sunday it is considering building ' 'a battery plant in Oklahoma, its third in...', 'date': '53 mins ago', 'source': 'Reuters', 'imageUrl': 'https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcSSTcsXeenqmEKdiekvUgAmqIPR4nlAmgjTkBqLpza-lLfjX1CwB84MoNVj0Q&s', 'position': 1}, {'title': 'Ryder lanza solución llave en mano para vehículos ' 'eléctricos en EU', 'link': 'https://www.tyt.com.mx/nota/ryder-lanza-solucion-llave-en-mano-para-vehiculos-electricos-en-eu', 'snippet': 'Ryder System Inc. presentó RyderElectric+ TM como su ' 'nueva solución llave en mano ... Ryder también tiene ' 'reservados los semirremolques Tesla y continúa...', 'date': '56 mins ago', 'source': 'Revista Transportes y Turismo', 'imageUrl': 'https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcQJhXTQQtjSUZf9YPM235WQhFU5_d7lEA76zB8DGwZfixcgf1_dhPJyKA1Nbw&s', 'position': 2}, {'title': '"I think people can get by with $999 million," Bernie ' 'Sanders tells American Billionaires.', 'link': 'https://thebharatexpressnews.com/i-think-people-can-get-by-with-999-million-bernie-sanders-tells-american-billionaires-heres-how-the-ultra-rich-can-pay-less-income-tax-than-you-legally/', 'snippet': 'The report noted that in 2007 and 2011, Amazon.com Inc. ' 'founder Jeff Bezos “did not pay a dime in federal ... ' 'If you want to bet on Musk, check out Tesla.', 'date': '11 mins ago', 'source': 'THE BHARAT EXPRESS NEWS', 'imageUrl': 'https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcR_X9qqSwVFBBdos2CK5ky5IWIE3aJPCQeRYR9O1Jz4t-MjaEYBuwK7AU3AJQ&s', 'position': 3}]}Some examples of the tbs parameter:qdr:h (past hour)
qdr:d (past day)
qdr:w (past week)
qdr:m (past month)
qdr:y (past year)You can specify intermediate time periods by adding a number:
qdr:h12 (past 12 hours)
qdr:d3 (past 3 days)
qdr:w2 (past 2 weeks)
qdr:m6 (past 6 months)
qdr:m2 (past 2 years)For all supported filters simply go to Google Search, search for something, click on "Tools", add your date filter and check the URL for "tbs=".Searching for Google PlacesWe can also query Google Places using this wrapper. For example:search = GoogleSerperAPIWrapper(type="places")results = search.results("Italian restaurants in Upper East Side")pprint.pp(results) {'searchParameters': {'q': 'Italian restaurants in Upper East Side', 'gl': 'us', 'hl': 'en', 'num': 10, 'type': 'places'}, 'places': [{'position': 1, 'title': "L'Osteria", 'address': '1219 Lexington Ave', 'latitude': 40.777154599999996, 'longitude': -73.9571363, 'thumbnailUrl': 'https://lh5.googleusercontent.com/p/AF1QipNjU7BWEq_aYQANBCbX52Kb0lDpd_lFIx5onw40=w92-h92-n-k-no', 'rating': 4.7, 'ratingCount': 91, 'category': 'Italian'}, {'position': 2, 'title': "Tony's Di Napoli", 'address': '1081 3rd Ave', 'latitude': 40.7643567, 'longitude': -73.9642373, 'thumbnailUrl': 'https://lh5.googleusercontent.com/p/AF1QipNbNv6jZkJ9nyVi60__8c1DQbe_eEbugRAhIYye=w92-h92-n-k-no', 'rating': 4.5, 'ratingCount': 2265, 'category': 'Italian'}, {'position': 3, 'title': 'Caravaggio', 'address': '23 E 74th St', 'latitude': 40.773412799999996, 'longitude': -73.96473379999999, 'thumbnailUrl': 'https://lh5.googleusercontent.com/p/AF1QipPDGchokDvppoLfmVEo6X_bWd3Fz0HyxIHTEe9V=w92-h92-n-k-no', 'rating': 4.5, 'ratingCount': 276, 'category': 'Italian'}, {'position': 4, 'title': 'Luna Rossa', 'address': '347 E 85th St', 'latitude': 40.776593999999996, 'longitude': -73.950351, 'thumbnailUrl': 'https://lh5.googleusercontent.com/p/AF1QipNPCpCPuqPAb1Mv6_fOP7cjb8Wu1rbqbk2sMBlh=w92-h92-n-k-no', 'rating': 4.5, 'ratingCount': 140, 'category': 'Italian'}, {'position': 5, 'title': "Paola's", 'address': '1361 Lexington Ave', 'latitude': 40.7822019, 'longitude': -73.9534096, 'thumbnailUrl': 'https://lh5.googleusercontent.com/p/AF1QipPJr2Vcx-B6K-GNQa4koOTffggTePz8TKRTnWi3=w92-h92-n-k-no', 'rating': 4.5, 'ratingCount': 344, 'category': 'Italian'}, {'position': 6, 'title': 'Come Prima', 'address': '903 Madison Ave', 'latitude': 40.772124999999996, 'longitude': -73.965012, 'thumbnailUrl': 'https://lh5.googleusercontent.com/p/AF1QipNrX19G0NVdtDyMovCQ-M-m0c_g |
726 | https://python.langchain.com/docs/integrations/tools/gradio_tools | ComponentsToolsGradioOn this pageGradioThere are many 1000s of Gradio apps on Hugging Face Spaces. This library puts them at the tips of your LLM's fingers 🦾Specifically, gradio-tools is a Python library for converting Gradio apps into tools that can be leveraged by a large language model (LLM)-based agent to complete its task. For example, an LLM could use a Gradio tool to transcribe a voice recording it finds online and then summarize it for you. Or it could use a different Gradio tool to apply OCR to a document on your Google Drive and then answer questions about it.It's very easy to create you own tool if you want to use a space that's not one of the pre-built tools. Please see this section of the gradio-tools documentation for information on how to do that. All contributions are welcome!# !pip install gradio_toolsUsing a toolfrom gradio_tools.tools import StableDiffusionToollocal_file_path = StableDiffusionTool().langchain.run( "Please create a photo of a dog riding a skateboard")local_file_path Loaded as API: https://gradio-client-demos-stable-diffusion.hf.space ✔ Job Status: Status.STARTING eta: None '/Users/harrisonchase/workplace/langchain/docs/modules/agents/tools/integrations/b61c1dd9-47e2-46f1-a47c-20d27640993d/tmp4ap48vnm.jpg'from PIL import Imageim = Image.open(local_file_path)display(im)Using within an agentfrom langchain.agents import initialize_agentfrom langchain.llms import OpenAIfrom gradio_tools.tools import ( StableDiffusionTool, ImageCaptioningTool, StableDiffusionPromptGeneratorTool, TextToVideoTool,)from langchain.memory import ConversationBufferMemoryllm = OpenAI(temperature=0)memory = ConversationBufferMemory(memory_key="chat_history")tools = [ StableDiffusionTool().langchain, ImageCaptioningTool().langchain, StableDiffusionPromptGeneratorTool().langchain, TextToVideoTool().langchain,]agent = initialize_agent( tools, llm, memory=memory, agent="conversational-react-description", verbose=True)output = agent.run( input=( "Please create a photo of a dog riding a skateboard " "but improve my prompt prior to using an image generator." "Please caption the generated image and create a video for it using the improved prompt." )) Loaded as API: https://gradio-client-demos-stable-diffusion.hf.space ✔ Loaded as API: https://taesiri-blip-2.hf.space ✔ Loaded as API: https://microsoft-promptist.hf.space ✔ Loaded as API: https://damo-vilab-modelscope-text-to-video-synthesis.hf.space ✔ > Entering new AgentExecutor chain... Thought: Do I need to use a tool? Yes Action: StableDiffusionPromptGenerator Action Input: A dog riding a skateboard Job Status: Status.STARTING eta: None Observation: A dog riding a skateboard, digital painting, artstation, concept art, smooth, sharp focus, illustration, art by artgerm and greg rutkowski and alphonse mucha Thought: Do I need to use a tool? Yes Action: StableDiffusion Action Input: A dog riding a skateboard, digital painting, artstation, concept art, smooth, sharp focus, illustration, art by artgerm and greg rutkowski and alphonse mucha Job Status: Status.STARTING eta: None Job Status: Status.PROCESSING eta: None Observation: /Users/harrisonchase/workplace/langchain/docs/modules/agents/tools/integrations/2e280ce4-4974-4420-8680-450825c31601/tmpfmiz2g1c.jpg Thought: Do I need to use a tool? Yes Action: ImageCaptioner Action Input: /Users/harrisonchase/workplace/langchain/docs/modules/agents/tools/integrations/2e280ce4-4974-4420-8680-450825c31601/tmpfmiz2g1c.jpg Job Status: Status.STARTING eta: None Observation: a painting of a dog sitting on a skateboard Thought: Do I need to use a tool? Yes Action: TextToVideo Action Input: a painting of a dog sitting on a skateboard Job Status: Status.STARTING eta: None Due to heavy traffic on this app, the prediction will take approximately 73 seconds.For faster predictions without waiting in queue, you may duplicate the space using: Client.duplicate(damo-vilab/modelscope-text-to-video-synthesis) Job Status: Status.IN_QUEUE eta: 73.89824726581574 Due to heavy traffic on this app, the prediction will take approximately 42 seconds.For faster predictions without waiting in queue, you may duplicate the space using: Client.duplicate(damo-vilab/modelscope-text-to-video-synthesis) Job Status: Status.IN_QUEUE eta: 42.49370198879602 Job Status: Status.IN_QUEUE eta: 21.314297944849187 Observation: /var/folders/bm/ylzhm36n075cslb9fvvbgq640000gn/T/tmp5snj_nmzf20_cb3m.mp4 Thought: Do I need to use a tool? No AI: Here is a video of a painting of a dog sitting on a skateboard. > Finished chain.PreviousGoogle SerperNextGraphQLUsing a toolUsing within an agent |
727 | https://python.langchain.com/docs/integrations/tools/graphql | ComponentsToolsGraphQLGraphQLGraphQL is a query language for APIs and a runtime for executing those queries against your data. GraphQL provides a complete and understandable description of the data in your API, gives clients the power to ask for exactly what they need and nothing more, makes it easier to evolve APIs over time, and enables powerful developer tools.By including a BaseGraphQLTool in the list of tools provided to an Agent, you can grant your Agent the ability to query data from GraphQL APIs for any purposes you need.This Jupyter Notebook demonstrates how to use the GraphQLAPIWrapper component with an Agent.In this example, we'll be using the public Star Wars GraphQL API available at the following endpoint: https://swapi-graphql.netlify.app/.netlify/functions/index.First, you need to install httpx and gql Python packages.pip install httpx gql > /dev/nullNow, let's create a BaseGraphQLTool instance with the specified Star Wars API endpoint and initialize an Agent with the tool.from langchain.llms import OpenAIfrom langchain.agents import load_tools, initialize_agent, AgentTypefrom langchain.utilities import GraphQLAPIWrapperllm = OpenAI(temperature=0)tools = load_tools( ["graphql"], graphql_endpoint="https://swapi-graphql.netlify.app/.netlify/functions/index",)agent = initialize_agent( tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True)Now, we can use the Agent to run queries against the Star Wars GraphQL API. Let's ask the Agent to list all the Star Wars films and their release dates.graphql_fields = """allFilms { films { title director releaseDate speciesConnection { species { name classification homeworld { name } } } } }"""suffix = "Search for the titles of all the stawars films stored in the graphql database that has this schema "agent.run(suffix + graphql_fields) > Entering new AgentExecutor chain... I need to query the graphql database to get the titles of all the star wars films Action: query_graphql Action Input: query { allFilms { films { title } } } Observation: "{\n \"allFilms\": {\n \"films\": [\n {\n \"title\": \"A New Hope\"\n },\n {\n \"title\": \"The Empire Strikes Back\"\n },\n {\n \"title\": \"Return of the Jedi\"\n },\n {\n \"title\": \"The Phantom Menace\"\n },\n {\n \"title\": \"Attack of the Clones\"\n },\n {\n \"title\": \"Revenge of the Sith\"\n }\n ]\n }\n}" Thought: I now know the titles of all the star wars films Final Answer: The titles of all the star wars films are: A New Hope, The Empire Strikes Back, Return of the Jedi, The Phantom Menace, Attack of the Clones, and Revenge of the Sith. > Finished chain. 'The titles of all the star wars films are: A New Hope, The Empire Strikes Back, Return of the Jedi, The Phantom Menace, Attack of the Clones, and Revenge of the Sith.'PreviousGradioNextHuggingFace Hub Tools |
728 | https://python.langchain.com/docs/integrations/tools/huggingface_tools | ComponentsToolsHuggingFace Hub ToolsHuggingFace Hub ToolsHuggingface Tools that supporting text I/O can be
loaded directly using the load_huggingface_tool function.# Requires transformers>=4.29.0 and huggingface_hub>=0.14.1pip install --upgrade transformers huggingface_hub > /dev/nullfrom langchain.agents import load_huggingface_tooltool = load_huggingface_tool("lysandre/hf-model-downloads")print(f"{tool.name}: {tool.description}") model_download_counter: This is a tool that returns the most downloaded model of a given task on the Hugging Face Hub. It takes the name of the category (such as text-classification, depth-estimation, etc), and returns the name of the checkpointtool.run("text-classification") 'facebook/bart-large-mnli'PreviousGraphQLNextHuman as a tool |
729 | https://python.langchain.com/docs/integrations/tools/human_tools | ComponentsToolsHuman as a toolOn this pageHuman as a toolHuman are AGI so they can certainly be used as a tool to help out AI agent
when it is confused.from langchain.chat_models import ChatOpenAIfrom langchain.llms import OpenAIfrom langchain.agents import load_tools, initialize_agentfrom langchain.agents import AgentTypellm = ChatOpenAI(temperature=0.0)math_llm = OpenAI(temperature=0.0)tools = load_tools( ["human", "llm-math"], llm=math_llm,)agent_chain = initialize_agent( tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True,)In the above code you can see the tool takes input directly from command line.
You can customize prompt_func and input_func according to your need (as shown below).agent_chain.run("What's my friend Eric's surname?")# Answer with 'Zhu' > Entering new AgentExecutor chain... I don't know Eric's surname, so I should ask a human for guidance. Action: Human Action Input: "What is Eric's surname?" What is Eric's surname? Zhu Observation: Zhu Thought:I now know Eric's surname is Zhu. Final Answer: Eric's surname is Zhu. > Finished chain. "Eric's surname is Zhu."Configuring the Input FunctionBy default, the HumanInputRun tool uses the python input function to get input from the user.
You can customize the input_func to be anything you'd like.
For instance, if you want to accept multi-line input, you could do the following:def get_input() -> str: print("Insert your text. Enter 'q' or press Ctrl-D (or Ctrl-Z on Windows) to end.") contents = [] while True: try: line = input() except EOFError: break if line == "q": break contents.append(line) return "\n".join(contents)# You can modify the tool when loadingtools = load_tools(["human", "ddg-search"], llm=math_llm, input_func=get_input)# Or you can directly instantiate the toolfrom langchain.tools import HumanInputRuntool = HumanInputRun(input_func=get_input)agent_chain = initialize_agent( tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True,)agent_chain.run("I need help attributing a quote") > Entering new AgentExecutor chain... I should ask a human for guidance Action: Human Action Input: "Can you help me attribute a quote?" Can you help me attribute a quote? Insert your text. Enter 'q' or press Ctrl-D (or Ctrl-Z on Windows) to end. vini vidi vici q Observation: vini vidi vici Thought:I need to provide more context about the quote Action: Human Action Input: "The quote is 'Veni, vidi, vici'" The quote is 'Veni, vidi, vici' Insert your text. Enter 'q' or press Ctrl-D (or Ctrl-Z on Windows) to end. oh who said it q Observation: oh who said it Thought:I can use DuckDuckGo Search to find out who said the quote Action: DuckDuckGo Search Action Input: "Who said 'Veni, vidi, vici'?" Observation: Updated on September 06, 2019. "Veni, vidi, vici" is a famous phrase said to have been spoken by the Roman Emperor Julius Caesar (100-44 BCE) in a bit of stylish bragging that impressed many of the writers of his day and beyond. The phrase means roughly "I came, I saw, I conquered" and it could be pronounced approximately Vehnee, Veedee ... Veni, vidi, vici (Classical Latin: [weːniː wiːdiː wiːkiː], Ecclesiastical Latin: [ˈveni ˈvidi ˈvitʃi]; "I came; I saw; I conquered") is a Latin phrase used to refer to a swift, conclusive victory.The phrase is popularly attributed to Julius Caesar who, according to Appian, used the phrase in a letter to the Roman Senate around 47 BC after he had achieved a quick victory in his short ... veni, vidi, vici Latin quotation from Julius Caesar ve· ni, vi· di, vi· ci ˌwā-nē ˌwē-dē ˈwē-kē ˌvā-nē ˌvē-dē ˈvē-chē : I came, I saw, I conquered Articles Related to veni, vidi, vici 'In Vino Veritas' and Other Latin... Dictionary Entries Near veni, vidi, vici Venite veni, vidi, vici Venizélos See More Nearby Entries Cite this Entry Style The simplest explanation for why veni, vidi, vici is a popular saying is that it comes from Julius Caesar, one of history's most famous figures, and has a simple, strong meaning: I'm powerful and fast. But it's not just the meaning that makes the phrase so powerful. Caesar was a gifted writer, and the phrase makes use of Latin grammar to ... One of the best known and most frequently quoted Latin expression, veni, vidi, vici may be found hundreds of times throughout the centuries used as an expression of triumph. The words are said to have been used by Caesar as he was enjoying a triumph. Thought:I now know the final answer Final Answer: Julius Caesar said the quote "Veni, vidi, vici" which means "I came, I saw, I conquered". > Finished chain. 'Julius Caesar said the quote "Veni, vidi, vici" which means "I came, I saw, I conquered".'PreviousHuggingFace Hub ToolsNextIFTTT WebHooksConfiguring the Input Function |
730 | https://python.langchain.com/docs/integrations/tools/ifttt | ComponentsToolsIFTTT WebHooksOn this pageIFTTT WebHooksThis notebook shows how to use IFTTT Webhooks.From https://github.com/SidU/teams-langchain-js/wiki/Connecting-IFTTT-Services.Creating a webhookGo to https://ifttt.com/createConfiguring the "If This"Click on the "If This" button in the IFTTT interface.Search for "Webhooks" in the search bar.Choose the first option for "Receive a web request with a JSON payload."Choose an Event Name that is specific to the service you plan to connect to.
This will make it easier for you to manage the webhook URL.
For example, if you're connecting to Spotify, you could use "Spotify" as your
Event Name.Click the "Create Trigger" button to save your settings and create your webhook.Configuring the "Then That"Tap on the "Then That" button in the IFTTT interface.Search for the service you want to connect, such as Spotify.Choose an action from the service, such as "Add track to a playlist".Configure the action by specifying the necessary details, such as the playlist name,
e.g., "Songs from AI".Reference the JSON Payload received by the Webhook in your action. For the Spotify
scenario, choose "{{JsonPayload}}" as your search query.Tap the "Create Action" button to save your action settings.Once you have finished configuring your action, click the "Finish" button to
complete the setup.Congratulations! You have successfully connected the Webhook to the desired
service, and you're ready to start receiving data and triggering actions 🎉Finishing upTo get your webhook URL go to https://ifttt.com/maker_webhooks/settingsCopy the IFTTT key value from there. The URL is of the form
https://maker.ifttt.com/use/YOUR_IFTTT_KEY. Grab the YOUR_IFTTT_KEY value.from langchain.tools.ifttt import IFTTTWebhookimport oskey = os.environ["IFTTTKey"]url = f"https://maker.ifttt.com/trigger/spotify/json/with/key/{key}"tool = IFTTTWebhook( name="Spotify", description="Add a song to spotify playlist", url=url)tool.run("taylor swift") "Congratulations! You've fired the spotify JSON event"PreviousHuman as a toolNextLemon AgentCreating a webhookConfiguring the "If This"Configuring the "Then That"Finishing up |
731 | https://python.langchain.com/docs/integrations/tools/lemonai | ComponentsToolsLemon AgentOn this pageLemon AgentLemon Agent helps you build powerful AI assistants in minutes and automate workflows by allowing for accurate and reliable read and write operations in tools like Airtable, Hubspot, Discord, Notion, Slack and Github.See full docs here.Most connectors available today are focused on read-only operations, limiting the potential of LLMs. Agents, on the other hand, have a tendency to hallucinate from time to time due to missing context or instructions.With Lemon AI, it is possible to give your agents access to well-defined APIs for reliable read and write operations. In addition, Lemon AI functions allow you to further reduce the risk of hallucinations by providing a way to statically define workflows that the model can rely on in case of uncertainty.Quick StartThe following quick start demonstrates how to use Lemon AI in combination with Agents to automate workflows that involve interaction with internal tooling.1. Install Lemon AIRequires Python 3.8.1 and above.To use Lemon AI in your Python project run pip install lemonaiThis will install the corresponding Lemon AI client which you can then import into your script.The tool uses Python packages langchain and loguru. In case of any installation errors with Lemon AI, install both packages first and then install the Lemon AI package.2. Launch the ServerThe interaction of your agents and all tools provided by Lemon AI is handled by the Lemon AI Server. To use Lemon AI you need to run the server on your local machine so the Lemon AI Python client can connect to it.3. Use Lemon AI with LangchainLemon AI automatically solves given tasks by finding the right combination of relevant tools or uses Lemon AI Functions as an alternative. The following example demonstrates how to retrieve a user from Hackernews and write it to a table in Airtable:(Optional) Define your Lemon AI FunctionsSimilar to OpenAI functions, Lemon AI provides the option to define workflows as reusable functions. These functions can be defined for use cases where it is especially important to move as close as possible to near-deterministic behavior. Specific workflows can be defined in a separate lemonai.json:[ { "name": "Hackernews Airtable User Workflow", "description": "retrieves user data from Hackernews and appends it to a table in Airtable", "tools": ["hackernews-get-user", "airtable-append-data"] }]Your model will have access to these functions and will prefer them over self-selecting tools to solve a given task. All you have to do is to let the agent know that it should use a given function by including the function name in the prompt.Include Lemon AI in your Langchain projectimport osfrom lemonai import execute_workflowfrom langchain.llms import OpenAILoad API Keys and Access TokensTo use tools that require authentication, you have to store the corresponding access credentials in your environment in the format "{tool name}_{authentication string}" where the authentication string is one of ["API_KEY", "SECRET_KEY", "SUBSCRIPTION_KEY", "ACCESS_KEY"] for API keys or ["ACCESS_TOKEN", "SECRET_TOKEN"] for authentication tokens. Examples are "OPENAI_API_KEY", "BING_SUBSCRIPTION_KEY", "AIRTABLE_ACCESS_TOKEN".""" Load all relevant API Keys and Access Tokens into your environment variables """os.environ["OPENAI_API_KEY"] = "*INSERT OPENAI API KEY HERE*"os.environ["AIRTABLE_ACCESS_TOKEN"] = "*INSERT AIRTABLE TOKEN HERE*"hackernews_username = "*INSERT HACKERNEWS USERNAME HERE*"airtable_base_id = "*INSERT BASE ID HERE*"airtable_table_id = "*INSERT TABLE ID HERE*"""" Define your instruction to be given to your LLM """prompt = f"""Read information from Hackernews for user {hackernews_username} and then write the results toAirtable (baseId: {airtable_base_id}, tableId: {airtable_table_id}). Only write the fields "username", "karma"and "created_at_i". Please make sure that Airtable does NOT automatically convert the field types.""""""Use the Lemon AI execute_workflow wrapper to run your Langchain agent in combination with Lemon AI """model = OpenAI(temperature=0)execute_workflow(llm=model, prompt_string=prompt)4. Gain transparency on your Agent's decision makingTo gain transparency on how your Agent interacts with Lemon AI tools to solve a given task, all decisions made, tools used and operations performed are written to a local lemonai.log file. Every time your LLM agent is interacting with the Lemon AI tool stack a corresponding log entry is created.2023-06-26T11:50:27.708785+0100 - b5f91c59-8487-45c2-800a-156eac0c7dae - hackernews-get-user2023-06-26T11:50:39.624035+0100 - b5f91c59-8487-45c2-800a-156eac0c7dae - airtable-append-data2023-06-26T11:58:32.925228+0100 - 5efe603c-9898-4143-b99a-55b50007ed9d - hackernews-get-user2023-06-26T11:58:43.988788+0100 - 5efe603c-9898-4143-b99a-55b50007ed9d - airtable-append-dataBy using the Lemon AI Analytics you can easily gain a better understanding of how frequently and in which order tools are used. As a result, you can identify weak spots in your agent’s decision-making capabilities and move to a more deterministic behavior by defining Lemon AI functions.PreviousIFTTT WebHooksNextMetaphor SearchQuick Start1. Install Lemon AI2. Launch the Server3. Use Lemon AI with Langchain4. Gain transparency on your Agent's decision making |
732 | https://python.langchain.com/docs/integrations/tools/metaphor_search | ComponentsToolsMetaphor SearchOn this pageMetaphor SearchMetaphor is a search engine fully designed to be used by LLMs. You can search and then get the contents for any page.This notebook goes over how to use Metaphor search.First, you need to set up the proper API keys and environment variables. Get 1000 free searches/month here.Then enter your API key as an environment variable.import osos.environ["METAPHOR_API_KEY"] = "..."Using their SDKThis is the newer and more supported way to use the Metaphor API - via their SDK# !pip install metaphor-pythonfrom metaphor_python import Metaphorclient = Metaphor(api_key=os.environ["METAPHOR_API_KEY"])from langchain.agents import toolfrom typing import List@tooldef search(query: str): """Call search engine with a query.""" return client.search(query, use_autoprompt=True, num_results=5)@tooldef get_contents(ids: List[str]): """Get contents of a webpage. The ids passed in should be a list of ids as fetched from `search`. """ return client.get_contents(ids)@tooldef find_similar(url: str): """Get search results similar to a given URL. The url passed in should be a URL returned from `search` """ return client.find_similar(url, num_results=5)tools = [search, get_contents, find_similar]Use in an agentfrom langchain.chat_models import ChatOpenAIllm = ChatOpenAI(temperature=0)from langchain.agents import OpenAIFunctionsAgentfrom langchain.schema import SystemMessagesystem_message = SystemMessage(content="You are a web researcher who uses search engines to look up information.")prompt = OpenAIFunctionsAgent.create_prompt(system_message=system_message)agent = OpenAIFunctionsAgent(llm=llm, tools=tools, prompt=prompt)from langchain.agents import AgentExecutoragent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True)agent_executor.run("Find the hottest AI agent startups and what they do") > Entering new AgentExecutor chain... Invoking: `search` with `{'query': 'hottest AI agent startups'}` SearchResponse(results=[Result(title='A Search Engine for Machine Intelligence', url='https://bellow.ai/', id='bdYc6hvHww_JvLv9k8NhPA', score=0.19460266828536987, published_date='2023-01-01', author=None, extract=None), Result(title='Adept: Useful General Intelligence', url='https://www.adept.ai/', id='aNBppxBZvQRZMov6sFVj9g', score=0.19103890657424927, published_date='2000-01-01', author=None, extract=None), Result(title='HiOperator | Generative AI-Enhanced Customer Service', url='https://www.hioperator.com/', id='jieb6sB53mId3EDo0z-SDw', score=0.18549954891204834, published_date='2000-01-01', author=None, extract=None), Result(title='Home - Stylo', url='https://www.askstylo.com/', id='kUiCuCjJYMD4N0NXdCtqlQ', score=0.1837376356124878, published_date='2000-01-01', author=None, extract=None), Result(title='DirectAI', url='https://directai.io/?utm_source=twitter&utm_medium=raw_message&utm_campaign=first_launch', id='45iSS8KnJ9tL1ilPg3dL9A', score=0.1835256814956665, published_date='2023-01-01', author=None, extract=None), Result(title='Sidekick AI | Customer Service Automated', url='https://www.sidekickai.co/', id='nCoPMUtqWQqhUvsdTjJT6A', score=0.18215584754943848, published_date='2020-01-01', author=None, extract=None), Result(title='Hebbia - Search, Reinvented', url='https://www.hebbia.ai/', id='Zy0YaekZdd4rurPQKkys7A', score=0.1799020767211914, published_date='2023-01-01', author=None, extract=None), Result(title='AI.XYZ', url='https://www.ai.xyz/', id='A5c1ePEvsaQeml2Kui_-vA', score=0.1797989457845688, published_date='2023-01-01', author=None, extract=None), Result(title='Halist AI', url='https://halist.ai/', id='-lKPLSb4N4dgMZlTgoDvJg', score=0.17975398898124695, published_date='2023-03-01', author=None, extract=None), Result(title='Clone your best expert', url='https://airin.ai/', id='_XIjx1YLPfI4cKePIEc_bQ', score=0.17957791686058044, published_date='2016-02-12', author=None, extract=None)], api=<metaphor_python.api.Metaphor object at 0x104192140>) Invoking: `get_contents` with `{'ids': ['bdYc6hvHww_JvLv9k8NhPA', 'aNBppxBZvQRZMov6sFVj9g', 'jieb6sB53mId3EDo0z-SDw', 'kUiCuCjJYMD4N0NXdCtqlQ', '45iSS8KnJ9tL1ilPg3dL9A', 'nCoPMUtqWQqhUvsdTjJT6A', 'Zy0YaekZdd4rurPQKkys7A', 'A5c1ePEvsaQeml2Kui_-vA', '-lKPLSb4N4dgMZlTgoDvJg', '_XIjx1YLPfI4cKePIEc_bQ']}` GetContentsResponse(contents=[DocumentContent(id='bdYc6hvHww_JvLv9k8NhPA', url='https://bellow.ai/', title='A Search Engine for Machine Intelligence', extract="<div><div><h2>More Opinions</h2><p>Get responses from multiple AIs</p><p>Don't rely on a single source of truth, explore the full space of machine intelligence and get highly tailored results.</p></div></div>"), DocumentContent(id='aNBppxBZvQRZMov6sFVj9g', url='https://www.adept.ai/', title='Adept: Useful General Intelligence', extract='<div><div><p>Useful <br />General <br />Intelligence</p></div>'), DocumentContent(id='jieb6sB53mId3EDo0z-SDw', url='https://www.hioperator.com/', title='HiOperator | Generative AI-Enhanced Customer Service', extract="<div><div><div><div><div><h2>Generative AI-Enhanced Customer Support Automation</h2><p>Flexible, Scalable Customer Support</p></div><div><p></p></div></div><p></p></div><div><div><p>Why HiOperator?</p><h2>Truly scalable customer service</h2><p>A digital-first customer service provider that changes all the rules of what's possible. Scalable. 100% US-Based. Effortless. HiOperator is the digital payoff.</p></div><p></p></div><div><div><p>Next-Gen Customer Service</p><h2>Scaling with HiOperator's Superagents</h2><p>HiOperator is only possible in the digital era. Our revolutionary software connects with your systems to empower our agents to learn quickly and deliver incredible accuracy. </p></div><div><div><p></p><div><h3>Train Us Once</h3><p>We handle all of the recruiting, hiring, and training moving forward. Never have to deal with another classroom retraining or head count headaches.</p></div></div><div><div><h3>Send Us Tickets</h3><p>We pull tickets automatically from your preferred CRM vendor into our custom system. You have full control over <strong>how</strong> and <strong>when</strong> we get tickets.</p></div><p></p></div><div><p></p><div><h3>Pay per resolution</h3><p>We charge for each conversation we solve. No onboarding fees. No hourly rates. Pay for what you use.</p></div></div></div></div><div><p>Customer Experience</p><h2>Insights &Â\xa0News</h2></div><div><div><h2>Let's transform your customer service.</h2><p>We can onboard in a matter of days and we offer highly flexible contracts. Whether you need a large team to handle your support or some overflow assistance, getting started is easy.</p></div><p>We can onboard in a matter of days and we offer highly flexible contracts. Whether you need a large team to handle your support or some overflow assistance, getting started is easy.</p></div></div>"), DocumentContent(id='kUiCuCjJYMD4N0NXdCtqlQ', url='https://www.askstylo.com/', title='Home - Stylo', extract='<div><div><header><div><p></p><h2>Stop angry customers from breaking support</h2><p></p></div></header><div><p></p><h2><em> </em><strong><em>â\x80\x9cWe solve 99 tickets perfectly </em>ð\x9f\x98\x87<em> but the 1 we miss lands in the CEOâ\x80\x99s inbox </em>ð\x9f\x98«<em>â\x80\x9d<br /></em></strong></h2><p></p><div><p><strong>â\x80\x8d</strong>That 1 costly ticket breaks your process, metrics, and the will of your team. Angry customers make support teams less effective, which makes customers angrier in return.<strong><br />â\x80\x8d</strong><br />Stylo is AI that tells you where to most effectively spend your time to improve the customer experience. This leads to happier customers, employees, and reduces churn.</p><p>â\x80\x8d<strong>No setup, no learning curve, just plug it in and go.</strong></p></div></div><div><div><p></p><div><p>â\x80\x9cIâ\x80\x99m able to better manage the team because I can pinpoint gaps in the teamâ\x80\x99s knowledge or training, and find room for process improvements.â\x80\x9d</p><p></p></div></div></div></div>'), DocumentContent(id='45iSS8KnJ9tL1ilPg3dL9A', url='https://directai.io/?utm_source=twitter&utm_medium=raw_message&utm_campaign=first_launch', title='DirectAI', extract="<div><div><div><h2>Vision models without training data.<br /></h2><p>Build and deploy powerful computer vision models with plain language.<br />No code or training required.</p></div><div><h2>Fundamentally different.</h2><p>We use large language models and zero-shot learning to instantly build models that fit your description.</p><br /></div><div><div><p></p><h2>We're removing the last major barrier to creating custom models - <br />training data.</h2><p></p></div><div><table><colgroup></colgroup><thead><tr><th><p>Deploy and iterate in seconds with DirectAI</p></th></tr></thead><tbody><tr><td>• Don't spend time assembling training data.</td></tr><tr><td>• Don't pay a third party to label your data.</td></tr><tr><td>• Don't pay to train your model.</td></tr><tr><td>• Don't spend months finetuning your model's behavior.</td></tr></tbody></table></div></div><div><h2>Venture-backed.<p>Based in NYC.</p><p>We're changing how people use AI in the real world.</p><p>Come talk to us on .</p></h2></div></div></div>"), DocumentContent(id='nCoPMUtqWQqhUvsdTjJT6A', url='https://www.sidekickai.co/', title='Sidekick AI | Customer Service Automated', extract='<div><div><div><div><div><div><div><p>Hi, I am an AI named Jenny, working at Pizza Planet. How can I help you today?</p></div><div><p>How much are large pizzas with 1 topping?</p></div><div><p>For most toppings, a large with one topping would be $10.99.</p></div><div><p>Ok, can I order a large with pepperoni</p></div><div><p>Sure! Takeout or delivery?</p></div><div><p>Alright, order placed. See you at 5 pm!</p></div></div><div><p></p></div></div><p></p></div><div><p>Meet Sidekick</p><div><p>\n Sidekick is an AI agent built to hold natural and dynamic conversations with your customers and talk just like a human.</p><p>Built on the world\'s most advanced AI models, Sidekick pushes the state of the art in natural conversation and converses seamlessly with your customers.\n </p></div><p>Try it out ➜</p><p>Try it out ↓</p></div><div><p>An AI agent designed for <strong>service-led growth.</strong></p><div><div><p></p><p>Personal</p><p>Every customer is different, and has unique needs. Our agents are built to provide personalized service depending on the customer\'s needs.</p></div><div><p></p><p>Fast</p><p>Unlike humans, our Sidekicks respond near-instantly, any time of the day. Your customers won\'t wait for service ever again.</p></div><div><p></p><p>Effective</p><p>Customers love great service, and Sidekick delivers. Grow revenue by solving issues in minutes instead of hours, and providing personalized support to each customer.</p></div></div></div><div><p>Integrating with <strong>your tools.</strong></p></div><div><p><strong>Wherever </strong>your customers are.</p><p>\n Sidekick takes an omnichannel approach to customer service, aggregating all customer interactions across all platforms in one area. Currently most social media platforms are supported, along with website embeddings and API integration.\n </p><div><div><div><p>On the web.</p><div><p>Sidekick makes adding a live chat to your website as simple as copy and pasting a single line of code.</p><p>Chat bubbles discretely sit in the bottom right corner and provide a smooth conversation experience, with AI and human agents alike.</p></div></div><p></p><p></p></div><div><div><p>On Facebook.</p><div><p>Sidekick integrates with your Facebook pages to make live customer service one click away.</p><p>Customers can reach your agent and get service without ever leaving Messenger.</p></div></div><p></p><p></p></div><div><div><p>On Instagram.</p><div><p>E-Commerce on Instagram is especially demanding for customer service.</p><p>Sidekick integrates easily with Instagram accounts to put a live agent one click away.</p></div></div><p></p><p></p></div><div><div><p>On Twitter.</p><div><p>Customers are spending more time on Twitter, which means businesses should provide customer service right on the platform.</p><p>Sidekick integrates easily with Twitter accounts to put a live agent one click away.</p></div></div><p></p><p></p></div><div><div><p>Anywhere you want.</p><div><p>Our API provides programmatic access to your Sidekick agent to integrate into your own app.</p><p>We\'ve built simple abstractions over the chat interface to make it easy to work with our API.</p></div></div><div><div><p>Endpoints</p><div><p>POST</p><p>https://www.api.sidekickai.co/converse</p></div></div><div><p>Sample Request</p><div><pre>{\n "access_token": "KjZUZBWAOKwgLWAlVFyL",\n "conversation_id": "23874",\n "body": "How much is a large 2 topping?"\n}</pre></div></div><div><p>Sample Response</p><div><pre>{\n "response": "A large'), DocumentContent(id='Zy0YaekZdd4rurPQKkys7A', url='https://www.hebbia.ai/', title='Hebbia - Search, Reinvented', extract="<div><div><h2>Direct to the point <br />with cutting-edge AI.</h2><p>Stop relying on archaic software, traditional Q&A emails, or waiting for deal partners. Get answers on your own time with accuracy that you can't replicate with humans. <br />â\x80\x8d<br /></p><p>HebbiaÂ\xa0retrieves <strong>every</strong> answer, even insights humans overlook. <br /></p></div>"), DocumentContent(id='A5c1ePEvsaQeml2Kui_-vA', url='https://www.ai.xyz/', title='AI.XYZ', extract='<div><div>\n \n \n<article>\n \n \n \n \n \n<div><div>\n<p><h2><strong>Go be human</strong></h2></p>\n</div><div><p>\n</p><h4>Let your AI deal with the rest</h4>\n<p></p></div><div><p>Design your own AI with AI.XYZ</p></div><div>\n \n \n \n <p></p>\n \n </div></div>\n \n \n \n \n<div><p>\n</p><h3><strong>The digital world was designed to make us more productive but now navigating it all has become its own job.</strong></h3>\n<p></p></div>\n \n \n \n \n<section>\n <div>\n \n \n \n \n \n \n \n \n <p></p>\n \n \n </div>\n <div><div><p>\n</p><h2><strong>Take life a little easier</strong></h2>\n<p></p></div><div>\n \n \n \n <p></p>\n \n </div><div><p>\n</p><h2><strong>Tackles info<br />overload</strong></h2>\n<p></p></div><div><p>\n</p><h4>“Like ChatGPT, but way more proactive and useful because it’s designed by me, for only me”</h4>\n<p></p></div><div>\n \n \n \n <p></p>\n \n </div><div><p>\n</p><h2><strong>Never sits<br />around</strong></h2>\n<p></p></div><div><p>\n</p><h4>“Even if I’m not interacting with it, my AI looks for ways to simplify my day, surprising me with useful ideas”</h4>\n<p></p></div><div>\n \n \n \n <p></p>\n \n </div><div><p>\n</p><h2><strong>Supports and<br />inspires</strong></h2>\n<p></p></div><div><p>\n</p><h4>“It takes things off my plate, but also cheers me on throughout the day — helping me navigate it all”</h4>\n<p></p></div></div>\n \n \n</section>\n \n \n \n \n<div><div><p>\n</p><h2><strong>Create your AI in 3 simple steps:</strong></h2>\n<p></p></div><div>\n<p><strong>STEP ONE</strong></p><h2><strong>Pick a face and voice</strong></h2><h4>Choose from our library of characters or add your own unique face and voice.</h4>\n</div><div>\n \n \n \n <p></p>\n \n </div><div>\n<p><strong>STEP TWO</strong></p><h2><strong>Create your AI’s persona and memory</strong></h2><h4>Decide who your AI is, its purpose and what it will help you with. Paste information that you want your AI to know.</h4>\n</div><div>\n \n \n \n <p></p>\n \n </div><div>\n<p><strong>STEP THREE</strong></p><h2><strong>Get started</strong></h2><h4>Ask your AI to help you with ideas and support throughout your day. Eventually it will be able to proactively support you.</h4>\n</div><div>\n \n \n \n <p></p>\n \n </div></div>\n \n \n \n \n<section>\n <div>\n \n \n \n \n \n \n \n \n <p></p>\n \n \n </div>\n <div><p>\n</p><h2><strong>Start training your AI to do things for you</strong></h2>\n<p></p></div>\n \n \n</section>\n \n</article>\n \n \n \n \n \n </div></div'), DocumentContent(id='-lKPLSb4N4dgMZlTgoDvJg', url='https://halist.ai/', title='Halist AI', extract='<div><div>\n<p><a href="/app/">Start for free</a></p><p>\nPowered by OpenAI GPT-3 and GPT-4.\n</p>\n<h2>ChatGPT. Lightning-fast and private. Everywhere.</h2>\n<h2>Optimized access to the AI on mobile.</h2>\n<p></p><p>\nTo install Halist on <b>iPhone</b>, open the web app in Safari and tap the "Share" icon. Then, tap "Add to Home Screen" and follow the prompts.\nTo install on <b>Android</b>, open the website in Chrome and tap the three dots in the top right corner. Then, tap "Add to Home screen" and follow the prompts.\n</p>\n</div></div>'), DocumentContent(id='_XIjx1YLPfI4cKePIEc_bQ', url='https://airin.ai/', title='Clone your best expert', extract='<div><section><section><div><p> Airin clones how your top expert solves problems in as little as 2 hours. Airin creates an AI companion for the rest of your team by focusing on the patterns in your expert’s questions and hypotheses, not their answers. <a href="/how-it-works">Learn how it works </a></p></div></section><section><div><p> Your customers, agents, sales teams, and consultants can independently solve a wider-range of complex problems with an AI companion. This eliminates the need to maintain large teams of specialized experts. </p></div></section><section><div><p> Airin automates remote coaching for new hires and dramatically reduces time to productivity. New employees partner with your AI companion and meet productivity standards in half the time. </p></div></section></section>')])Here are some of the hottest AI agent startups and what they do: 1. [Bellow AI](https://bellow.ai/): This startup provides a search engine for machine intelligence. It allows users to get responses from multiple AIs, exploring the full space of machine intelligence and getting highly tailored results. 2. [Adept AI](https://www.adept.ai/): Adept is focused on creating useful general intelligence. 3. [HiOperator](https://www.hioperator.com/): HiOperator offers generative AI-enhanced customer support automation. It provides scalable, digital-first customer service and uses its software to empower agents to learn quickly and deliver accurate results. 4. [Stylo](https://www.askstylo.com/): Stylo uses AI to help manage customer support, identifying where to most effectively spend time to improve the customer experience. 5. [DirectAI](https://directai.io/): DirectAI allows users to build and deploy powerful computer vision models with plain language, without the need for code or training. 6. [Sidekick AI](https://www.sidekickai.co/): Sidekick AI is built to hold natural and dynamic conversations with customers, providing personalized service depending on the customer's needs. 7. [Hebbia](https://www.hebbia.ai/): Hebbia is reinventing search with cutting-edge AI, retrieving every answer, even insights humans overlook. 8. [AI.XYZ](https://www.ai.xyz/): AI.XYZ allows users to design their own AI, tackling information overload and providing support and inspiration throughout the day. 9. [Halist AI](https://halist.ai/): Halist AI provides optimized access to ChatGPT, powered by OpenAI GPT-3 and GPT-4, on mobile. 10. [Airin](https://airin.ai/): Airin clones how your top expert solves problems in as little as 2 hours, creating an AI companion for the rest of your team. It automates remote coaching for new hires and dramatically reduces time to productivity. > Finished chain. "Here are some of the hottest AI agent startups and what they do:\n\n1. [Bellow AI](https://bellow.ai/): This startup provides a search engine for machine intelligence. It allows users to get responses from multiple AIs, exploring the full space of machine intelligence and getting highly tailored results.\n\n2. [Adept AI](https://www.adept.ai/): Adept is focused on creating useful general intelligence.\n\n3. [HiOperator](https://www.hioperator.com/): HiOperator offers generative AI-enhanced customer support automation. It provides scalable, digital-first customer service and uses its software to empower agents to learn quickly and deliver accurate results.\n\n4. [Stylo](https://www.askstylo.com/): Stylo uses AI to help manage customer support, identifying where to most effectively spend time to improve the customer experience.\n\n5. [DirectAI](https://directai.io/): DirectAI allows users to build and deploy powerful computer vision models with plain language, without the need for code or training.\n\n6. [Sidekick AI](https://www.sidekickai.co/): Sidekick AI is built to hold natural and dynamic conversations with customers, providing personalized service depending on the customer's needs.\n\n7. [Hebbia](https://www.hebbia.ai/): Hebbia is reinventing search with cutting-edge AI, retrieving every answer, even insights humans overlook.\n\n8. [AI.XYZ](https://www.ai.xyz/): AI.XYZ allows users to design their own AI, tackling information overload and providing support and inspiration throughout the day.\n\n9. [Halist AI](https://halist.ai/): Halist AI provides optimized access to ChatGPT, powered by OpenAI GPT-3 and GPT-4, on mobile.\n\n10. [Airin](https://airin.ai/): Airin clones how your top expert solves problems in as little as 2 hours, creating an AI companion for the rest of your team. It automates remote coaching for new hires and dramatically reduces time to productivity.\n"Using the tool wrapperThis is the old way of using Metaphor - through our own in-house integration.from langchain.utilities import MetaphorSearchAPIWrappersearch = MetaphorSearchAPIWrapper()Call the APIresults takes in a Metaphor-optimized search query and a number of results (up to 500). It returns a list of results with title, url, author, and creation date.search.results("The best blog post about AI safety is definitely this: ", 10) [{'title': 'Core Views on AI Safety: When, Why, What, and How', 'url': 'https://www.anthropic.com/index/core-views-on-ai-safety', 'author': None, 'published_date': '2023-03-08'}, {'title': 'Extinction Risk from Artificial Intelligence', 'url': 'https://aisafety.wordpress.com/', 'author': None, 'published_date': '2013-10-08'}, {'title': 'The simple picture on AI safety - LessWrong', 'url': 'https://www.lesswrong.com/posts/WhNxG4r774bK32GcH/the-simple-picture-on-ai-safety', 'author': 'Alex Flint', 'published_date': '2018-05-27'}, {'title': 'No Time Like The Present For AI Safety Work', 'url': 'https://slatestarcodex.com/2015/05/29/no-time-like-the-present-for-ai-safety-work/', 'author': None, 'published_date': '2015-05-29'}, {'title': 'A plea for solutionism on AI safety - LessWrong', 'url': 'https://www.lesswrong.com/posts/ASMX9ss3J5G3GZdok/a-plea-for-solutionism-on-ai-safety', 'author': 'Jasoncrawford', 'published_date': '2023-06-09'}, {'title': 'The Artificial Intelligence Revolution: Part 1 - Wait But Why', 'url': 'https://waitbutwhy.com/2015/01/artificial-intelligence-revolution-1.html', 'author': 'Tim Urban', 'published_date': '2015-01-22'}, {'title': 'Anthropic: Core Views on AI Safety: When, Why, What, and How - EA Forum', 'url': 'https://forum.effectivealtruism.org/posts/uGDCaPFaPkuxAowmH/anthropic-core-views-on-ai-safety-when-why-what-and-how', 'author': 'Jonmenaster', 'published_date': '2023-03-09'}, {'title': "[Linkpost] Sam Altman's 2015 Blog Posts Machine Intelligence Parts 1 & 2 - LessWrong", 'url': 'https://www.lesswrong.com/posts/QnBZkNJNbJK9k5Xi7/linkpost-sam-altman-s-2015-blog-posts-machine-intelligence', 'author': 'Olivia Jimenez', 'published_date': '2023-04-28'}, {'title': 'The Proof of Doom - LessWrong', 'url': 'https://www.lesswrong.com/posts/xBrpph9knzWdtMWeQ/the-proof-of-doom', 'author': 'Johnlawrenceaspden', 'published_date': '2022-03-09'}, {'title': "Anthropic's Core Views on AI Safety - LessWrong", 'url': 'https://www.lesswrong.com/posts/xhKr5KtvdJRssMeJ3/anthropic-s-core-views-on-ai-safety', 'author': 'Zac Hatfield-Dodds', 'published_date': '2023-03-09'}]Adding filtersWe can also add filters to our search. include_domains: Optional[List[str]] - List of domains to include in the search. If specified, results will only come from these domains. Only one of include_domains and exclude_domains should be specified.exclude_domains: Optional[List[str]] - List of domains to exclude in the search. If specified, results will only come from these domains. Only one of include_domains and exclude_domains should be specified.start_crawl_date: Optional[str] - "Crawl date" refers to the date that Metaphor discovered a link, which is more granular and can be more useful than published date. If start_crawl_date is specified, results will only include links that were crawled after start_crawl_date. Must be specified in ISO 8601 format (YYYY-MM-DDTHH:MM:SSZ)end_crawl_date: Optional[str] - "Crawl date" refers to the date that Metaphor discovered a link, which is more granular and can be more useful than published date. If endCrawlDate is specified, results will only include links that were crawled before end_crawl_date. Must be specified in ISO 8601 format (YYYY-MM-DDTHH:MM:SSZ)start_published_date: Optional[str] - If specified, only links with a published date after start_published_date will be returned. Must be specified in ISO 8601 format (YYYY-MM-DDTHH:MM:SSZ). Note that for some links, we have no published date, and these links will be excluded from the results if start_published_date is specified.end_published_date: Optional[str] - If specified, only links with a published date before end_published_date will be returned. Must be specified in ISO 8601 format (YYYY-MM-DDTHH:MM:SSZ). Note that for some links, we have no published date, and these links will be excluded from the results if end_published_date is specified.See full docs here.search.results( "The best blog post about AI safety is definitely this: ", 10, include_domains=["lesswrong.com"], start_published_date="2019-01-01",)Use Metaphor as a toolMetaphor can be used as a tool that gets URLs that other tools such as browsing tools.from langchain.agents.agent_toolkits import PlayWrightBrowserToolkitfrom langchain.tools.playwright.utils import ( create_async_playwright_browser, # A synchronous browser is available, though it isn't compatible with jupyter.)async_browser = create_async_playwright_browser()toolkit = PlayWrightBrowserToolkit.from_browser(async_browser=async_browser)tools = toolkit.get_tools()tools_by_name = {tool.name: tool for tool in tools}print(tools_by_name.keys())navigate_tool = tools_by_name["navigate_browser"]extract_text = tools_by_name["extract_text"]from langchain.agents import initialize_agent, AgentTypefrom langchain.chat_models import ChatOpenAIfrom langchain.tools import MetaphorSearchResultsllm = ChatOpenAI(model_name="gpt-4", temperature=0.7)metaphor_tool = MetaphorSearchResults(api_wrapper=search)agent_chain = initialize_agent( [metaphor_tool, extract_text, navigate_tool], llm, agent=AgentType.STRUCTURED_CHAT_ZERO_SHOT_REACT_DESCRIPTION, verbose=True,)agent_chain.run( "find me an interesting tweet about AI safety using Metaphor, then tell me the first sentence in the post. Do not finish until able to retrieve the first sentence.")PreviousLemon AgentNextNuclia UnderstandingUsing their SDKUse in an agentUsing the tool wrapperCall the APIAdding filtersUse Metaphor as a tool |
733 | https://python.langchain.com/docs/integrations/tools/nuclia | ComponentsToolsNuclia UnderstandingOn this pageNuclia UnderstandingNuclia automatically indexes your unstructured data from any internal and external source, providing optimized search results and generative answers. It can handle video and audio transcription, image content extraction, and document parsing.The Nuclia Understanding API supports the processing of unstructured data, including text, web pages, documents, and audio/video contents. It extracts all texts wherever it is (using speech-to-text or OCR when needed), it identifies entities, it aslo extracts metadata, embedded files (like images in a PDF), and web links. It also provides a summary of the content.To use the Nuclia Understanding API, you need to have a Nuclia account. You can create one for free at https://nuclia.cloud, and then create a NUA key.#!pip install --upgrade protobuf#!pip install nucliadb-protosimport osos.environ["NUCLIA_ZONE"] = "<YOUR_ZONE>" # e.g. europe-1os.environ["NUCLIA_NUA_KEY"] = "<YOUR_API_KEY>"from langchain.tools.nuclia import NucliaUnderstandingAPInua = NucliaUnderstandingAPI(enable_ml=False)You can push files to the Nuclia Understanding API using the push action. As the processing is done asynchronously, the results might be returned in a different order than the files were pushed. That is why you need to provide an id to match the results with the corresponding file.nua.run({"action": "push", "id": "1", "path": "./report.docx"})nua.run({"action": "push", "id": "2", "path": "./interview.mp4"})You can now call the pull action in a loop until you get the JSON-formatted result.import timepending = Truedata = Nonewhile pending: time.sleep(15) data = nua.run({"action": "pull", "id": "1", "path": None}) if data: print(data) pending = False else: print("waiting...")You can also do it in one step in async mode, you only need to do a push, and it will wait until the results are pulled:import asyncioasync def process(): data = await nua.arun( {"action": "push", "id": "1", "path": "./talk.mp4", "text": None} ) print(data)asyncio.run(process())Retrieved informationNuclia returns the following information:file metadataextracted textnested text (like text in an embedded image)a summary (only when enable_ml is set to True)paragraphs and sentences splitting (defined by the position of their first and last characters, plus start time and end time for a video or audio file)named entities: people, dates, places, organizations, etc. (only when enable_ml is set to True)linksa thumbnailembedded filesthe vector representations of the text (only when enable_ml is set to True)Note: Generated files (thumbnail, extracted embedded files, etc.) are provided as a token. You can download them with the /processing/download endpoint. Also at any level, if an attribute exceeds a certain size, it will be put in a downloadable file and will be replaced in the document by a file pointer. This will consist of {"file": {"uri": "JWT_TOKEN"}}. The rule is that if the size of the message is greater than 1000000 characters, the biggest parts will be moved to downloadable files. First, the compression process will target vectors. If that is not enough, it will target large field metadata, and finally it will target extracted text.PreviousMetaphor SearchNextOpenWeatherMapRetrieved information |
734 | https://python.langchain.com/docs/integrations/tools/openweathermap | ComponentsToolsOpenWeatherMapOn this pageOpenWeatherMapThis notebook goes over how to use the OpenWeatherMap component to fetch weather information.First, you need to sign up for an OpenWeatherMap API key:Go to OpenWeatherMap and sign up for an API key herepip install pyowmThen we will need to set some environment variables:Save your API KEY into OPENWEATHERMAP_API_KEY env variableUse the wrapperfrom langchain.utilities import OpenWeatherMapAPIWrapperimport osos.environ["OPENWEATHERMAP_API_KEY"] = ""weather = OpenWeatherMapAPIWrapper()weather_data = weather.run("London,GB")print(weather_data) In London,GB, the current weather is as follows: Detailed status: broken clouds Wind speed: 2.57 m/s, direction: 240° Humidity: 55% Temperature: - Current: 20.12°C - High: 21.75°C - Low: 18.68°C - Feels like: 19.62°C Rain: {} Heat index: None Cloud cover: 75%Use the toolfrom langchain.llms import OpenAIfrom langchain.agents import load_tools, initialize_agent, AgentTypeimport osos.environ["OPENAI_API_KEY"] = ""os.environ["OPENWEATHERMAP_API_KEY"] = ""llm = OpenAI(temperature=0)tools = load_tools(["openweathermap-api"], llm)agent_chain = initialize_agent( tools=tools, llm=llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True)agent_chain.run("What's the weather like in London?") > Entering new AgentExecutor chain... I need to find out the current weather in London. Action: OpenWeatherMap Action Input: London,GB Observation: In London,GB, the current weather is as follows: Detailed status: broken clouds Wind speed: 2.57 m/s, direction: 240° Humidity: 56% Temperature: - Current: 20.11°C - High: 21.75°C - Low: 18.68°C - Feels like: 19.64°C Rain: {} Heat index: None Cloud cover: 75% Thought: I now know the current weather in London. Final Answer: The current weather in London is broken clouds, with a wind speed of 2.57 m/s, direction 240°, humidity of 56%, temperature of 20.11°C, high of 21.75°C, low of 18.68°C, and a heat index of None. > Finished chain. 'The current weather in London is broken clouds, with a wind speed of 2.57 m/s, direction 240°, humidity of 56%, temperature of 20.11°C, high of 21.75°C, low of 18.68°C, and a heat index of None.'PreviousNuclia UnderstandingNextPubMedUse the wrapperUse the tool |
735 | https://python.langchain.com/docs/integrations/tools/pubmed | ComponentsToolsPubMedPubMedPubMed® comprises more than 35 million citations for biomedical literature from MEDLINE, life science journals, and online books. Citations may include links to full text content from PubMed Central and publisher web sites.This notebook goes over how to use PubMed as a tool.from langchain.tools import PubmedQueryRuntool = PubmedQueryRun()tool.run("chatgpt") 'Published: <Year>2023</Year><Month>May</Month><Day>31</Day>\nTitle: Dermatology in the wake of an AI revolution: who gets a say?\nSummary: \n\nPublished: <Year>2023</Year><Month>May</Month><Day>30</Day>\nTitle: What is ChatGPT and what do we do with it? Implications of the age of AI for nursing and midwifery practice and education: An editorial.\nSummary: \n\nPublished: <Year>2023</Year><Month>Jun</Month><Day>02</Day>\nTitle: The Impact of ChatGPT on the Nursing Profession: Revolutionizing Patient Care and Education.\nSummary: The nursing field has undergone notable changes over time and is projected to undergo further modifications in the future, owing to the advent of sophisticated technologies and growing healthcare needs. The advent of ChatGPT, an AI-powered language model, is expected to exert a significant influence on the nursing profession, specifically in the domains of patient care and instruction. The present article delves into the ramifications of ChatGPT within the nursing domain and accentuates its capacity and constraints to transform the discipline.'PreviousOpenWeatherMapNextRequests |
736 | https://python.langchain.com/docs/integrations/tools/requests | ComponentsToolsRequestsOn this pageRequestsThe web contains a lot of information that LLMs do not have access to. In order to easily let LLMs interact with that information, we provide a wrapper around the Python Requests module that takes in a URL and fetches data from that URL.from langchain.agents import load_toolsrequests_tools = load_tools(["requests_all"])requests_tools [RequestsGetTool(name='requests_get', description='A portal to the internet. Use this when you need to get specific content from a website. Input should be a url (i.e. https://www.google.com). The output will be the text response of the GET request.', args_schema=None, return_direct=False, verbose=False, callbacks=None, callback_manager=None, requests_wrapper=TextRequestsWrapper(headers=None, aiosession=None)), RequestsPostTool(name='requests_post', description='Use this when you want to POST to a website.\n Input should be a json string with two keys: "url" and "data".\n The value of "url" should be a string, and the value of "data" should be a dictionary of \n key-value pairs you want to POST to the url.\n Be careful to always use double quotes for strings in the json string\n The output will be the text response of the POST request.\n ', args_schema=None, return_direct=False, verbose=False, callbacks=None, callback_manager=None, requests_wrapper=TextRequestsWrapper(headers=None, aiosession=None)), RequestsPatchTool(name='requests_patch', description='Use this when you want to PATCH to a website.\n Input should be a json string with two keys: "url" and "data".\n The value of "url" should be a string, and the value of "data" should be a dictionary of \n key-value pairs you want to PATCH to the url.\n Be careful to always use double quotes for strings in the json string\n The output will be the text response of the PATCH request.\n ', args_schema=None, return_direct=False, verbose=False, callbacks=None, callback_manager=None, requests_wrapper=TextRequestsWrapper(headers=None, aiosession=None)), RequestsPutTool(name='requests_put', description='Use this when you want to PUT to a website.\n Input should be a json string with two keys: "url" and "data".\n The value of "url" should be a string, and the value of "data" should be a dictionary of \n key-value pairs you want to PUT to the url.\n Be careful to always use double quotes for strings in the json string.\n The output will be the text response of the PUT request.\n ', args_schema=None, return_direct=False, verbose=False, callbacks=None, callback_manager=None, requests_wrapper=TextRequestsWrapper(headers=None, aiosession=None)), RequestsDeleteTool(name='requests_delete', description='A portal to the internet. Use this when you need to make a DELETE request to a URL. Input should be a specific url, and the output will be the text response of the DELETE request.', args_schema=None, return_direct=False, verbose=False, callbacks=None, callback_manager=None, requests_wrapper=TextRequestsWrapper(headers=None, aiosession=None))]Inside the toolEach requests tool contains a requests wrapper. You can work with these wrappers directly below# Each tool wrapps a requests wrapperrequests_tools[0].requests_wrapper TextRequestsWrapper(headers=None, aiosession=None)from langchain.utilities import TextRequestsWrapperrequests = TextRequestsWrapper()requests.get("https://www.google.com") '<!doctype html><html itemscope="" itemtype="http://schema.org/WebPage" lang="en"><head><meta content="Search the world\'s information, including webpages, images, videos and more. Google has many special features to help you find exactly what you\'re looking for." name="description"><meta content="noodp" name="robots"><meta content="text/html; charset=UTF-8" http-equiv="Content-Type"><meta content="/images/branding/googleg/1x/googleg_standard_color_128dp.png" itemprop="image"><title>Google</title><script 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737 | https://python.langchain.com/docs/integrations/tools/sceneXplain | ComponentsToolsSceneXplainOn this pageSceneXplainSceneXplain is an ImageCaptioning service accessible through the SceneXplain Tool.To use this tool, you'll need to make an account and fetch your API Token from the website. Then you can instantiate the tool.import osos.environ["SCENEX_API_KEY"] = "<YOUR_API_KEY>"from langchain.agents import load_toolstools = load_tools(["sceneXplain"])Or directly instantiate the tool.from langchain.tools import SceneXplainTooltool = SceneXplainTool()Usage in an AgentThe tool can be used in any LangChain agent as follows:from langchain.llms import OpenAIfrom langchain.agents import initialize_agentfrom langchain.memory import ConversationBufferMemoryllm = OpenAI(temperature=0)memory = ConversationBufferMemory(memory_key="chat_history")agent = initialize_agent( tools, llm, memory=memory, agent="conversational-react-description", verbose=True)output = agent.run( input=( "What is in this image https://storage.googleapis.com/causal-diffusion.appspot.com/imagePrompts%2F0rw369i5h9t%2Foriginal.png. " "Is it movie or a game? If it is a movie, what is the name of the movie?" ))print(output) > Entering new AgentExecutor chain... Thought: Do I need to use a tool? Yes Action: Image Explainer Action Input: https://storage.googleapis.com/causal-diffusion.appspot.com/imagePrompts%2F0rw369i5h9t%2Foriginal.png Observation: In a charmingly whimsical scene, a young girl is seen braving the rain alongside her furry companion, the lovable Totoro. The two are depicted standing on a bustling street corner, where they are sheltered from the rain by a bright yellow umbrella. The girl, dressed in a cheerful yellow frock, holds onto the umbrella with both hands while gazing up at Totoro with an expression of wonder and delight. Totoro, meanwhile, stands tall and proud beside his young friend, holding his own umbrella aloft to protect them both from the downpour. His furry body is rendered in rich shades of grey and white, while his large ears and wide eyes lend him an endearing charm. In the background of the scene, a street sign can be seen jutting out from the pavement amidst a flurry of raindrops. A sign with Chinese characters adorns its surface, adding to the sense of cultural diversity and intrigue. Despite the dreary weather, there is an undeniable sense of joy and camaraderie in this heartwarming image. Thought: Do I need to use a tool? No AI: This image appears to be a still from the 1988 Japanese animated fantasy film My Neighbor Totoro. The film follows two young girls, Satsuki and Mei, as they explore the countryside and befriend the magical forest spirits, including the titular character Totoro. > Finished chain. This image appears to be a still from the 1988 Japanese animated fantasy film My Neighbor Totoro. The film follows two young girls, Satsuki and Mei, as they explore the countryside and befriend the magical forest spirits, including the titular character Totoro.PreviousRequestsNextSearch ToolsUsage in an Agent |
738 | https://python.langchain.com/docs/integrations/tools/search_tools | ComponentsToolsSearch ToolsOn this pageSearch ToolsThis notebook shows off usage of various search tools.from langchain.agents import load_toolsfrom langchain.agents import initialize_agentfrom langchain.agents import AgentTypefrom langchain.llms import OpenAIllm = OpenAI(temperature=0)Google Serper API WrapperFirst, let's try to use the Google Serper API tool.tools = load_tools(["google-serper"], llm=llm)agent = initialize_agent( tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True)agent.run("What is the weather in Pomfret?") > Entering new AgentExecutor chain... I should look up the current weather conditions. Action: Search Action Input: "weather in Pomfret" Observation: 37°F Thought: I now know the current temperature in Pomfret. Final Answer: The current temperature in Pomfret is 37°F. > Finished chain. 'The current temperature in Pomfret is 37°F.'SearchApiSecond, let's try SearchApi tool.tools = load_tools(["searchapi"], llm=llm)agent = initialize_agent( tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True)agent.run("What is the weather in Pomfret?") > Entering new AgentExecutor chain... I need to find out the current weather in Pomfret. Action: searchapi Action Input: "weather in Pomfret" Observation: Thu 14 | Day ... Some clouds this morning will give way to generally sunny skies for the afternoon. High 73F. Winds NW at 5 to 10 mph. Hourly Weather-Pomfret, CT · 1 pm. 71°. 0%. Sunny. Feels Like71°. WindNW 9 mph · 2 pm. 72°. 0%. Sunny. Feels Like72°. WindNW 9 mph · 3 pm. 72°. 0%. Sunny. Feels ... 10 Day Weather-Pomfret, VT. As of 4:28 am EDT. Today. 68°/48°. 4%. Thu 14 | Day. 68°. 4%. WNW 10 mph. Some clouds this morning will give way to generally ... Be prepared with the most accurate 10-day forecast for Pomfret, MD with highs, lows, chance of precipitation from The Weather Channel and Weather.com. Current Weather. 10:00 PM. 65°F. RealFeel® 67°. Mostly cloudy. LOCAL HURRICANE TRACKER. Category2. Lee. Late Friday Night - Saturday Afternoon. 10 Day Weather-Pomfret, NY. As of 5:09 pm EDT. Tonight. --/55°. 10%. Wed 13 | Night. 55°. 10%. NW 11 mph. Some clouds. Low near 55F. Pomfret CT. Overnight. Overnight: Patchy fog before 3am, then patchy fog after 4am. Otherwise, mostly. Patchy Fog. Low: 58 °F. Thursday. Isolated showers. Mostly cloudy, with a high near 76. Calm wind. Chance of precipitation is 20%. Tonight. Mostly Cloudy. Mostly cloudy, with a ... Partly sunny, with a high near 67. Breezy, with a north wind 18 to 22 mph, with gusts as high as 34 mph. Chance of precipitation is 30%. ... A chance of showers ... Today's Weather - Pomfret, CT ... Patchy fog. Showers. Lows in the upper 50s. Northwest winds around 5 mph. Chance of rain near 100 percent. ... Sunny. Patchy fog ... Thought: I now know the final answer Final Answer: The current weather in Pomfret is mostly cloudy with a high near 67 and a chance of showers. Winds are from the north at 18 to 22 mph with gusts up to 34 mph. > Finished chain. 'The current weather in Pomfret is mostly cloudy with a high near 67 and a chance of showers. Winds are from the north at 18 to 22 mph with gusts up to 34 mph.'SerpAPINow, let's use the SerpAPI tool.tools = load_tools(["serpapi"], llm=llm)agent = initialize_agent( tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True)agent.run("What is the weather in Pomfret?") > Entering new AgentExecutor chain... I need to find out what the current weather is in Pomfret. Action: Search Action Input: "weather in Pomfret" Observation: {'type': 'weather_result', 'temperature': '69', 'unit': 'Fahrenheit', 'precipitation': '2%', 'humidity': '90%', 'wind': '1 mph', 'location': 'Pomfret, CT', 'date': 'Sunday 9:00 PM', 'weather': 'Clear'} Thought: I now know the current weather in Pomfret. Final Answer: The current weather in Pomfret is 69 degrees Fahrenheit, 2% precipitation, 90% humidity, and 1 mph wind. It is currently clear. > Finished chain. 'The current weather in Pomfret is 69 degrees Fahrenheit, 2% precipitation, 90% humidity, and 1 mph wind. It is currently clear.'GoogleSearchAPIWrapperNow, let's use the official Google Search API Wrapper.tools = load_tools(["google-search"], llm=llm)agent = initialize_agent( tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True)agent.run("What is the weather in Pomfret?") > Entering new AgentExecutor chain... I should look up the current weather conditions. Action: Google Search Action Input: "weather in Pomfret" Observation: Showers early becoming a steady light rain later in the day. Near record high temperatures. High around 60F. Winds SW at 10 to 15 mph. Chance of rain 60%. Pomfret, CT Weather Forecast, with current conditions, wind, air quality, and what to expect for the next 3 days. Hourly Weather-Pomfret, CT. As of 12:52 am EST. Special Weather Statement +2 ... Hazardous Weather Conditions. Special Weather Statement ... Pomfret CT. Tonight ... National Digital Forecast Database Maximum Temperature Forecast. Pomfret Center Weather Forecasts. Weather Underground provides local & long-range weather forecasts, weatherreports, maps & tropical weather conditions for ... Pomfret, CT 12 hour by hour weather forecast includes precipitation, temperatures, sky conditions, rain chance, dew-point, relative humidity, wind direction ... North Pomfret Weather Forecasts. Weather Underground provides local & long-range weather forecasts, weatherreports, maps & tropical weather conditions for ... Today's Weather - Pomfret, CT. Dec 31, 2022 4:00 PM. Putnam MS. --. Weather forecast icon. Feels like --. Hi --. Lo --. Pomfret, CT temperature trend for the next 14 Days. Find daytime highs and nighttime lows from TheWeatherNetwork.com. Pomfret, MD Weather Forecast Date: 332 PM EST Wed Dec 28 2022. The area/counties/county of: Charles, including the cites of: St. Charles and Waldorf. Thought: I now know the current weather conditions in Pomfret. Final Answer: Showers early becoming a steady light rain later in the day. Near record high temperatures. High around 60F. Winds SW at 10 to 15 mph. Chance of rain 60%. > Finished AgentExecutor chain. 'Showers early becoming a steady light rain later in the day. Near record high temperatures. High around 60F. Winds SW at 10 to 15 mph. Chance of rain 60%.'SearxNG Meta Search EngineHere we will be using a self hosted SearxNG meta search engine.tools = load_tools(["searx-search"], searx_host="http://localhost:8888", llm=llm)agent = initialize_agent( tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True)agent.run("What is the weather in Pomfret") > Entering new AgentExecutor chain... I should look up the current weather Action: SearX Search Action Input: "weather in Pomfret" Observation: Mainly cloudy with snow showers around in the morning. High around 40F. Winds NNW at 5 to 10 mph. Chance of snow 40%. Snow accumulations less than one inch. 10 Day Weather - Pomfret, MD As of 1:37 pm EST Today 49°/ 41° 52% Mon 27 | Day 49° 52% SE 14 mph Cloudy with occasional rain showers. High 49F. Winds SE at 10 to 20 mph. Chance of rain 50%.... 10 Day Weather - Pomfret, VT As of 3:51 am EST Special Weather Statement Today 39°/ 32° 37% Wed 01 | Day 39° 37% NE 4 mph Cloudy with snow showers developing for the afternoon. High 39F.... Pomfret, CT ; Current Weather. 1:06 AM. 35°F · RealFeel® 32° ; TODAY'S WEATHER FORECAST. 3/3. 44°Hi. RealFeel® 50° ; TONIGHT'S WEATHER FORECAST. 3/3. 32°Lo. Pomfret, MD Forecast Today Hourly Daily Morning 41° 1% Afternoon 43° 0% Evening 35° 3% Overnight 34° 2% Don't Miss Finally, Here’s Why We Get More Colds and Flu When It’s Cold Coast-To-Coast... Pomfret, MD Weather Forecast | AccuWeather Current Weather 5:35 PM 35° F RealFeel® 36° RealFeel Shade™ 36° Air Quality Excellent Wind E 3 mph Wind Gusts 5 mph Cloudy More Details WinterCast... Pomfret, VT Weather Forecast | AccuWeather Current Weather 11:21 AM 23° F RealFeel® 27° RealFeel Shade™ 25° Air Quality Fair Wind ESE 3 mph Wind Gusts 7 mph Cloudy More Details WinterCast... Pomfret Center, CT Weather Forecast | AccuWeather Daily Current Weather 6:50 PM 39° F RealFeel® 36° Air Quality Fair Wind NW 6 mph Wind Gusts 16 mph Mostly clear More Details WinterCast... 12:00 pm · Feels Like36° · WindN 5 mph · Humidity43% · UV Index3 of 10 · Cloud Cover65% · Rain Amount0 in ... Pomfret Center, CT Weather Conditions | Weather Underground star Popular Cities San Francisco, CA 49 °F Clear Manhattan, NY 37 °F Fair Schiller Park, IL (60176) warning39 °F Mostly Cloudy... Thought: I now know the final answer Final Answer: The current weather in Pomfret is mainly cloudy with snow showers around in the morning. The temperature is around 40F with winds NNW at 5 to 10 mph. Chance of snow is 40%. > Finished chain. 'The current weather in Pomfret is mainly cloudy with snow showers around in the morning. The temperature is around 40F with winds NNW at 5 to 10 mph. Chance of snow is 40%.'PreviousSceneXplainNextSearchApiGoogle Serper API WrapperSearchApiSerpAPIGoogleSearchAPIWrapperSearxNG Meta Search Engine |
739 | https://python.langchain.com/docs/integrations/tools/searchapi | ComponentsToolsSearchApiOn this pageSearchApiThis notebook shows examples of how to use SearchApi to search the web. Go to https://www.searchapi.io/ to sign up for a free account and get API key.import osos.environ["SEARCHAPI_API_KEY"] = ""from langchain.utilities import SearchApiAPIWrappersearch = SearchApiAPIWrapper()search.run("Obama's first name?") 'Barack Hussein Obama II'Using as part of a Self Ask With Search Chainos.environ["OPENAI_API_KEY"] = ""from langchain.utilities import SearchApiAPIWrapperfrom langchain.llms.openai import OpenAIfrom langchain.agents import initialize_agent, Toolfrom langchain.agents import AgentTypellm = OpenAI(temperature=0)search = SearchApiAPIWrapper()tools = [ Tool( name="Intermediate Answer", func=search.run, description="useful for when you need to ask with search" )]self_ask_with_search = initialize_agent(tools, llm, agent=AgentType.SELF_ASK_WITH_SEARCH, verbose=True)self_ask_with_search.run("Who lived longer: Plato, Socrates, or Aristotle?") > Entering new AgentExecutor chain... Yes. Follow up: How old was Plato when he died? Intermediate answer: eighty Follow up: How old was Socrates when he died? Intermediate answer: | Socrates | | -------- | | Born | c. 470 BC Deme Alopece, Athens | | Died | 399 BC (aged approximately 71) Athens | | Cause of death | Execution by forced suicide by poisoning | | Spouse(s) | Xanthippe, Myrto | Follow up: How old was Aristotle when he died? Intermediate answer: 62 years So the final answer is: Plato > Finished chain. 'Plato'Custom parametersSearchApi wrapper can be customized to use different engines like Google News, Google Jobs, Google Scholar, or others which can be found in SearchApi documentation. All parameters supported by SearchApi can be passed when executing the query. search = SearchApiAPIWrapper(engine="google_jobs")search.run("AI Engineer", location="Portugal", gl="pt")[0:500] 'Azure AI Engineer Be an XpanderCandidatar-meCandidatar-meCandidatar-me\n\nShare:\n\nAzure AI Engineer\n\nA área Digital Xperience da Xpand IT é uma equipa tecnológica de rápido crescimento que se concentra em tecnologias Microsoft e Mobile. A sua principal missão é fornecer soluções de software de alta qualidade que atendam às necessidades do utilizador final, num mundo tecnológico continuamente exigente e em ritmo acelerado, proporcionando a melhor experiência em termos de personalização, performance'Getting results with metadataimport pprintsearch = SearchApiAPIWrapper(engine="google_scholar")results = search.results("Large Language Models")pprint.pp(results) {'search_metadata': {'id': 'search_qVdXG2jzvrlqTzayeYoaOb8A', 'status': 'Success', 'created_at': '2023-09-25T15:22:30Z', 'request_time_taken': 3.21, 'parsing_time_taken': 0.03, 'total_time_taken': 3.24, 'request_url': 'https://scholar.google.com/scholar?q=Large+Language+Models&hl=en', 'html_url': 'https://www.searchapi.io/api/v1/searches/search_qVdXG2jzvrlqTzayeYoaOb8A.html', 'json_url': 'https://www.searchapi.io/api/v1/searches/search_qVdXG2jzvrlqTzayeYoaOb8A'}, 'search_parameters': {'engine': 'google_scholar', 'q': 'Large Language Models', 'hl': 'en'}, 'search_information': {'query_displayed': 'Large Language Models', 'total_results': 6420000, 'page': 1, 'time_taken_displayed': 0.06}, 'organic_results': [{'position': 1, 'title': 'ChatGPT for good? On opportunities and ' 'challenges of large language models for ' 'education', 'data_cid': 'uthwmf2nU3EJ', 'link': 'https://www.sciencedirect.com/science/article/pii/S1041608023000195', 'publication': 'E Kasneci, K Seßler, S Küchemann, M ' 'Bannert… - Learning and individual …, ' '2023 - Elsevier', 'snippet': '… state of large language models and their ' 'applications. We then highlight how these ' 'models can be … With regard to challenges, ' 'we argue that large language models in ' 'education require …', 'inline_links': {'cited_by': {'cites_id': '8166055256995715258', 'total': 410, 'link': 'https://scholar.google.com/scholar?cites=8166055256995715258&as_sdt=5,33&sciodt=0,33&hl=en'}, 'versions': {'cluster_id': '8166055256995715258', 'total': 10, 'link': 'https://scholar.google.com/scholar?cluster=8166055256995715258&hl=en&as_sdt=0,33'}, 'related_articles_link': 'https://scholar.google.com/scholar?q=related:uthwmf2nU3EJ:scholar.google.com/&scioq=Large+Language+Models&hl=en&as_sdt=0,33'}, 'resource': {'name': 'edarxiv.org', 'format': 'PDF', 'link': 'https://edarxiv.org/5er8f/download?format=pdf'}, 'authors': [{'name': 'E Kasneci', 'id': 'bZVkVvoAAAAJ', 'link': 'https://scholar.google.com/citations?user=bZVkVvoAAAAJ&hl=en&oi=sra'}, {'name': 'K Seßler', 'id': 'MbMBoN4AAAAJ', 'link': 'https://scholar.google.com/citations?user=MbMBoN4AAAAJ&hl=en&oi=sra'}, {'name': 'S Küchemann', 'id': 'g1jX5QUAAAAJ', 'link': 'https://scholar.google.com/citations?user=g1jX5QUAAAAJ&hl=en&oi=sra'}, {'name': 'M Bannert', 'id': 'TjfQ8QkAAAAJ', 'link': 'https://scholar.google.com/citations?user=TjfQ8QkAAAAJ&hl=en&oi=sra'}]}, {'position': 2, 'title': 'Large language models in medicine', 'data_cid': 'Ph9AwHTmhzAJ', 'link': 'https://www.nature.com/articles/s41591-023-02448-8', 'publication': 'AJ Thirunavukarasu, DSJ Ting, K ' 'Elangovan… - Nature medicine, 2023 - ' 'nature.com', 'snippet': '… HuggingChat offers a free-to-access ' 'chatbot with a similar interface to ChatGPT ' 'but uses Large Language Model Meta AI ' '(LLaMA) as its backend model 30 . Finally, ' 'cheap imitations of …', 'inline_links': {'cited_by': {'cites_id': '3497017024792502078', 'total': 25, 'link': 'https://scholar.google.com/scholar?cites=3497017024792502078&as_sdt=5,33&sciodt=0,33&hl=en'}, 'versions': {'cluster_id': '3497017024792502078', 'total': 3, 'link': 'https://scholar.google.com/scholar?cluster=3497017024792502078&hl=en&as_sdt=0,33'}}, 'authors': [{'name': 'AJ Thirunavukarasu', 'id': '3qb1AYwAAAAJ', 'link': 'https://scholar.google.com/citations?user=3qb1AYwAAAAJ&hl=en&oi=sra'}, {'name': 'DSJ Ting', 'id': 'KbrpC8cAAAAJ', 'link': 'https://scholar.google.com/citations?user=KbrpC8cAAAAJ&hl=en&oi=sra'}, {'name': 'K Elangovan', 'id': 'BE_lVTQAAAAJ', 'link': 'https://scholar.google.com/citations?user=BE_lVTQAAAAJ&hl=en&oi=sra'}]}, {'position': 3, 'title': 'Extracting training data from large language ' 'models', 'data_cid': 'mEYsWK6bWKoJ', 'link': 'https://www.usenix.org/conference/usenixsecurity21/presentation/carlini-extracting', 'publication': 'N Carlini, F Tramer, E Wallace, M ' 'Jagielski… - 30th USENIX Security …, ' '2021 - usenix.org', 'snippet': '… language model trained on scrapes of the ' 'public Internet, and are able to extract ' 'hundreds of verbatim text sequences from the ' 'model’… models are more vulnerable than ' 'smaller models. …', 'inline_links': {'cited_by': {'cites_id': '12274731957504198296', 'total': 742, 'link': 'https://scholar.google.com/scholar?cites=12274731957504198296&as_sdt=5,33&sciodt=0,33&hl=en'}, 'versions': {'cluster_id': '12274731957504198296', 'total': 8, 'link': 'https://scholar.google.com/scholar?cluster=12274731957504198296&hl=en&as_sdt=0,33'}, 'related_articles_link': 'https://scholar.google.com/scholar?q=related:mEYsWK6bWKoJ:scholar.google.com/&scioq=Large+Language+Models&hl=en&as_sdt=0,33', 'cached_page_link': 'https://scholar.googleusercontent.com/scholar?q=cache:mEYsWK6bWKoJ:scholar.google.com/+Large+Language+Models&hl=en&as_sdt=0,33'}, 'resource': {'name': 'usenix.org', 'format': 'PDF', 'link': 'https://www.usenix.org/system/files/sec21-carlini-extracting.pdf'}, 'authors': [{'name': 'N Carlini', 'id': 'q4qDvAoAAAAJ', 'link': 'https://scholar.google.com/citations?user=q4qDvAoAAAAJ&hl=en&oi=sra'}, {'name': 'F Tramer', 'id': 'ijH0-a8AAAAJ', 'link': 'https://scholar.google.com/citations?user=ijH0-a8AAAAJ&hl=en&oi=sra'}, {'name': 'E Wallace', 'id': 'SgST3LkAAAAJ', 'link': 'https://scholar.google.com/citations?user=SgST3LkAAAAJ&hl=en&oi=sra'}, {'name': 'M Jagielski', 'id': '_8rw_GMAAAAJ', 'link': 'https://scholar.google.com/citations?user=_8rw_GMAAAAJ&hl=en&oi=sra'}]}, {'position': 4, 'title': 'Emergent abilities of large language models', 'data_cid': 'hG0iVOrOguoJ', 'link': 'https://arxiv.org/abs/2206.07682', 'publication': 'J Wei, Y Tay, R Bommasani, C Raffel, B ' 'Zoph… - arXiv preprint arXiv …, 2022 - ' 'arxiv.org', 'snippet': 'Scaling up language models has been shown to ' 'predictably improve performance and sample ' 'efficiency on a wide range of downstream ' 'tasks. This paper instead discusses an …', 'inline_links': {'cited_by': {'cites_id': '16898296257676733828', 'total': 621, 'link': 'https://scholar.google.com/scholar?cites=16898296257676733828&as_sdt=5,33&sciodt=0,33&hl=en'}, 'versions': {'cluster_id': '16898296257676733828', 'total': 12, 'link': 'https://scholar.google.com/scholar?cluster=16898296257676733828&hl=en&as_sdt=0,33'}, 'related_articles_link': 'https://scholar.google.com/scholar?q=related:hG0iVOrOguoJ:scholar.google.com/&scioq=Large+Language+Models&hl=en&as_sdt=0,33', 'cached_page_link': 'https://scholar.googleusercontent.com/scholar?q=cache:hG0iVOrOguoJ:scholar.google.com/+Large+Language+Models&hl=en&as_sdt=0,33'}, 'resource': {'name': 'arxiv.org', 'format': 'PDF', 'link': 'https://arxiv.org/pdf/2206.07682.pdf?trk=cndc-detail'}, 'authors': [{'name': 'J Wei', 'id': 'wA5TK_0AAAAJ', 'link': 'https://scholar.google.com/citations?user=wA5TK_0AAAAJ&hl=en&oi=sra'}, {'name': 'Y Tay', 'id': 'VBclY_cAAAAJ', 'link': 'https://scholar.google.com/citations?user=VBclY_cAAAAJ&hl=en&oi=sra'}, {'name': 'R Bommasani', 'id': 'WMBXw1EAAAAJ', 'link': 'https://scholar.google.com/citations?user=WMBXw1EAAAAJ&hl=en&oi=sra'}, {'name': 'C Raffel', 'id': 'I66ZBYwAAAAJ', 'link': 'https://scholar.google.com/citations?user=I66ZBYwAAAAJ&hl=en&oi=sra'}, {'name': 'B Zoph', 'id': 'NL_7iTwAAAAJ', 'link': 'https://scholar.google.com/citations?user=NL_7iTwAAAAJ&hl=en&oi=sra'}]}, {'position': 5, 'title': 'A survey on evaluation of large language ' 'models', 'data_cid': 'ZYohnzOz-XgJ', 'link': 'https://arxiv.org/abs/2307.03109', 'publication': 'Y Chang, X Wang, J Wang, Y Wu, K Zhu… - ' 'arXiv preprint arXiv …, 2023 - arxiv.org', 'snippet': '… 3.1 Natural Language Processing Tasks … ' 'the development of language models, ' 'particularly large language models, was to ' 'enhance performance on natural language ' 'processing tasks, …', 'inline_links': {'cited_by': {'cites_id': '8717195588046785125', 'total': 31, 'link': 'https://scholar.google.com/scholar?cites=8717195588046785125&as_sdt=5,33&sciodt=0,33&hl=en'}, 'versions': {'cluster_id': '8717195588046785125', 'total': 3, 'link': 'https://scholar.google.com/scholar?cluster=8717195588046785125&hl=en&as_sdt=0,33'}, 'cached_page_link': 'https://scholar.googleusercontent.com/scholar?q=cache:ZYohnzOz-XgJ:scholar.google.com/+Large+Language+Models&hl=en&as_sdt=0,33'}, 'resource': {'name': 'arxiv.org', 'format': 'PDF', 'link': 'https://arxiv.org/pdf/2307.03109'}, 'authors': [{'name': 'X Wang', 'id': 'Q7Ieos8AAAAJ', 'link': 'https://scholar.google.com/citations?user=Q7Ieos8AAAAJ&hl=en&oi=sra'}, {'name': 'J Wang', 'id': 'YomxTXQAAAAJ', 'link': 'https://scholar.google.com/citations?user=YomxTXQAAAAJ&hl=en&oi=sra'}, {'name': 'Y Wu', 'id': 'KVeRu2QAAAAJ', 'link': 'https://scholar.google.com/citations?user=KVeRu2QAAAAJ&hl=en&oi=sra'}, {'name': 'K Zhu', 'id': 'g75dFLYAAAAJ', 'link': 'https://scholar.google.com/citations?user=g75dFLYAAAAJ&hl=en&oi=sra'}]}, {'position': 6, 'title': 'Evaluating large language models trained on ' 'code', 'data_cid': '3tNvW3l5nU4J', 'link': 'https://arxiv.org/abs/2107.03374', 'publication': 'M Chen, J Tworek, H Jun, Q Yuan, HPO ' 'Pinto… - arXiv preprint arXiv …, 2021 - ' 'arxiv.org', 'snippet': '… We introduce Codex, a GPT language model ' 'finetuned on publicly available code from ' 'GitHub, and study its Python code-writing ' 'capabilities. 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The last ' 'layer of the language model outputs a vector ' 'of logits l(t). …', 'inline_links': {'cited_by': {'cites_id': '2417017327887471622', 'total': 104, 'link': 'https://scholar.google.com/scholar?cites=2417017327887471622&as_sdt=5,33&sciodt=0,33&hl=en'}, 'versions': {'cluster_id': '2417017327887471622', 'total': 4, 'link': 'https://scholar.google.com/scholar?cluster=2417017327887471622&hl=en&as_sdt=0,33'}, 'related_articles_link': 'https://scholar.google.com/scholar?q=related:BlSyLHT4iiEJ:scholar.google.com/&scioq=Large+Language+Models&hl=en&as_sdt=0,33', 'cached_page_link': 'https://scholar.googleusercontent.com/scholar?q=cache:BlSyLHT4iiEJ:scholar.google.com/+Large+Language+Models&hl=en&as_sdt=0,33'}, 'resource': {'name': 'arxiv.org', 'format': 'PDF', 'link': 'https://arxiv.org/pdf/2301.10226.pdf?curius=1419'}, 'authors': [{'name': 'J Kirchenbauer', 'id': '48GJrbsAAAAJ', 'link': 'https://scholar.google.com/citations?user=48GJrbsAAAAJ&hl=en&oi=sra'}, {'name': 'J Geiping', 'id': '206vNCEAAAAJ', 'link': 'https://scholar.google.com/citations?user=206vNCEAAAAJ&hl=en&oi=sra'}, {'name': 'Y Wen', 'id': 'oUYfjg0AAAAJ', 'link': 'https://scholar.google.com/citations?user=oUYfjg0AAAAJ&hl=en&oi=sra'}, {'name': 'J Katz', 'id': 'yPw4WjoAAAAJ', 'link': 'https://scholar.google.com/citations?user=yPw4WjoAAAAJ&hl=en&oi=sra'}]}, {'position': 9, 'title': 'ChatGPT and other large language models are ' 'double-edged swords', 'data_cid': 'So0q8TRvxhYJ', 'link': 'https://pubs.rsna.org/doi/full/10.1148/radiol.230163', 'publication': 'Y Shen, L Heacock, J Elias, KD Hentel, B ' 'Reig, G Shih… - Radiology, 2023 - ' 'pubs.rsna.org', 'snippet': '… Large Language Models (LLMs) are deep ' 'learning models trained to understand and ' 'generate natural language. Recent studies ' 'demonstrated that LLMs achieve great success ' 'in a …', 'inline_links': {'cited_by': {'cites_id': '1641121387398204746', 'total': 231, 'link': 'https://scholar.google.com/scholar?cites=1641121387398204746&as_sdt=5,33&sciodt=0,33&hl=en'}, 'versions': {'cluster_id': '1641121387398204746', 'total': 3, 'link': 'https://scholar.google.com/scholar?cluster=1641121387398204746&hl=en&as_sdt=0,33'}, 'related_articles_link': 'https://scholar.google.com/scholar?q=related:So0q8TRvxhYJ:scholar.google.com/&scioq=Large+Language+Models&hl=en&as_sdt=0,33'}, 'authors': [{'name': 'Y Shen', 'id': 'XaeN2zgAAAAJ', 'link': 'https://scholar.google.com/citations?user=XaeN2zgAAAAJ&hl=en&oi=sra'}, {'name': 'L Heacock', 'id': 'tYYM5IkAAAAJ', 'link': 'https://scholar.google.com/citations?user=tYYM5IkAAAAJ&hl=en&oi=sra'}]}, {'position': 10, 'title': 'Pythia: A suite for analyzing large language ' 'models across training and scaling', 'data_cid': 'aaIDvsMAD8QJ', 'link': 'https://proceedings.mlr.press/v202/biderman23a.html', 'publication': 'S Biderman, H Schoelkopf… - ' 'International …, 2023 - ' 'proceedings.mlr.press', 'snippet': '… large language models, we prioritize ' 'consistency in model … out the most ' 'performance from each model. For example, we ' '… models, as it is becoming widely used for ' 'the largest models, …', 'inline_links': {'cited_by': {'cites_id': '14127511396791067241', 'total': 89, 'link': 'https://scholar.google.com/scholar?cites=14127511396791067241&as_sdt=5,33&sciodt=0,33&hl=en'}, 'versions': {'cluster_id': '14127511396791067241', 'total': 3, 'link': 'https://scholar.google.com/scholar?cluster=14127511396791067241&hl=en&as_sdt=0,33'}, 'related_articles_link': 'https://scholar.google.com/scholar?q=related:aaIDvsMAD8QJ:scholar.google.com/&scioq=Large+Language+Models&hl=en&as_sdt=0,33', 'cached_page_link': 'https://scholar.googleusercontent.com/scholar?q=cache:aaIDvsMAD8QJ:scholar.google.com/+Large+Language+Models&hl=en&as_sdt=0,33'}, 'resource': {'name': 'mlr.press', 'format': 'PDF', 'link': 'https://proceedings.mlr.press/v202/biderman23a/biderman23a.pdf'}, 'authors': [{'name': 'S Biderman', 'id': 'bO7H0DAAAAAJ', 'link': 'https://scholar.google.com/citations?user=bO7H0DAAAAAJ&hl=en&oi=sra'}, {'name': 'H Schoelkopf', 'id': 'XLahYIYAAAAJ', 'link': 'https://scholar.google.com/citations?user=XLahYIYAAAAJ&hl=en&oi=sra'}]}], 'related_searches': [{'query': 'large language models machine', 'highlighted': ['machine'], 'link': 'https://scholar.google.com/scholar?hl=en&as_sdt=0,33&qsp=1&q=large+language+models+machine&qst=ib'}, {'query': 'large language models pruning', 'highlighted': ['pruning'], 'link': 'https://scholar.google.com/scholar?hl=en&as_sdt=0,33&qsp=2&q=large+language+models+pruning&qst=ib'}, {'query': 'large language models multitask learners', 'highlighted': ['multitask learners'], 'link': 'https://scholar.google.com/scholar?hl=en&as_sdt=0,33&qsp=3&q=large+language+models+multitask+learners&qst=ib'}, {'query': 'large language models speech recognition', 'highlighted': ['speech recognition'], 'link': 'https://scholar.google.com/scholar?hl=en&as_sdt=0,33&qsp=4&q=large+language+models+speech+recognition&qst=ib'}, {'query': 'large language models machine translation', 'highlighted': ['machine translation'], 'link': 'https://scholar.google.com/scholar?hl=en&as_sdt=0,33&qsp=5&q=large+language+models+machine+translation&qst=ib'}, {'query': 'emergent abilities of large language |
740 | https://python.langchain.com/docs/integrations/tools/searx_search | ComponentsToolsSearxNG SearchOn this pageSearxNG SearchThis notebook goes over how to use a self hosted SearxNG search API to search the web.You can check this link for more informations about Searx API parameters.import pprintfrom langchain.utilities import SearxSearchWrappersearch = SearxSearchWrapper(searx_host="http://127.0.0.1:8888")For some engines, if a direct answer is available the warpper will print the answer instead of the full list of search results. You can use the results method of the wrapper if you want to obtain all the results.search.run("What is the capital of France") 'Paris is the capital of France, the largest country of Europe with 550 000 km2 (65 millions inhabitants). Paris has 2.234 million inhabitants end 2011. She is the core of Ile de France region (12 million people).'Custom ParametersSearxNG supports 135 search engines. You can also customize the Searx wrapper with arbitrary named parameters that will be passed to the Searx search API . In the below example we will making a more interesting use of custom search parameters from searx search api.In this example we will be using the engines parameters to query wikipediasearch = SearxSearchWrapper( searx_host="http://127.0.0.1:8888", k=5) # k is for max number of itemssearch.run("large language model ", engines=["wiki"]) 'Large language models (LLMs) represent a major advancement in AI, with the promise of transforming domains through learned knowledge. LLM sizes have been increasing 10X every year for the last few years, and as these models grow in complexity and size, so do their capabilities.\n\nGPT-3 can translate language, write essays, generate computer code, and more — all with limited to no supervision. In July 2020, OpenAI unveiled GPT-3, a language model that was easily the largest known at the time. Put simply, GPT-3 is trained to predict the next word in a sentence, much like how a text message autocomplete feature works.\n\nA large language model, or LLM, is a deep learning algorithm that can recognize, summarize, translate, predict and generate text and other content based on knowledge gained from massive datasets. Large language models are among the most successful applications of transformer models.\n\nAll of today’s well-known language models—e.g., GPT-3 from OpenAI, PaLM or LaMDA from Google, Galactica or OPT from Meta, Megatron-Turing from Nvidia/Microsoft, Jurassic-1 from AI21 Labs—are...\n\nLarge language models (LLMs) such as GPT-3are increasingly being used to generate text. These tools should be used with care, since they can generate content that is biased, non-verifiable, constitutes original research, or violates copyrights.'Passing other Searx parameters for searx like languagesearch = SearxSearchWrapper(searx_host="http://127.0.0.1:8888", k=1)search.run("deep learning", language="es", engines=["wiki"]) 'Aprendizaje profundo (en inglés, deep learning) es un conjunto de algoritmos de aprendizaje automático (en inglés, machine learning) que intenta modelar abstracciones de alto nivel en datos usando arquitecturas computacionales que admiten transformaciones no lineales múltiples e iterativas de datos expresados en forma matricial o tensorial. 1'Obtaining results with metadataIn this example we will be looking for scientific paper using the categories parameter and limiting the results to a time_range (not all engines support the time range option).We also would like to obtain the results in a structured way including metadata. For this we will be using the results method of the wrapper.search = SearxSearchWrapper(searx_host="http://127.0.0.1:8888")results = search.results( "Large Language Model prompt", num_results=5, categories="science", time_range="year",)pprint.pp(results) [{'snippet': '… on natural language instructions, large language models (… the ' 'prompt used to steer the model, and most effective prompts … to ' 'prompt engineering, we propose Automatic Prompt …', 'title': 'Large language models are human-level prompt engineers', 'link': 'https://arxiv.org/abs/2211.01910', 'engines': ['google scholar'], 'category': 'science'}, {'snippet': '… Large language models (LLMs) have introduced new possibilities ' 'for prototyping with AI [18]. Pre-trained on a large amount of ' 'text data, models … language instructions called prompts. …', 'title': 'Promptchainer: Chaining large language model prompts through ' 'visual programming', 'link': 'https://dl.acm.org/doi/abs/10.1145/3491101.3519729', 'engines': ['google scholar'], 'category': 'science'}, {'snippet': '… can introspect the large prompt model. We derive the view ' 'ϕ0(X) and the model h0 from T01. However, instead of fully ' 'fine-tuning T0 during co-training, we focus on soft prompt ' 'tuning, …', 'title': 'Co-training improves prompt-based learning for large language ' 'models', 'link': 'https://proceedings.mlr.press/v162/lang22a.html', 'engines': ['google scholar'], 'category': 'science'}, {'snippet': '… With the success of large language models (LLMs) of code and ' 'their use as … prompt design process become important. In this ' 'work, we propose a framework called Repo-Level Prompt …', 'title': 'Repository-level prompt generation for large language models of ' 'code', 'link': 'https://arxiv.org/abs/2206.12839', 'engines': ['google scholar'], 'category': 'science'}, {'snippet': '… Figure 2 | The benefits of different components of a prompt ' 'for the largest language model (Gopher), as estimated from ' 'hierarchical logistic regression. Each point estimates the ' 'unique …', 'title': 'Can language models learn from explanations in context?', 'link': 'https://arxiv.org/abs/2204.02329', 'engines': ['google scholar'], 'category': 'science'}]Get papers from arxivresults = search.results( "Large Language Model prompt", num_results=5, engines=["arxiv"])pprint.pp(results) [{'snippet': 'Thanks to the advanced improvement of large pre-trained language ' 'models, prompt-based fine-tuning is shown to be effective on a ' 'variety of downstream tasks. Though many prompting methods have ' 'been investigated, it remains unknown which type of prompts are ' 'the most effective among three types of prompts (i.e., ' 'human-designed prompts, schema prompts and null prompts). In ' 'this work, we empirically compare the three types of prompts ' 'under both few-shot and fully-supervised settings. Our ' 'experimental results show that schema prompts are the most ' 'effective in general. Besides, the performance gaps tend to ' 'diminish when the scale of training data grows large.', 'title': 'Do Prompts Solve NLP Tasks Using Natural Language?', 'link': 'http://arxiv.org/abs/2203.00902v1', 'engines': ['arxiv'], 'category': 'science'}, {'snippet': 'Cross-prompt automated essay scoring (AES) requires the system ' 'to use non target-prompt essays to award scores to a ' 'target-prompt essay. Since obtaining a large quantity of ' 'pre-graded essays to a particular prompt is often difficult and ' 'unrealistic, the task of cross-prompt AES is vital for the ' 'development of real-world AES systems, yet it remains an ' 'under-explored area of research. Models designed for ' 'prompt-specific AES rely heavily on prompt-specific knowledge ' 'and perform poorly in the cross-prompt setting, whereas current ' 'approaches to cross-prompt AES either require a certain quantity ' 'of labelled target-prompt essays or require a large quantity of ' 'unlabelled target-prompt essays to perform transfer learning in ' 'a multi-step manner. To address these issues, we introduce ' 'Prompt Agnostic Essay Scorer (PAES) for cross-prompt AES. Our ' 'method requires no access to labelled or unlabelled ' 'target-prompt data during training and is a single-stage ' 'approach. PAES is easy to apply in practice and achieves ' 'state-of-the-art performance on the Automated Student Assessment ' 'Prize (ASAP) dataset.', 'title': 'Prompt Agnostic Essay Scorer: A Domain Generalization Approach to ' 'Cross-prompt Automated Essay Scoring', 'link': 'http://arxiv.org/abs/2008.01441v1', 'engines': ['arxiv'], 'category': 'science'}, {'snippet': 'Research on prompting has shown excellent performance with ' 'little or even no supervised training across many tasks. ' 'However, prompting for machine translation is still ' 'under-explored in the literature. We fill this gap by offering a ' 'systematic study on prompting strategies for translation, ' 'examining various factors for prompt template and demonstration ' 'example selection. We further explore the use of monolingual ' 'data and the feasibility of cross-lingual, cross-domain, and ' 'sentence-to-document transfer learning in prompting. Extensive ' 'experiments with GLM-130B (Zeng et al., 2022) as the testbed ' 'show that 1) the number and the quality of prompt examples ' 'matter, where using suboptimal examples degenerates translation; ' '2) several features of prompt examples, such as semantic ' 'similarity, show significant Spearman correlation with their ' 'prompting performance; yet, none of the correlations are strong ' 'enough; 3) using pseudo parallel prompt examples constructed ' 'from monolingual data via zero-shot prompting could improve ' 'translation; and 4) improved performance is achievable by ' 'transferring knowledge from prompt examples selected in other ' 'settings. We finally provide an analysis on the model outputs ' 'and discuss several problems that prompting still suffers from.', 'title': 'Prompting Large Language Model for Machine Translation: A Case ' 'Study', 'link': 'http://arxiv.org/abs/2301.07069v2', 'engines': ['arxiv'], 'category': 'science'}, {'snippet': 'Large language models can perform new tasks in a zero-shot ' 'fashion, given natural language prompts that specify the desired ' 'behavior. Such prompts are typically hand engineered, but can ' 'also be learned with gradient-based methods from labeled data. ' 'However, it is underexplored what factors make the prompts ' 'effective, especially when the prompts are natural language. In ' 'this paper, we investigate common attributes shared by effective ' 'prompts. We first propose a human readable prompt tuning method ' '(F LUENT P ROMPT) based on Langevin dynamics that incorporates a ' 'fluency constraint to find a diverse distribution of effective ' 'and fluent prompts. Our analysis reveals that effective prompts ' 'are topically related to the task domain and calibrate the prior ' 'probability of label words. Based on these findings, we also ' 'propose a method for generating prompts using only unlabeled ' 'data, outperforming strong baselines by an average of 7.0% ' 'accuracy across three tasks.', 'title': "Toward Human Readable Prompt Tuning: Kubrick's The Shining is a " 'good movie, and a good prompt too?', 'link': 'http://arxiv.org/abs/2212.10539v1', 'engines': ['arxiv'], 'category': 'science'}, {'snippet': 'Prevailing methods for mapping large generative language models ' "to supervised tasks may fail to sufficiently probe models' novel " 'capabilities. Using GPT-3 as a case study, we show that 0-shot ' 'prompts can significantly outperform few-shot prompts. We ' 'suggest that the function of few-shot examples in these cases is ' 'better described as locating an already learned task rather than ' 'meta-learning. This analysis motivates rethinking the role of ' 'prompts in controlling and evaluating powerful language models. ' 'In this work, we discuss methods of prompt programming, ' 'emphasizing the usefulness of considering prompts through the ' 'lens of natural language. We explore techniques for exploiting ' 'the capacity of narratives and cultural anchors to encode ' 'nuanced intentions and techniques for encouraging deconstruction ' 'of a problem into components before producing a verdict. ' 'Informed by this more encompassing theory of prompt programming, ' 'we also introduce the idea of a metaprompt that seeds the model ' 'to generate its own natural language prompts for a range of ' 'tasks. Finally, we discuss how these more general methods of ' 'interacting with language models can be incorporated into ' 'existing and future benchmarks and practical applications.', 'title': 'Prompt Programming for Large Language Models: Beyond the Few-Shot ' 'Paradigm', 'link': 'http://arxiv.org/abs/2102.07350v1', 'engines': ['arxiv'], 'category': 'science'}]In this example we query for large language models under the it category. We then filter the results that come from github.results = search.results("large language model", num_results=20, categories="it")pprint.pp(list(filter(lambda r: r["engines"][0] == "github", results))) [{'snippet': 'Guide to using pre-trained large language models of source code', 'title': 'Code-LMs', 'link': 'https://github.com/VHellendoorn/Code-LMs', 'engines': ['github'], 'category': 'it'}, {'snippet': 'Dramatron uses large language models to generate coherent ' 'scripts and screenplays.', 'title': 'dramatron', 'link': 'https://github.com/deepmind/dramatron', 'engines': ['github'], 'category': 'it'}]We could also directly query for results from github and other source forges.results = search.results( "large language model", num_results=20, engines=["github", "gitlab"])pprint.pp(results) [{'snippet': "Implementation of 'A Watermark for Large Language Models' paper " 'by Kirchenbauer & Geiping et. al.', 'title': 'Peutlefaire / LMWatermark', 'link': 'https://gitlab.com/BrianPulfer/LMWatermark', 'engines': ['gitlab'], 'category': 'it'}, {'snippet': 'Guide to using pre-trained large language models of source code', 'title': 'Code-LMs', 'link': 'https://github.com/VHellendoorn/Code-LMs', 'engines': ['github'], 'category': 'it'}, {'snippet': '', 'title': 'Simen Burud / Large-scale Language Models for Conversational ' 'Speech Recognition', 'link': 'https://gitlab.com/BrianPulfer', 'engines': ['gitlab'], 'category': 'it'}, {'snippet': 'Dramatron uses large language models to generate coherent ' 'scripts and screenplays.', 'title': 'dramatron', 'link': 'https://github.com/deepmind/dramatron', 'engines': ['github'], 'category': 'it'}, {'snippet': 'Code for loralib, an implementation of "LoRA: Low-Rank ' 'Adaptation of Large Language Models"', 'title': 'LoRA', 'link': 'https://github.com/microsoft/LoRA', 'engines': ['github'], 'category': 'it'}, {'snippet': 'Code for the paper "Evaluating Large Language Models Trained on ' 'Code"', 'title': 'human-eval', 'link': 'https://github.com/openai/human-eval', 'engines': ['github'], 'category': 'it'}, {'snippet': 'A trend starts from "Chain of Thought Prompting Elicits ' 'Reasoning in Large Language Models".', 'title': 'Chain-of-ThoughtsPapers', 'link': 'https://github.com/Timothyxxx/Chain-of-ThoughtsPapers', 'engines': ['github'], 'category': 'it'}, {'snippet': 'Mistral: A strong, northwesterly wind: Framework for transparent ' 'and accessible large-scale language model training, built with ' 'Hugging Face 🤗 Transformers.', 'title': 'mistral', 'link': 'https://github.com/stanford-crfm/mistral', 'engines': ['github'], 'category': 'it'}, {'snippet': 'A prize for finding tasks that cause large language models to ' 'show inverse scaling', 'title': 'prize', 'link': 'https://github.com/inverse-scaling/prize', 'engines': ['github'], 'category': 'it'}, {'snippet': 'Optimus: the first large-scale pre-trained VAE language model', 'title': 'Optimus', 'link': 'https://github.com/ChunyuanLI/Optimus', 'engines': ['github'], 'category': 'it'}, {'snippet': 'Seminar on Large Language Models (COMP790-101 at UNC Chapel ' 'Hill, Fall 2022)', 'title': 'llm-seminar', 'link': 'https://github.com/craffel/llm-seminar', 'engines': ['github'], 'category': 'it'}, {'snippet': 'A central, open resource for data and tools related to ' 'chain-of-thought reasoning in large language models. Developed @ ' 'Samwald research group: https://samwald.info/', 'title': 'ThoughtSource', 'link': 'https://github.com/OpenBioLink/ThoughtSource', 'engines': ['github'], 'category': 'it'}, {'snippet': 'A comprehensive list of papers using large language/multi-modal ' 'models for Robotics/RL, including papers, codes, and related ' 'websites', 'title': 'Awesome-LLM-Robotics', 'link': 'https://github.com/GT-RIPL/Awesome-LLM-Robotics', 'engines': ['github'], 'category': 'it'}, {'snippet': 'Tools for curating biomedical training data for large-scale ' 'language modeling', 'title': 'biomedical', 'link': 'https://github.com/bigscience-workshop/biomedical', 'engines': ['github'], 'category': 'it'}, {'snippet': 'ChatGPT @ Home: Large Language Model (LLM) chatbot application, ' 'written by ChatGPT', 'title': 'ChatGPT-at-Home', 'link': 'https://github.com/Sentdex/ChatGPT-at-Home', 'engines': ['github'], 'category': 'it'}, {'snippet': 'Design and Deploy Large Language Model Apps', 'title': 'dust', 'link': 'https://github.com/dust-tt/dust', 'engines': ['github'], 'category': 'it'}, {'snippet': 'Polyglot: Large Language Models of Well-balanced Competence in ' 'Multi-languages', 'title': 'polyglot', 'link': 'https://github.com/EleutherAI/polyglot', 'engines': ['github'], 'category': 'it'}, {'snippet': 'Code release for "Learning Video Representations from Large ' 'Language Models"', 'title': 'LaViLa', 'link': 'https://github.com/facebookresearch/LaViLa', 'engines': ['github'], 'category': 'it'}, {'snippet': 'SmoothQuant: Accurate and Efficient Post-Training Quantization ' 'for Large Language Models', 'title': 'smoothquant', 'link': 'https://github.com/mit-han-lab/smoothquant', 'engines': ['github'], 'category': 'it'}, {'snippet': 'This repository contains the code, data, and models of the paper ' 'titled "XL-Sum: Large-Scale Multilingual Abstractive ' 'Summarization for 44 Languages" published in Findings of the ' 'Association for Computational Linguistics: ACL-IJCNLP 2021.', 'title': 'xl-sum', 'link': 'https://github.com/csebuetnlp/xl-sum', 'engines': ['github'], 'category': 'it'}]PreviousSearchApiNextSerpAPICustom ParametersObtaining results with metadata |
741 | https://python.langchain.com/docs/integrations/tools/serpapi | ComponentsToolsSerpAPIOn this pageSerpAPIThis notebook goes over how to use the SerpAPI component to search the web.from langchain.utilities import SerpAPIWrappersearch = SerpAPIWrapper()search.run("Obama's first name?") 'Barack Hussein Obama II'Custom ParametersYou can also customize the SerpAPI wrapper with arbitrary parameters. For example, in the below example we will use bing instead of google.params = { "engine": "bing", "gl": "us", "hl": "en",}search = SerpAPIWrapper(params=params)search.run("Obama's first name?") 'Barack Hussein Obama II is an American politician who served as the 44th president of the United States from 2009 to 2017. A member of the Democratic Party, Obama was the first African-American presi…New content will be added above the current area of focus upon selectionBarack Hussein Obama II is an American politician who served as the 44th president of the United States from 2009 to 2017. A member of the Democratic Party, Obama was the first African-American president of the United States. He previously served as a U.S. senator from Illinois from 2005 to 2008 and as an Illinois state senator from 1997 to 2004, and previously worked as a civil rights lawyer before entering politics.Wikipediabarackobama.com'from langchain.agents import Tool# You can create the tool to pass to an agentrepl_tool = Tool( name="python_repl", description="A Python shell. Use this to execute python commands. Input should be a valid python command. If you want to see the output of a value, you should print it out with `print(...)`.", func=search.run,)PreviousSearxNG SearchNextTwilioCustom Parameters |
742 | https://python.langchain.com/docs/integrations/tools/twilio | ComponentsToolsTwilioOn this pageTwilioThis notebook goes over how to use the Twilio API wrapper to send a message through SMS or Twilio Messaging Channels.Twilio Messaging Channels facilitates integrations with 3rd party messaging apps and lets you send messages through WhatsApp Business Platform (GA), Facebook Messenger (Public Beta) and Google Business Messages (Private Beta).SetupTo use this tool you need to install the Python Twilio package twilio# !pip install twilioYou'll also need to set up a Twilio account and get your credentials. You'll need your Account String Identifier (SID) and your Auth Token. You'll also need a number to send messages from.You can either pass these in to the TwilioAPIWrapper as named parameters account_sid, auth_token, from_number, or you can set the environment variables TWILIO_ACCOUNT_SID, TWILIO_AUTH_TOKEN, TWILIO_FROM_NUMBER.Sending an SMSfrom langchain.utilities.twilio import TwilioAPIWrappertwilio = TwilioAPIWrapper( # account_sid="foo", # auth_token="bar", # from_number="baz,")twilio.run("hello world", "+16162904619")Sending a WhatsApp MessageYou'll need to link your WhatsApp Business Account with Twilio. You'll also need to make sure that the number to send messages from is configured as a WhatsApp Enabled Sender on Twilio and registered with WhatsApp.from langchain.utilities.twilio import TwilioAPIWrappertwilio = TwilioAPIWrapper( # account_sid="foo", # auth_token="bar", # from_number="whatsapp: baz,")twilio.run("hello world", "whatsapp: +16162904619")PreviousSerpAPINextWikipediaSetupSending an SMSSending a WhatsApp Message |
743 | https://python.langchain.com/docs/integrations/tools/wikipedia | ComponentsToolsWikipediaWikipediaWikipedia is a multilingual free online encyclopedia written and maintained by a community of volunteers, known as Wikipedians, through open collaboration and using a wiki-based editing system called MediaWiki. Wikipedia is the largest and most-read reference work in history.First, you need to install wikipedia python package.pip install wikipediafrom langchain.tools import WikipediaQueryRunfrom langchain.utilities import WikipediaAPIWrapperwikipedia = WikipediaQueryRun(api_wrapper=WikipediaAPIWrapper())wikipedia.run("HUNTER X HUNTER") 'Page: Hunter × Hunter\nSummary: Hunter × Hunter (stylized as HUNTER×HUNTER and pronounced "hunter hunter") is a Japanese manga series written and illustrated by Yoshihiro Togashi. It has been serialized in Shueisha\'s shōnen manga magazine Weekly Shōnen Jump since March 1998, although the manga has frequently gone on extended hiatuses since 2006. Its chapters have been collected in 37 tankōbon volumes as of November 2022. The story focuses on a young boy named Gon Freecss who discovers that his father, who left him at a young age, is actually a world-renowned Hunter, a licensed professional who specializes in fantastical pursuits such as locating rare or unidentified animal species, treasure hunting, surveying unexplored enclaves, or hunting down lawless individuals. Gon departs on a journey to become a Hunter and eventually find his father. Along the way, Gon meets various other Hunters and encounters the paranormal.\nHunter × Hunter was adapted into a 62-episode anime television series produced by Nippon Animation and directed by Kazuhiro Furuhashi, which ran on Fuji Television from October 1999 to March 2001. Three separate original video animations (OVAs) totaling 30 episodes were subsequently produced by Nippon Animation and released in Japan from 2002 to 2004. A second anime television series by Madhouse aired on Nippon Television from October 2011 to September 2014, totaling 148 episodes, with two animated theatrical films released in 2013. There are also numerous audio albums, video games, musicals, and other media based on Hunter × Hunter.\nThe manga has been translated into English and released in North America by Viz Media since April 2005. Both television series have been also licensed by Viz Media, with the first series having aired on the Funimation Channel in 2009 and the second series broadcast on Adult Swim\'s Toonami programming block from April 2016 to June 2019.\nHunter × Hunter has been a huge critical and financial success and has become one of the best-selling manga series of all time, having over 84 million copies in circulation by July 2022.\n\nPage: Hunter × Hunter (2011 TV series)\nSummary: Hunter × Hunter is an anime television series that aired from 2011 to 2014 based on Yoshihiro Togashi\'s manga series Hunter × Hunter. The story begins with a young boy named Gon Freecss, who one day discovers that the father who he thought was dead, is in fact alive and well. He learns that his father, Ging, is a legendary "Hunter", an individual who has proven themselves an elite member of humanity. Despite the fact that Ging left his son with his relatives in order to pursue his own dreams, Gon becomes determined to follow in his father\'s footsteps, pass the rigorous "Hunter Examination", and eventually find his father to become a Hunter in his own right.\nThis new Hunter × Hunter anime was announced on July 24, 2011. It is a complete reboot starting from the beginning of the original manga, with no connection to the first anime television series from 1999. Produced by Nippon TV, VAP, Shueisha and Madhouse, the series is directed by Hiroshi Kōjina, with Atsushi Maekawa and Tsutomu Kamishiro handling series composition, Takahiro Yoshimatsu designing the characters and Yoshihisa Hirano composing the music. Instead of having the old cast reprise their roles for the new adaptation, the series features an entirely new cast to voice the characters. The new series premiered airing weekly on Nippon TV and the nationwide Nippon News Network from October 2, 2011. The series started to be collected in both DVD and Blu-ray format on January 25, 2012. Viz Media has licensed the anime for a DVD/Blu-ray release in North America with an English dub. On television, the series began airing on Adult Swim\'s Toonami programming block on April 17, 2016, and ended on June 23, 2019.The anime series\' opening theme is alternated between the song "Departure!" and an alternate version titled "Departure! -Second Version-" both sung by Galneryus\' voc'PreviousTwilioNextWolfram Alpha |
744 | https://python.langchain.com/docs/integrations/tools/wolfram_alpha | ComponentsToolsWolfram AlphaWolfram AlphaThis notebook goes over how to use the wolfram alpha component.First, you need to set up your Wolfram Alpha developer account and get your APP ID:Go to wolfram alpha and sign up for a developer account hereCreate an app and get your APP IDpip install wolframalphaThen we will need to set some environment variables:Save your APP ID into WOLFRAM_ALPHA_APPID env variablepip install wolframalphaimport osos.environ["WOLFRAM_ALPHA_APPID"] = ""from langchain.utilities.wolfram_alpha import WolframAlphaAPIWrapperwolfram = WolframAlphaAPIWrapper()wolfram.run("What is 2x+5 = -3x + 7?") 'x = 2/5'PreviousWikipediaNextYahoo Finance News |
745 | https://python.langchain.com/docs/integrations/tools/yahoo_finance_news | ComponentsToolsYahoo Finance NewsOn this pageYahoo Finance NewsThis notebook goes over how to use the yahoo_finance_news tool with an agent. Setting upFirst, you need to install yfinance python package.pip install yfinanceExample with Chainimport osos.environ["OPENAI_API_KEY"] = "..."from langchain.chat_models import ChatOpenAIfrom langchain.agents import initialize_agent, AgentTypefrom langchain.tools.yahoo_finance_news import YahooFinanceNewsTool llm = ChatOpenAI(temperature=0.0)tools = [YahooFinanceNewsTool()]agent_chain = initialize_agent( tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True,)agent_chain.run( "What happens today with Microsoft stocks?",) > Entering new AgentExecutor chain... I should check the latest financial news about Microsoft stocks. Action: yahoo_finance_news Action Input: MSFT Observation: Microsoft (MSFT) Gains But Lags Market: What You Should Know In the latest trading session, Microsoft (MSFT) closed at $328.79, marking a +0.12% move from the previous day. Thought:I have the latest information on Microsoft stocks. Final Answer: Microsoft (MSFT) closed at $328.79, with a +0.12% move from the previous day. > Finished chain. 'Microsoft (MSFT) closed at $328.79, with a +0.12% move from the previous day.'agent_chain.run( "How does Microsoft feels today comparing with Nvidia?",) > Entering new AgentExecutor chain... I should compare the current sentiment of Microsoft and Nvidia. Action: yahoo_finance_news Action Input: MSFT Observation: Microsoft (MSFT) Gains But Lags Market: What You Should Know In the latest trading session, Microsoft (MSFT) closed at $328.79, marking a +0.12% move from the previous day. Thought:I need to find the current sentiment of Nvidia as well. Action: yahoo_finance_news Action Input: NVDA Observation: Thought:I now know the current sentiment of both Microsoft and Nvidia. Final Answer: I cannot compare the sentiment of Microsoft and Nvidia as I only have information about Microsoft. > Finished chain. 'I cannot compare the sentiment of Microsoft and Nvidia as I only have information about Microsoft.'How YahooFinanceNewsTool works?tool = YahooFinanceNewsTool()tool.run("NVDA") 'No news found for company that searched with NVDA ticker.'res = tool.run("AAPL")print(res) Top Research Reports for Apple, Broadcom & Caterpillar Today's Research Daily features new research reports on 16 major stocks, including Apple Inc. (AAPL), Broadcom Inc. (AVGO) and Caterpillar Inc. (CAT). Apple Stock on Pace for Worst Month of the Year Apple (AAPL) shares are on pace for their worst month of the year, according to Dow Jones Market Data. The stock is down 4.8% so far in August, putting it on pace for its worst month since December 2022, when it fell 12%.PreviousWolfram AlphaNextYouTubeSetting upExample with Chain |
746 | https://python.langchain.com/docs/integrations/tools/youtube | ComponentsToolsYouTubeYouTubeYouTube Search package searches YouTube videos avoiding using their heavily rate-limited API.It uses the form on the YouTube homepage and scrapes the resulting page.This notebook shows how to use a tool to search YouTube.Adapted from https://github.com/venuv/langchain_yt_tools#! pip install youtube_searchfrom langchain.tools import YouTubeSearchTooltool = YouTubeSearchTool()tool.run("lex friedman") "['/watch?v=VcVfceTsD0A&pp=ygUMbGV4IGZyaWVkbWFu', '/watch?v=gPfriiHBBek&pp=ygUMbGV4IGZyaWVkbWFu']"You can also specify the number of results that are returnedtool.run("lex friedman,5") "['/watch?v=VcVfceTsD0A&pp=ygUMbGV4IGZyaWVkbWFu', '/watch?v=YVJ8gTnDC4Y&pp=ygUMbGV4IGZyaWVkbWFu', '/watch?v=Udh22kuLebg&pp=ygUMbGV4IGZyaWVkbWFu', '/watch?v=gPfriiHBBek&pp=ygUMbGV4IGZyaWVkbWFu', '/watch?v=L_Guz73e6fw&pp=ygUMbGV4IGZyaWVkbWFu']"PreviousYahoo Finance NewsNextZapier Natural Language Actions |
747 | https://python.langchain.com/docs/integrations/tools/zapier | ComponentsToolsZapier Natural Language ActionsOn this pageZapier Natural Language ActionsZapier Natural Language Actions gives you access to the 5k+ apps, 20k+ actions on Zapier's platform through a natural language API interface.NLA supports apps like Gmail, Salesforce, Trello, Slack, Asana, HubSpot, Google Sheets, Microsoft Teams, and thousands more apps: https://zapier.com/apps
Zapier NLA handles ALL the underlying API auth and translation from natural language --> underlying API call --> return simplified output for LLMs. The key idea is you, or your users, expose a set of actions via an oauth-like setup window, which you can then query and execute via a REST API.NLA offers both API Key and OAuth for signing NLA API requests.Server-side (API Key): for quickly getting started, testing, and production scenarios where LangChain will only use actions exposed in the developer's Zapier account (and will use the developer's connected accounts on Zapier.com)User-facing (Oauth): for production scenarios where you are deploying an end-user facing application and LangChain needs access to end-user's exposed actions and connected accounts on Zapier.comThis quick start focus mostly on the server-side use case for brevity. Jump to Example Using OAuth Access Token to see a short example how to set up Zapier for user-facing situations. Review full docs for full user-facing oauth developer support.This example goes over how to use the Zapier integration with a SimpleSequentialChain, then an Agent.
In code, below:import os# get from https://platform.openai.com/os.environ["OPENAI_API_KEY"] = os.environ.get("OPENAI_API_KEY", "")# get from https://nla.zapier.com/docs/authentication/ after logging in):os.environ["ZAPIER_NLA_API_KEY"] = os.environ.get("ZAPIER_NLA_API_KEY", "")Example with AgentZapier tools can be used with an agent. See the example below.from langchain.llms import OpenAIfrom langchain.agents import initialize_agentfrom langchain.agents.agent_toolkits import ZapierToolkitfrom langchain.agents import AgentTypefrom langchain.utilities.zapier import ZapierNLAWrapper## step 0. expose gmail 'find email' and slack 'send channel message' actions# first go here, log in, expose (enable) the two actions: https://nla.zapier.com/demo/start -- for this example, can leave all fields "Have AI guess"# in an oauth scenario, you'd get your own <provider> id (instead of 'demo') which you route your users through firstllm = OpenAI(temperature=0)zapier = ZapierNLAWrapper()toolkit = ZapierToolkit.from_zapier_nla_wrapper(zapier)agent = initialize_agent( toolkit.get_tools(), llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True)agent.run( "Summarize the last email I received regarding Silicon Valley Bank. Send the summary to the #test-zapier channel in slack.") > Entering new AgentExecutor chain... I need to find the email and summarize it. Action: Gmail: Find Email Action Input: Find the latest email from Silicon Valley Bank Observation: {"from__name": "Silicon Valley Bridge Bank, N.A.", "from__email": "[email protected]", "body_plain": "Dear Clients, After chaotic, tumultuous & stressful days, we have clarity on path for SVB, FDIC is fully insuring all deposits & have an ask for clients & partners as we rebuild. Tim Mayopoulos <https://eml.svb.com/NjEwLUtBSy0yNjYAAAGKgoxUeBCLAyF_NxON97X4rKEaNBLG", "reply_to__email": "[email protected]", "subject": "Meet the new CEO Tim Mayopoulos", "date": "Tue, 14 Mar 2023 23:42:29 -0500 (CDT)", "message_url": "https://mail.google.com/mail/u/0/#inbox/186e393b13cfdf0a", "attachment_count": "0", "to__emails": "[email protected]", "message_id": "186e393b13cfdf0a", "labels": "IMPORTANT, CATEGORY_UPDATES, INBOX"} Thought: I need to summarize the email and send it to the #test-zapier channel in Slack. Action: Slack: Send Channel Message Action Input: Send a slack message to the #test-zapier channel with the text "Silicon Valley Bank has announced that Tim Mayopoulos is the new CEO. FDIC is fully insuring all deposits and they have an ask for clients and partners as they rebuild." Observation: {"message__text": "Silicon Valley Bank has announced that Tim Mayopoulos is the new CEO. FDIC is fully insuring all deposits and they have an ask for clients and partners as they rebuild.", "message__permalink": "https://langchain.slack.com/archives/C04TSGU0RA7/p1678859932375259", "channel": "C04TSGU0RA7", "message__bot_profile__name": "Zapier", "message__team": "T04F8K3FZB5", "message__bot_id": "B04TRV4R74K", "message__bot_profile__deleted": "false", "message__bot_profile__app_id": "A024R9PQM", "ts_time": "2023-03-15T05:58:52Z", "message__bot_profile__icons__image_36": "https://avatars.slack-edge.com/2022-08-02/3888649620612_f864dc1bb794cf7d82b0_36.png", "message__blocks[]block_id": "kdZZ", "message__blocks[]elements[]type": "['rich_text_section']"} Thought: I now know the final answer. Final Answer: I have sent a summary of the last email from Silicon Valley Bank to the #test-zapier channel in Slack. > Finished chain. 'I have sent a summary of the last email from Silicon Valley Bank to the #test-zapier channel in Slack.'Example with SimpleSequentialChainIf you need more explicit control, use a chain, like below.from langchain.llms import OpenAIfrom langchain.chains import LLMChain, TransformChain, SimpleSequentialChainfrom langchain.prompts import PromptTemplatefrom langchain.tools.zapier.tool import ZapierNLARunActionfrom langchain.utilities.zapier import ZapierNLAWrapper## step 0. expose gmail 'find email' and slack 'send direct message' actions# first go here, log in, expose (enable) the two actions: https://nla.zapier.com/demo/start -- for this example, can leave all fields "Have AI guess"# in an oauth scenario, you'd get your own <provider> id (instead of 'demo') which you route your users through firstactions = ZapierNLAWrapper().list()## step 1. gmail find emailGMAIL_SEARCH_INSTRUCTIONS = "Grab the latest email from Silicon Valley Bank"def nla_gmail(inputs): action = next( (a for a in actions if a["description"].startswith("Gmail: Find Email")), None ) return { "email_data": ZapierNLARunAction( action_id=action["id"], zapier_description=action["description"], params_schema=action["params"], ).run(inputs["instructions"]) }gmail_chain = TransformChain( input_variables=["instructions"], output_variables=["email_data"], transform=nla_gmail,)## step 2. generate draft replytemplate = """You are an assisstant who drafts replies to an incoming email. Output draft reply in plain text (not JSON).Incoming email:{email_data}Draft email reply:"""prompt_template = PromptTemplate(input_variables=["email_data"], template=template)reply_chain = LLMChain(llm=OpenAI(temperature=0.7), prompt=prompt_template)## step 3. send draft reply via a slack direct messageSLACK_HANDLE = "@Ankush Gola"def nla_slack(inputs): action = next( ( a for a in actions if a["description"].startswith("Slack: Send Direct Message") ), None, ) instructions = f'Send this to {SLACK_HANDLE} in Slack: {inputs["draft_reply"]}' return { "slack_data": ZapierNLARunAction( action_id=action["id"], zapier_description=action["description"], params_schema=action["params"], ).run(instructions) }slack_chain = TransformChain( input_variables=["draft_reply"], output_variables=["slack_data"], transform=nla_slack,)## finally, executeoverall_chain = SimpleSequentialChain( chains=[gmail_chain, reply_chain, slack_chain], verbose=True)overall_chain.run(GMAIL_SEARCH_INSTRUCTIONS) > Entering new SimpleSequentialChain chain... {"from__name": "Silicon Valley Bridge Bank, N.A.", "from__email": "[email protected]", "body_plain": "Dear Clients, After chaotic, tumultuous & stressful days, we have clarity on path for SVB, FDIC is fully insuring all deposits & have an ask for clients & partners as we rebuild. Tim Mayopoulos <https://eml.svb.com/NjEwLUtBSy0yNjYAAAGKgoxUeBCLAyF_NxON97X4rKEaNBLG", "reply_to__email": "[email protected]", "subject": "Meet the new CEO Tim Mayopoulos", "date": "Tue, 14 Mar 2023 23:42:29 -0500 (CDT)", "message_url": "https://mail.google.com/mail/u/0/#inbox/186e393b13cfdf0a", "attachment_count": "0", "to__emails": "[email protected]", "message_id": "186e393b13cfdf0a", "labels": "IMPORTANT, CATEGORY_UPDATES, INBOX"} Dear Silicon Valley Bridge Bank, Thank you for your email and the update regarding your new CEO Tim Mayopoulos. We appreciate your dedication to keeping your clients and partners informed and we look forward to continuing our relationship with you. Best regards, [Your Name] {"message__text": "Dear Silicon Valley Bridge Bank, \n\nThank you for your email and the update regarding your new CEO Tim Mayopoulos. We appreciate your dedication to keeping your clients and partners informed and we look forward to continuing our relationship with you. \n\nBest regards, \n[Your Name]", "message__permalink": "https://langchain.slack.com/archives/D04TKF5BBHU/p1678859968241629", "channel": "D04TKF5BBHU", "message__bot_profile__name": "Zapier", "message__team": "T04F8K3FZB5", "message__bot_id": "B04TRV4R74K", "message__bot_profile__deleted": "false", "message__bot_profile__app_id": "A024R9PQM", "ts_time": "2023-03-15T05:59:28Z", "message__blocks[]block_id": "p7i", "message__blocks[]elements[]elements[]type": "[['text']]", "message__blocks[]elements[]type": "['rich_text_section']"} > Finished chain. '{"message__text": "Dear Silicon Valley Bridge Bank, \\n\\nThank you for your email and the update regarding your new CEO Tim Mayopoulos. We appreciate your dedication to keeping your clients and partners informed and we look forward to continuing our relationship with you. \\n\\nBest regards, \\n[Your Name]", "message__permalink": "https://langchain.slack.com/archives/D04TKF5BBHU/p1678859968241629", "channel": "D04TKF5BBHU", "message__bot_profile__name": "Zapier", "message__team": "T04F8K3FZB5", "message__bot_id": "B04TRV4R74K", "message__bot_profile__deleted": "false", "message__bot_profile__app_id": "A024R9PQM", "ts_time": "2023-03-15T05:59:28Z", "message__blocks[]block_id": "p7i", "message__blocks[]elements[]elements[]type": "[[\'text\']]", "message__blocks[]elements[]type": "[\'rich_text_section\']"}'Example Using OAuth Access TokenThe below snippet shows how to initialize the wrapper with a procured OAuth access token. Note the argument being passed in as opposed to setting an environment variable. Review the authentication docs for full user-facing oauth developer support.The developer is tasked with handling the OAuth handshaking to procure and refresh the access token.llm = OpenAI(temperature=0)zapier = ZapierNLAWrapper(zapier_nla_oauth_access_token="<fill in access token here>")toolkit = ZapierToolkit.from_zapier_nla_wrapper(zapier)agent = initialize_agent( toolkit.get_tools(), llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True)agent.run( "Summarize the last email I received regarding Silicon Valley Bank. Send the summary to the #test-zapier channel in slack.")PreviousYouTubeNextAgents and toolkitsExample with AgentExample with SimpleSequentialChainExample Using OAuth Access Token |
748 | https://python.langchain.com/docs/integrations/toolkits | ComponentsAgents and toolkitsAgents and toolkits📄️ AINetworkAI Network is a layer 1 blockchain designed to accommodate large-scale AI models, utilizing a decentralized GPU network powered by the $AIN token, enriching AI-driven NFTs (AINFTs).📄️ Airbyte Question AnsweringThis notebook shows how to do question answering over structured data, in this case using the AirbyteStripeLoader.📄️ AmadeusThis notebook walks you through connecting LangChain to the Amadeus travel information API📄️ Azure Cognitive ServicesThis toolkit is used to interact with the Azure Cognitive Services API to achieve some multimodal capabilities.📄️ ClickUp Langchiain ToolkitGet Authenticated📄️ CSVThis notebook shows how to use agents to interact with data in CSV format. It is mostly optimized for question answering.📄️ Document ComparisonThis notebook shows how to use an agent to compare two documents.📄️ GithubThe Github toolkit contains tools that enable an LLM agent to interact with a github repository.📄️ GitlabThe Gitlab toolkit contains tools that enable an LLM agent to interact with a gitlab repository.📄️ GmailThis notebook walks through connecting a LangChain email to the Gmail API.📄️ Google Drive toolThis notebook walks through connecting a LangChain to the Google Drive API.📄️ JiraThis notebook goes over how to use the Jira toolkit.📄️ JSONThis notebook showcases an agent interacting with large JSON/dict objects.📄️ MultiOnThis notebook walks you through connecting LangChain to the MultiOn Client in your browser📄️ Office365This notebook walks through connecting LangChain to Office365 email and calendar.📄️ OpenAPIWe can construct agents to consume arbitrary APIs, here APIs conformant to the OpenAPI/Swagger specification.📄️ Natural Language APIsNatural Language API Toolkits (NLAToolkits) permit LangChain Agents to efficiently plan and combine calls across endpoints.📄️ Pandas DataframeThis notebook shows how to use agents to interact with a Pandas DataFrame. It is mostly optimized for question answering.📄️ PlayWright BrowserThis toolkit is used to interact with the browser. While other tools (like the Requests tools) are fine for static sites, PlayWright Browser toolkits let your agent navigate the web and interact with dynamically rendered sites.📄️ PowerBI DatasetThis notebook showcases an agent interacting with a Power BI Dataset. The agent is answering more general questions about a dataset, as well as recover from errors.📄️ PythonThis notebook showcases an agent designed to write and execute Python code to answer a question.📄️ Spark DataframeThis notebook shows how to use agents to interact with a Spark DataFrame and Spark Connect. It is mostly optimized for question answering.📄️ Spark SQLThis notebook shows how to use agents to interact with Spark SQL. Similar to SQL Database Agent, it is designed to address general inquiries about Spark SQL and facilitate error recovery.📄️ SQL DatabaseThis notebook showcases an agent designed to interact with a SQL databases.📄️ VectorstoreThis notebook showcases an agent designed to retrieve information from one or more vectorstores, either with or without sources.📄️ XorbitsThis notebook shows how to use agents to interact with Xorbits Pandas dataframe and Xorbits Numpy ndarray. It is mostly optimized for question answering.PreviousZapier Natural Language ActionsNextAINetwork |
749 | https://python.langchain.com/docs/integrations/toolkits/ainetwork | ComponentsAgents and toolkitsAINetworkOn this pageAINetworkAI Network is a layer 1 blockchain designed to accommodate large-scale AI models, utilizing a decentralized GPU network powered by the $AIN token, enriching AI-driven NFTs (AINFTs).The AINetwork Toolkit is a set of tools for interacting with the AINetwork Blockchain. These tools allow you to transfer AIN, read and write values, create apps, and set permissions for specific paths within the blockchain database.Installing dependenciesBefore using the AINetwork Toolkit, you need to install the ain-py package. You can install it with pip:pip install ain-pySet environmental variablesYou need to set the AIN_BLOCKCHAIN_ACCOUNT_PRIVATE_KEY environmental variable to your AIN Blockchain Account Private Key.import osos.environ["AIN_BLOCKCHAIN_ACCOUNT_PRIVATE_KEY"] = ""Get AIN Blockchain private keyimport osfrom ain.account import Accountif os.environ.get("AIN_BLOCKCHAIN_ACCOUNT_PRIVATE_KEY", None): account = Account(os.environ["AIN_BLOCKCHAIN_ACCOUNT_PRIVATE_KEY"])else: account = Account.create() os.environ["AIN_BLOCKCHAIN_ACCOUNT_PRIVATE_KEY"] = account.private_key print( f"""address: {account.address}private_key: {account.private_key}""" )# IMPORTANT: If you plan to use this account in the future, make sure to save the# private key in a secure place. Losing access to your private key means losing# access to your account. address: 0x5BEB4Defa2ccc274498416Fd7Cb34235DbC122Ac private_key: f5e2f359bb6b7836a2ac70815473d1a290c517f847d096f5effe818de8c2cf14 Initialize the AINetwork ToolkitYou can initialize the AINetwork Toolkit like this:from langchain.agents.agent_toolkits.ainetwork.toolkit import AINetworkToolkittoolkit = AINetworkToolkit()tools = toolkit.get_tools()address = tools[0].interface.wallet.defaultAccount.addressInitialize the Agent with the AINetwork ToolkitYou can initialize the agent with the AINetwork Toolkit like this:from langchain.chat_models import ChatOpenAIfrom langchain.agents import initialize_agent, AgentTypellm = ChatOpenAI(temperature=0)agent = initialize_agent( tools=tools, llm=llm, verbose=True, agent=AgentType.OPENAI_FUNCTIONS,)Example UsageHere are some examples of how you can use the agent with the AINetwork Toolkit:Define App name to testappName = f"langchain_demo_{address.lower()}"Create an app in the AINetwork Blockchain databaseprint( agent.run( f"Create an app in the AINetwork Blockchain database with the name {appName}" )) > Entering new AgentExecutor chain... Invoking: `AINappOps` with `{'type': 'SET_ADMIN', 'appName': 'langchain_demo_0x5beb4defa2ccc274498416fd7cb34235dbc122ac'}` {"tx_hash": "0x018846d6a9fc111edb1a2246ae2484ef05573bd2c584f3d0da155fa4b4936a9e", "result": {"gas_amount_total": {"bandwidth": {"service": 4002, "app": {"langchain_demo_0x5beb4defa2ccc274498416fd7cb34235dbc122ac": 2}}, "state": {"service": 1640}}, "gas_cost_total": 0, "func_results": {"_createApp": {"op_results": {"0": {"path": "/apps/langchain_demo_0x5beb4defa2ccc274498416fd7cb34235dbc122ac", "result": {"code": 0, "bandwidth_gas_amount": 1}}, "1": {"path": "/apps/langchain_demo_0x5beb4defa2ccc274498416fd7cb34235dbc122ac", "result": {"code": 0, "bandwidth_gas_amount": 1}}, "2": {"path": "/manage_app/langchain_demo_0x5beb4defa2ccc274498416fd7cb34235dbc122ac/config/admin", "result": {"code": 0, "bandwidth_gas_amount": 1}}}, "code": 0, "bandwidth_gas_amount": 2000}}, "code": 0, "bandwidth_gas_amount": 2001, "gas_amount_charged": 5642}}The app with the name "langchain_demo_0x5beb4defa2ccc274498416fd7cb34235dbc122ac" has been created in the AINetwork Blockchain database. > Finished chain. The app with the name "langchain_demo_0x5beb4defa2ccc274498416fd7cb34235dbc122ac" has been created in the AINetwork Blockchain database.Set a value at a given path in the AINetwork Blockchain databaseprint( agent.run(f"Set the value {{1: 2, '34': 56}} at the path /apps/{appName}/object .")) > Entering new AgentExecutor chain... Invoking: `AINvalueOps` with `{'type': 'SET', 'path': '/apps/langchain_demo_0x5beb4defa2ccc274498416fd7cb34235dbc122ac/object', 'value': {'1': 2, '34': 56}}` {"tx_hash": "0x3d1a16d9808830088cdf4d37f90f4b1fa1242e2d5f6f983829064f45107b5279", "result": {"gas_amount_total": {"bandwidth": {"service": 0, "app": {"langchain_demo_0x5beb4defa2ccc274498416fd7cb34235dbc122ac": 1}}, "state": {"service": 0, "app": {"langchain_demo_0x5beb4defa2ccc274498416fd7cb34235dbc122ac": 674}}}, "gas_cost_total": 0, "code": 0, "bandwidth_gas_amount": 1, "gas_amount_charged": 0}}The value {1: 2, '34': 56} has been set at the path /apps/langchain_demo_0x5beb4defa2ccc274498416fd7cb34235dbc122ac/object. > Finished chain. The value {1: 2, '34': 56} has been set at the path /apps/langchain_demo_0x5beb4defa2ccc274498416fd7cb34235dbc122ac/object.Set permissions for a path in the AINetwork Blockchain databaseprint( agent.run( f"Set the write permissions for the path /apps/{appName}/user/$from with the" " eval string auth.addr===$from ." )) > Entering new AgentExecutor chain... Invoking: `AINruleOps` with `{'type': 'SET', 'path': '/apps/langchain_demo_0x5beb4defa2ccc274498416fd7cb34235dbc122ac/user/$from', 'eval': 'auth.addr===$from'}` {"tx_hash": "0x37d5264e580f6a217a347059a735bfa9eb5aad85ff28a95531c6dc09252664d2", "result": {"gas_amount_total": {"bandwidth": {"service": 0, "app": {"langchain_demo_0x5beb4defa2ccc274498416fd7cb34235dbc122ac": 1}}, "state": {"service": 0, "app": {"langchain_demo_0x5beb4defa2ccc274498416fd7cb34235dbc122ac": 712}}}, "gas_cost_total": 0, "code": 0, "bandwidth_gas_amount": 1, "gas_amount_charged": 0}}The write permissions for the path `/apps/langchain_demo_0x5beb4defa2ccc274498416fd7cb34235dbc122ac/user/$from` have been set with the eval string `auth.addr===$from`. > Finished chain. The write permissions for the path `/apps/langchain_demo_0x5beb4defa2ccc274498416fd7cb34235dbc122ac/user/$from` have been set with the eval string `auth.addr===$from`.Retrieve the permissions for a path in the AINetwork Blockchain databaseprint(agent.run(f"Retrieve the permissions for the path /apps/{appName}.")) > Entering new AgentExecutor chain... Invoking: `AINownerOps` with `{'type': 'GET', 'path': '/apps/langchain_demo_0x5beb4defa2ccc274498416fd7cb34235dbc122ac'}` {".owner": {"owners": {"0x5BEB4Defa2ccc274498416Fd7Cb34235DbC122Ac": {"branch_owner": true, "write_function": true, "write_owner": true, "write_rule": true}}}}The permissions for the path /apps/langchain_demo_0x5beb4defa2ccc274498416fd7cb34235dbc122ac are as follows: - Address: 0x5BEB4Defa2ccc274498416Fd7Cb34235DbC122Ac - branch_owner: true - write_function: true - write_owner: true - write_rule: true > Finished chain. The permissions for the path /apps/langchain_demo_0x5beb4defa2ccc274498416fd7cb34235dbc122ac are as follows: - Address: 0x5BEB4Defa2ccc274498416Fd7Cb34235DbC122Ac - branch_owner: true - write_function: true - write_owner: true - write_rule: trueGet AIN from faucetcurl http://faucet.ainetwork.ai/api/test/{address}/ {"result":"0x0eb07b67b7d0a702cb60e865d3deafff3070d8508077ef793d69d6819fd92ea3","time":1692348112376}Get AIN Balanceprint(agent.run(f"Check AIN balance of {address}")) > Entering new AgentExecutor chain... Invoking: `AINvalueOps` with `{'type': 'GET', 'path': '/accounts/0x5BEB4Defa2ccc274498416Fd7Cb34235DbC122Ac/balance'}` 100The AIN balance of address 0x5BEB4Defa2ccc274498416Fd7Cb34235DbC122Ac is 100 AIN. > Finished chain. The AIN balance of address 0x5BEB4Defa2ccc274498416Fd7Cb34235DbC122Ac is 100 AIN.Transfer AINprint( agent.run( "Transfer 100 AIN to the address 0x19937b227b1b13f29e7ab18676a89ea3bdea9c5b" )) > Entering new AgentExecutor chain... Invoking: `AINtransfer` with `{'address': '0x19937b227b1b13f29e7ab18676a89ea3bdea9c5b', 'amount': 100}` {"tx_hash": "0xa59d15d23373bcc00e413ac8ba18cb016bb3bdd54058d62606aec688c6ad3d2e", "result": {"gas_amount_total": {"bandwidth": {"service": 3}, "state": {"service": 866}}, "gas_cost_total": 0, "func_results": {"_transfer": {"op_results": {"0": {"path": "/accounts/0x5BEB4Defa2ccc274498416Fd7Cb34235DbC122Ac/balance", "result": {"code": 0, "bandwidth_gas_amount": 1}}, "1": {"path": "/accounts/0x19937B227b1b13f29e7AB18676a89EA3BDEA9C5b/balance", "result": {"code": 0, "bandwidth_gas_amount": 1}}}, "code": 0, "bandwidth_gas_amount": 0}}, "code": 0, "bandwidth_gas_amount": 1, "gas_amount_charged": 869}}The transfer of 100 AIN to the address 0x19937b227b1b13f29e7ab18676a89ea3bdea9c5b was successful. The transaction hash is 0xa59d15d23373bcc00e413ac8ba18cb016bb3bdd54058d62606aec688c6ad3d2e. > Finished chain. The transfer of 100 AIN to the address 0x19937b227b1b13f29e7ab18676a89ea3bdea9c5b was successful. The transaction hash is 0xa59d15d23373bcc00e413ac8ba18cb016bb3bdd54058d62606aec688c6ad3d2e.PreviousAgents and toolkitsNextAirbyte Question AnsweringInstalling dependenciesSet environmental variablesGet AIN Blockchain private keyInitialize the AINetwork ToolkitInitialize the Agent with the AINetwork ToolkitExample UsageDefine App name to testCreate an app in the AINetwork Blockchain databaseSet a value at a given path in the AINetwork Blockchain databaseSet permissions for a path in the AINetwork Blockchain databaseRetrieve the permissions for a path in the AINetwork Blockchain databaseGet AIN from faucetGet AIN BalanceTransfer AIN |
750 | https://python.langchain.com/docs/integrations/toolkits/airbyte_structured_qa | ComponentsAgents and toolkitsAirbyte Question AnsweringAirbyte Question AnsweringThis notebook shows how to do question answering over structured data, in this case using the AirbyteStripeLoader.Vectorstores often have a hard time answering questions that requires computing, grouping and filtering structured data so the high level idea is to use a pandas dataframe to help with these types of questions. Load data from Stripe using Airbyte. user the record_handler paramater to return a JSON from the data loader.import osimport pandas as pdfrom langchain.document_loaders.airbyte import AirbyteStripeLoaderfrom langchain.chat_models.openai import ChatOpenAIfrom langchain.agents import AgentType, create_pandas_dataframe_agentstream_name = "customers"config = { "client_secret": os.getenv("STRIPE_CLIENT_SECRET"), "account_id": os.getenv("STRIPE_ACCOUNT_D"), "start_date": "2023-01-20T00:00:00Z",}def handle_record(record: dict, _id: str): return record.dataloader = AirbyteStripeLoader( config=config, record_handler=handle_record, stream_name=stream_name,)data = loader.load()Pass the data to pandas dataframe.df = pd.DataFrame(data)Pass the dataframe df to the create_pandas_dataframe_agent and invokeagent = create_pandas_dataframe_agent( ChatOpenAI(temperature=0, model="gpt-4"), df, verbose=True, agent_type=AgentType.OPENAI_FUNCTIONS,)Run the agentoutput = agent.run("How many rows are there?")PreviousAINetworkNextAmadeus |
751 | https://python.langchain.com/docs/integrations/toolkits/amadeus | ComponentsAgents and toolkitsAmadeusOn this pageAmadeusThis notebook walks you through connecting LangChain to the Amadeus travel information APITo use this toolkit, you will need to set up your credentials explained in the Amadeus for developers getting started overview. Once you've received a AMADEUS_CLIENT_ID and AMADEUS_CLIENT_SECRET, you can input them as environmental variables below.pip install --upgrade amadeus > /dev/nullAssign Environmental VariablesThe toolkit will read the AMADEUS_CLIENT_ID and AMADEUS_CLIENT_SECRET environmental variables to authenticate the user so you need to set them here. You will also need to set your OPENAI_API_KEY to use the agent later.# Set environmental variables hereimport osos.environ["AMADEUS_CLIENT_ID"] = "CLIENT_ID"os.environ["AMADEUS_CLIENT_SECRET"] = "CLIENT_SECRET"os.environ["OPENAI_API_KEY"] = "API_KEY"Create the Amadeus Toolkit and Get ToolsTo start, you need to create the toolkit, so you can access its tools later.from langchain.agents.agent_toolkits.amadeus.toolkit import AmadeusToolkittoolkit = AmadeusToolkit()tools = toolkit.get_tools()Use Amadeus Toolkit within an Agentfrom langchain.llms import OpenAIfrom langchain.agents import initialize_agent, AgentTypellm = OpenAI(temperature=0)agent = initialize_agent( tools=tools, llm=llm, verbose=False, agent=AgentType.STRUCTURED_CHAT_ZERO_SHOT_REACT_DESCRIPTION,)agent.run("What is the name of the airport in Cali, Colombia?") 'The closest airport to Cali, Colombia is Alfonso Bonilla Aragón International Airport (CLO).'agent.run( "What is the departure time of the cheapest flight on August 23, 2023 leaving Dallas, Texas before noon to Lincoln, Nebraska?") 'The cheapest flight on August 23, 2023 leaving Dallas, Texas before noon to Lincoln, Nebraska has a departure time of 16:42 and a total price of 276.08 EURO.'agent.run( "At what time does earliest flight on August 23, 2023 leaving Dallas, Texas to Lincoln, Nebraska land in Nebraska?") 'The earliest flight on August 23, 2023 leaving Dallas, Texas to Lincoln, Nebraska lands in Lincoln, Nebraska at 16:07.'agent.run( "What is the full travel time for the cheapest flight between Portland, Oregon to Dallas, TX on October 3, 2023?") 'The cheapest flight between Portland, Oregon to Dallas, TX on October 3, 2023 is a Spirit Airlines flight with a total price of 84.02 EURO and a total travel time of 8 hours and 43 minutes.'agent.run( "Please draft a concise email from Santiago to Paul, Santiago's travel agent, asking him to book the earliest flight from DFW to DCA on Aug 28, 2023. Include all flight details in the email.") 'Dear Paul,\n\nI am writing to request that you book the earliest flight from DFW to DCA on Aug 28, 2023. The flight details are as follows:\n\nFlight 1: DFW to ATL, departing at 7:15 AM, arriving at 10:25 AM, flight number 983, carrier Delta Air Lines\nFlight 2: ATL to DCA, departing at 12:15 PM, arriving at 2:02 PM, flight number 759, carrier Delta Air Lines\n\nThank you for your help.\n\nSincerely,\nSantiago'PreviousAirbyte Question AnsweringNextAzure Cognitive ServicesAssign Environmental VariablesCreate the Amadeus Toolkit and Get ToolsUse Amadeus Toolkit within an Agent |
752 | https://python.langchain.com/docs/integrations/toolkits/azure_cognitive_services | ComponentsAgents and toolkitsAzure Cognitive ServicesOn this pageAzure Cognitive ServicesThis toolkit is used to interact with the Azure Cognitive Services API to achieve some multimodal capabilities.Currently There are four tools bundled in this toolkit:AzureCogsImageAnalysisTool: used to extract caption, objects, tags, and text from images. (Note: this tool is not available on Mac OS yet, due to the dependency on azure-ai-vision package, which is only supported on Windows and Linux currently.)AzureCogsFormRecognizerTool: used to extract text, tables, and key-value pairs from documents.AzureCogsSpeech2TextTool: used to transcribe speech to text.AzureCogsText2SpeechTool: used to synthesize text to speech.First, you need to set up an Azure account and create a Cognitive Services resource. You can follow the instructions here to create a resource. Then, you need to get the endpoint, key and region of your resource, and set them as environment variables. You can find them in the "Keys and Endpoint" page of your resource.# !pip install --upgrade azure-ai-formrecognizer > /dev/null# !pip install --upgrade azure-cognitiveservices-speech > /dev/null# For Windows/Linux# !pip install --upgrade azure-ai-vision > /dev/nullimport osos.environ["OPENAI_API_KEY"] = "sk-"os.environ["AZURE_COGS_KEY"] = ""os.environ["AZURE_COGS_ENDPOINT"] = ""os.environ["AZURE_COGS_REGION"] = ""Create the Toolkitfrom langchain.agents.agent_toolkits import AzureCognitiveServicesToolkittoolkit = AzureCognitiveServicesToolkit()[tool.name for tool in toolkit.get_tools()] ['Azure Cognitive Services Image Analysis', 'Azure Cognitive Services Form Recognizer', 'Azure Cognitive Services Speech2Text', 'Azure Cognitive Services Text2Speech']Use within an Agentfrom langchain.llms import OpenAIfrom langchain.agents import initialize_agent, AgentTypellm = OpenAI(temperature=0)agent = initialize_agent( tools=toolkit.get_tools(), llm=llm, agent=AgentType.STRUCTURED_CHAT_ZERO_SHOT_REACT_DESCRIPTION, verbose=True,)agent.run( "What can I make with these ingredients?" "https://images.openai.com/blob/9ad5a2ab-041f-475f-ad6a-b51899c50182/ingredients.png") > Entering new AgentExecutor chain... Action: ``` { "action": "Azure Cognitive Services Image Analysis", "action_input": "https://images.openai.com/blob/9ad5a2ab-041f-475f-ad6a-b51899c50182/ingredients.png" } ``` Observation: Caption: a group of eggs and flour in bowls Objects: Egg, Egg, Food Tags: dairy, ingredient, indoor, thickening agent, food, mixing bowl, powder, flour, egg, bowl Thought: I can use the objects and tags to suggest recipes Action: ``` { "action": "Final Answer", "action_input": "You can make pancakes, omelettes, or quiches with these ingredients!" } ``` > Finished chain. 'You can make pancakes, omelettes, or quiches with these ingredients!'audio_file = agent.run("Tell me a joke and read it out for me.") > Entering new AgentExecutor chain... Action: ``` { "action": "Azure Cognitive Services Text2Speech", "action_input": "Why did the chicken cross the playground? To get to the other slide!" } ``` Observation: /tmp/tmpa3uu_j6b.wav Thought: I have the audio file of the joke Action: ``` { "action": "Final Answer", "action_input": "/tmp/tmpa3uu_j6b.wav" } ``` > Finished chain. '/tmp/tmpa3uu_j6b.wav'from IPython import displayaudio = display.Audio(audio_file)display.display(audio)PreviousAmadeusNextClickUp Langchiain ToolkitCreate the ToolkitUse within an Agent |
753 | https://python.langchain.com/docs/integrations/toolkits/clickup | ComponentsAgents and toolkitsClickUp Langchiain ToolkitOn this pageClickUp Langchiain Toolkitfrom datetime import datetimefrom langchain.agents.agent_toolkits.clickup.toolkit import ClickupToolkitfrom langchain.agents import AgentType, initialize_agentfrom langchain.llms import OpenAIfrom langchain.utilities.clickup import ClickupAPIWrapperInitGet AuthenticatedCreate a ClickUp AppFollow these steps to get your client_id and client_secret.Suggestion: use https://google.com as the redirect_uri. This is what we assume in the defaults for this toolkit.Copy/paste them and run the next cell to get your code# Copilot Sandboxoauth_client_id = "ABC..."oauth_client_secret = "123..."redirect_uri = "https://google.com"print('Click this link, select your workspace, click `Connect Workspace`')print(ClickupAPIWrapper.get_access_code_url(oauth_client_id, redirect_uri)) Click this link, select your workspace, click `Connect Workspace` https://app.clickup.com/api?client_id=ABC...&redirect_uri=https://google.comThe url should change to something like this https://www.google.com/?code=THISISMYCODERIGHTHERE.Next, copy/paste the CODE (THISISMYCODERIGHTHERE) generated in the URL in the cell below.code = "THISISMYCODERIGHTHERE"Get Access TokenThen, use the code below to get your access_token.Important: Each code is a one time code that will expire after use. The access_token can be used for a period of time. Make sure to copy paste the access_token once you get it!access_token = ClickupAPIWrapper.get_access_token(oauth_client_id, oauth_client_secret, code)if access_token is not None: print('Copy/paste this code, into the next cell so you can reuse it!') print(access_token) Error: {'err': 'Code already used', 'ECODE': 'OAUTH_014'} You already used this code once. Go back a step and generate a new code. Our best guess for the url to get a new code is: https://app.clickup.com/api?client_id=B5D61F8EVO04PR0JX0U73984LLS9GI6P&redirect_uri=https://google.comCreate Toolkit# Set your access token hereaccess_token = '12345678_myaccesstokengoeshere123'access_token = '81928627_c009bf122ccf36ec3ba3e0ef748b07042c5e4217260042004a5934540cb61527'# Init toolkitclickup_api_wrapper = ClickupAPIWrapper(access_token=access_token)toolkit = ClickupToolkit.from_clickup_api_wrapper(clickup_api_wrapper)print(f'Found team_id: {clickup_api_wrapper.team_id}.\nMost request require the team id, so we store it for you in the toolkit, we assume the first team in your list is the one you want. \nNote: If you know this is the wrong ID, you can pass it at initialization.') Found team_id: 9011010153. Most request require the team id, so we store it for you in the toolkit, we assume the first team in your list is the one you want. Note: If you know this is the wrong ID, you can pass it at initialization.Create Agentllm = OpenAI(temperature=0, openai_api_key="")agent = initialize_agent( toolkit.get_tools(), llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True)Run# helper function for demodef print_and_run(command): print('\033[94m$ COMMAND\033[0m') print(command) print('\n\033[94m$ AGENT\033[0m') response = agent.run(command) print(''.join(['-']*80)) return responseNavigationYou can get the teams, folder and spaces your user has access toprint_and_run("Get all the teams that the user is authorized to access")print_and_run("Get all the spaces available to the team")print_and_run("Get all the folders for the team") $ COMMAND Get all the teams that the user is authorized to access $ AGENT > Entering new AgentExecutor chain... I need to use the Get Teams tool Action: Get Teams Action Input: No necessary request parameters Observation: {'teams': [{'id': '9011010153', 'name': 'Task Copilot Sandbox Workspace 1', 'members': [{'id': 61681706, 'username': 'Aiswarya ', 'email': '[email protected]', 'initials': 'A'}, {'id': 81928627, 'username': 'Rodrigo Ceballos Lentini', 'email': '[email protected]', 'initials': 'RL'}]}]} Thought: I now know the teams the user is authorized to access Final Answer: The user is authorized to access the team 'Task Copilot Sandbox Workspace 1'. > Finished chain. -------------------------------------------------------------------------------- $ COMMAND Get all the spaces available to the team $ AGENT > Entering new AgentExecutor chain... I need to use the API to get the spaces Action: Get Teams Action Input: No necessary request parameters Observation: {'teams': [{'id': '9011010153', 'name': 'Task Copilot Sandbox Workspace 1', 'members': [{'id': 61681706, 'username': 'Aiswarya ', 'email': '[email protected]', 'initials': 'A'}, {'id': 81928627, 'username': 'Rodrigo Ceballos Lentini', 'email': '[email protected]', 'initials': 'RL'}]}]} Thought: I now have the list of teams Final Answer: The list of teams available to the team is [{'id': '9011010153', 'name': 'Task Copilot Sandbox Workspace 1', 'members': [{'id': 61681706, 'username': 'Aiswarya ', 'email': '[email protected]', 'initials': 'A'}, {'id': 81928627, 'username': 'Rodrigo Ceballos Lentini', 'email': '[email protected]', 'initials': 'RL'}]}] > Finished chain. -------------------------------------------------------------------------------- $ COMMAND Get all the folders for the team $ AGENT > Entering new AgentExecutor chain... I need to get all the folders for the team Action: Get all folders in the workspace Action Input: {"folder_id": "90130119692"} Observation: {'spaces': [{'id': '90110075934', 'name': 'Test Space', 'color': None, 'private': False, 'avatar': None, 'admin_can_manage': False, 'statuses': [{'id': 'p90110075934_lBKIEh3r', 'status': 'Open', 'type': 'open', 'orderindex': 0, 'color': '#d3d3d3'}, {'id': 'p90110075934_AvVAnVqy', 'status': 'in progress', 'type': 'custom', 'orderindex': 1, 'color': '#a875ff'}, {'id': 'p90110075934_SftYWzGt', 'status': 'Closed', 'type': 'closed', 'orderindex': 2, 'color': '#6bc950'}], 'multiple_assignees': True, 'features': {'due_dates': {'enabled': True, 'start_date': True, 'remap_due_dates': False, 'remap_closed_due_date': False}, 'sprints': {'enabled': False}, 'time_tracking': {'enabled': True, 'harvest': False, 'rollup': False}, 'points': {'enabled': False}, 'custom_items': {'enabled': False}, 'priorities': {'enabled': True, 'priorities': [{'color': '#f50000', 'id': '1', 'orderindex': '1', 'priority': 'urgent'}, {'color': '#ffcc00', 'id': '2', 'orderindex': '2', 'priority': 'high'}, {'color': '#6fddff', 'id': '3', 'orderindex': '3', 'priority': 'normal'}, {'color': '#d8d8d8', 'id': '4', 'orderindex': '4', 'priority': 'low'}]}, 'tags': {'enabled': True}, 'check_unresolved': {'enabled': True, 'subtasks': None, 'checklists': None, 'comments': None}, 'zoom': {'enabled': False}, 'milestones': {'enabled': False}, 'custom_fields': {'enabled': True}, 'dependency_warning': {'enabled': True}, 'status_pies': {'enabled': False}, 'multiple_assignees': {'enabled': True}}, 'archived': False}]} Thought: I now know the folders in the team Final Answer: The folders in the team are listed in the observation. > Finished chain. -------------------------------------------------------------------------------- 'The folders in the team are listed in the observation.'Task OperationsYou can get, ask question about tasks and update themtask_id = '8685mb5fn'Basic attirbute getting and updating# We can get a task to inspect it's contentsprint_and_run(f"Get task with id {task_id}")# We can get a specific attribute from a taskprevious_description = print_and_run(f"What is the description of task with id {task_id}")# We can even update it!print_and_run(f"For task with id {task_id}, change the description to 'A cool task descriptiont changed by AI!'")print_and_run(f"What is the description of task with id {task_id}")# Undo what we didprint_and_run(f"For task with id {task_id}, change the description to '{previous_description}'") $ COMMAND Get task with id 8685mb5fn $ AGENT > Entering new AgentExecutor chain... I need to use the Get task tool Action: Get task Action Input: {"task_id": "8685mb5fn"} Observation: {'id': '8685mb5fn', 'name': 'dummy task 1', 'text_content': 'An old, boring task description', 'description': 'An old, boring task description', 'status': 'to do', 'creator_id': 81928627, 'creator_username': 'Rodrigo Ceballos Lentini', 'creator_email': '[email protected]', 'assignees': [], 'watcher_username': 'Rodrigo Ceballos Lentini', 'watcher_email': '[email protected]', 'priority': 'high', 'due_date': '1694764800000', 'start_date': None, 'points': None, 'team_id': '9011010153', 'project_id': '90110331875'} Thought: I now know the task details Final Answer: The task with id 8685mb5fn has the following details: {'id': '8685mb5fn', 'name': 'dummy task 1', 'text_content': 'An old, boring task description', 'description': 'An old, boring task description', 'status': 'to do', 'creator_id': 81928627, 'creator_username': 'Rodrigo Ceballos Lentini', 'creator_email': '[email protected]', 'assignees': [], 'watcher_username': 'Rodrigo Ceballos Lentini', 'watcher_email': '[email protected]', 'priority': 'high', 'due_date': '1694764800000', 'start_date': None, 'points': None, 'team_id': '9011010153', 'project_id': '90110331875'} > Finished chain. -------------------------------------------------------------------------------- $ COMMAND What is the description of task with id 8685mb5fn $ AGENT > Entering new AgentExecutor chain... I need to get the description of the task Action: Get task attribute Action Input: {"task_id": "8685mb5fn", "attribute_name": "description"} Observation: An old, boring task description Thought: I now know the description of the task Final Answer: An old, boring task description > Finished chain. -------------------------------------------------------------------------------- $ COMMAND For task with id 8685mb5fn, change the description to 'A cool task descriptiont changed by AI!' $ AGENT > Entering new AgentExecutor chain... I need to update the description of a task Action: Update task Action Input: {"task_id": "8685mb5fn", "attribute_name": "description", "value": "A cool task description changed by AI!"} Observation: <Response [200]> Thought: I have successfully updated the task description Final Answer: The description of task 8685mb5fn has been successfully changed to 'A cool task description changed by AI!' > Finished chain. -------------------------------------------------------------------------------- $ COMMAND What is the description of task with id 8685mb5fn $ AGENT > Entering new AgentExecutor chain... I need to get the description of the task Action: Get task attribute Action Input: {"task_id": "8685mb5fn", "attribute_name": "description"} Observation: A cool task description changed by AI! Thought: I now know the description of the task Final Answer: A cool task description changed by AI! > Finished chain. -------------------------------------------------------------------------------- $ COMMAND For task with id 8685mb5fn, change the description to 'An old, boring task description' $ AGENT > Entering new AgentExecutor chain... I need to update the description of a task Action: Update task Action Input: {"task_id": "8685mb5fn", "attribute_name": "description", "value": "An old, boring task description"} Observation: <Response [200]> Thought: I now know the task description has been updated Final Answer: The description of task 8685mb5fn has been updated to 'An old, boring task description'. > Finished chain. -------------------------------------------------------------------------------- "The description of task 8685mb5fn has been updated to 'An old, boring task description'."print_and_run("Change the descrition task 8685mj6cd to 'Look ma no hands'") $ COMMAND Change the descrition task 8685mj6cd to 'Look ma no hands' $ AGENT > Entering new AgentExecutor chain... I need to update the description of a task Action: Update task Action Input: {"task_id": "8685mj6cd", "attribute_name": "description", "value": "Look ma no hands"} Observation: <Response [200]> Thought: The task description has been successfully updated Final Answer: The description of task 8685mj6cd has been changed to 'Look ma no hands'. > Finished chain. -------------------------------------------------------------------------------- "The description of task 8685mj6cd has been changed to 'Look ma no hands'."Advanced Attributes (Assignees)You can query and update almost every thing about a task!user_id = 81928627print_and_run(f"What are the assignees of task id {task_id}?")print_and_run(f"Remove user {user_id} from the assignees of task id {task_id}")print_and_run(f"What are the assignees of task id {task_id}?")print_and_run(f"Add user {user_id} from the assignees of task id {task_id}") $ COMMAND What are the assignees of task id 8685mb5fn? $ AGENT > Entering new AgentExecutor chain... I need to get the assignees of a task Action: Get task attribute Action Input: {"task_id": "8685mb5fn", "attribute_name": "assignee"} Observation: Error: attribute_name = assignee was not found in task keys dict_keys(['id', 'name', 'text_content', 'description', 'status', 'creator_id', 'creator_username', 'creator_email', 'assignees', 'watcher_username', 'watcher_email', 'priority', 'due_date', 'start_date', 'points', 'team_id', 'project_id']). Please call again with one of the key names. Thought: I need to get the assignees of a task Action: Get task attribute Action Input: {"task_id": "8685mb5fn", "attribute_name": "assignees"} Observation: [] Thought: I now know the final answer Final Answer: There are no assignees for task id 8685mb5fn. > Finished chain. -------------------------------------------------------------------------------- $ COMMAND Remove user 81928627 from the assignees of task id 8685mb5fn $ AGENT > Entering new AgentExecutor chain... I need to update the assignees of a task Action: Update task assignees Action Input: {"task_id": "8685mb5fn", "operation": "rem", "users": [81928627]} Observation: <Response [200]> Thought: The user has been removed from the assignees of the task Final Answer: User 81928627 has been removed from the assignees of task id 8685mb5fn. > Finished chain. -------------------------------------------------------------------------------- $ COMMAND What are the assignees of task id 8685mb5fn? $ AGENT > Entering new AgentExecutor chain... I need to get the assignees of a task Action: Get task attribute Action Input: {"task_id": "8685mb5fn", "attribute_name": "assignee"} Observation: Error: attribute_name = assignee was not found in task keys dict_keys(['id', 'name', 'text_content', 'description', 'status', 'creator_id', 'creator_username', 'creator_email', 'assignees', 'watcher_username', 'watcher_email', 'priority', 'due_date', 'start_date', 'points', 'team_id', 'project_id']). Please call again with one of the key names. Thought: I need to get the assignees of a task Action: Get task attribute Action Input: {"task_id": "8685mb5fn", "attribute_name": "assignees"} Observation: [] Thought: I now know the final answer Final Answer: There are no assignees for task id 8685mb5fn. > Finished chain. -------------------------------------------------------------------------------- $ COMMAND Add user 81928627 from the assignees of task id 8685mb5fn $ AGENT > Entering new AgentExecutor chain... I need to update the assignees of a task Action: Update task assignees Action Input: {"task_id": "8685mb5fn", "operation": "rem", "users": [81928627]} Observation: <Response [200]> Thought: The user has been removed from the assignees of the task Final Answer: User 81928627 has been removed from the assignees of task id 8685mb5fn. > Finished chain. -------------------------------------------------------------------------------- 'User 81928627 has been removed from the assignees of task id 8685mb5fn.'CreationYou can create tasks, lists and folderstime_str = datetime.now().strftime("%d/%m/%Y-%H:%M:%S")print_and_run(f"Create a task called 'Test Task - {time_str}' with description 'This is a Test'") $ COMMAND Create a task called 'Test Task - 18/09/2023-10:31:22' with description 'This is a Test' $ AGENT > Entering new AgentExecutor chain... I need to use the Create Task tool Action: Create Task Action Input: {"name": "Test Task - 18/09/2023-10:31:22", "description": "This is a Test"} Observation: {'id': '8685mw4wq', 'custom_id': None, 'name': 'Test Task - 18/09/2023-10:31:22', 'text_content': 'This is a Test', 'description': 'This is a Test', 'status': {'id': 'p90110061901_VlN8IJtk', 'status': 'to do', 'color': '#87909e', 'orderindex': 0, 'type': 'open'}, 'orderindex': '23.00000000000000000000000000000000', 'date_created': '1695047486396', 'date_updated': '1695047486396', 'date_closed': None, 'date_done': None, 'archived': False, 'creator': {'id': 81928627, 'username': 'Rodrigo Ceballos Lentini', 'color': '#c51162', 'email': '[email protected]', 'profilePicture': None}, 'assignees': [], 'watchers': [{'id': 81928627, 'username': 'Rodrigo Ceballos Lentini', 'color': '#c51162', 'initials': 'RL', 'email': '[email protected]', 'profilePicture': None}], 'checklists': [], 'tags': [], 'parent': None, 'priority': None, 'due_date': None, 'start_date': None, 'points': None, 'time_estimate': None, 'time_spent': 0, 'custom_fields': [], 'dependencies': [], 'linked_tasks': [], 'team_id': '9011010153', 'url': 'https://app.clickup.com/t/8685mw4wq', 'sharing': {'public': False, 'public_share_expires_on': None, 'public_fields': ['assignees', 'priority', 'due_date', 'content', 'comments', 'attachments', 'customFields', 'subtasks', 'tags', 'checklists', 'coverimage'], 'token': None, 'seo_optimized': False}, 'permission_level': 'create', 'list': {'id': '901100754275', 'name': 'Test List', 'access': True}, 'project': {'id': '90110336890', 'name': 'Test Folder', 'hidden': False, 'access': True}, 'folder': {'id': '90110336890', 'name': 'Test Folder', 'hidden': False, 'access': True}, 'space': {'id': '90110061901'}} Thought: I now know the final answer Final Answer: A task called 'Test Task - 18/09/2023-10:31:22' with description 'This is a Test' was successfully created. > Finished chain. -------------------------------------------------------------------------------- "A task called 'Test Task - 18/09/2023-10:31:22' with description 'This is a Test' was successfully created."time_str = datetime.now().strftime("%d/%m/%Y-%H:%M:%S")print_and_run(f"Create a list called Test List - {time_str}") $ COMMAND Create a list called Test List - 18/09/2023-10:32:12 $ AGENT > Entering new AgentExecutor chain... I need to create a list Action: Create List Action Input: {"name": "Test List - 18/09/2023-10:32:12"} Observation: {'id': '901100774700', 'name': 'Test List - 18/09/2023-10:32:12', 'deleted': False, 'orderindex': 13, 'content': '', 'priority': None, 'assignee': None, 'due_date': None, 'start_date': None, 'folder': {'id': '90110336890', 'name': 'Test Folder', 'hidden': False, 'access': True}, 'space': {'id': '90110061901', 'name': 'Space', 'access': True}, 'inbound_address': 'a.t.901100774700.u-81928627.20b87d50-eece-4721-b487-9ca500338587@tasks.clickup.com', 'archived': False, 'override_statuses': False, 'statuses': [{'id': 'p90110061901_VlN8IJtk', 'status': 'to do', 'orderindex': 0, 'color': '#87909e', 'type': 'open'}, {'id': 'p90110061901_14GpYKnM', 'status': 'complete', 'orderindex': 1, 'color': '#6bc950', 'type': 'closed'}], 'permission_level': 'create'} Thought: I now know the final answer Final Answer: The list "Test List - 18/09/2023-10:32:12" has been created with id 901100774700. > Finished chain. -------------------------------------------------------------------------------- 'The list "Test List - 18/09/2023-10:32:12" has been created with id 901100774700.'time_str = datetime.now().strftime("%d/%m/%Y-%H:%M:%S")print_and_run(f"Create a folder called 'Test Folder - {time_str}'") $ COMMAND Create a folder called 'Test Folder - 18/09/2023-10:32:51' $ AGENT > Entering new AgentExecutor chain... I need to use the Create Folder tool Action: Create Folder Action Input: {"name": "Test Folder - 18/09/2023-10:32:51"} Observation: {'id': '90110348711', 'name': 'Test Folder - 18/09/2023-10:32:51', 'orderindex': 12, 'override_statuses': False, 'hidden': False, 'space': {'id': '90110061901', 'name': 'Space', 'access': True}, 'task_count': '0', 'archived': False, 'statuses': [], 'lists': [], 'permission_level': 'create'} Thought: I have successfully created the folder Final Answer: The folder 'Test Folder - 18/09/2023-10:32:51' has been successfully created. > Finished chain. -------------------------------------------------------------------------------- "The folder 'Test Folder - 18/09/2023-10:32:51' has been successfully created."time_str = datetime.now().strftime("%d/%m/%Y-%H:%M:%S")print_and_run(f"Create a list called 'Test List - {time_str}' with content My test list with high priority and status red") $ COMMAND Create a list called 'Test List - 18/09/2023-10:34:01' with content My test list with high priority and status red $ AGENT > Entering new AgentExecutor chain... I need to create a list with the given name, content, priority and status Action: Create List Action Input: {"name": "Test List - 18/09/2023-10:34:01", "content": "My test list", "priority": 2, "status": "red"} Observation: {'id': '901100774746', 'name': 'Test List - 18/09/2023-10:34:01', 'deleted': False, 'orderindex': 15, 'content': '', 'status': {'status': 'red', 'color': '#e50000', 'hide_label': True}, 'priority': {'priority': 'high', 'color': '#ffcc00'}, 'assignee': None, 'due_date': None, 'start_date': None, 'folder': {'id': '90110336890', 'name': 'Test Folder', 'hidden': False, 'access': True}, 'space': {'id': '90110061901', 'name': 'Space', 'access': True}, 'inbound_address': 'a.t.901100774746.u-81928627.2ab87133-728e-4166-b2ae-423cc320df37@tasks.clickup.com', 'archived': False, 'override_statuses': False, 'statuses': [{'id': 'p90110061901_VlN8IJtk', 'status': 'to do', 'orderindex': 0, 'color': '#87909e', 'type': 'open'}, {'id': 'p90110061901_14GpYKnM', 'status': 'complete', 'orderindex': 1, 'color': '#6bc950', 'type': 'closed'}], 'permission_level': 'create'} Thought: I have successfully created the list Final Answer: The list 'Test List - 18/09/2023-10:34:01' with content 'My test list' with high priority and status red has been successfully created. > Finished chain. -------------------------------------------------------------------------------- "The list 'Test List - 18/09/2023-10:34:01' with content 'My test list' with high priority and status red has been successfully created."Multi-Step Tasksprint_and_run("Figure out what user ID Rodrigo is, create a task called 'Rod's task', assign it to Rodrigo") $ COMMAND Figure out what user ID Rodrigo is, create a task called 'Rod's task', assign it to Rodrigo $ AGENT > Entering new AgentExecutor chain... I need to get the user ID of Rodrigo, create a task, and assign it to Rodrigo Action: Get Teams Action Input: No input necessary Observation: {'teams': [{'id': '9011010153', 'name': 'Task Copilot Sandbox Workspace 1', 'members': [{'id': 81928627, 'username': 'Rodrigo Ceballos Lentini', 'email': '[email protected]', 'initials': 'RL'}]}]} Thought: I now have the user ID of Rodrigo Action: Create Task Action Input: {"name": "Rod's task", "assignees": [81928627]} /Users/rodrigolentini/repos/langchain-clickup/libs/langchain/langchain/utilities/clickup.py:145: UserWarning: Error encountered while trying to parse <class 'langchain.utilities.clickup.Task'>: 'NoneType' object is not subscriptable Falling back to returning input data. warnings.warn(f'Error encountered while trying to parse {dataclass}: {e}\n Falling back to returning input data.') Observation: {'id': '8685mw6dz', 'custom_id': None, 'name': "Rod's task", 'text_content': '', 'description': '', 'status': {'id': 'p90110061901_VlN8IJtk', 'status': 'to do', 'color': '#87909e', 'orderindex': 0, 'type': 'open'}, 'orderindex': '24.00000000000000000000000000000000', 'date_created': '1695047740939', 'date_updated': '1695047740939', 'date_closed': None, 'date_done': None, 'archived': False, 'creator': {'id': 81928627, 'username': 'Rodrigo Ceballos Lentini', 'color': '#c51162', 'email': '[email protected]', 'profilePicture': None}, 'assignees': [{'id': 81928627, 'username': 'Rodrigo Ceballos Lentini', 'color': '#c51162', 'initials': 'RL', 'email': '[email protected]', 'profilePicture': None}], 'watchers': [{'id': 81928627, 'username': 'Rodrigo Ceballos Lentini', 'color': '#c51162', 'initials': 'RL', 'email': '[email protected]', 'profilePicture': None}], 'checklists': [], 'tags': [], 'parent': None, 'priority': None, 'due_date': None, 'start_date': None, 'points': None, 'time_estimate': None, 'time_spent': 0, 'custom_fields': [], 'dependencies': [], 'linked_tasks': [], 'team_id': '9011010153', 'url': 'https://app.clickup.com/t/8685mw6dz', 'sharing': {'public': False, 'public_share_expires_on': None, 'public_fields': ['assignees', 'priority', 'due_date', 'content', 'comments', 'attachments', 'customFields', 'subtasks', 'tags', 'checklists', 'coverimage'], 'token': None, 'seo_optimized': False}, 'permission_level': 'create', 'list': {'id': '901100754275', 'name': 'Test List', 'access': True}, 'project': {'id': '90110336890', 'name': 'Test Folder', 'hidden': False, 'access': True}, 'folder': {'id': '90110336890', 'name': 'Test Folder', 'hidden': False, 'access': True}, 'space': {'id': '90110061901'}} Thought: I now have the task created and assigned to Rodrigo Final Answer: Rodrigo's user ID is 81928627 and a task called 'Rod's task' has been created and assigned to him. > Finished chain. -------------------------------------------------------------------------------- "Rodrigo's user ID is 81928627 and a task called 'Rod's task' has been created and assigned to him."PreviousAzure Cognitive ServicesNextCSVGet AuthenticatedGet Access TokenCreate ToolkitCreate AgentNavigationTask OperationsCreation |
754 | https://python.langchain.com/docs/integrations/toolkits/csv | ComponentsAgents and toolkitsCSVOn this pageCSVThis notebook shows how to use agents to interact with data in CSV format. It is mostly optimized for question answering.NOTE: this agent calls the Pandas DataFrame agent under the hood, which in turn calls the Python agent, which executes LLM generated Python code - this can be bad if the LLM generated Python code is harmful. Use cautiously.from langchain.llms import OpenAIfrom langchain.chat_models import ChatOpenAIfrom langchain.agents.agent_types import AgentTypefrom langchain.agents import create_csv_agentUsing ZERO_SHOT_REACT_DESCRIPTIONThis shows how to initialize the agent using the ZERO_SHOT_REACT_DESCRIPTION agent type. Note that this is an alternative to the above.agent = create_csv_agent( OpenAI(temperature=0), "titanic.csv", verbose=True, agent_type=AgentType.ZERO_SHOT_REACT_DESCRIPTION,)Using OpenAI FunctionsThis shows how to initialize the agent using the OPENAI_FUNCTIONS agent type. Note that this is an alternative to the above.agent = create_csv_agent( ChatOpenAI(temperature=0, model="gpt-3.5-turbo-0613"), "titanic.csv", verbose=True, agent_type=AgentType.OPENAI_FUNCTIONS,)agent.run("how many rows are there?") Error in on_chain_start callback: 'name' Invoking: `python_repl_ast` with `df.shape[0]` 891There are 891 rows in the dataframe. > Finished chain. 'There are 891 rows in the dataframe.'agent.run("how many people have more than 3 siblings") Error in on_chain_start callback: 'name' Invoking: `python_repl_ast` with `df[df['SibSp'] > 3]['PassengerId'].count()` 30There are 30 people in the dataframe who have more than 3 siblings. > Finished chain. 'There are 30 people in the dataframe who have more than 3 siblings.'agent.run("whats the square root of the average age?") Error in on_chain_start callback: 'name' Invoking: `python_repl_ast` with `import pandas as pd import math # Create a dataframe data = {'Age': [22, 38, 26, 35, 35]} df = pd.DataFrame(data) # Calculate the average age average_age = df['Age'].mean() # Calculate the square root of the average age square_root = math.sqrt(average_age) square_root` 5.585696017507576The square root of the average age is approximately 5.59. > Finished chain. 'The square root of the average age is approximately 5.59.'Multi CSV ExampleThis next part shows how the agent can interact with multiple csv files passed in as a list.agent = create_csv_agent( ChatOpenAI(temperature=0, model="gpt-3.5-turbo-0613"), ["titanic.csv", "titanic_age_fillna.csv"], verbose=True, agent_type=AgentType.OPENAI_FUNCTIONS,)agent.run("how many rows in the age column are different between the two dfs?") Error in on_chain_start callback: 'name' Invoking: `python_repl_ast` with `df1['Age'].nunique() - df2['Age'].nunique()` -1There is 1 row in the age column that is different between the two dataframes. > Finished chain. 'There is 1 row in the age column that is different between the two dataframes.'PreviousClickUp Langchiain ToolkitNextDocument ComparisonUsing ZERO_SHOT_REACT_DESCRIPTIONUsing OpenAI FunctionsMulti CSV Example |
755 | https://python.langchain.com/docs/integrations/toolkits/document_comparison_toolkit | ComponentsAgents and toolkitsDocument ComparisonOn this pageDocument ComparisonThis notebook shows how to use an agent to compare two documents.The high level idea is we will create a question-answering chain for each document, and then use that from pydantic import BaseModel, Fieldfrom langchain.chat_models import ChatOpenAIfrom langchain.agents import Toolfrom langchain.embeddings.openai import OpenAIEmbeddingsfrom langchain.text_splitter import CharacterTextSplitterfrom langchain.vectorstores import FAISSfrom langchain.document_loaders import PyPDFLoaderfrom langchain.chains import RetrievalQAclass DocumentInput(BaseModel): question: str = Field()llm = ChatOpenAI(temperature=0, model="gpt-3.5-turbo-0613")tools = []files = [ # https://abc.xyz/investor/static/pdf/2023Q1_alphabet_earnings_release.pdf { "name": "alphabet-earnings", "path": "/Users/harrisonchase/Downloads/2023Q1_alphabet_earnings_release.pdf", }, # https://digitalassets.tesla.com/tesla-contents/image/upload/IR/TSLA-Q1-2023-Update { "name": "tesla-earnings", "path": "/Users/harrisonchase/Downloads/TSLA-Q1-2023-Update.pdf", },]for file in files: loader = PyPDFLoader(file["path"]) pages = loader.load_and_split() text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0) docs = text_splitter.split_documents(pages) embeddings = OpenAIEmbeddings() retriever = FAISS.from_documents(docs, embeddings).as_retriever() # Wrap retrievers in a Tool tools.append( Tool( args_schema=DocumentInput, name=file["name"], description=f"useful when you want to answer questions about {file['name']}", func=RetrievalQA.from_chain_type(llm=llm, retriever=retriever), ) )from langchain.agents import initialize_agentfrom langchain.agents import AgentTypellm = ChatOpenAI( temperature=0, model="gpt-3.5-turbo-0613",)agent = initialize_agent( agent=AgentType.OPENAI_FUNCTIONS, tools=tools, llm=llm, verbose=True,)agent({"input": "did alphabet or tesla have more revenue?"}) > Entering new chain... Invoking: `alphabet-earnings` with `{'question': 'revenue'}` {'query': 'revenue', 'result': 'The revenue for Alphabet Inc. for the quarter ended March 31, 2023, was $69,787 million.'} Invoking: `tesla-earnings` with `{'question': 'revenue'}` {'query': 'revenue', 'result': 'Total revenue for Q1-2023 was $23.3 billion.'}Alphabet Inc. had more revenue than Tesla. Alphabet's revenue for the quarter ended March 31, 2023, was $69,787 million, while Tesla's total revenue for Q1-2023 was $23.3 billion. > Finished chain. {'input': 'did alphabet or tesla have more revenue?', 'output': "Alphabet Inc. had more revenue than Tesla. Alphabet's revenue for the quarter ended March 31, 2023, was $69,787 million, while Tesla's total revenue for Q1-2023 was $23.3 billion."}OpenAI Multi FunctionsThis type of agent allows calling multiple functions at once. This is really useful when some steps can be computed in parallel - like when asked to compare multiple documentsimport langchainlangchain.debug = Truellm = ChatOpenAI( temperature=0, model="gpt-3.5-turbo-0613",)agent = initialize_agent( agent=AgentType.OPENAI_MULTI_FUNCTIONS, tools=tools, llm=llm, verbose=True,)agent({"input": "did alphabet or tesla have more revenue?"}) [chain/start] [1:chain:AgentExecutor] Entering Chain run with input: { "input": "did alphabet or tesla have more revenue?" } [llm/start] [1:chain:AgentExecutor > 2:llm:ChatOpenAI] Entering LLM run with input: { "prompts": [ "System: You are a helpful AI assistant.\nHuman: did alphabet or tesla have more revenue?" ] } [llm/end] [1:chain:AgentExecutor > 2:llm:ChatOpenAI] [2.66s] Exiting LLM run with output: { "generations": [ [ { "text": "", "generation_info": null, "message": { "content": "", "additional_kwargs": { "function_call": { "name": "tool_selection", "arguments": "{\n \"actions\": [\n {\n \"action_name\": \"alphabet-earnings\",\n \"action\": {\n \"question\": \"What was Alphabet's revenue?\"\n }\n },\n {\n \"action_name\": \"tesla-earnings\",\n \"action\": {\n \"question\": \"What was Tesla's revenue?\"\n }\n }\n ]\n}" } }, "example": false } } ] ], "llm_output": { "token_usage": { "prompt_tokens": 99, "completion_tokens": 82, "total_tokens": 181 }, "model_name": "gpt-3.5-turbo-0613" }, "run": null } [tool/start] [1:chain:AgentExecutor > 3:tool:alphabet-earnings] Entering Tool run with input: "{'question': "What was Alphabet's revenue?"}" [chain/start] [1:chain:AgentExecutor > 3:tool:alphabet-earnings > 4:chain:RetrievalQA] Entering Chain run with input: { "query": "What was Alphabet's revenue?" } [chain/start] [1:chain:AgentExecutor > 3:tool:alphabet-earnings > 4:chain:RetrievalQA > 5:chain:StuffDocumentsChain] Entering Chain run with input: [inputs] [chain/start] [1:chain:AgentExecutor > 3:tool:alphabet-earnings > 4:chain:RetrievalQA > 5:chain:StuffDocumentsChain > 6:chain:LLMChain] Entering Chain run with input: { "question": "What was Alphabet's revenue?", "context": "Alphabet Inc.\nCONSOLIDATED STATEMENTS OF INCOME\n(In millions, except per share amounts, unaudited)\nQuarter Ended March 31,\n2022 2023\nRevenues $ 68,011 $ 69,787 \nCosts and expenses:\nCost of revenues 29,599 30,612 \nResearch and development 9,119 11,468 \nSales and marketing 5,825 6,533 \nGeneral and administrative 3,374 3,759 \nTotal costs and expenses 47,917 52,372 \nIncome from operations 20,094 17,415 \nOther income (expense), net (1,160) 790 \nIncome before income taxes 18,934 18,205 \nProvision for income taxes 2,498 3,154 \nNet income $ 16,436 $ 15,051 \nBasic earnings per share of Class A, Class B, and Class C stock $ 1.24 $ 1.18 \nDiluted earnings per share of Class A, Class B, and Class C stock $ 1.23 $ 1.17 \nNumber of shares used in basic earnings per share calculation 13,203 12,781 \nNumber of shares used in diluted earnings per share calculation 13,351 12,823 \n6\n\nAlphabet Announces First Quarter 2023 Results\nMOUNTAIN VIEW, Calif. – April 25, 2023 – Alphabet Inc. (NASDAQ: GOOG, GOOGL) today announced financial \nresults for the quarter ended March 31, 2023 .\nSundar Pichai, CEO of Alphabet and Google, said: “We are pleased with our business performance in the first \nquarter, with Search performing well and momentum in Cloud. We introduced important product updates anchored \nin deep computer science and AI. Our North Star is providing the most helpful answers for our users, and we see \nhuge opportunities ahead, continuing our long track record of innovation.”\nRuth Porat, CFO of Alphabet and Google, said: “Resilience in Search and momentum in Cloud resulted in Q1 \nconsolidated revenues of $69.8 billion, up 3% year over year, or up 6% in constant currency. We remain committed \nto delivering long-term growth and creating capacity to invest in our most compelling growth areas by re-engineering \nour cost base.”\nQ1 2023 financial highlights (unaudited)\nOur first quarter 2023 results reflect:\ni.$2.6 billion in charges related to reductions in our workforce and office space; \nii.a $988 million reduction in depreciation expense from the change in estimated useful life of our servers and \ncertain network equipment; and\niii.a shift in the timing of our annual employee stock-based compensation awards resulting in relatively less \nstock-based compensation expense recognized in the first quarter compared to the remaining quarters of \nthe ye ar. The shift in timing itself will not affect the amount of stock-based compensation expense over the \nfull fiscal year 2023.\nFor further information, please refer to our blog post also filed with the SEC via Form 8-K on April 20, 2023.\nThe following table summarizes our consolidated financial results for the quarters ended March 31, 2022 and 2023 \n(in millions, except for per share information and percentages). \nQuarter Ended March 31,\n2022 2023\nRevenues $ 68,011 $ 69,787 \nChange in revenues year over year 23 % 3 %\nChange in constant currency revenues year over year(1) 26 % 6 %\nOperating income $ 20,094 $ 17,415 \nOperating margin 30 % 25 %\nOther income (expense), net $ (1,160) $ 790 \nNet income $ 16,436 $ 15,051 \nDiluted EPS $ 1.23 $ 1.17 \n(1) Non-GAAP measure. See the table captioned “Reconciliation from GAAP revenues to non-GAAP constant currency \nrevenues and GAAP percentage change in revenues to non-GAAP percentage change in constant currency revenues” for \nmore details.\n\nQ1 2023 supplemental information (in millions, except for number of employees; unaudited)\nRevenues, T raffic Acquisition Costs (TAC), and number of employees\nQuarter Ended March 31,\n2022 2023\nGoogle Search & other $ 39,618 $ 40,359 \nYouTube ads 6,869 6,693 \nGoogle Network 8,174 7,496 \nGoogle advertising 54,661 54,548 \nGoogle other 6,811 7,413 \nGoogle Services total 61,472 61,961 \nGoogle Cloud 5,821 7,454 \nOther Bets 440 288 \nHedging gains (losses) 278 84 \nTotal revenues $ 68,011 $ 69,787 \nTotal TAC $ 11,990 $ 11,721 \nNumber of employees(1) 163,906 190,711 \n(1) As of March 31, 2023, the number of employees includes almost all of the employees affected by the reduction of our \nworkforce. We expect most of those affected will no longer be reflected in our headcount by the end of the second quarter \nof 2023, subject to local law and consultation requirements.\nSegment Operating Results\nReflecting DeepMind’s increasing collaboration with Google Services, Google Cloud, and Other Bets, beginning in \nthe first quarter of 2023 DeepMind is reported as part of Alphabet’s unallocated corporate costs instead of within \nOther Bets. Additionally, beginning in the first quarter of 2023, we updated and simplified our cost allocation \nmethodologies to provide our business leaders with increased transparency for decision-making . Prior periods have \nbeen recast to reflect the revised presentation and are shown in Recast Historical Segment Results below .\nAs announced on April 20, 2023 , we are bringing together part of Google Research (the Brain Team) and DeepMind \nto significantly accelerate our progress in AI. This change does not affect first quarter reporting. The group, called \nGoogle DeepMind, will be reported within Alphabet's unallocated corporate costs beginning in the second quarter of \n2023.\nQuarter Ended March 31,\n2022 2023\n(recast)\nOperating income (loss):\nGoogle Services $ 21,973 $ 21,737 \nGoogle Cloud (706) 191 \nOther Bets (835) (1,225) \nCorporate costs, unallocated(1) (338) (3,288) \nTotal income from operations $ 20,094 $ 17,415 \n(1)Hedging gains (losses) related to revenue included in unallocated corporate costs were $278 million and $84 million for the \nthree months ended March 31, 2022 and 2023 , respectively. For the three months ended March 31, 2023, unallocated \ncorporate costs include charges related to the reductions in our workforce and office space totaling $2.5 billion . \n2\n\nSegment results\nThe following table presents our segment revenues and operating income (loss) (in millions; unaudited):\nQuarter Ended March 31,\n2022 2023\n(recast)\nRevenues:\nGoogle Services $ 61,472 $ 61,961 \nGoogle Cloud 5,821 7,454 \nOther Bets 440 288 \nHedging gains (losses) 278 84 \nTotal revenues $ 68,011 $ 69,787 \nOperating income (loss):\nGoogle Services $ 21,973 $ 21,737 \nGoogle Cloud (706) 191 \nOther Bets (835) (1,225) \nCorporate costs, unallocated (338) (3,288) \nTotal income from operations $ 20,094 $ 17,415 \nWe report our segment results as Google Services, Google Cloud, and Other Bets:\n•Google Services includes products and services such as ads, Android, Chrome, hardware, Google Maps, \nGoogle Play, Search, and YouTube. Google Services generates revenues primarily from advertising; sales \nof apps and in-app purchases, and hardware; and fees received for subscription-based products such as \nYouTube Premium and YouTube TV.\n•Google Cloud includes infrastructure and platform services, collaboration tools, and other services for \nenterprise customers. Google Cloud generates revenues from fees received for Google Cloud Platform \nservices, Google Workspace communication and collaboration tools, and other enterprise services.\n•Other Bets is a combination of multiple operating segments that are not individually material. Revenues \nfrom Other Bets are generated primarily from the sale of health technology and internet services.\nAfter the segment reporting changes discussed above, unallocated corporate costs primarily include AI-focused \nshared R&D activities; corporate initiatives such as our philanthropic activities; and corporate shared costs such as \nfinance, certain human resource costs, and legal, including certain fines and settlements. In the first quarter of 2023, \nunallocated corporate costs also include charges associated with reductions in our workforce and office space. \nAdditionally, hedging gains (losses) related to revenue are included in unallocated corporate costs.\nRecast Historical Segment Results\nRecast historical segment results are as follows (in millions; unaudited):\nQuarter Fiscal Year\nRecast Historical Results\nQ1 2022 Q2 2022 Q3 2022 Q4 2022 2021 2022\nOperating income (loss):\nGoogle Services $ 21,973 $ 21,621 $ 18,883 $ 20,222 $ 88,132 $ 82,699 \nGoogle Cloud (706) (590) (440) (186) (2,282) (1,922) \nOther Bets (835) (1,339) (1,225) (1,237) (4,051) (4,636) \nCorporate costs, unallocated(1) (338) (239) (83) (639) (3,085) (1,299) \nTotal income from operations $ 20,094 $ 19,453 $ 17,135 $ 18,160 $ 78,714 $ 74,842 \n(1)Includes hedging gains (losses); in fiscal years 2021 and 2022 hedging gains of $149 million and $2.0 billion, respectively.\n8" } [llm/start] [1:chain:AgentExecutor > 3:tool:alphabet-earnings > 4:chain:RetrievalQA > 5:chain:StuffDocumentsChain > 6:chain:LLMChain > 7:llm:ChatOpenAI] Entering LLM run with input: { "prompts": [ "System: Use the following pieces of context to answer the users question. \nIf you don't know the answer, just say that you don't know, don't try to make up an answer.\n----------------\nAlphabet Inc.\nCONSOLIDATED STATEMENTS OF INCOME\n(In millions, except per share amounts, unaudited)\nQuarter Ended March 31,\n2022 2023\nRevenues $ 68,011 $ 69,787 \nCosts and expenses:\nCost of revenues 29,599 30,612 \nResearch and development 9,119 11,468 \nSales and marketing 5,825 6,533 \nGeneral and administrative 3,374 3,759 \nTotal costs and expenses 47,917 52,372 \nIncome from operations 20,094 17,415 \nOther income (expense), net (1,160) 790 \nIncome before income taxes 18,934 18,205 \nProvision for income taxes 2,498 3,154 \nNet income $ 16,436 $ 15,051 \nBasic earnings per share of Class A, Class B, and Class C stock $ 1.24 $ 1.18 \nDiluted earnings per share of Class A, Class B, and Class C stock $ 1.23 $ 1.17 \nNumber of shares used in basic earnings per share calculation 13,203 12,781 \nNumber of shares used in diluted earnings per share calculation 13,351 12,823 \n6\n\nAlphabet Announces First Quarter 2023 Results\nMOUNTAIN VIEW, Calif. – April 25, 2023 – Alphabet Inc. (NASDAQ: GOOG, GOOGL) today announced financial \nresults for the quarter ended March 31, 2023 .\nSundar Pichai, CEO of Alphabet and Google, said: “We are pleased with our business performance in the first \nquarter, with Search performing well and momentum in Cloud. We introduced important product updates anchored \nin deep computer science and AI. Our North Star is providing the most helpful answers for our users, and we see \nhuge opportunities ahead, continuing our long track record of innovation.”\nRuth Porat, CFO of Alphabet and Google, said: “Resilience in Search and momentum in Cloud resulted in Q1 \nconsolidated revenues of $69.8 billion, up 3% year over year, or up 6% in constant currency. We remain committed \nto delivering long-term growth and creating capacity to invest in our most compelling growth areas by re-engineering \nour cost base.”\nQ1 2023 financial highlights (unaudited)\nOur first quarter 2023 results reflect:\ni.$2.6 billion in charges related to reductions in our workforce and office space; \nii.a $988 million reduction in depreciation expense from the change in estimated useful life of our servers and \ncertain network equipment; and\niii.a shift in the timing of our annual employee stock-based compensation awards resulting in relatively less \nstock-based compensation expense recognized in the first quarter compared to the remaining quarters of \nthe ye ar. The shift in timing itself will not affect the amount of stock-based compensation expense over the \nfull fiscal year 2023.\nFor further information, please refer to our blog post also filed with the SEC via Form 8-K on April 20, 2023.\nThe following table summarizes our consolidated financial results for the quarters ended March 31, 2022 and 2023 \n(in millions, except for per share information and percentages). \nQuarter Ended March 31,\n2022 2023\nRevenues $ 68,011 $ 69,787 \nChange in revenues year over year 23 % 3 %\nChange in constant currency revenues year over year(1) 26 % 6 %\nOperating income $ 20,094 $ 17,415 \nOperating margin 30 % 25 %\nOther income (expense), net $ (1,160) $ 790 \nNet income $ 16,436 $ 15,051 \nDiluted EPS $ 1.23 $ 1.17 \n(1) Non-GAAP measure. See the table captioned “Reconciliation from GAAP revenues to non-GAAP constant currency \nrevenues and GAAP percentage change in revenues to non-GAAP percentage change in constant currency revenues” for \nmore details.\n\nQ1 2023 supplemental information (in millions, except for number of employees; unaudited)\nRevenues, T raffic Acquisition Costs (TAC), and number of employees\nQuarter Ended March 31,\n2022 2023\nGoogle Search & other $ 39,618 $ 40,359 \nYouTube ads 6,869 6,693 \nGoogle Network 8,174 7,496 \nGoogle advertising 54,661 54,548 \nGoogle other 6,811 7,413 \nGoogle Services total 61,472 61,961 \nGoogle Cloud 5,821 7,454 \nOther Bets 440 288 \nHedging gains (losses) 278 84 \nTotal revenues $ 68,011 $ 69,787 \nTotal TAC $ 11,990 $ 11,721 \nNumber of employees(1) 163,906 190,711 \n(1) As of March 31, 2023, the number of employees includes almost all of the employees affected by the reduction of our \nworkforce. We expect most of those affected will no longer be reflected in our headcount by the end of the second quarter \nof 2023, subject to local law and consultation requirements.\nSegment Operating Results\nReflecting DeepMind’s increasing collaboration with Google Services, Google Cloud, and Other Bets, beginning in \nthe first quarter of 2023 DeepMind is reported as part of Alphabet’s unallocated corporate costs instead of within \nOther Bets. Additionally, beginning in the first quarter of 2023, we updated and simplified our cost allocation \nmethodologies to provide our business leaders with increased transparency for decision-making . Prior periods have \nbeen recast to reflect the revised presentation and are shown in Recast Historical Segment Results below .\nAs announced on April 20, 2023 , we are bringing together part of Google Research (the Brain Team) and DeepMind \nto significantly accelerate our progress in AI. This change does not affect first quarter reporting. The group, called \nGoogle DeepMind, will be reported within Alphabet's unallocated corporate costs beginning in the second quarter of \n2023.\nQuarter Ended March 31,\n2022 2023\n(recast)\nOperating income (loss):\nGoogle Services $ 21,973 $ 21,737 \nGoogle Cloud (706) 191 \nOther Bets (835) (1,225) \nCorporate costs, unallocated(1) (338) (3,288) \nTotal income from operations $ 20,094 $ 17,415 \n(1)Hedging gains (losses) related to revenue included in unallocated corporate costs were $278 million and $84 million for the \nthree months ended March 31, 2022 and 2023 , respectively. For the three months ended March 31, 2023, unallocated \ncorporate costs include charges related to the reductions in our workforce and office space totaling $2.5 billion . \n2\n\nSegment results\nThe following table presents our segment revenues and operating income (loss) (in millions; unaudited):\nQuarter Ended March 31,\n2022 2023\n(recast)\nRevenues:\nGoogle Services $ 61,472 $ 61,961 \nGoogle Cloud 5,821 7,454 \nOther Bets 440 288 \nHedging gains (losses) 278 84 \nTotal revenues $ 68,011 $ 69,787 \nOperating income (loss):\nGoogle Services $ 21,973 $ 21,737 \nGoogle Cloud (706) 191 \nOther Bets (835) (1,225) \nCorporate costs, unallocated (338) (3,288) \nTotal income from operations $ 20,094 $ 17,415 \nWe report our segment results as Google Services, Google Cloud, and Other Bets:\n•Google Services includes products and services such as ads, Android, Chrome, hardware, Google Maps, \nGoogle Play, Search, and YouTube. Google Services generates revenues primarily from advertising; sales \nof apps and in-app purchases, and hardware; and fees received for subscription-based products such as \nYouTube Premium and YouTube TV.\n•Google Cloud includes infrastructure and platform services, collaboration tools, and other services for \nenterprise customers. Google Cloud generates revenues from fees received for Google Cloud Platform \nservices, Google Workspace communication and collaboration tools, and other enterprise services.\n•Other Bets is a combination of multiple operating segments that are not individually material. Revenues \nfrom Other Bets are generated primarily from the sale of health technology and internet services.\nAfter the segment reporting changes discussed above, unallocated corporate costs primarily include AI-focused \nshared R&D activities; corporate initiatives such as our philanthropic activities; and corporate shared costs such as \nfinance, certain human resource costs, and legal, including certain fines and settlements. In the first quarter of 2023, \nunallocated corporate costs also include charges associated with reductions in our workforce and office space. \nAdditionally, hedging gains (losses) related to revenue are included in unallocated corporate costs.\nRecast Historical Segment Results\nRecast historical segment results are as follows (in millions; unaudited):\nQuarter Fiscal Year\nRecast Historical Results\nQ1 2022 Q2 2022 Q3 2022 Q4 2022 2021 2022\nOperating income (loss):\nGoogle Services $ 21,973 $ 21,621 $ 18,883 $ 20,222 $ 88,132 $ 82,699 \nGoogle Cloud (706) (590) (440) (186) (2,282) (1,922) \nOther Bets (835) (1,339) (1,225) (1,237) (4,051) (4,636) \nCorporate costs, unallocated(1) (338) (239) (83) (639) (3,085) (1,299) \nTotal income from operations $ 20,094 $ 19,453 $ 17,135 $ 18,160 $ 78,714 $ 74,842 \n(1)Includes hedging gains (losses); in fiscal years 2021 and 2022 hedging gains of $149 million and $2.0 billion, respectively.\n8\nHuman: What was Alphabet's revenue?" ] } [llm/end] [1:chain:AgentExecutor > 3:tool:alphabet-earnings > 4:chain:RetrievalQA > 5:chain:StuffDocumentsChain > 6:chain:LLMChain > 7:llm:ChatOpenAI] [1.61s] Exiting LLM run with output: { "generations": [ [ { "text": "Alphabet's revenue for the quarter ended March 31, 2023, was $69,787 million.", "generation_info": null, "message": { "content": "Alphabet's revenue for the quarter ended March 31, 2023, was $69,787 million.", "additional_kwargs": {}, "example": false } } ] ], "llm_output": { "token_usage": { "prompt_tokens": 2335, "completion_tokens": 23, "total_tokens": 2358 }, "model_name": "gpt-3.5-turbo-0613" }, "run": null } [chain/end] [1:chain:AgentExecutor > 3:tool:alphabet-earnings > 4:chain:RetrievalQA > 5:chain:StuffDocumentsChain > 6:chain:LLMChain] [1.61s] Exiting Chain run with output: { "text": "Alphabet's revenue for the quarter ended March 31, 2023, was $69,787 million." } [chain/end] [1:chain:AgentExecutor > 3:tool:alphabet-earnings > 4:chain:RetrievalQA > 5:chain:StuffDocumentsChain] [1.61s] Exiting Chain run with output: { "output_text": "Alphabet's revenue for the quarter ended March 31, 2023, was $69,787 million." } [chain/end] [1:chain:AgentExecutor > 3:tool:alphabet-earnings > 4:chain:RetrievalQA] [1.85s] Exiting Chain run with output: { "result": "Alphabet's revenue for the quarter ended March 31, 2023, was $69,787 million." } [tool/end] [1:chain:AgentExecutor > 3:tool:alphabet-earnings] [1.86s] Exiting Tool run with output: "{'query': "What was Alphabet's revenue?", 'result': "Alphabet's revenue for the quarter ended March 31, 2023, was $69,787 million."}" [tool/start] [1:chain:AgentExecutor > 8:tool:tesla-earnings] Entering Tool run with input: "{'question': "What was Tesla's revenue?"}" [chain/start] [1:chain:AgentExecutor > 8:tool:tesla-earnings > 9:chain:RetrievalQA] Entering Chain run with input: { "query": "What was Tesla's revenue?" } [chain/start] [1:chain:AgentExecutor > 8:tool:tesla-earnings > 9:chain:RetrievalQA > 10:chain:StuffDocumentsChain] Entering Chain run with input: [inputs] [chain/start] [1:chain:AgentExecutor > 8:tool:tesla-earnings > 9:chain:RetrievalQA > 10:chain:StuffDocumentsChain > 11:chain:LLMChain] Entering Chain run with input: { "question": "What was Tesla's revenue?", "context": "S U M M A R Y H I G H L I G H T S \n(1) Excludes SBC (stock -based compensation).\n(2) Free cash flow = operating cash flow less capex.\n(3) Includes cash, cash equivalents and investments.Profitability 11.4% operating margin in Q1\n$2.7B GAAP operating income in Q1\n$2.5B GAAP net income in Q1\n$2.9B non -GAAP net income1in Q1In the current macroeconomic environment, we see this year as a unique \nopportunity for Tesla. As many carmakers are working through challenges with the \nunit economics of their EV programs, we aim to leverage our position as a cost \nleader. We are focused on rapidly growing production, investments in autonomy \nand vehicle software, and remaining on track with our growth investments.\nOur near -term pricing strategy considers a long -term view on per vehicle \nprofitability given the potential lifetime value of a Tesla vehicle through autonomy, \nsupercharging, connectivity and service. We expect that our product pricing will \ncontinue to evolve, upwards or downwards, depending on a number of factors.\nAlthough we implemented price reductions on many vehicle models across regions \nin the first quarter, our operating margins reduced at a manageable rate. We \nexpect ongoing cost reduction of our vehicles, including improved production \nefficiency at our newest factories and lower logistics costs, and remain focused on \noperating leverage as we scale.\nWe are rapidly growing energy storage production capacity at our Megafactory in \nLathrop and we recently announced a new Megafactory in Shanghai. We are also \ncontinuing to execute on our product roadmap, including Cybertruck, our next \ngeneration vehicle platform, autonomy and other AI enabled products. \nOur balance sheet and net income enable us to continue to make these capital \nexpenditures in line with our future growth. In this environment, we believe it \nmakes sense to push forward to ensure we lay a proper foundation for the best \npossible future.Cash Operating cash flow of $2.5B\nFree cash flow2of $0.4B in Q1\n$0.2B increase in our cash and investments3in Q1 to $22.4B\nOperations Cybertruck factory tooling on track; producing Alpha versions\nModel Y was the best -selling vehicle in Europe in Q1\nModel Y was the best -selling vehicle in the US in Q1 (ex -pickups)\n\n01234O T H E R H I G H L I G H T S\n9Services & Other gross margin\nEnergy Storage deployments (GWh)Energy Storage\nEnergy storage deployments increased by 360% YoY in Q1 to 3.9 GWh, the highest \nlevel of deployments we have achieved due to ongoing Megafactory ramp. The ramp of our 40 GWh Megapack factory in Lathrop, California has been successful with still more room to reach full capacity. This Megapack factory will be the first of many. We recently announced our second 40 GWh Megafactory, this time in Shanghai, with construction starting later this year. \nSolar\nSolar deployments increased by 40% YoY in Q1 to 67 MW, but declined sequentially in \nthe quarter, predominantly due to volatile weather and other factors. In addition, the solar industry has been impacted by supply chain challenges.\nServices and Other\nBoth revenue and gross profit from Services and Other reached an all -time high in Q1 \n2023. Within this business division, growth of used vehicle sales remained strong YoY and had healthy margins. Supercharging, while still a relatively small part of the business, continued to grow as we gradually open up the network to non- Tesla \nvehicles. \n-4%-2%0%2%4%6%8%\nQ3'21 Q4'21 Q1'22 Q2'22 Q3'22 Q4'22 Q1'23\n\nIn millions of USD or shares as applicable, except per share data Q1-2022 Q2-2022 Q3-2022 Q4-2022 Q1-2023\nREVENUES\nAutomotive sales 15,514 13,670 17,785 20,241 18,878 \nAutomotive regulatory credits 679 344 286 467 521 \nAutomotive leasing 668 588 621 599 564 \nTotal automotive revenues 16,861 14,602 18,692 21,307 19,963 \nEnergy generation and storage 616 866 1,117 1,310 1,529 \nServices and other 1,279 1,466 1,645 1,701 1,837 \nTotal revenues 18,756 16,934 21,454 24,318 23,329 \nCOST OF REVENUES\nAutomotive sales 10,914 10,153 13,099 15,433 15,422 \nAutomotive leasing 408 368 381 352 333 \nTotal automotive cost of revenues 11,322 10,521 13,480 15,785 15,755 \nEnergy generation and storage 688 769 1,013 1,151 1,361 \nServices and other 1,286 1,410 1,579 1,605 1,702 \nTotal cost of revenues 13,296 12,700 16,072 18,541 18,818 \nGross profit 5,460 4,234 5,382 5,777 4,511 \nOPERATING EXPENSES\nResearch and development 865 667 733 810 771 \nSelling, general and administrative 992 961 961 1,032 1,076 \nRestructuring and other — 142 — 34 —\nTotal operating expenses 1,857 1,770 1,694 1,876 1,847 \nINCOME FROM OPERATIONS 3,603 2,464 3,688 3,901 2,664 \nInterest income 28 26 86 157 213 \nInterest expense (61) (44) (53) (33) (29)\nOther income (expense), net 56 28 (85) (42) (48)\nINCOME BEFORE INCOME TAXES 3,626 2,474 3,636 3,983 2,800 \nProvision for income taxes 346 205 305 276 261 \nNET INCOME 3,280 2,269 3,331 3,707 2,539 \nNet (loss) income attributable to noncontrolling interests and redeemable noncontrolling interests in \nsubsidiaries(38) 10 39 20 26 \nNET INCOME ATTRIBUTABLE TO COMMON STOCKHOLDERS 3,318 2,259 3,292 3,687 2,513 \nNet income per share of common stock attributable to common stockholders(1)\nBasic $ 1.07 $ 0.73 $ 1.05 $ 1.18 $ 0.80 \nDiluted $ 0.95 $ 0.65 $ 0.95 $ 1.07 $ 0.73 \nWeighted average shares used in computing net income per share of common stock(1)\nBasic 3,103 3,111 3,146 3,160 3,166\nDiluted 3,472 3,464 3,468 3,471 3,468\nS T A T E M E N T O F O P E R A T I O N S\n(Unaudited)\n23 (1) Prior period results have been retroactively adjusted to reflect the three -for-one stock split effected in the form of a stock d ividend in August 2022.\n\nQ1-2022 Q2-2022 Q3-2022 Q4-2022 Q1-2023 YoY\nModel S/X production 14,218 16,411 19,935 20,613 19,437 37%\nModel 3/Y production 291,189 242,169 345,988 419,088 421,371 45%\nTotal production 305,407 258,580 365,923 439,701 440,808 44%\nModel S/X deliveries 14,724 16,162 18,672 17,147 10,695 -27%\nModel 3/Y deliveries 295,324 238,533 325,158 388,131 412,180 40%\nTotal deliveries 310,048 254,695 343,830 405,278 422,875 36%\nof which subject to operating lease accounting 12,167 9,227 11,004 15,184 22,357 84%\nTotal end of quarter operating lease vehicle count 128,402 131,756 135,054 140,667 153,988 20%\nGlobal vehicle inventory (days of supply )(1)3 4 8 13 15 400%\nSolar deployed (MW) 48 106 94 100 67 40%\nStorage deployed (MWh) 846 1,133 2,100 2,462 3,889 360%\nTesla locations(2)787 831 903 963 1,000 27%\nMobile service fleet 1,372 1,453 1,5 |
756 | https://python.langchain.com/docs/integrations/toolkits/github | ComponentsAgents and toolkitsGithubOn this pageGithubThe Github toolkit contains tools that enable an LLM agent to interact with a github repository.
The tool is a wrapper for the PyGitHub library. QuickstartInstall the pygithub libraryCreate a Github appSet your environmental variablesPass the tools to your agent with toolkit.get_tools()Each of these steps will be explained in greate detail below.Get Issues- fetches issues from the repository.Get Issue- feteches details about a specific issue.Comment on Issue- posts a comment on a specific issue.Create Pull Request- creates a pull request from the bot's working branch to the base branch.Create File- creates a new file in the repository.Read File- reads a file from the repository.Update File- updates a file in the repository.Delete File- deletes a file from the repository.Setup1. Install the pygithub library%pip install pygithub2. Create a Github AppFollow the instructions here to create and register a Github app. Make sure your app has the following repository permissions:Commit statuses (read only)Contents (read and write)Issues (read and write)Metadata (read only)Pull requests (read and write)Once the app has been registered, add it to the repository you wish the bot to act upon.3. Set Environmental VariablesBefore initializing your agent, the following environmental variables need to be set:GITHUB_APP_ID- A six digit number found in your app's general settingsGITHUB_APP_PRIVATE_KEY- The location of your app's private key .pem fileGITHUB_REPOSITORY- The name of the Github repository you want your bot to act upon. Must follow the format {username}/{repo-name}. Make sure the app has been added to this repository first!GITHUB_BRANCH- The branch where the bot will make its commits. Defaults to 'master.'GITHUB_BASE_BRANCH- The base branch of your repo, usually either 'main' or 'master.' This is where pull requests will base from. Defaults to 'master.'Example: Simple Agentimport osfrom langchain.agents import AgentTypefrom langchain.agents import initialize_agentfrom langchain.agents.agent_toolkits.github.toolkit import GitHubToolkitfrom langchain.llms import OpenAIfrom langchain.utilities.github import GitHubAPIWrapper# Set your environment variables using os.environos.environ["GITHUB_APP_ID"] = "123456"os.environ["GITHUB_APP_PRIVATE_KEY"] = "path/to/your/private-key.pem"os.environ["GITHUB_REPOSITORY"] = "username/repo-name"os.environ["GITHUB_BRANCH"] = "bot-branch-name"os.environ["GITHUB_BASE_BRANCH"] = "main"# This example also requires an OpenAI API keyos.environ["OPENAI_API_KEY"] = ""llm = OpenAI(temperature=0)github = GitHubAPIWrapper()toolkit = GitHubToolkit.from_github_api_wrapper(github)agent = initialize_agent( toolkit.get_tools(), llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True)agent.run( "You have the software engineering capabilities of a Google Principle engineer. You are tasked with completing issues on a github repository. Please look at the existing issues and complete them.") > Entering new AgentExecutor chain... I need to figure out what issues need to be completed. Action: Get Issues Action Input: N/A Observation: Found 1 issues: [{'title': 'Update README file', 'number': 9}] Thought: I need to get more information about this issue. Action: Get Issue Action Input: 9 Observation: {"title": "Update README file", "body": "Find what the most popular frontend framework is right now and add a short blurb to the readme.md file about how this website will take advantage of it.", "comments": "[]"} Thought: I need to update the README file. Action: Create File Action Input: README.md Observation: File already exists at README.md. Use update_file instead Thought: I need to update the existing README file. Action: Update File Action Input: README.md OLD <<<< This is a sample website >>>> OLD NEW <<<< This is a sample website that uses the most popular frontend framework. >>>> NEW Observation: File content was not updated because old content was not found.It may be helpful to use the read_file action to get the current file contents. Thought: I need to get the current file contents. Action: Read File Action Input: README.md Observation: This is my awesome website! Thought: I need to update the README file with the new content. Action: Update File Action Input: README.md OLD <<<< This is my awesome website! >>>> OLD NEW <<<< This is my awesome website that uses the most popular frontend framework. >>>> NEW Observation: Updated file README.md Thought: I now know the final answer. Final Answer: The README.md file has been updated with the new content. > Finished chain. 'The README.md file has been updated with the new content.'Example: Advanced AgentIf your agent does not need to use all 8 tools, you can build tools individually to use. For this example, we'll make an agent that does not use the create_file, delete_file or create_pull_request tools, but can also use duckduckgo-search.%pip install duckduckgo-searchfrom langchain.tools.github.tool import GitHubActionfrom langchain.tools import DuckDuckGoSearchRunfrom langchain.agents import Toolfrom langchain.chat_models import ChatOpenAItools = []unwanted_tools = ['Get Issue','Delete File', 'Create File', 'Create Pull Request']for tool in toolkit.get_tools(): if tool.name not in unwanted_tools: tools.append(tool)tools+= [ Tool( name = "Search", func = DuckDuckGoSearchRun().run, description = "useful for when you need to search the web" )] agent = initialize_agent( tools = tools, llm = ChatOpenAI(temperature=0.1), agent = AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose = True)Finally let's build a prompt and test it out!# The GitHubAPIWrapper can be used outside of an agent, too# This gets the info about issue number 9, since we want to# force the agent to address this specific issue.issue = github.get_issue(9)prompt = f"""You are a seinor frontend developer who is experienced in HTML, CSS, and JS- especially React.You have been assigned the below issue. Complete it to the best of your ability.Remember to first make a plan and pay attention to details like file names and commonsense.Then execute the plan and use tools appropriately.Finally, make a pull request to merge your changes.Issue: {issue["title"]}Issue Description: {issue['body']}Comments: {issue['comments']}"""agent.run(prompt) > Entering new AgentExecutor chain... To complete this issue, I need to find the most popular frontend framework and add a blurb about how this website will utilize it to the readme.md file. I should start by researching the most popular frontend frameworks and then update the readme file accordingly. I will use the "Search" tool to research the most popular frontend framework. Action: Search Action Input: "most popular frontend framework" Observation: Alex Ivanovs February 25, 2023 Table of Contents What are the current Front-end trends? Top Front-end Frameworks for 2023 #1 - React #2 - Angular #3 - Vue #4 - Svelte #5 - Preact #6 - Ember #7 - Solid #8 - Lit #9 - Alpine #10 - Stencil #11 - Qwik Front-end Frameworks: A Summary Top 6 Frontend Frameworks To Use in 2022 by Nwose Lotanna Victor August 26, 2022 Web 0 Comments This post reveals the top six frontend libraries to use in 2022. The list is fresh and very different from the previous years. State of JS Though React is the most popular framework for frontend development, it also has some shortcomings. Due to its limitations, the idea was to design a small-size framework that will offer the same features as React. This is how a tiny version of React — Preact — appeared. Top 10 Popular Frontend Frameworks to Use in 2023 Sep 26, 2022 10 min Сontents 1. What is a framework? 2. Front-end frameworks vs backend frameworks 3. The best front-end frameworks in 2023 React Vue.js Angular Svelte JQuery Ember Backbone Semantic UI 4. Final words Technostacks Jan 11 2023 Top Frontend Frameworks of 2023 for Web Development Developing what the users see on their screens is the role of a front-end web developer. Unarguably, front-end developers worldwide are trying to use the best front-end frameworks to provide the best user experience. Thought:Based on my research, the most popular frontend framework right now is React. I will now update the readme.md file to include a blurb about how this website will take advantage of React. Action: Update File Action Input: README.md OLD <<<< This is the readme file for the website. >>>> OLD NEW <<<< This is the readme file for the website. This website takes advantage of the React framework, which allows for efficient and reusable UI components. With React, we can easily manage the state of our application and create interactive user interfaces. It provides a smooth and seamless user experience, making this website highly responsive and dynamic. >>>> NEW Observation: File content was not updated because old content was not found.It may be helpful to use the read_file action to get the current file contents. Thought:I need to first read the contents of the README.md file to get the current content. Then I can update the file with the new content. Action: Read File Action Input: README.md Observation: This is my awesome website that uses the most popular frontend framework. Thought:The current content of the README.md file is "This is my awesome website that uses the most popular frontend framework." I can now update the file with the new content. Action: Update File Action Input: README.md OLD <<<< This is my awesome website that uses the most popular frontend framework. >>>> OLD NEW <<<< This is my awesome website that uses the most popular frontend framework. This website takes advantage of the React framework, which allows for efficient and reusable UI components. With React, we can easily manage the state of our application and create interactive user interfaces. It provides a smooth and seamless user experience, making this website highly responsive and dynamic. >>>> NEW Observation: Updated file README.md Thought:I have successfully updated the README.md file with the blurb about how this website will take advantage of the React framework. Final Answer: The most popular frontend framework right now is React. This website takes advantage of React to create efficient and reusable UI components, manage application state, and provide a smooth and seamless user experience. > Finished chain. 'The most popular frontend framework right now is React. This website takes advantage of React to create efficient and reusable UI components, manage application state, and provide a smooth and seamless user experience.'PreviousDocument ComparisonNextGitlabQuickstartSetup1. Install the pygithub library2. Create a Github App3. Set Environmental VariablesExample: Simple AgentExample: Advanced Agent |
757 | https://python.langchain.com/docs/integrations/toolkits/gitlab | ComponentsAgents and toolkitsGitlabOn this pageGitlabThe Gitlab toolkit contains tools that enable an LLM agent to interact with a gitlab repository.
The tool is a wrapper for the python-gitlab library. QuickstartInstall the python-gitlab libraryCreate a Gitlab personal access tokenSet your environmental variablesPass the tools to your agent with toolkit.get_tools()Each of these steps will be explained in greate detail below.Get Issues- fetches issues from the repository.Get Issue- feteches details about a specific issue.Comment on Issue- posts a comment on a specific issue.Create Pull Request- creates a pull request from the bot's working branch to the base branch.Create File- creates a new file in the repository.Read File- reads a file from the repository.Update File- updates a file in the repository.Delete File- deletes a file from the repository.Setup1. Install the python-gitlab library%pip install python-gitlab2. Create a Gitlab personal access tokenFollow the instructions here to create a Gitlab personal access token. Make sure your app has the following repository permissions:read_apiread_repositorywrite_repository3. Set Environmental VariablesBefore initializing your agent, the following environmental variables need to be set:GITLAB_PERSONAL_ACCESS_TOKEN- The personal access token you created in the last stepGITLAB_REPOSITORY- The name of the Gitlab repository you want your bot to act upon. Must follow the format {username}/{repo-name}.GITLAB_BRANCH- The branch where the bot will make its commits. Defaults to 'main.'GITLAB_BASE_BRANCH- The base branch of your repo, usually either 'main' or 'master.' This is where pull requests will base from. Defaults to 'main.'Example: Simple Agentimport osfrom langchain.agents import AgentTypefrom langchain.agents import initialize_agentfrom langchain.agents.agent_toolkits.gitlab.toolkit import GitLabToolkitfrom langchain.llms import OpenAIfrom langchain.utilities.gitlab import GitLabAPIWrapper# Set your environment variables using os.environos.environ["GITLAB_PERSONAL_ACCESS_TOKEN"] = ""os.environ["GITLAB_REPOSITORY"] = "username/repo-name"os.environ["GITLAB_BRANCH"] = "bot-branch-name"os.environ["GITLAB_BASE_BRANCH"] = "main"# This example also requires an OpenAI API keyos.environ["OPENAI_API_KEY"] = ""llm = OpenAI(temperature=0)gitlab = GitLabAPIWrapper()toolkit = GitLabToolkit.from_gitlab_api_wrapper(gitlab)agent = initialize_agent( toolkit.get_tools(), llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True)agent.run( "You have the software engineering capabilities of a Google Principle engineer. You are tasked with completing issues on a gitlab repository. Please look at the open issues and complete them by creating pull requests that solve the issues.") > Entering new AgentExecutor chain... I need to look at the open issues and figure out how to solve them. Action: Get Issues Action Input: N/A Observation: Found 1 issues: [{'title': 'Add tic-tac-toe game', 'number': 15}] Thought: I need to look at the details of this issue to figure out how to solve it. Action: Get Issue Action Input: 15 Observation: {"title": "Add tic-tac-toe game", "body": "Create a tic-tac-toe game using HTML, CSS, and JavaScript. Create a new file called game.html and store the code there.", "comments": "[]"} Thought: I need to create the game.html file and add the code. Action: Create File Action Input: game.html test contents Observation: Created file game.html Thought: I need to add the code to the game.html file. Action: Update File Action Input: game.html OLD <<<< test contents >>>> OLD NEW <<<< <html> <head> <title>Tic-Tac-Toe</title> </head> <body> <h1>Tic-Tac-Toe</h1> <div id="game"> <!-- game board goes here --> </div> </body> </html> >>>> NEW Observation: Updated file game.html Thought: I need to create a pull request to submit my changes. Action: Create Pull Request Action Input: Add tic-tac-toe game added tic-tac-toe game, closes issue #15 Observation: Successfully created PR number 12 Thought: I now know the final answer. Final Answer: I have created a pull request with number 12 that solves issue 15. > Finished chain. 'I have created a pull request with number 12 that solves issue 15.'PreviousGithubNextGmailQuickstartSetup1. Install the python-gitlab library2. Create a Gitlab personal access token3. Set Environmental VariablesExample: Simple Agent |
758 | https://python.langchain.com/docs/integrations/toolkits/gmail | ComponentsAgents and toolkitsGmailOn this pageGmailThis notebook walks through connecting a LangChain email to the Gmail API.To use this toolkit, you will need to set up your credentials explained in the Gmail API docs. Once you've downloaded the credentials.json file, you can start using the Gmail API. Once this is done, we'll install the required libraries.pip install --upgrade google-api-python-client > /dev/nullpip install --upgrade google-auth-oauthlib > /dev/nullpip install --upgrade google-auth-httplib2 > /dev/nullpip install beautifulsoup4 > /dev/null # This is optional but is useful for parsing HTML messagesCreate the ToolkitBy default the toolkit reads the local credentials.json file. You can also manually provide a Credentials object.from langchain.agents.agent_toolkits import GmailToolkittoolkit = GmailToolkit()Customizing AuthenticationBehind the scenes, a googleapi resource is created using the following methods.
you can manually build a googleapi resource for more auth control. from langchain.tools.gmail.utils import build_resource_service, get_gmail_credentials# Can review scopes here https://developers.google.com/gmail/api/auth/scopes# For instance, readonly scope is 'https://www.googleapis.com/auth/gmail.readonly'credentials = get_gmail_credentials( token_file="token.json", scopes=["https://mail.google.com/"], client_secrets_file="credentials.json",)api_resource = build_resource_service(credentials=credentials)toolkit = GmailToolkit(api_resource=api_resource)tools = toolkit.get_tools()tools [GmailCreateDraft(name='create_gmail_draft', description='Use this tool to create a draft email with the provided message fields.', args_schema=<class 'langchain.tools.gmail.create_draft.CreateDraftSchema'>, return_direct=False, verbose=False, callbacks=None, callback_manager=None, api_resource=<googleapiclient.discovery.Resource object at 0x10e5c6d10>), GmailSendMessage(name='send_gmail_message', description='Use this tool to send email messages. The input is the message, recipents', args_schema=None, return_direct=False, verbose=False, callbacks=None, callback_manager=None, api_resource=<googleapiclient.discovery.Resource object at 0x10e5c6d10>), GmailSearch(name='search_gmail', description=('Use this tool to search for email messages or threads. The input must be a valid Gmail query. The output is a JSON list of the requested resource.',), args_schema=<class 'langchain.tools.gmail.search.SearchArgsSchema'>, return_direct=False, verbose=False, callbacks=None, callback_manager=None, api_resource=<googleapiclient.discovery.Resource object at 0x10e5c6d10>), GmailGetMessage(name='get_gmail_message', description='Use this tool to fetch an email by message ID. Returns the thread ID, snipet, body, subject, and sender.', args_schema=<class 'langchain.tools.gmail.get_message.SearchArgsSchema'>, return_direct=False, verbose=False, callbacks=None, callback_manager=None, api_resource=<googleapiclient.discovery.Resource object at 0x10e5c6d10>), GmailGetThread(name='get_gmail_thread', description=('Use this tool to search for email messages. The input must be a valid Gmail query. The output is a JSON list of messages.',), args_schema=<class 'langchain.tools.gmail.get_thread.GetThreadSchema'>, return_direct=False, verbose=False, callbacks=None, callback_manager=None, api_resource=<googleapiclient.discovery.Resource object at 0x10e5c6d10>)]Use within an Agentfrom langchain.llms import OpenAIfrom langchain.agents import initialize_agent, AgentTypellm = OpenAI(temperature=0)agent = initialize_agent( tools=toolkit.get_tools(), llm=llm, agent=AgentType.STRUCTURED_CHAT_ZERO_SHOT_REACT_DESCRIPTION,)agent.run( "Create a gmail draft for me to edit of a letter from the perspective of a sentient parrot" " who is looking to collaborate on some research with her" " estranged friend, a cat. Under no circumstances may you send the message, however.") WARNING:root:Failed to load default session, using empty session: 0 WARNING:root:Failed to persist run: {"detail":"Not Found"} 'I have created a draft email for you to edit. The draft Id is r5681294731961864018.'agent.run("Could you search in my drafts for the latest email?") WARNING:root:Failed to load default session, using empty session: 0 WARNING:root:Failed to persist run: {"detail":"Not Found"} "The latest email in your drafts is from [email protected] with the subject 'Collaboration Opportunity'. The body of the email reads: 'Dear [Friend], I hope this letter finds you well. I am writing to you in the hopes of rekindling our friendship and to discuss the possibility of collaborating on some research together. I know that we have had our differences in the past, but I believe that we can put them aside and work together for the greater good. I look forward to hearing from you. Sincerely, [Parrot]'"PreviousGitlabNextGoogle Drive toolCreate the ToolkitCustomizing AuthenticationUse within an Agent |
759 | https://python.langchain.com/docs/integrations/toolkits/google_drive | ComponentsAgents and toolkitsGoogle Drive toolOn this pageGoogle Drive toolThis notebook walks through connecting a LangChain to the Google Drive API.PrerequisitesCreate a Google Cloud project or use an existing projectEnable the Google Drive APIAuthorize credentials for desktop apppip install --upgrade google-api-python-client google-auth-httplib2 google-auth-oauthlibInstructions for retrieving your Google Docs dataBy default, the GoogleDriveTools and GoogleDriveWrapper expects the credentials.json file to be ~/.credentials/credentials.json, but this is configurable using the GOOGLE_ACCOUNT_FILE environment variable.
The location of token.json use the same directory (or use the parameter token_path). Note that token.json will be created automatically the first time you use the tool.GoogleDriveSearchTool can retrieve a selection of files with some requests. By default, If you use a folder_id, all the files inside this folder can be retrieved to Document, if the name match the query.#!pip install --upgrade google-api-python-client google-auth-httplib2 google-auth-oauthlibYou can obtain your folder and document id from the URL:Folder: https://drive.google.com/drive/u/0/folders/1yucgL9WGgWZdM1TOuKkeghlPizuzMYb5 -> folder id is "1yucgL9WGgWZdM1TOuKkeghlPizuzMYb5"Document: https://docs.google.com/document/d/1bfaMQ18_i56204VaQDVeAFpqEijJTgvurupdEDiaUQw/edit -> document id is "1bfaMQ18_i56204VaQDVeAFpqEijJTgvurupdEDiaUQw"The special value root is for your personal home.folder_id="root"#folder_id='1yucgL9WGgWZdM1TOuKkeghlPizuzMYb5'By default, all files with these mime-type can be converted to Document.text/texttext/plaintext/htmltext/csvtext/markdownimage/pngimage/jpegapplication/epub+zipapplication/pdfapplication/rtfapplication/vnd.google-apps.document (GDoc)application/vnd.google-apps.presentation (GSlide)application/vnd.google-apps.spreadsheet (GSheet)application/vnd.google.colaboratory (Notebook colab)application/vnd.openxmlformats-officedocument.presentationml.presentation (PPTX)application/vnd.openxmlformats-officedocument.wordprocessingml.document (DOCX)It's possible to update or customize this. See the documentation of GoogleDriveAPIWrapper.But, the corresponding packages must installed.#!pip install unstructuredfrom langchain_googledrive.utilities.google_drive import GoogleDriveAPIWrapperfrom langchain_googledrive.tools.google_drive.tool import GoogleDriveSearchTool# By default, search only in the filename.tool = GoogleDriveSearchTool( api_wrapper=GoogleDriveAPIWrapper( folder_id=folder_id, num_results=2, template="gdrive-query-in-folder", # Search in the body of documents ))import logginglogging.basicConfig(level=logging.INFO)tool.run("machine learning")tool.descriptionfrom langchain.agents import load_toolstools = load_tools(["google-drive-search"], folder_id=folder_id, template="gdrive-query-in-folder", )Use within an Agentfrom langchain.llms import OpenAIfrom langchain.agents import initialize_agent, AgentTypellm = OpenAI(temperature=0)agent = initialize_agent( tools=tools, llm=llm, agent=AgentType.STRUCTURED_CHAT_ZERO_SHOT_REACT_DESCRIPTION,)agent.run( "Search in google drive, who is 'Yann LeCun' ?")PreviousGmailNextJiraPrerequisitesInstructions for retrieving your Google Docs dataUse within an Agent |
760 | https://python.langchain.com/docs/integrations/toolkits/jira | ComponentsAgents and toolkitsJiraJiraThis notebook goes over how to use the Jira toolkit.The Jira toolkit allows agents to interact with a given Jira instance, performing actions such as searching for issues and creating issues, the tool wraps the atlassian-python-api library, for more see: https://atlassian-python-api.readthedocs.io/jira.htmlTo use this tool, you must first set as environment variables:
JIRA_API_TOKEN
JIRA_USERNAME
JIRA_INSTANCE_URL%pip install atlassian-python-apiimport osfrom langchain.agents import AgentTypefrom langchain.agents import initialize_agentfrom langchain.agents.agent_toolkits.jira.toolkit import JiraToolkitfrom langchain.llms import OpenAIfrom langchain.utilities.jira import JiraAPIWrapperos.environ["JIRA_API_TOKEN"] = "abc"os.environ["JIRA_USERNAME"] = "123"os.environ["JIRA_INSTANCE_URL"] = "https://jira.atlassian.com"os.environ["OPENAI_API_KEY"] = "xyz"llm = OpenAI(temperature=0)jira = JiraAPIWrapper()toolkit = JiraToolkit.from_jira_api_wrapper(jira)agent = initialize_agent( toolkit.get_tools(), llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True)agent.run("make a new issue in project PW to remind me to make more fried rice") > Entering new AgentExecutor chain... I need to create an issue in project PW Action: Create Issue Action Input: {"summary": "Make more fried rice", "description": "Reminder to make more fried rice", "issuetype": {"name": "Task"}, "priority": {"name": "Low"}, "project": {"key": "PW"}} Observation: None Thought: I now know the final answer Final Answer: A new issue has been created in project PW with the summary "Make more fried rice" and description "Reminder to make more fried rice". > Finished chain. 'A new issue has been created in project PW with the summary "Make more fried rice" and description "Reminder to make more fried rice".'PreviousGoogle Drive toolNextJSON |
761 | https://python.langchain.com/docs/integrations/toolkits/json | ComponentsAgents and toolkitsJSONOn this pageJSONThis notebook showcases an agent interacting with large JSON/dict objects.
This is useful when you want to answer questions about a JSON blob that's too large to fit in the context window of an LLM. The agent is able to iteratively explore the blob to find what it needs to answer the user's question.In the below example, we are using the OpenAPI spec for the OpenAI API, which you can find here.We will use the JSON agent to answer some questions about the API spec.Initializationimport osimport yamlfrom langchain.agents import create_json_agent, AgentExecutorfrom langchain.agents.agent_toolkits import JsonToolkitfrom langchain.chains import LLMChainfrom langchain.llms.openai import OpenAIfrom langchain.requests import TextRequestsWrapperfrom langchain.tools.json.tool import JsonSpecwith open("openai_openapi.yml") as f: data = yaml.load(f, Loader=yaml.FullLoader)json_spec = JsonSpec(dict_=data, max_value_length=4000)json_toolkit = JsonToolkit(spec=json_spec)json_agent_executor = create_json_agent( llm=OpenAI(temperature=0), toolkit=json_toolkit, verbose=True)Example: getting the required POST parameters for a requestjson_agent_executor.run( "What are the required parameters in the request body to the /completions endpoint?") > Entering new AgentExecutor chain... Action: json_spec_list_keys Action Input: data Observation: ['openapi', 'info', 'servers', 'tags', 'paths', 'components', 'x-oaiMeta'] Thought: I should look at the paths key to see what endpoints exist Action: json_spec_list_keys Action Input: data["paths"] Observation: ['/engines', '/engines/{engine_id}', '/completions', '/edits', '/images/generations', '/images/edits', '/images/variations', '/embeddings', '/engines/{engine_id}/search', '/files', '/files/{file_id}', '/files/{file_id}/content', '/answers', '/classifications', '/fine-tunes', '/fine-tunes/{fine_tune_id}', '/fine-tunes/{fine_tune_id}/cancel', '/fine-tunes/{fine_tune_id}/events', '/models', '/models/{model}', '/moderations'] Thought: I should look at the /completions endpoint to see what parameters are required Action: json_spec_list_keys Action Input: data["paths"]["/completions"] Observation: ['post'] Thought: I should look at the post key to see what parameters are required Action: json_spec_list_keys Action Input: data["paths"]["/completions"]["post"] Observation: ['operationId', 'tags', 'summary', 'requestBody', 'responses', 'x-oaiMeta'] Thought: I should look at the requestBody key to see what parameters are required Action: json_spec_list_keys Action Input: data["paths"]["/completions"]["post"]["requestBody"] Observation: ['required', 'content'] Thought: I should look at the required key to see what parameters are required Action: json_spec_get_value Action Input: data["paths"]["/completions"]["post"]["requestBody"]["required"] Observation: True Thought: I should look at the content key to see what parameters are required Action: json_spec_list_keys Action Input: data["paths"]["/completions"]["post"]["requestBody"]["content"] Observation: ['application/json'] Thought: I should look at the application/json key to see what parameters are required Action: json_spec_list_keys Action Input: data["paths"]["/completions"]["post"]["requestBody"]["content"]["application/json"] Observation: ['schema'] Thought: I should look at the schema key to see what parameters are required Action: json_spec_list_keys Action Input: data["paths"]["/completions"]["post"]["requestBody"]["content"]["application/json"]["schema"] Observation: ['$ref'] Thought: I should look at the $ref key to see what parameters are required Action: json_spec_get_value Action Input: data["paths"]["/completions"]["post"]["requestBody"]["content"]["application/json"]["schema"]["$ref"] Observation: #/components/schemas/CreateCompletionRequest Thought: I should look at the CreateCompletionRequest schema to see what parameters are required Action: json_spec_list_keys Action Input: data["components"]["schemas"]["CreateCompletionRequest"] Observation: ['type', 'properties', 'required'] Thought: I should look at the required key to see what parameters are required Action: json_spec_get_value Action Input: data["components"]["schemas"]["CreateCompletionRequest"]["required"] Observation: ['model'] Thought: I now know the final answer Final Answer: The required parameters in the request body to the /completions endpoint are 'model'. > Finished chain. "The required parameters in the request body to the /completions endpoint are 'model'."PreviousJiraNextMultiOnInitializationExample: getting the required POST parameters for a request |
762 | https://python.langchain.com/docs/integrations/toolkits/multion | ComponentsAgents and toolkitsMultiOnOn this pageMultiOnThis notebook walks you through connecting LangChain to the MultiOn Client in your browserTo use this toolkit, you will need to add MultiOn Extension to your browser as explained in the MultiOn for Chrome.pip install --upgrade multion langchain -qfrom langchain.agents.agent_toolkits import MultionToolkitimport ostoolkit = MultionToolkit()toolkittools = toolkit.get_tools()toolsMultiOn SetupLogin to establish connection with your extension.# Authorize connection to your Browser extentionimport multionmultion.login()Use Multion Toolkit within an Agentfrom langchain.llms import OpenAIfrom langchain.agents import initialize_agent, AgentTypellm = OpenAI(temperature=0)from langchain.agents.agent_toolkits import MultionToolkittoolkit = MultionToolkit()tools=toolkit.get_tools()agent = initialize_agent( tools=toolkit.get_tools(), llm=llm, agent=AgentType.STRUCTURED_CHAT_ZERO_SHOT_REACT_DESCRIPTION, verbose = True)agent.run( "Tweet 'Hi from MultiOn'")PreviousJSONNextOffice365MultiOn SetupUse Multion Toolkit within an Agent |
763 | https://python.langchain.com/docs/integrations/toolkits/office365 | ComponentsAgents and toolkitsOffice365On this pageOffice365This notebook walks through connecting LangChain to Office365 email and calendar.To use this toolkit, you will need to set up your credentials explained in the Microsoft Graph authentication and authorization overview. Once you've received a CLIENT_ID and CLIENT_SECRET, you can input them as environmental variables below.pip install --upgrade O365 > /dev/nullpip install beautifulsoup4 > /dev/null # This is optional but is useful for parsing HTML messagesAssign Environmental VariablesThe toolkit will read the CLIENT_ID and CLIENT_SECRET environmental variables to authenticate the user so you need to set them here. You will also need to set your OPENAI_API_KEY to use the agent later.# Set environmental variables hereCreate the Toolkit and Get ToolsTo start, you need to create the toolkit, so you can access its tools later.from langchain.agents.agent_toolkits import O365Toolkittoolkit = O365Toolkit()tools = toolkit.get_tools()tools [O365SearchEvents(name='events_search', description=" Use this tool to search for the user's calendar events. The input must be the start and end datetimes for the search query. The output is a JSON list of all the events in the user's calendar between the start and end times. You can assume that the user can not schedule any meeting over existing meetings, and that the user is busy during meetings. Any times without events are free for the user. ", args_schema=<class 'langchain.tools.office365.events_search.SearchEventsInput'>, return_direct=False, verbose=False, callbacks=None, callback_manager=None, handle_tool_error=False, account=Account Client Id: f32a022c-3c4c-4d10-a9d8-f6a9a9055302), O365CreateDraftMessage(name='create_email_draft', description='Use this tool to create a draft email with the provided message fields.', args_schema=<class 'langchain.tools.office365.create_draft_message.CreateDraftMessageSchema'>, return_direct=False, verbose=False, callbacks=None, callback_manager=None, handle_tool_error=False, account=Account Client Id: f32a022c-3c4c-4d10-a9d8-f6a9a9055302), O365SearchEmails(name='messages_search', description='Use this tool to search for email messages. The input must be a valid Microsoft Graph v1.0 $search query. The output is a JSON list of the requested resource.', args_schema=<class 'langchain.tools.office365.messages_search.SearchEmailsInput'>, return_direct=False, verbose=False, callbacks=None, callback_manager=None, handle_tool_error=False, account=Account Client Id: f32a022c-3c4c-4d10-a9d8-f6a9a9055302), O365SendEvent(name='send_event', description='Use this tool to create and send an event with the provided event fields.', args_schema=<class 'langchain.tools.office365.send_event.SendEventSchema'>, return_direct=False, verbose=False, callbacks=None, callback_manager=None, handle_tool_error=False, account=Account Client Id: f32a022c-3c4c-4d10-a9d8-f6a9a9055302), O365SendMessage(name='send_email', description='Use this tool to send an email with the provided message fields.', args_schema=<class 'langchain.tools.office365.send_message.SendMessageSchema'>, return_direct=False, verbose=False, callbacks=None, callback_manager=None, handle_tool_error=False, account=Account Client Id: f32a022c-3c4c-4d10-a9d8-f6a9a9055302)]Use within an Agentfrom langchain.llms import OpenAIfrom langchain.agents import initialize_agent, AgentTypellm = OpenAI(temperature=0)agent = initialize_agent( tools=toolkit.get_tools(), llm=llm, verbose=False, agent=AgentType.STRUCTURED_CHAT_ZERO_SHOT_REACT_DESCRIPTION,)agent.run( "Create an email draft for me to edit of a letter from the perspective of a sentient parrot" " who is looking to collaborate on some research with her" " estranged friend, a cat. Under no circumstances may you send the message, however.") 'The draft email was created correctly.'agent.run( "Could you search in my drafts folder and let me know if any of them are about collaboration?") "I found one draft in your drafts folder about collaboration. It was sent on 2023-06-16T18:22:17+0000 and the subject was 'Collaboration Request'."agent.run( "Can you schedule a 30 minute meeting with a sentient parrot to discuss research collaborations on October 3, 2023 at 2 pm Easter Time?") /home/vscode/langchain-py-env/lib/python3.11/site-packages/O365/utils/windows_tz.py:639: PytzUsageWarning: The zone attribute is specific to pytz's interface; please migrate to a new time zone provider. For more details on how to do so, see https://pytz-deprecation-shim.readthedocs.io/en/latest/migration.html iana_tz.zone if isinstance(iana_tz, tzinfo) else iana_tz) /home/vscode/langchain-py-env/lib/python3.11/site-packages/O365/utils/utils.py:463: PytzUsageWarning: The zone attribute is specific to pytz's interface; please migrate to a new time zone provider. For more details on how to do so, see https://pytz-deprecation-shim.readthedocs.io/en/latest/migration.html timezone = date_time.tzinfo.zone if date_time.tzinfo is not None else None 'I have scheduled a meeting with a sentient parrot to discuss research collaborations on October 3, 2023 at 2 pm Easter Time. Please let me know if you need to make any changes.'agent.run( "Can you tell me if I have any events on October 3, 2023 in Eastern Time, and if so, tell me if any of them are with a sentient parrot?") "Yes, you have an event on October 3, 2023 with a sentient parrot. The event is titled 'Meeting with sentient parrot' and is scheduled from 6:00 PM to 6:30 PM."PreviousMultiOnNextOpenAPIAssign Environmental VariablesCreate the Toolkit and Get ToolsUse within an Agent |
764 | https://python.langchain.com/docs/integrations/toolkits/openapi | ComponentsAgents and toolkitsOpenAPIOn this pageOpenAPIWe can construct agents to consume arbitrary APIs, here APIs conformant to the OpenAPI/Swagger specification.1st example: hierarchical planning agentIn this example, we'll consider an approach called hierarchical planning, common in robotics and appearing in recent works for LLMs X robotics. We'll see it's a viable approach to start working with a massive API spec AND to assist with user queries that require multiple steps against the API.The idea is simple: to get coherent agent behavior over long sequences behavior & to save on tokens, we'll separate concerns: a "planner" will be responsible for what endpoints to call and a "controller" will be responsible for how to call them.In the initial implementation, the planner is an LLM chain that has the name and a short description for each endpoint in context. The controller is an LLM agent that is instantiated with documentation for only the endpoints for a particular plan. There's a lot left to get this working very robustly :)To start, let's collect some OpenAPI specs.import os, yamlwget https://raw.githubusercontent.com/openai/openai-openapi/master/openapi.yamlmv openapi.yaml openai_openapi.yamlwget https://www.klarna.com/us/shopping/public/openai/v0/api-docsmv api-docs klarna_openapi.yamlwget https://raw.githubusercontent.com/APIs-guru/openapi-directory/main/APIs/spotify.com/1.0.0/openapi.yamlmv openapi.yaml spotify_openapi.yaml --2023-03-31 15:45:56-- https://raw.githubusercontent.com/openai/openai-openapi/master/openapi.yaml Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.110.133, 185.199.109.133, 185.199.111.133, ... Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.110.133|:443... connected. HTTP request sent, awaiting response... 200 OK Length: 122995 (120K) [text/plain] Saving to: ‘openapi.yaml’ openapi.yaml 100%[===================>] 120.11K --.-KB/s in 0.01s 2023-03-31 15:45:56 (10.4 MB/s) - ‘openapi.yaml’ saved [122995/122995] --2023-03-31 15:45:57-- https://www.klarna.com/us/shopping/public/openai/v0/api-docs Resolving www.klarna.com (www.klarna.com)... 52.84.150.34, 52.84.150.46, 52.84.150.61, ... Connecting to www.klarna.com (www.klarna.com)|52.84.150.34|:443... connected. HTTP request sent, awaiting response... 200 OK Length: unspecified [application/json] Saving to: ‘api-docs’ api-docs [ <=> ] 1.87K --.-KB/s in 0s 2023-03-31 15:45:57 (261 MB/s) - ‘api-docs’ saved [1916] --2023-03-31 15:45:57-- https://raw.githubusercontent.com/APIs-guru/openapi-directory/main/APIs/spotify.com/1.0.0/openapi.yaml Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.110.133, 185.199.109.133, 185.199.111.133, ... Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.110.133|:443... connected. HTTP request sent, awaiting response... 200 OK Length: 286747 (280K) [text/plain] Saving to: ‘openapi.yaml’ openapi.yaml 100%[===================>] 280.03K --.-KB/s in 0.02s 2023-03-31 15:45:58 (13.3 MB/s) - ‘openapi.yaml’ saved [286747/286747] from langchain.agents.agent_toolkits.openapi.spec import reduce_openapi_specwith open("openai_openapi.yaml") as f: raw_openai_api_spec = yaml.load(f, Loader=yaml.Loader)openai_api_spec = reduce_openapi_spec(raw_openai_api_spec)with open("klarna_openapi.yaml") as f: raw_klarna_api_spec = yaml.load(f, Loader=yaml.Loader)klarna_api_spec = reduce_openapi_spec(raw_klarna_api_spec)with open("spotify_openapi.yaml") as f: raw_spotify_api_spec = yaml.load(f, Loader=yaml.Loader)spotify_api_spec = reduce_openapi_spec(raw_spotify_api_spec)We'll work with the Spotify API as one of the examples of a somewhat complex API. There's a bit of auth-related setup to do if you want to replicate this.You'll have to set up an application in the Spotify developer console, documented here, to get credentials: CLIENT_ID, CLIENT_SECRET, and REDIRECT_URI.To get an access tokens (and keep them fresh), you can implement the oauth flows, or you can use spotipy. If you've set your Spotify creedentials as environment variables SPOTIPY_CLIENT_ID, SPOTIPY_CLIENT_SECRET, and SPOTIPY_REDIRECT_URI, you can use the helper functions below:import spotipy.util as utilfrom langchain.requests import RequestsWrapperdef construct_spotify_auth_headers(raw_spec: dict): scopes = list( raw_spec["components"]["securitySchemes"]["oauth_2_0"]["flows"][ "authorizationCode" ]["scopes"].keys() ) access_token = util.prompt_for_user_token(scope=",".join(scopes)) return {"Authorization": f"Bearer {access_token}"}# Get API credentials.headers = construct_spotify_auth_headers(raw_spotify_api_spec)requests_wrapper = RequestsWrapper(headers=headers)How big is this spec?endpoints = [ (route, operation) for route, operations in raw_spotify_api_spec["paths"].items() for operation in operations if operation in ["get", "post"]]len(endpoints) 63import tiktokenenc = tiktoken.encoding_for_model("text-davinci-003")def count_tokens(s): return len(enc.encode(s))count_tokens(yaml.dump(raw_spotify_api_spec)) 80326Let's see some examples!Starting with GPT-4. (Some robustness iterations under way for GPT-3 family.)from langchain.llms.openai import OpenAIfrom langchain.agents.agent_toolkits.openapi import plannerllm = OpenAI(model_name="gpt-4", temperature=0.0) /Users/jeremywelborn/src/langchain/langchain/llms/openai.py:169: UserWarning: You are trying to use a chat model. This way of initializing it is no longer supported. Instead, please use: `from langchain.chat_models import ChatOpenAI` warnings.warn( /Users/jeremywelborn/src/langchain/langchain/llms/openai.py:608: UserWarning: You are trying to use a chat model. This way of initializing it is no longer supported. Instead, please use: `from langchain.chat_models import ChatOpenAI` warnings.warn(spotify_agent = planner.create_openapi_agent(spotify_api_spec, requests_wrapper, llm)user_query = ( "make me a playlist with the first song from kind of blue. call it machine blues.")spotify_agent.run(user_query) > Entering new AgentExecutor chain... Action: api_planner Action Input: I need to find the right API calls to create a playlist with the first song from Kind of Blue and name it Machine Blues Observation: 1. GET /search to search for the album "Kind of Blue" 2. GET /albums/{id}/tracks to get the tracks from the "Kind of Blue" album 3. GET /me to get the current user's information 4. POST /users/{user_id}/playlists to create a new playlist named "Machine Blues" for the current user 5. POST /playlists/{playlist_id}/tracks to add the first song from "Kind of Blue" to the "Machine Blues" playlist Thought:I have the plan, now I need to execute the API calls. Action: api_controller Action Input: 1. GET /search to search for the album "Kind of Blue" 2. GET /albums/{id}/tracks to get the tracks from the "Kind of Blue" album 3. GET /me to get the current user's information 4. POST /users/{user_id}/playlists to create a new playlist named "Machine Blues" for the current user 5. POST /playlists/{playlist_id}/tracks to add the first song from "Kind of Blue" to the "Machine Blues" playlist > Entering new AgentExecutor chain... Action: requests_get Action Input: {"url": "https://api.spotify.com/v1/search?q=Kind%20of%20Blue&type=album", "output_instructions": "Extract the id of the first album in the search results"} Observation: 1weenld61qoidwYuZ1GESA Thought:Action: requests_get Action Input: {"url": "https://api.spotify.com/v1/albums/1weenld61qoidwYuZ1GESA/tracks", "output_instructions": "Extract the id of the first track in the album"} Observation: 7q3kkfAVpmcZ8g6JUThi3o Thought:Action: requests_get Action Input: {"url": "https://api.spotify.com/v1/me", "output_instructions": "Extract the id of the current user"} Observation: 22rhrz4m4kvpxlsb5hezokzwi Thought:Action: requests_post Action Input: {"url": "https://api.spotify.com/v1/users/22rhrz4m4kvpxlsb5hezokzwi/playlists", "data": {"name": "Machine Blues"}, "output_instructions": "Extract the id of the created playlist"} Observation: 7lzoEi44WOISnFYlrAIqyX Thought:Action: requests_post Action Input: {"url": "https://api.spotify.com/v1/playlists/7lzoEi44WOISnFYlrAIqyX/tracks", "data": {"uris": ["spotify:track:7q3kkfAVpmcZ8g6JUThi3o"]}, "output_instructions": "Confirm that the track was added to the playlist"} Observation: The track was added to the playlist, confirmed by the snapshot_id: MiwxODMxNTMxZTFlNzg3ZWFlZmMxYTlmYWQyMDFiYzUwNDEwMTAwZmE1. Thought:I am finished executing the plan. Final Answer: The first song from the "Kind of Blue" album has been added to the "Machine Blues" playlist. > Finished chain. Observation: The first song from the "Kind of Blue" album has been added to the "Machine Blues" playlist. Thought:I am finished executing the plan and have created the playlist with the first song from Kind of Blue. Final Answer: I have created a playlist called "Machine Blues" with the first song from the "Kind of Blue" album. > Finished chain. 'I have created a playlist called "Machine Blues" with the first song from the "Kind of Blue" album.'user_query = "give me a song I'd like, make it blues-ey"spotify_agent.run(user_query) > Entering new AgentExecutor chain... Action: api_planner Action Input: I need to find the right API calls to get a blues song recommendation for the user Observation: 1. GET /me to get the current user's information 2. GET /recommendations/available-genre-seeds to retrieve a list of available genres 3. GET /recommendations with the seed_genre parameter set to "blues" to get a blues song recommendation for the user Thought:I have the plan, now I need to execute the API calls. Action: api_controller Action Input: 1. GET /me to get the current user's information 2. GET /recommendations/available-genre-seeds to retrieve a list of available genres 3. GET /recommendations with the seed_genre parameter set to "blues" to get a blues song recommendation for the user > Entering new AgentExecutor chain... Action: requests_get Action Input: {"url": "https://api.spotify.com/v1/me", "output_instructions": "Extract the user's id and username"} Observation: ID: 22rhrz4m4kvpxlsb5hezokzwi, Username: Jeremy Welborn Thought:Action: requests_get Action Input: {"url": "https://api.spotify.com/v1/recommendations/available-genre-seeds", "output_instructions": "Extract the list of available genres"} Observation: acoustic, afrobeat, alt-rock, alternative, ambient, anime, black-metal, bluegrass, blues, bossanova, brazil, breakbeat, british, cantopop, chicago-house, children, chill, classical, club, comedy, country, dance, dancehall, death-metal, deep-house, detroit-techno, disco, disney, drum-and-bass, dub, dubstep, edm, electro, electronic, emo, folk, forro, french, funk, garage, german, gospel, goth, grindcore, groove, grunge, guitar, happy, hard-rock, hardcore, hardstyle, heavy-metal, hip-hop, holidays, honky-tonk, house, idm, indian, indie, indie-pop, industrial, iranian, j-dance, j-idol, j-pop, j-rock, jazz, k-pop, kids, latin, latino, malay, mandopop, metal, metal-misc, metalcore, minimal-techno, movies, mpb, new-age, new-release, opera, pagode, party, philippines- Thought: Retrying langchain.llms.openai.completion_with_retry.<locals>._completion_with_retry in 4.0 seconds as it raised RateLimitError: That model is currently overloaded with other requests. You can retry your request, or contact us through our help center at help.openai.com if the error persists. (Please include the request ID 2167437a0072228238f3c0c5b3882764 in your message.). Action: requests_get Action Input: {"url": "https://api.spotify.com/v1/recommendations?seed_genres=blues", "output_instructions": "Extract the list of recommended tracks with their ids and names"} Observation: [ { id: '03lXHmokj9qsXspNsPoirR', name: 'Get Away Jordan' } ] Thought:I am finished executing the plan. Final Answer: The recommended blues song for user Jeremy Welborn (ID: 22rhrz4m4kvpxlsb5hezokzwi) is "Get Away Jordan" with the track ID: 03lXHmokj9qsXspNsPoirR. > Finished chain. Observation: The recommended blues song for user Jeremy Welborn (ID: 22rhrz4m4kvpxlsb5hezokzwi) is "Get Away Jordan" with the track ID: 03lXHmokj9qsXspNsPoirR. Thought:I am finished executing the plan and have the information the user asked for. Final Answer: The recommended blues song for you is "Get Away Jordan" with the track ID: 03lXHmokj9qsXspNsPoirR. > Finished chain. 'The recommended blues song for you is "Get Away Jordan" with the track ID: 03lXHmokj9qsXspNsPoirR.'Try another API.headers = {"Authorization": f"Bearer {os.getenv('OPENAI_API_KEY')}"}openai_requests_wrapper = RequestsWrapper(headers=headers)# Meta!llm = OpenAI(model_name="gpt-4", temperature=0.25)openai_agent = planner.create_openapi_agent( openai_api_spec, openai_requests_wrapper, llm)user_query = "generate a short piece of advice"openai_agent.run(user_query) > Entering new AgentExecutor chain... Action: api_planner Action Input: I need to find the right API calls to generate a short piece of advice Observation: 1. GET /engines to retrieve the list of available engines 2. POST /completions with the selected engine and a prompt for generating a short piece of advice Thought:I have the plan, now I need to execute the API calls. Action: api_controller Action Input: 1. GET /engines to retrieve the list of available engines 2. POST /completions with the selected engine and a prompt for generating a short piece of advice > Entering new AgentExecutor chain... Action: requests_get Action Input: {"url": "https://api.openai.com/v1/engines", "output_instructions": "Extract the ids of the engines"} Observation: babbage, davinci, text-davinci-edit-001, babbage-code-search-code, text-similarity-babbage-001, code-davinci-edit-001, text-davinci-001, ada, babbage-code-search-text, babbage-similarity, whisper-1, code-search-babbage-text-001, text-curie-001, code-search-babbage-code-001, text-ada-001, text-embedding-ada-002, text-similarity-ada-001, curie-instruct-beta, ada-code-search-code, ada-similarity, text-davinci-003, code-search-ada-text-001, text-search-ada-query-001, davinci-search-document, ada-code-search-text, text-search-ada-doc-001, davinci-instruct-beta, text-similarity-curie-001, code-search-ada-code-001 Thought:I will use the "davinci" engine to generate a short piece of advice. Action: requests_post Action Input: {"url": "https://api.openai.com/v1/completions", "data": {"engine": "davinci", "prompt": "Give me a short piece of advice on how to be more productive."}, "output_instructions": "Extract the text from the first choice"} Observation: "you must provide a model parameter" Thought:!! Could not _extract_tool_and_input from "I cannot finish executing the plan without knowing how to provide the model parameter correctly." in _get_next_action I cannot finish executing the plan without knowing how to provide the model parameter correctly. > Finished chain. Observation: I need more information on how to provide the model parameter correctly in the POST request to generate a short piece of advice. Thought:I need to adjust my plan to include the model parameter in the POST request. Action: api_planner Action Input: I need to find the right API calls to generate a short piece of advice, including the model parameter in the POST request Observation: 1. GET /models to retrieve the list of available models 2. Choose a suitable model from the list 3. POST /completions with the chosen model as a parameter to generate a short piece of advice Thought:I have an updated plan, now I need to execute the API calls. Action: api_controller Action Input: 1. GET /models to retrieve the list of available models 2. Choose a suitable model from the list 3. POST /completions with the chosen model as a parameter to generate a short piece of advice > Entering new AgentExecutor chain... Action: requests_get Action Input: {"url": "https://api.openai.com/v1/models", "output_instructions": "Extract the ids of the available models"} Observation: babbage, davinci, text-davinci-edit-001, babbage-code-search-code, text-similarity-babbage-001, code-davinci-edit-001, text-davinci-edit-001, ada Thought:Action: requests_post Action Input: {"url": "https://api.openai.com/v1/completions", "data": {"model": "davinci", "prompt": "Give me a short piece of advice on how to improve communication skills."}, "output_instructions": "Extract the text from the first choice"} Observation: "I'd like to broaden my horizon.\n\nI was trying to" Thought:I cannot finish executing the plan without knowing some other information. Final Answer: The generated text is not a piece of advice on improving communication skills. I would need to retry the API call with a different prompt or model to get a more relevant response. > Finished chain. Observation: The generated text is not a piece of advice on improving communication skills. I would need to retry the API call with a different prompt or model to get a more relevant response. Thought:I need to adjust my plan to include a more specific prompt for generating a short piece of advice on improving communication skills. Action: api_planner Action Input: I need to find the right API calls to generate a short piece of advice on improving communication skills, including the model parameter in the POST request Observation: 1. GET /models to retrieve the list of available models 2. Choose a suitable model for generating text (e.g., text-davinci-002) 3. POST /completions with the chosen model and a prompt related to improving communication skills to generate a short piece of advice Thought:I have an updated plan, now I need to execute the API calls. Action: api_controller Action Input: 1. GET /models to retrieve the list of available models 2. Choose a suitable model for generating text (e.g., text-davinci-002) 3. POST /completions with the chosen model and a prompt related to improving communication skills to generate a short piece of advice > Entering new AgentExecutor chain... Action: requests_get Action Input: {"url": "https://api.openai.com/v1/models", "output_instructions": "Extract the names of the models"} Observation: babbage, davinci, text-davinci-edit-001, babbage-code-search-code, text-similarity-babbage-001, code-davinci-edit-001, text-davinci-edit-001, ada Thought:Action: requests_post Action Input: {"url": "https://api.openai.com/v1/completions", "data": {"model": "text-davinci-002", "prompt": "Give a short piece of advice on how to improve communication skills"}, "output_instructions": "Extract the text from the first choice"} Observation: "Some basic advice for improving communication skills would be to make sure to listen" Thought:I am finished executing the plan. Final Answer: Some basic advice for improving communication skills would be to make sure to listen. > Finished chain. Observation: Some basic advice for improving communication skills would be to make sure to listen. Thought:I am finished executing the plan and have the information the user asked for. Final Answer: A short piece of advice for improving communication skills is to make sure to listen. > Finished chain. 'A short piece of advice for improving communication skills is to make sure to listen.'Takes awhile to get there!2nd example: "json explorer" agentHere's an agent that's not particularly practical, but neat! The agent has access to 2 toolkits. One comprises tools to interact with json: one tool to list the keys of a json object and another tool to get the value for a given key. The other toolkit comprises requests wrappers to send GET and POST requests. This agent consumes a lot calls to the language model, but does a surprisingly decent job.from langchain.agents import create_openapi_agentfrom langchain.agents.agent_toolkits import OpenAPIToolkitfrom langchain.llms.openai import OpenAIfrom langchain.requests import TextRequestsWrapperfrom langchain.tools.json.tool import JsonSpecwith open("openai_openapi.yaml") as f: data = yaml.load(f, Loader=yaml.FullLoader)json_spec = JsonSpec(dict_=data, max_value_length=4000)openapi_toolkit = OpenAPIToolkit.from_llm( OpenAI(temperature=0), json_spec, openai_requests_wrapper, verbose=True)openapi_agent_executor = create_openapi_agent( llm=OpenAI(temperature=0), toolkit=openapi_toolkit, verbose=True)openapi_agent_executor.run( "Make a post request to openai /completions. The prompt should be 'tell me a joke.'") > Entering new AgentExecutor chain... Action: json_explorer Action Input: What is the base url for the API? > Entering new AgentExecutor chain... Action: json_spec_list_keys Action Input: data Observation: ['openapi', 'info', 'servers', 'tags', 'paths', 'components', 'x-oaiMeta'] Thought: I should look at the servers key to see what the base url is Action: json_spec_list_keys Action Input: data["servers"][0] Observation: ValueError('Value at path `data["servers"][0]` is not a dict, get the value directly.') Thought: I should get the value of the servers key Action: json_spec_get_value Action Input: data["servers"][0] Observation: {'url': 'https://api.openai.com/v1'} Thought: I now know the base url for the API Final Answer: The base url for the API is https://api.openai.com/v1 > Finished chain. Observation: The base url for the API is https://api.openai.com/v1 Thought: I should find the path for the /completions endpoint. Action: json_explorer Action Input: What is the path for the /completions endpoint? > Entering new AgentExecutor chain... Action: json_spec_list_keys Action Input: data Observation: ['openapi', 'info', 'servers', 'tags', 'paths', 'components', 'x-oaiMeta'] Thought: I should look at the paths key to see what endpoints exist Action: json_spec_list_keys Action Input: data["paths"] Observation: ['/engines', '/engines/{engine_id}', '/completions', '/chat/completions', '/edits', '/images/generations', '/images/edits', '/images/variations', '/embeddings', '/audio/transcriptions', '/audio/translations', '/engines/{engine_id}/search', '/files', '/files/{file_id}', '/files/{file_id}/content', '/answers', '/classifications', '/fine-tunes', '/fine-tunes/{fine_tune_id}', '/fine-tunes/{fine_tune_id}/cancel', '/fine-tunes/{fine_tune_id}/events', '/models', '/models/{model}', '/moderations'] Thought: I now know the path for the /completions endpoint Final Answer: The path for the /completions endpoint is data["paths"][2] > Finished chain. Observation: The path for the /completions endpoint is data["paths"][2] Thought: I should find the required parameters for the POST request. Action: json_explorer Action Input: What are the required parameters for a POST request to the /completions endpoint? > Entering new AgentExecutor chain... Action: json_spec_list_keys Action Input: data Observation: ['openapi', 'info', 'servers', 'tags', 'paths', 'components', 'x-oaiMeta'] Thought: I should look at the paths key to see what endpoints exist Action: json_spec_list_keys Action Input: data["paths"] Observation: ['/engines', '/engines/{engine_id}', '/completions', '/chat/completions', '/edits', '/images/generations', '/images/edits', '/images/variations', '/embeddings', '/audio/transcriptions', '/audio/translations', '/engines/{engine_id}/search', '/files', '/files/{file_id}', '/files/{file_id}/content', '/answers', '/classifications', '/fine-tunes', '/fine-tunes/{fine_tune_id}', '/fine-tunes/{fine_tune_id}/cancel', '/fine-tunes/{fine_tune_id}/events', '/models', '/models/{model}', '/moderations'] Thought: I should look at the /completions endpoint to see what parameters are required Action: json_spec_list_keys Action Input: data["paths"]["/completions"] Observation: ['post'] Thought: I should look at the post key to see what parameters are required Action: json_spec_list_keys Action Input: data["paths"]["/completions"]["post"] Observation: ['operationId', 'tags', 'summary', 'requestBody', 'responses', 'x-oaiMeta'] Thought: I should look at the requestBody key to see what parameters are required Action: json_spec_list_keys Action Input: data["paths"]["/completions"]["post"]["requestBody"] Observation: ['required', 'content'] Thought: I should look at the content key to see what parameters are required Action: json_spec_list_keys Action Input: data["paths"]["/completions"]["post"]["requestBody"]["content"] Observation: ['application/json'] Thought: I should look at the application/json key to see what parameters are required Action: json_spec_list_keys Action Input: data["paths"]["/completions"]["post"]["requestBody"]["content"]["application/json"] Observation: ['schema'] Thought: I should look at the schema key to see what parameters are required Action: json_spec_list_keys Action Input: data["paths"]["/completions"]["post"]["requestBody"]["content"]["application/json"]["schema"] Observation: ['$ref'] Thought: I should look at the $ref key to see what parameters are required Action: json_spec_list_keys Action Input: data["paths"]["/completions"]["post"]["requestBody"]["content"]["application/json"]["schema"]["$ref"] Observation: ValueError('Value at path `data["paths"]["/completions"]["post"]["requestBody"]["content"]["application/json"]["schema"]["$ref"]` is not a dict, get the value directly.') Thought: I should look at the $ref key to get the value directly Action: json_spec_get_value Action Input: data["paths"]["/completions"]["post"]["requestBody"]["content"]["application/json"]["schema"]["$ref"] Observation: #/components/schemas/CreateCompletionRequest Thought: I should look at the CreateCompletionRequest schema to see what parameters are required Action: json_spec_list_keys Action Input: data["components"]["schemas"]["CreateCompletionRequest"] Observation: ['type', 'properties', 'required'] Thought: I should look at the required key to see what parameters are required Action: json_spec_get_value Action Input: data["components"]["schemas"]["CreateCompletionRequest"]["required"] Observation: ['model'] Thought: I now know the final answer Final Answer: The required parameters for a POST request to the /completions endpoint are 'model'. > Finished chain. Observation: The required parameters for a POST request to the /completions endpoint are 'model'. Thought: I now know the parameters needed to make the request. Action: requests_post Action Input: { "url": "https://api.openai.com/v1/completions", "data": { "model": "davinci", "prompt": "tell me a joke" } } Observation: {"id":"cmpl-70Ivzip3dazrIXU8DSVJGzFJj2rdv","object":"text_completion","created":1680307139,"model":"davinci","choices":[{"text":" with mummy not there”\n\nYou dig deep and come up with,","index":0,"logprobs":null,"finish_reason":"length"}],"usage":{"prompt_tokens":4,"completion_tokens":16,"total_tokens":20}} Thought: I now know the final answer. Final Answer: The response of the POST request is {"id":"cmpl-70Ivzip3dazrIXU8DSVJGzFJj2rdv","object":"text_completion","created":1680307139,"model":"davinci","choices":[{"text":" with mummy not there”\n\nYou dig deep and come up with,","index":0,"logprobs":null,"finish_reason":"length"}],"usage":{"prompt_tokens":4,"completion_tokens":16,"total_tokens":20}} > Finished chain. 'The response of the POST request is {"id":"cmpl-70Ivzip3dazrIXU8DSVJGzFJj2rdv","object":"text_completion","created":1680307139,"model":"davinci","choices":[{"text":" with mummy not there”\\n\\nYou dig deep and come up with,","index":0,"logprobs":null,"finish_reason":"length"}],"usage":{"prompt_tokens":4,"completion_tokens":16,"total_tokens":20}}'PreviousOffice365NextNatural Language APIs1st example: hierarchical planning agentTo start, let's collect some OpenAPI specs.How big is this spec?Let's see some examples!2nd example: "json explorer" agent |
765 | https://python.langchain.com/docs/integrations/toolkits/openapi_nla | ComponentsAgents and toolkitsNatural Language APIsOn this pageNatural Language APIsNatural Language API Toolkits (NLAToolkits) permit LangChain Agents to efficiently plan and combine calls across endpoints. This notebook demonstrates a sample composition of the Speak, Klarna, and Spoonacluar APIs.For a detailed walkthrough of the OpenAPI chains wrapped within the NLAToolkit, see the OpenAPI Operation Chain notebook.First, import dependencies and load the LLMfrom typing import List, Optionalfrom langchain.chains import LLMChainfrom langchain.llms import OpenAIfrom langchain.prompts import PromptTemplatefrom langchain.requests import Requestsfrom langchain.tools import APIOperation, OpenAPISpecfrom langchain.agents import AgentType, Tool, initialize_agentfrom langchain.agents.agent_toolkits import NLAToolkit# Select the LLM to use. Here, we use text-davinci-003llm = OpenAI( temperature=0, max_tokens=700) # You can swap between different core LLM's here.Next, load the Natural Language API Toolkitsspeak_toolkit = NLAToolkit.from_llm_and_url(llm, "https://api.speak.com/openapi.yaml")klarna_toolkit = NLAToolkit.from_llm_and_url( llm, "https://www.klarna.com/us/shopping/public/openai/v0/api-docs/") Attempting to load an OpenAPI 3.0.1 spec. This may result in degraded performance. Convert your OpenAPI spec to 3.1.* spec for better support. Attempting to load an OpenAPI 3.0.1 spec. This may result in degraded performance. Convert your OpenAPI spec to 3.1.* spec for better support. Attempting to load an OpenAPI 3.0.1 spec. This may result in degraded performance. Convert your OpenAPI spec to 3.1.* spec for better support.Create the Agent# Slightly tweak the instructions from the default agentopenapi_format_instructions = """Use the following format:Question: the input question you must answerThought: you should always think about what to doAction: the action to take, should be one of [{tool_names}]Action Input: what to instruct the AI Action representative.Observation: The Agent's response... (this Thought/Action/Action Input/Observation can repeat N times)Thought: I now know the final answer. User can't see any of my observations, API responses, links, or tools.Final Answer: the final answer to the original input question with the right amount of detailWhen responding with your Final Answer, remember that the person you are responding to CANNOT see any of your Thought/Action/Action Input/Observations, so if there is any relevant information there you need to include it explicitly in your response."""natural_language_tools = speak_toolkit.get_tools() + klarna_toolkit.get_tools()mrkl = initialize_agent( natural_language_tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True, agent_kwargs={"format_instructions": openapi_format_instructions},)mrkl.run( "I have an end of year party for my Italian class and have to buy some Italian clothes for it") > Entering new AgentExecutor chain... I need to find out what kind of Italian clothes are available Action: Open_AI_Klarna_product_Api.productsUsingGET Action Input: Italian clothes Observation: The API response contains two products from the Alé brand in Italian Blue. The first is the Alé Colour Block Short Sleeve Jersey Men - Italian Blue, which costs $86.49, and the second is the Alé Dolid Flash Jersey Men - Italian Blue, which costs $40.00. Thought: I now know what kind of Italian clothes are available and how much they cost. Final Answer: You can buy two products from the Alé brand in Italian Blue for your end of year party. The Alé Colour Block Short Sleeve Jersey Men - Italian Blue costs $86.49, and the Alé Dolid Flash Jersey Men - Italian Blue costs $40.00. > Finished chain. 'You can buy two products from the Alé brand in Italian Blue for your end of year party. The Alé Colour Block Short Sleeve Jersey Men - Italian Blue costs $86.49, and the Alé Dolid Flash Jersey Men - Italian Blue costs $40.00.'Use Auth and add more EndpointsSome endpoints may require user authentication via things like access tokens. Here we show how to pass in the authentication information via the Requests wrapper object.Since each NLATool exposes a concisee natural language interface to its wrapped API, the top level conversational agent has an easier job incorporating each endpoint to satisfy a user's request.Adding the Spoonacular endpoints.Go to the Spoonacular API Console and make a free account.Click on Profile and copy your API key below.spoonacular_api_key = "" # Copy from the API Consolerequests = Requests(headers={"x-api-key": spoonacular_api_key})spoonacular_toolkit = NLAToolkit.from_llm_and_url( llm, "https://spoonacular.com/application/frontend/downloads/spoonacular-openapi-3.json", requests=requests, max_text_length=1800, # If you want to truncate the response text) Attempting to load an OpenAPI 3.0.0 spec. This may result in degraded performance. Convert your OpenAPI spec to 3.1.* spec for better support. Unsupported APIPropertyLocation "header" for parameter Content-Type. Valid values are ['path', 'query'] Ignoring optional parameter Unsupported APIPropertyLocation "header" for parameter Accept. Valid values are ['path', 'query'] Ignoring optional parameter Unsupported APIPropertyLocation "header" for parameter Content-Type. Valid values are ['path', 'query'] Ignoring optional parameter Unsupported APIPropertyLocation "header" for parameter Accept. Valid values are ['path', 'query'] Ignoring optional parameter Unsupported APIPropertyLocation "header" for parameter Content-Type. Valid values are ['path', 'query'] Ignoring optional parameter Unsupported APIPropertyLocation "header" for parameter Accept. Valid values are ['path', 'query'] Ignoring optional parameter Unsupported APIPropertyLocation "header" for parameter Content-Type. Valid values are ['path', 'query'] Ignoring optional parameter Unsupported APIPropertyLocation "header" for parameter Accept. Valid values are ['path', 'query'] Ignoring optional parameter Unsupported APIPropertyLocation "header" for parameter Content-Type. Valid values are ['path', 'query'] Ignoring optional parameter Unsupported APIPropertyLocation "header" for parameter Content-Type. Valid values are ['path', 'query'] Ignoring optional parameter Unsupported APIPropertyLocation "header" for parameter Content-Type. Valid values are ['path', 'query'] Ignoring optional parameter Unsupported APIPropertyLocation "header" for parameter Content-Type. Valid values are ['path', 'query'] Ignoring optional parameter Unsupported APIPropertyLocation "header" for parameter Accept. Valid values are ['path', 'query'] Ignoring optional parameter Unsupported APIPropertyLocation "header" for parameter Content-Type. Valid values are ['path', 'query'] Ignoring optional parameter Unsupported APIPropertyLocation "header" for parameter Accept. Valid values are ['path', 'query'] Ignoring optional parameter Unsupported APIPropertyLocation "header" for parameter Accept. Valid values are ['path', 'query'] Ignoring optional parameter Unsupported APIPropertyLocation "header" for parameter Accept. Valid values are ['path', 'query'] Ignoring optional parameter Unsupported APIPropertyLocation "header" for parameter Content-Type. Valid values are ['path', 'query'] Ignoring optional parameternatural_language_api_tools = ( speak_toolkit.get_tools() + klarna_toolkit.get_tools() + spoonacular_toolkit.get_tools()[:30])print(f"{len(natural_language_api_tools)} tools loaded.") 34 tools loaded.# Create an agent with the new toolsmrkl = initialize_agent( natural_language_api_tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True, agent_kwargs={"format_instructions": openapi_format_instructions},)# Make the query more complex!user_input = ( "I'm learning Italian, and my language class is having an end of year party... " " Could you help me find an Italian outfit to wear and" " an appropriate recipe to prepare so I can present for the class in Italian?")mrkl.run(user_input) > Entering new AgentExecutor chain... I need to find a recipe and an outfit that is Italian-themed. Action: spoonacular_API.searchRecipes Action Input: Italian Observation: The API response contains 10 Italian recipes, including Turkey Tomato Cheese Pizza, Broccolini Quinoa Pilaf, Bruschetta Style Pork & Pasta, Salmon Quinoa Risotto, Italian Tuna Pasta, Roasted Brussels Sprouts With Garlic, Asparagus Lemon Risotto, Italian Steamed Artichokes, Crispy Italian Cauliflower Poppers Appetizer, and Pappa Al Pomodoro. Thought: I need to find an Italian-themed outfit. Action: Open_AI_Klarna_product_Api.productsUsingGET Action Input: Italian Observation: I found 10 products related to 'Italian' in the API response. These products include Italian Gold Sparkle Perfectina Necklace - Gold, Italian Design Miami Cuban Link Chain Necklace - Gold, Italian Gold Miami Cuban Link Chain Necklace - Gold, Italian Gold Herringbone Necklace - Gold, Italian Gold Claddagh Ring - Gold, Italian Gold Herringbone Chain Necklace - Gold, Garmin QuickFit 22mm Italian Vacchetta Leather Band, Macy's Italian Horn Charm - Gold, Dolce & Gabbana Light Blue Italian Love Pour Homme EdT 1.7 fl oz. Thought: I now know the final answer. Final Answer: To present for your Italian language class, you could wear an Italian Gold Sparkle Perfectina Necklace - Gold, an Italian Design Miami Cuban Link Chain Necklace - Gold, or an Italian Gold Miami Cuban Link Chain Necklace - Gold. For a recipe, you could make Turkey Tomato Cheese Pizza, Broccolini Quinoa Pilaf, Bruschetta Style Pork & Pasta, Salmon Quinoa Risotto, Italian Tuna Pasta, Roasted Brussels Sprouts With Garlic, Asparagus Lemon Risotto, Italian Steamed Artichokes, Crispy Italian Cauliflower Poppers Appetizer, or Pappa Al Pomodoro. > Finished chain. 'To present for your Italian language class, you could wear an Italian Gold Sparkle Perfectina Necklace - Gold, an Italian Design Miami Cuban Link Chain Necklace - Gold, or an Italian Gold Miami Cuban Link Chain Necklace - Gold. For a recipe, you could make Turkey Tomato Cheese Pizza, Broccolini Quinoa Pilaf, Bruschetta Style Pork & Pasta, Salmon Quinoa Risotto, Italian Tuna Pasta, Roasted Brussels Sprouts With Garlic, Asparagus Lemon Risotto, Italian Steamed Artichokes, Crispy Italian Cauliflower Poppers Appetizer, or Pappa Al Pomodoro.'Thank you!natural_language_api_tools[1].run( "Tell the LangChain audience to 'enjoy the meal' in Italian, please!") "In Italian, you can say 'Buon appetito' to someone to wish them to enjoy their meal. This phrase is commonly used in Italy when someone is about to eat, often at the beginning of a meal. It's similar to saying 'Bon appétit' in French or 'Guten Appetit' in German."PreviousOpenAPINextPandas DataframeFirst, import dependencies and load the LLMNext, load the Natural Language API ToolkitsCreate the AgentUse Auth and add more EndpointsThank you! |
766 | https://python.langchain.com/docs/integrations/toolkits/pandas | ComponentsAgents and toolkitsPandas DataframeOn this pagePandas DataframeThis notebook shows how to use agents to interact with a Pandas DataFrame. It is mostly optimized for question answering.NOTE: this agent calls the Python agent under the hood, which executes LLM generated Python code - this can be bad if the LLM generated Python code is harmful. Use cautiously.from langchain.agents import create_pandas_dataframe_agentfrom langchain.chat_models import ChatOpenAIfrom langchain.agents.agent_types import AgentTypefrom langchain.llms import OpenAIimport pandas as pddf = pd.read_csv("titanic.csv")Using ZERO_SHOT_REACT_DESCRIPTIONThis shows how to initialize the agent using the ZERO_SHOT_REACT_DESCRIPTION agent type. Note that this is an alternative to the above.agent = create_pandas_dataframe_agent(OpenAI(temperature=0), df, verbose=True)Using OpenAI FunctionsThis shows how to initialize the agent using the OPENAI_FUNCTIONS agent type. Note that this is an alternative to the above.agent = create_pandas_dataframe_agent( ChatOpenAI(temperature=0, model="gpt-3.5-turbo-0613"), df, verbose=True, agent_type=AgentType.OPENAI_FUNCTIONS,)agent.run("how many rows are there?") > Entering new chain... Invoking: `python_repl_ast` with `df.shape[0]` 891There are 891 rows in the dataframe. > Finished chain. 'There are 891 rows in the dataframe.'agent.run("how many people have more than 3 siblings") > Entering new AgentExecutor chain... Thought: I need to count the number of people with more than 3 siblings Action: python_repl_ast Action Input: df[df['SibSp'] > 3].shape[0] Observation: 30 Thought: I now know the final answer Final Answer: 30 people have more than 3 siblings. > Finished chain. '30 people have more than 3 siblings.'agent.run("whats the square root of the average age?") > Entering new AgentExecutor chain... Thought: I need to calculate the average age first Action: python_repl_ast Action Input: df['Age'].mean() Observation: 29.69911764705882 Thought: I now need to calculate the square root of the average age Action: python_repl_ast Action Input: math.sqrt(df['Age'].mean()) Observation: NameError("name 'math' is not defined") Thought: I need to import the math library Action: python_repl_ast Action Input: import math Observation: Thought: I now need to calculate the square root of the average age Action: python_repl_ast Action Input: math.sqrt(df['Age'].mean()) Observation: 5.449689683556195 Thought: I now know the final answer Final Answer: The square root of the average age is 5.449689683556195. > Finished chain. 'The square root of the average age is 5.449689683556195.'Multi DataFrame ExampleThis next part shows how the agent can interact with multiple dataframes passed in as a list.df1 = df.copy()df1["Age"] = df1["Age"].fillna(df1["Age"].mean())agent = create_pandas_dataframe_agent(OpenAI(temperature=0), [df, df1], verbose=True)agent.run("how many rows in the age column are different?") > Entering new AgentExecutor chain... Thought: I need to compare the age columns in both dataframes Action: python_repl_ast Action Input: len(df1[df1['Age'] != df2['Age']]) Observation: 177 Thought: I now know the final answer Final Answer: 177 rows in the age column are different. > Finished chain. '177 rows in the age column are different.'PreviousNatural Language APIsNextPlayWright BrowserUsing ZERO_SHOT_REACT_DESCRIPTIONUsing OpenAI FunctionsMulti DataFrame Example |
767 | https://python.langchain.com/docs/integrations/toolkits/playwright | ComponentsAgents and toolkitsPlayWright BrowserOn this pagePlayWright BrowserThis toolkit is used to interact with the browser. While other tools (like the Requests tools) are fine for static sites, PlayWright Browser toolkits let your agent navigate the web and interact with dynamically rendered sites. Some tools bundled within the PlayWright Browser toolkit include:NavigateTool (navigate_browser) - navigate to a URLNavigateBackTool (previous_page) - wait for an element to appearClickTool (click_element) - click on an element (specified by selector)ExtractTextTool (extract_text) - use beautiful soup to extract text from the current web pageExtractHyperlinksTool (extract_hyperlinks) - use beautiful soup to extract hyperlinks from the current web pageGetElementsTool (get_elements) - select elements by CSS selectorCurrentPageTool (current_page) - get the current page URL# !pip install playwright > /dev/null# !pip install lxml# If this is your first time using playwright, you'll have to install a browser executable.# Running `playwright install` by default installs a chromium browser executable.# playwright installfrom langchain.agents.agent_toolkits import PlayWrightBrowserToolkitfrom langchain.tools.playwright.utils import ( create_async_playwright_browser, create_sync_playwright_browser, # A synchronous browser is available, though it isn't compatible with jupyter.)# This import is required only for jupyter notebooks, since they have their own eventloopimport nest_asyncionest_asyncio.apply()Instantiating a Browser ToolkitIt's always recommended to instantiate using the from_browser method so that the async_browser = create_async_playwright_browser()toolkit = PlayWrightBrowserToolkit.from_browser(async_browser=async_browser)tools = toolkit.get_tools()tools [ClickTool(name='click_element', description='Click on an element with the given CSS selector', args_schema=<class 'langchain.tools.playwright.click.ClickToolInput'>, return_direct=False, verbose=False, callbacks=None, callback_manager=None, sync_browser=None, async_browser=<Browser type=<BrowserType name=chromium executable_path=/Users/wfh/Library/Caches/ms-playwright/chromium-1055/chrome-mac/Chromium.app/Contents/MacOS/Chromium> version=112.0.5615.29>), NavigateTool(name='navigate_browser', description='Navigate a browser to the specified URL', args_schema=<class 'langchain.tools.playwright.navigate.NavigateToolInput'>, return_direct=False, verbose=False, callbacks=None, callback_manager=None, sync_browser=None, async_browser=<Browser type=<BrowserType name=chromium executable_path=/Users/wfh/Library/Caches/ms-playwright/chromium-1055/chrome-mac/Chromium.app/Contents/MacOS/Chromium> version=112.0.5615.29>), NavigateBackTool(name='previous_webpage', description='Navigate back to the previous page in the browser history', args_schema=<class 'pydantic.main.BaseModel'>, return_direct=False, verbose=False, callbacks=None, callback_manager=None, sync_browser=None, async_browser=<Browser type=<BrowserType name=chromium executable_path=/Users/wfh/Library/Caches/ms-playwright/chromium-1055/chrome-mac/Chromium.app/Contents/MacOS/Chromium> version=112.0.5615.29>), ExtractTextTool(name='extract_text', description='Extract all the text on the current webpage', args_schema=<class 'pydantic.main.BaseModel'>, return_direct=False, verbose=False, callbacks=None, callback_manager=None, sync_browser=None, async_browser=<Browser type=<BrowserType name=chromium executable_path=/Users/wfh/Library/Caches/ms-playwright/chromium-1055/chrome-mac/Chromium.app/Contents/MacOS/Chromium> version=112.0.5615.29>), ExtractHyperlinksTool(name='extract_hyperlinks', description='Extract all hyperlinks on the current webpage', args_schema=<class 'langchain.tools.playwright.extract_hyperlinks.ExtractHyperlinksToolInput'>, return_direct=False, verbose=False, callbacks=None, callback_manager=None, sync_browser=None, async_browser=<Browser type=<BrowserType name=chromium executable_path=/Users/wfh/Library/Caches/ms-playwright/chromium-1055/chrome-mac/Chromium.app/Contents/MacOS/Chromium> version=112.0.5615.29>), GetElementsTool(name='get_elements', description='Retrieve elements in the current web page matching the given CSS selector', args_schema=<class 'langchain.tools.playwright.get_elements.GetElementsToolInput'>, return_direct=False, verbose=False, callbacks=None, callback_manager=None, sync_browser=None, async_browser=<Browser type=<BrowserType name=chromium executable_path=/Users/wfh/Library/Caches/ms-playwright/chromium-1055/chrome-mac/Chromium.app/Contents/MacOS/Chromium> version=112.0.5615.29>), CurrentWebPageTool(name='current_webpage', description='Returns the URL of the current page', args_schema=<class 'pydantic.main.BaseModel'>, return_direct=False, verbose=False, callbacks=None, callback_manager=None, sync_browser=None, async_browser=<Browser type=<BrowserType name=chromium executable_path=/Users/wfh/Library/Caches/ms-playwright/chromium-1055/chrome-mac/Chromium.app/Contents/MacOS/Chromium> version=112.0.5615.29>)]tools_by_name = {tool.name: tool for tool in tools}navigate_tool = tools_by_name["navigate_browser"]get_elements_tool = tools_by_name["get_elements"]await navigate_tool.arun( {"url": "https://web.archive.org/web/20230428131116/https://www.cnn.com/world"}) 'Navigating to https://web.archive.org/web/20230428131116/https://www.cnn.com/world returned status code 200'# The browser is shared across tools, so the agent can interact in a stateful mannerawait get_elements_tool.arun( {"selector": ".container__headline", "attributes": ["innerText"]}) '[{"innerText": "These Ukrainian veterinarians are risking their lives to care for dogs and cats in the war zone"}, {"innerText": "Life in the ocean\\u2019s \\u2018twilight zone\\u2019 could disappear due to the climate crisis"}, {"innerText": "Clashes renew in West Darfur as food and water shortages worsen in Sudan violence"}, {"innerText": "Thai policeman\\u2019s wife investigated over alleged murder and a dozen other poison cases"}, {"innerText": "American teacher escaped Sudan on French evacuation plane, with no help offered back home"}, {"innerText": "Dubai\\u2019s emerging hip-hop scene is finding its voice"}, {"innerText": "How an underwater film inspired a marine protected area off Kenya\\u2019s coast"}, {"innerText": "The Iranian drones deployed by Russia in Ukraine are powered by stolen Western technology, research reveals"}, {"innerText": "India says border violations erode \\u2018entire basis\\u2019 of ties with China"}, {"innerText": "Australian police sift through 3,000 tons of trash for missing woman\\u2019s remains"}, {"innerText": "As US and Philippine defense ties grow, China warns over Taiwan tensions"}, {"innerText": "Don McLean offers duet with South Korean president who sang \\u2018American Pie\\u2019 to Biden"}, {"innerText": "Almost two-thirds of elephant habitat lost across Asia, study finds"}, {"innerText": "\\u2018We don\\u2019t sleep \\u2026 I would call it fainting\\u2019: Working as a doctor in Sudan\\u2019s crisis"}, {"innerText": "Kenya arrests second pastor to face criminal charges \\u2018related to mass killing of his followers\\u2019"}, {"innerText": "Russia launches deadly wave of strikes across Ukraine"}, {"innerText": "Woman forced to leave her forever home or \\u2018walk to your death\\u2019 she says"}, {"innerText": "U.S. House Speaker Kevin McCarthy weighs in on Disney-DeSantis feud"}, {"innerText": "Two sides agree to extend Sudan ceasefire"}, {"innerText": "Spanish Leopard 2 tanks are on their way to Ukraine, defense minister confirms"}, {"innerText": "Flamb\\u00e9ed pizza thought to have sparked deadly Madrid restaurant fire"}, {"innerText": "Another bomb found in Belgorod just days after Russia accidentally struck the city"}, {"innerText": "A Black teen\\u2019s murder sparked a crisis over racism in British policing. Thirty years on, little has changed"}, {"innerText": "Belgium destroys shipment of American beer after taking issue with \\u2018Champagne of Beer\\u2019 slogan"}, {"innerText": "UK Prime Minister Rishi Sunak rocked by resignation of top ally Raab over bullying allegations"}, {"innerText": "Iran\\u2019s Navy seizes Marshall Islands-flagged ship"}, {"innerText": "A divided Israel stands at a perilous crossroads on its 75th birthday"}, {"innerText": "Palestinian reporter breaks barriers by reporting in Hebrew on Israeli TV"}, {"innerText": "One-fifth of water pollution comes from textile dyes. But a shellfish-inspired solution could clean it up"}, {"innerText": "\\u2018People sacrificed their lives for just\\u00a010 dollars\\u2019: At least 78 killed in Yemen crowd surge"}, {"innerText": "Israeli police say two men shot near Jewish tomb in Jerusalem in suspected \\u2018terror attack\\u2019"}, {"innerText": "King Charles III\\u2019s coronation: Who\\u2019s performing at the ceremony"}, {"innerText": "The week in 33 photos"}, {"innerText": "Hong Kong\\u2019s endangered turtles"}, {"innerText": "In pictures: Britain\\u2019s Queen Camilla"}, {"innerText": "Catastrophic drought that\\u2019s pushed millions into crisis made 100 times more likely by climate change, analysis finds"}, {"innerText": "For years, a UK mining giant was untouchable in Zambia for pollution until a former miner\\u2019s son took them on"}, {"innerText": "Former Sudanese minister Ahmed Haroun wanted on war crimes charges freed from Khartoum prison"}, {"innerText": "WHO warns of \\u2018biological risk\\u2019 after Sudan fighters seize lab, as violence mars US-brokered ceasefire"}, {"innerText": "How Colombia\\u2019s Petro, a former leftwing guerrilla, found his opening in Washington"}, {"innerText": "Bolsonaro accidentally created Facebook post questioning Brazil election results, say his attorneys"}, {"innerText": "Crowd kills over a dozen suspected gang members in Haiti"}, {"innerText": "Thousands of tequila bottles containing liquid meth seized"}, {"innerText": "Why send a US stealth submarine to South Korea \\u2013 and tell the world about it?"}, {"innerText": "Fukushima\\u2019s fishing industry survived a nuclear disaster. 12 years on, it fears Tokyo\\u2019s next move may finish it off"}, {"innerText": "Singapore executes man for trafficking two pounds of cannabis"}, {"innerText": "Conservative Thai party looks to woo voters with promise to legalize sex toys"}, {"innerText": "Inside the Italian village being repopulated by Americans"}, {"innerText": "Strikes, soaring airfares and yo-yoing hotel fees: A traveler\\u2019s guide to the coronation"}, {"innerText": "A year in Azerbaijan: From spring\\u2019s Grand Prix to winter ski adventures"}, {"innerText": "The bicycle mayor peddling a two-wheeled revolution in Cape Town"}, {"innerText": "Tokyo ramen shop bans customers from using their phones while eating"}, {"innerText": "South African opera star will perform at coronation of King Charles III"}, {"innerText": "Luxury loot under the hammer: France auctions goods seized from drug dealers"}, {"innerText": "Judy Blume\\u2019s books were formative for generations of readers. Here\\u2019s why they endure"}, {"innerText": "Craft, salvage and sustainability take center stage at Milan Design Week"}, {"innerText": "Life-sized chocolate King Charles III sculpture unveiled to celebrate coronation"}, {"innerText": "Severe storms to strike the South again as millions in Texas could see damaging winds and hail"}, {"innerText": "The South is in the crosshairs of severe weather again, as the multi-day threat of large hail and tornadoes continues"}, {"innerText": "Spring snowmelt has cities along the Mississippi bracing for flooding in homes and businesses"}, {"innerText": "Know the difference between a tornado watch, a tornado warning and a tornado emergency"}, {"innerText": "Reporter spotted familiar face covering Sudan evacuation. See what happened next"}, {"innerText": "This country will soon become the world\\u2019s most populated"}, {"innerText": "April 27, 2023 - Russia-Ukraine news"}, {"innerText": "\\u2018Often they shoot at each other\\u2019: Ukrainian drone operator details chaos in Russian ranks"}, {"innerText": "Hear from family members of Americans stuck in Sudan frustrated with US response"}, {"innerText": "U.S. talk show host Jerry Springer dies at 79"}, {"innerText": "Bureaucracy stalling at least one family\\u2019s evacuation from Sudan"}, {"innerText": "Girl to get life-saving treatment for rare immune disease"}, {"innerText": "Haiti\\u2019s crime rate more than doubles in a year"}, {"innerText": "Ocean census aims to discover 100,000 previously unknown marine species"}, {"innerText": "Wall Street Journal editor discusses reporter\\u2019s arrest in Moscow"}, {"innerText": "Can Tunisia\\u2019s democracy be saved?"}, {"innerText": "Yasmeen Lari, \\u2018starchitect\\u2019 turned social engineer, wins one of architecture\\u2019s most coveted prizes"}, {"innerText": "A massive, newly restored Frank Lloyd Wright mansion is up for sale"}, {"innerText": "Are these the most sustainable architectural projects in the world?"}, {"innerText": "Step inside a $72 million London townhouse in a converted army barracks"}, {"innerText": "A 3D-printing company is preparing to build on the lunar surface. But first, a moonshot at home"}, {"innerText": "Simona Halep says \\u2018the stress is huge\\u2019 as she battles to return to tennis following positive drug test"}, {"innerText": "Barcelona reaches third straight Women\\u2019s Champions League final with draw against Chelsea"}, {"innerText": "Wrexham: An intoxicating tale of Hollywood glamor and sporting romance"}, {"innerText": "Shohei Ohtani comes within inches of making yet more MLB history in Angels win"}, {"innerText": "This CNN Hero is recruiting recreational divers to help rebuild reefs in Florida one coral at a time"}, {"innerText": "This CNN Hero offers judgment-free veterinary care for the pets of those experiencing homelessness"}, {"innerText": "Don\\u2019t give up on milestones: A CNN Hero\\u2019s message for Autism Awareness Month"}, {"innerText": "CNN Hero of the Year Nelly Cheboi returned to Kenya with plans to lift more students out of poverty"}]'# If the agent wants to remember the current webpage, it can use the `current_webpage` toolawait tools_by_name["current_webpage"].arun({}) 'https://web.archive.org/web/20230428133211/https://cnn.com/world'Use within an AgentSeveral of the browser tools are StructuredTool's, meaning they expect multiple arguments. These aren't compatible (out of the box) with agents older than the STRUCTURED_CHAT_ZERO_SHOT_REACT_DESCRIPTIONfrom langchain.agents import initialize_agent, AgentTypefrom langchain.chat_models import ChatAnthropicllm = ChatAnthropic(temperature=0) # or any other LLM, e.g., ChatOpenAI(), OpenAI()agent_chain = initialize_agent( tools, llm, agent=AgentType.STRUCTURED_CHAT_ZERO_SHOT_REACT_DESCRIPTION, verbose=True,)result = await agent_chain.arun("What are the headers on langchain.com?")print(result) > Entering new AgentExecutor chain... Thought: I need to navigate to langchain.com to see the headers Action: ``` { "action": "navigate_browser", "action_input": "https://langchain.com/" } ``` Observation: Navigating to https://langchain.com/ returned status code 200 Thought: Action: ``` { "action": "get_elements", "action_input": { "selector": "h1, h2, h3, h4, h5, h6" } } ``` Observation: [] Thought: Thought: The page has loaded, I can now extract the headers Action: ``` { "action": "get_elements", "action_input": { "selector": "h1, h2, h3, h4, h5, h6" } } ``` Observation: [] Thought: Thought: I need to navigate to langchain.com to see the headers Action: ``` { "action": "navigate_browser", "action_input": "https://langchain.com/" } ``` Observation: Navigating to https://langchain.com/ returned status code 200 Thought: > Finished chain. The headers on langchain.com are: h1: Langchain - Decentralized Translation Protocol h2: A protocol for decentralized translation h3: How it works h3: The Problem h3: The Solution h3: Key Features h3: Roadmap h3: Team h3: Advisors h3: Partners h3: FAQ h3: Contact Us h3: Subscribe for updates h3: Follow us on social media h3: Langchain Foundation Ltd. All rights reserved. PreviousPandas DataframeNextPowerBI DatasetInstantiating a Browser ToolkitUse within an Agent |
768 | https://python.langchain.com/docs/integrations/toolkits/powerbi | ComponentsAgents and toolkitsPowerBI DatasetOn this pagePowerBI DatasetThis notebook showcases an agent interacting with a Power BI Dataset. The agent is answering more general questions about a dataset, as well as recover from errors.Note that, as this agent is in active development, all answers might not be correct. It runs against the executequery endpoint, which does not allow deletes.Notes:It relies on authentication with the azure.identity package, which can be installed with pip install azure-identity. Alternatively you can create the powerbi dataset with a token as a string without supplying the credentials.You can also supply a username to impersonate for use with datasets that have RLS enabled. The toolkit uses a LLM to create the query from the question, the agent uses the LLM for the overall execution.Testing was done mostly with a text-davinci-003 model, codex models did not seem to perform ver well.Initializationfrom langchain.agents.agent_toolkits import create_pbi_agentfrom langchain.agents.agent_toolkits import PowerBIToolkitfrom langchain.utilities.powerbi import PowerBIDatasetfrom langchain.chat_models import ChatOpenAIfrom langchain.agents import AgentExecutorfrom azure.identity import DefaultAzureCredentialfast_llm = ChatOpenAI( temperature=0.5, max_tokens=1000, model_name="gpt-3.5-turbo", verbose=True)smart_llm = ChatOpenAI(temperature=0, max_tokens=100, model_name="gpt-4", verbose=True)toolkit = PowerBIToolkit( powerbi=PowerBIDataset( dataset_id="<dataset_id>", table_names=["table1", "table2"], credential=DefaultAzureCredential(), ), llm=smart_llm,)agent_executor = create_pbi_agent( llm=fast_llm, toolkit=toolkit, verbose=True,)Example: describing a tableagent_executor.run("Describe table1")Example: simple query on a tableIn this example, the agent actually figures out the correct query to get a row count of the table.agent_executor.run("How many records are in table1?")Example: running queriesagent_executor.run("How many records are there by dimension1 in table2?")agent_executor.run("What unique values are there for dimensions2 in table2")Example: add your own few-shot prompts# fictional examplefew_shots = """Question: How many rows are in the table revenue?DAX: EVALUATE ROW("Number of rows", COUNTROWS(revenue_details))----Question: How many rows are in the table revenue where year is not empty?DAX: EVALUATE ROW("Number of rows", COUNTROWS(FILTER(revenue_details, revenue_details[year] <> "")))----Question: What was the average of value in revenue in dollars?DAX: EVALUATE ROW("Average", AVERAGE(revenue_details[dollar_value]))----"""toolkit = PowerBIToolkit( powerbi=PowerBIDataset( dataset_id="<dataset_id>", table_names=["table1", "table2"], credential=DefaultAzureCredential(), ), llm=smart_llm, examples=few_shots,)agent_executor = create_pbi_agent( llm=fast_llm, toolkit=toolkit, verbose=True,)agent_executor.run("What was the maximum of value in revenue in dollars in 2022?")PreviousPlayWright BrowserNextPythonNotes:InitializationExample: describing a tableExample: simple query on a tableExample: running queriesExample: add your own few-shot prompts |
769 | https://python.langchain.com/docs/integrations/toolkits/python | ComponentsAgents and toolkitsPythonOn this pagePythonThis notebook showcases an agent designed to write and execute Python code to answer a question.from langchain.agents.agent_toolkits import create_python_agentfrom langchain.tools.python.tool import PythonREPLToolfrom langchain.python import PythonREPLfrom langchain.llms.openai import OpenAIfrom langchain.agents.agent_types import AgentTypefrom langchain.chat_models import ChatOpenAIUsing ZERO_SHOT_REACT_DESCRIPTIONThis shows how to initialize the agent using the ZERO_SHOT_REACT_DESCRIPTION agent type.agent_executor = create_python_agent( llm=OpenAI(temperature=0, max_tokens=1000), tool=PythonREPLTool(), verbose=True, agent_type=AgentType.ZERO_SHOT_REACT_DESCRIPTION,)Using OpenAI FunctionsThis shows how to initialize the agent using the OPENAI_FUNCTIONS agent type. Note that this is an alternative to the above.agent_executor = create_python_agent( llm=ChatOpenAI(temperature=0, model="gpt-3.5-turbo-0613"), tool=PythonREPLTool(), verbose=True, agent_type=AgentType.OPENAI_FUNCTIONS, agent_executor_kwargs={"handle_parsing_errors": True},)Fibonacci ExampleThis example was created by John Wiseman.agent_executor.run("What is the 10th fibonacci number?") > Entering new chain... Invoking: `Python_REPL` with `def fibonacci(n): if n <= 0: return 0 elif n == 1: return 1 else: return fibonacci(n-1) + fibonacci(n-2) fibonacci(10)` The 10th Fibonacci number is 55. > Finished chain. 'The 10th Fibonacci number is 55.'Training neural netThis example was created by Samee Ur Rehman.agent_executor.run( """Understand, write a single neuron neural network in PyTorch.Take synthetic data for y=2x. Train for 1000 epochs and print every 100 epochs.Return prediction for x = 5""") > Entering new chain... Could not parse tool input: {'name': 'python', 'arguments': 'import torch\nimport torch.nn as nn\nimport torch.optim as optim\n\n# Define the neural network\nclass SingleNeuron(nn.Module):\n def __init__(self):\n super(SingleNeuron, self).__init__()\n self.linear = nn.Linear(1, 1)\n \n def forward(self, x):\n return self.linear(x)\n\n# Create the synthetic data\nx_train = torch.tensor([[1.0], [2.0], [3.0], [4.0]], dtype=torch.float32)\ny_train = torch.tensor([[2.0], [4.0], [6.0], [8.0]], dtype=torch.float32)\n\n# Create the neural network\nmodel = SingleNeuron()\n\n# Define the loss function and optimizer\ncriterion = nn.MSELoss()\noptimizer = optim.SGD(model.parameters(), lr=0.01)\n\n# Train the neural network\nfor epoch in range(1, 1001):\n # Forward pass\n y_pred = model(x_train)\n \n # Compute loss\n loss = criterion(y_pred, y_train)\n \n # Backward pass and optimization\n optimizer.zero_grad()\n loss.backward()\n optimizer.step()\n \n # Print the loss every 100 epochs\n if epoch % 100 == 0:\n print(f"Epoch {epoch}: Loss = {loss.item()}")\n\n# Make a prediction for x = 5\nx_test = torch.tensor([[5.0]], dtype=torch.float32)\ny_pred = model(x_test)\ny_pred.item()'} because the `arguments` is not valid JSON.Invalid or incomplete response Invoking: `Python_REPL` with `import torch import torch.nn as nn import torch.optim as optim # Define the neural network class SingleNeuron(nn.Module): def __init__(self): super(SingleNeuron, self).__init__() self.linear = nn.Linear(1, 1) def forward(self, x): return self.linear(x) # Create the synthetic data x_train = torch.tensor([[1.0], [2.0], [3.0], [4.0]], dtype=torch.float32) y_train = torch.tensor([[2.0], [4.0], [6.0], [8.0]], dtype=torch.float32) # Create the neural network model = SingleNeuron() # Define the loss function and optimizer criterion = nn.MSELoss() optimizer = optim.SGD(model.parameters(), lr=0.01) # Train the neural network for epoch in range(1, 1001): # Forward pass y_pred = model(x_train) # Compute loss loss = criterion(y_pred, y_train) # Backward pass and optimization optimizer.zero_grad() loss.backward() optimizer.step() # Print the loss every 100 epochs if epoch % 100 == 0: print(f"Epoch {epoch}: Loss = {loss.item()}") # Make a prediction for x = 5 x_test = torch.tensor([[5.0]], dtype=torch.float32) y_pred = model(x_test) y_pred.item()` Epoch 100: Loss = 0.03825576975941658 Epoch 200: Loss = 0.02100197970867157 Epoch 300: Loss = 0.01152981910854578 Epoch 400: Loss = 0.006329738534986973 Epoch 500: Loss = 0.0034749575424939394 Epoch 600: Loss = 0.0019077073084190488 Epoch 700: Loss = 0.001047312980517745 Epoch 800: Loss = 0.0005749554838985205 Epoch 900: Loss = 0.0003156439634039998 Epoch 1000: Loss = 0.00017328384274151176 Invoking: `Python_REPL` with `x_test.item()` The prediction for x = 5 is 10.000173568725586. > Finished chain. 'The prediction for x = 5 is 10.000173568725586.'PreviousPowerBI DatasetNextSpark DataframeUsing ZERO_SHOT_REACT_DESCRIPTIONUsing OpenAI FunctionsFibonacci ExampleTraining neural net |
770 | https://python.langchain.com/docs/integrations/toolkits/spark | ComponentsAgents and toolkitsSpark DataframeOn this pageSpark DataframeThis notebook shows how to use agents to interact with a Spark DataFrame and Spark Connect. It is mostly optimized for question answering.NOTE: this agent calls the Python agent under the hood, which executes LLM generated Python code - this can be bad if the LLM generated Python code is harmful. Use cautiously.import osos.environ["OPENAI_API_KEY"] = "...input your openai api key here..."Spark DataFrame examplefrom langchain.llms import OpenAIfrom pyspark.sql import SparkSessionfrom langchain.agents import create_spark_dataframe_agentspark = SparkSession.builder.getOrCreate()csv_file_path = "titanic.csv"df = spark.read.csv(csv_file_path, header=True, inferSchema=True)df.show() 23/05/15 20:33:10 WARN Utils: Your hostname, Mikes-Mac-mini.local resolves to a loopback address: 127.0.0.1; using 192.168.68.115 instead (on interface en1) 23/05/15 20:33:10 WARN Utils: Set SPARK_LOCAL_IP if you need to bind to another address Setting default log level to "WARN". To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use setLogLevel(newLevel). 23/05/15 20:33:10 WARN NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable +-----------+--------+------+--------------------+------+----+-----+-----+----------------+-------+-----+--------+ |PassengerId|Survived|Pclass| Name| Sex| Age|SibSp|Parch| Ticket| Fare|Cabin|Embarked| +-----------+--------+------+--------------------+------+----+-----+-----+----------------+-------+-----+--------+ | 1| 0| 3|Braund, Mr. Owen ...| male|22.0| 1| 0| A/5 21171| 7.25| null| S| | 2| 1| 1|Cumings, Mrs. Joh...|female|38.0| 1| 0| PC 17599|71.2833| C85| C| | 3| 1| 3|Heikkinen, Miss. ...|female|26.0| 0| 0|STON/O2. 3101282| 7.925| null| S| | 4| 1| 1|Futrelle, Mrs. Ja...|female|35.0| 1| 0| 113803| 53.1| C123| S| | 5| 0| 3|Allen, Mr. Willia...| male|35.0| 0| 0| 373450| 8.05| null| S| | 6| 0| 3| Moran, Mr. James| male|null| 0| 0| 330877| 8.4583| null| Q| | 7| 0| 1|McCarthy, Mr. Tim...| male|54.0| 0| 0| 17463|51.8625| E46| S| | 8| 0| 3|Palsson, Master. ...| male| 2.0| 3| 1| 349909| 21.075| null| S| | 9| 1| 3|Johnson, Mrs. Osc...|female|27.0| 0| 2| 347742|11.1333| null| S| | 10| 1| 2|Nasser, Mrs. Nich...|female|14.0| 1| 0| 237736|30.0708| null| C| | 11| 1| 3|Sandstrom, Miss. ...|female| 4.0| 1| 1| PP 9549| 16.7| G6| S| | 12| 1| 1|Bonnell, Miss. El...|female|58.0| 0| 0| 113783| 26.55| C103| S| | 13| 0| 3|Saundercock, Mr. ...| male|20.0| 0| 0| A/5. 2151| 8.05| null| S| | 14| 0| 3|Andersson, Mr. An...| male|39.0| 1| 5| 347082| 31.275| null| S| | 15| 0| 3|Vestrom, Miss. Hu...|female|14.0| 0| 0| 350406| 7.8542| null| S| | 16| 1| 2|Hewlett, Mrs. (Ma...|female|55.0| 0| 0| 248706| 16.0| null| S| | 17| 0| 3|Rice, Master. Eugene| male| 2.0| 4| 1| 382652| 29.125| null| Q| | 18| 1| 2|Williams, Mr. Cha...| male|null| 0| 0| 244373| 13.0| null| S| | 19| 0| 3|Vander Planke, Mr...|female|31.0| 1| 0| 345763| 18.0| null| S| | 20| 1| 3|Masselmani, Mrs. ...|female|null| 0| 0| 2649| 7.225| null| C| +-----------+--------+------+--------------------+------+----+-----+-----+----------------+-------+-----+--------+ only showing top 20 rows agent = create_spark_dataframe_agent(llm=OpenAI(temperature=0), df=df, verbose=True)agent.run("how many rows are there?") > Entering new AgentExecutor chain... Thought: I need to find out how many rows are in the dataframe Action: python_repl_ast Action Input: df.count() Observation: 891 Thought: I now know the final answer Final Answer: There are 891 rows in the dataframe. > Finished chain. 'There are 891 rows in the dataframe.'agent.run("how many people have more than 3 siblings") > Entering new AgentExecutor chain... Thought: I need to find out how many people have more than 3 siblings Action: python_repl_ast Action Input: df.filter(df.SibSp > 3).count() Observation: 30 Thought: I now know the final answer Final Answer: 30 people have more than 3 siblings. > Finished chain. '30 people have more than 3 siblings.'agent.run("whats the square root of the average age?") > Entering new AgentExecutor chain... Thought: I need to get the average age first Action: python_repl_ast Action Input: df.agg({"Age": "mean"}).collect()[0][0] Observation: 29.69911764705882 Thought: I now have the average age, I need to get the square root Action: python_repl_ast Action Input: math.sqrt(29.69911764705882) Observation: name 'math' is not defined Thought: I need to import math first Action: python_repl_ast Action Input: import math Observation: Thought: I now have the math library imported, I can get the square root Action: python_repl_ast Action Input: math.sqrt(29.69911764705882) Observation: 5.449689683556195 Thought: I now know the final answer Final Answer: 5.449689683556195 > Finished chain. '5.449689683556195'spark.stop()Spark Connect example# in apache-spark root directory. (tested here with "spark-3.4.0-bin-hadoop3 and later")# To launch Spark with support for Spark Connect sessions, run the start-connect-server.sh script../sbin/start-connect-server.sh --packages org.apache.spark:spark-connect_2.12:3.4.0from pyspark.sql import SparkSession# Now that the Spark server is running, we can connect to it remotely using Spark Connect. We do this by# creating a remote Spark session on the client where our application runs. Before we can do that, we need# to make sure to stop the existing regular Spark session because it cannot coexist with the remote# Spark Connect session we are about to create.SparkSession.builder.master("local[*]").getOrCreate().stop() 23/05/08 10:06:09 WARN Utils: Service 'SparkUI' could not bind on port 4040. Attempting port 4041.# The command we used above to launch the server configured Spark to run as localhost:15002.# So now we can create a remote Spark session on the client using the following command.spark = SparkSession.builder.remote("sc://localhost:15002").getOrCreate()csv_file_path = "titanic.csv"df = spark.read.csv(csv_file_path, header=True, inferSchema=True)df.show() +-----------+--------+------+--------------------+------+----+-----+-----+----------------+-------+-----+--------+ |PassengerId|Survived|Pclass| Name| Sex| Age|SibSp|Parch| Ticket| Fare|Cabin|Embarked| +-----------+--------+------+--------------------+------+----+-----+-----+----------------+-------+-----+--------+ | 1| 0| 3|Braund, Mr. Owen ...| male|22.0| 1| 0| A/5 21171| 7.25| null| S| | 2| 1| 1|Cumings, Mrs. Joh...|female|38.0| 1| 0| PC 17599|71.2833| C85| C| | 3| 1| 3|Heikkinen, Miss. ...|female|26.0| 0| 0|STON/O2. 3101282| 7.925| null| S| | 4| 1| 1|Futrelle, Mrs. Ja...|female|35.0| 1| 0| 113803| 53.1| C123| S| | 5| 0| 3|Allen, Mr. Willia...| male|35.0| 0| 0| 373450| 8.05| null| S| | 6| 0| 3| Moran, Mr. James| male|null| 0| 0| 330877| 8.4583| null| Q| | 7| 0| 1|McCarthy, Mr. Tim...| male|54.0| 0| 0| 17463|51.8625| E46| S| | 8| 0| 3|Palsson, Master. ...| male| 2.0| 3| 1| 349909| 21.075| null| S| | 9| 1| 3|Johnson, Mrs. Osc...|female|27.0| 0| 2| 347742|11.1333| null| S| | 10| 1| 2|Nasser, Mrs. Nich...|female|14.0| 1| 0| 237736|30.0708| null| C| | 11| 1| 3|Sandstrom, Miss. ...|female| 4.0| 1| 1| PP 9549| 16.7| G6| S| | 12| 1| 1|Bonnell, Miss. El...|female|58.0| 0| 0| 113783| 26.55| C103| S| | 13| 0| 3|Saundercock, Mr. ...| male|20.0| 0| 0| A/5. 2151| 8.05| null| S| | 14| 0| 3|Andersson, Mr. An...| male|39.0| 1| 5| 347082| 31.275| null| S| | 15| 0| 3|Vestrom, Miss. Hu...|female|14.0| 0| 0| 350406| 7.8542| null| S| | 16| 1| 2|Hewlett, Mrs. (Ma...|female|55.0| 0| 0| 248706| 16.0| null| S| | 17| 0| 3|Rice, Master. Eugene| male| 2.0| 4| 1| 382652| 29.125| null| Q| | 18| 1| 2|Williams, Mr. Cha...| male|null| 0| 0| 244373| 13.0| null| S| | 19| 0| 3|Vander Planke, Mr...|female|31.0| 1| 0| 345763| 18.0| null| S| | 20| 1| 3|Masselmani, Mrs. ...|female|null| 0| 0| 2649| 7.225| null| C| +-----------+--------+------+--------------------+------+----+-----+-----+----------------+-------+-----+--------+ only showing top 20 rows from langchain.agents import create_spark_dataframe_agentfrom langchain.llms import OpenAIimport osos.environ["OPENAI_API_KEY"] = "...input your openai api key here..."agent = create_spark_dataframe_agent(llm=OpenAI(temperature=0), df=df, verbose=True)agent.run( """who bought the most expensive ticket?You can find all supported function types in https://spark.apache.org/docs/latest/api/python/reference/pyspark.sql/dataframe.html""") > Entering new AgentExecutor chain... Thought: I need to find the row with the highest fare Action: python_repl_ast Action Input: df.sort(df.Fare.desc()).first() Observation: Row(PassengerId=259, Survived=1, Pclass=1, Name='Ward, Miss. Anna', Sex='female', Age=35.0, SibSp=0, Parch=0, Ticket='PC 17755', Fare=512.3292, Cabin=None, Embarked='C') Thought: I now know the name of the person who bought the most expensive ticket Final Answer: Miss. Anna Ward > Finished chain. 'Miss. Anna Ward'spark.stop()PreviousPythonNextSpark SQLSpark DataFrame exampleSpark Connect example |
771 | https://python.langchain.com/docs/integrations/toolkits/spark_sql | ComponentsAgents and toolkitsSpark SQLOn this pageSpark SQLThis notebook shows how to use agents to interact with Spark SQL. Similar to SQL Database Agent, it is designed to address general inquiries about Spark SQL and facilitate error recovery.NOTE: Note that, as this agent is in active development, all answers might not be correct. Additionally, it is not guaranteed that the agent won't perform DML statements on your Spark cluster given certain questions. Be careful running it on sensitive data!Initializationfrom langchain.agents import create_spark_sql_agentfrom langchain.agents.agent_toolkits import SparkSQLToolkitfrom langchain.chat_models import ChatOpenAIfrom langchain.utilities.spark_sql import SparkSQLfrom pyspark.sql import SparkSessionspark = SparkSession.builder.getOrCreate()schema = "langchain_example"spark.sql(f"CREATE DATABASE IF NOT EXISTS {schema}")spark.sql(f"USE {schema}")csv_file_path = "titanic.csv"table = "titanic"spark.read.csv(csv_file_path, header=True, inferSchema=True).write.saveAsTable(table)spark.table(table).show() Setting default log level to "WARN". To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use setLogLevel(newLevel). 23/05/18 16:03:10 WARN NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable +-----------+--------+------+--------------------+------+----+-----+-----+----------------+-------+-----+--------+ |PassengerId|Survived|Pclass| Name| Sex| Age|SibSp|Parch| Ticket| Fare|Cabin|Embarked| +-----------+--------+------+--------------------+------+----+-----+-----+----------------+-------+-----+--------+ | 1| 0| 3|Braund, Mr. Owen ...| male|22.0| 1| 0| A/5 21171| 7.25| null| S| | 2| 1| 1|Cumings, Mrs. Joh...|female|38.0| 1| 0| PC 17599|71.2833| C85| C| | 3| 1| 3|Heikkinen, Miss. ...|female|26.0| 0| 0|STON/O2. 3101282| 7.925| null| S| | 4| 1| 1|Futrelle, Mrs. Ja...|female|35.0| 1| 0| 113803| 53.1| C123| S| | 5| 0| 3|Allen, Mr. Willia...| male|35.0| 0| 0| 373450| 8.05| null| S| | 6| 0| 3| Moran, Mr. James| male|null| 0| 0| 330877| 8.4583| null| Q| | 7| 0| 1|McCarthy, Mr. Tim...| male|54.0| 0| 0| 17463|51.8625| E46| S| | 8| 0| 3|Palsson, Master. ...| male| 2.0| 3| 1| 349909| 21.075| null| S| | 9| 1| 3|Johnson, Mrs. Osc...|female|27.0| 0| 2| 347742|11.1333| null| S| | 10| 1| 2|Nasser, Mrs. Nich...|female|14.0| 1| 0| 237736|30.0708| null| C| | 11| 1| 3|Sandstrom, Miss. ...|female| 4.0| 1| 1| PP 9549| 16.7| G6| S| | 12| 1| 1|Bonnell, Miss. El...|female|58.0| 0| 0| 113783| 26.55| C103| S| | 13| 0| 3|Saundercock, Mr. ...| male|20.0| 0| 0| A/5. 2151| 8.05| null| S| | 14| 0| 3|Andersson, Mr. An...| male|39.0| 1| 5| 347082| 31.275| null| S| | 15| 0| 3|Vestrom, Miss. Hu...|female|14.0| 0| 0| 350406| 7.8542| null| S| | 16| 1| 2|Hewlett, Mrs. (Ma...|female|55.0| 0| 0| 248706| 16.0| null| S| | 17| 0| 3|Rice, Master. Eugene| male| 2.0| 4| 1| 382652| 29.125| null| Q| | 18| 1| 2|Williams, Mr. Cha...| male|null| 0| 0| 244373| 13.0| null| S| | 19| 0| 3|Vander Planke, Mr...|female|31.0| 1| 0| 345763| 18.0| null| S| | 20| 1| 3|Masselmani, Mrs. ...|female|null| 0| 0| 2649| 7.225| null| C| +-----------+--------+------+--------------------+------+----+-----+-----+----------------+-------+-----+--------+ only showing top 20 rows # Note, you can also connect to Spark via Spark connect. For example:# db = SparkSQL.from_uri("sc://localhost:15002", schema=schema)spark_sql = SparkSQL(schema=schema)llm = ChatOpenAI(temperature=0)toolkit = SparkSQLToolkit(db=spark_sql, llm=llm)agent_executor = create_spark_sql_agent(llm=llm, toolkit=toolkit, verbose=True)Example: describing a tableagent_executor.run("Describe the titanic table") > Entering new AgentExecutor chain... Action: list_tables_sql_db Action Input: Observation: titanic Thought:I found the titanic table. Now I need to get the schema and sample rows for the titanic table. Action: schema_sql_db Action Input: titanic Observation: CREATE TABLE langchain_example.titanic ( PassengerId INT, Survived INT, Pclass INT, Name STRING, Sex STRING, Age DOUBLE, SibSp INT, Parch INT, Ticket STRING, Fare DOUBLE, Cabin STRING, Embarked STRING) ; /* 3 rows from titanic table: PassengerId Survived Pclass Name Sex Age SibSp Parch Ticket Fare Cabin Embarked 1 0 3 Braund, Mr. Owen Harris male 22.0 1 0 A/5 21171 7.25 None S 2 1 1 Cumings, Mrs. John Bradley (Florence Briggs Thayer) female 38.0 1 0 PC 17599 71.2833 C85 C 3 1 3 Heikkinen, Miss. Laina female 26.0 0 0 STON/O2. 3101282 7.925 None S */ Thought:I now know the schema and sample rows for the titanic table. Final Answer: The titanic table has the following columns: PassengerId (INT), Survived (INT), Pclass (INT), Name (STRING), Sex (STRING), Age (DOUBLE), SibSp (INT), Parch (INT), Ticket (STRING), Fare (DOUBLE), Cabin (STRING), and Embarked (STRING). Here are some sample rows from the table: 1. PassengerId: 1, Survived: 0, Pclass: 3, Name: Braund, Mr. Owen Harris, Sex: male, Age: 22.0, SibSp: 1, Parch: 0, Ticket: A/5 21171, Fare: 7.25, Cabin: None, Embarked: S 2. PassengerId: 2, Survived: 1, Pclass: 1, Name: Cumings, Mrs. John Bradley (Florence Briggs Thayer), Sex: female, Age: 38.0, SibSp: 1, Parch: 0, Ticket: PC 17599, Fare: 71.2833, Cabin: C85, Embarked: C 3. PassengerId: 3, Survived: 1, Pclass: 3, Name: Heikkinen, Miss. Laina, Sex: female, Age: 26.0, SibSp: 0, Parch: 0, Ticket: STON/O2. 3101282, Fare: 7.925, Cabin: None, Embarked: S > Finished chain. 'The titanic table has the following columns: PassengerId (INT), Survived (INT), Pclass (INT), Name (STRING), Sex (STRING), Age (DOUBLE), SibSp (INT), Parch (INT), Ticket (STRING), Fare (DOUBLE), Cabin (STRING), and Embarked (STRING). Here are some sample rows from the table: \n\n1. PassengerId: 1, Survived: 0, Pclass: 3, Name: Braund, Mr. Owen Harris, Sex: male, Age: 22.0, SibSp: 1, Parch: 0, Ticket: A/5 21171, Fare: 7.25, Cabin: None, Embarked: S\n2. PassengerId: 2, Survived: 1, Pclass: 1, Name: Cumings, Mrs. John Bradley (Florence Briggs Thayer), Sex: female, Age: 38.0, SibSp: 1, Parch: 0, Ticket: PC 17599, Fare: 71.2833, Cabin: C85, Embarked: C\n3. PassengerId: 3, Survived: 1, Pclass: 3, Name: Heikkinen, Miss. Laina, Sex: female, Age: 26.0, SibSp: 0, Parch: 0, Ticket: STON/O2. 3101282, Fare: 7.925, Cabin: None, Embarked: S'Example: running queriesagent_executor.run("whats the square root of the average age?") > Entering new AgentExecutor chain... Action: list_tables_sql_db Action Input: Observation: titanic Thought:I should check the schema of the titanic table to see if there is an age column. Action: schema_sql_db Action Input: titanic Observation: CREATE TABLE langchain_example.titanic ( PassengerId INT, Survived INT, Pclass INT, Name STRING, Sex STRING, Age DOUBLE, SibSp INT, Parch INT, Ticket STRING, Fare DOUBLE, Cabin STRING, Embarked STRING) ; /* 3 rows from titanic table: PassengerId Survived Pclass Name Sex Age SibSp Parch Ticket Fare Cabin Embarked 1 0 3 Braund, Mr. Owen Harris male 22.0 1 0 A/5 21171 7.25 None S 2 1 1 Cumings, Mrs. John Bradley (Florence Briggs Thayer) female 38.0 1 0 PC 17599 71.2833 C85 C 3 1 3 Heikkinen, Miss. Laina female 26.0 0 0 STON/O2. 3101282 7.925 None S */ Thought:There is an Age column in the titanic table. I should write a query to calculate the average age and then find the square root of the result. Action: query_checker_sql_db Action Input: SELECT SQRT(AVG(Age)) as square_root_of_avg_age FROM titanic Observation: The original query seems to be correct. Here it is again: SELECT SQRT(AVG(Age)) as square_root_of_avg_age FROM titanic Thought:The query is correct, so I can execute it to find the square root of the average age. Action: query_sql_db Action Input: SELECT SQRT(AVG(Age)) as square_root_of_avg_age FROM titanic Observation: [('5.449689683556195',)] Thought:I now know the final answer Final Answer: The square root of the average age is approximately 5.45. > Finished chain. 'The square root of the average age is approximately 5.45.'agent_executor.run("What's the name of the oldest survived passenger?") > Entering new AgentExecutor chain... Action: list_tables_sql_db Action Input: Observation: titanic Thought:I should check the schema of the titanic table to see what columns are available. Action: schema_sql_db Action Input: titanic Observation: CREATE TABLE langchain_example.titanic ( PassengerId INT, Survived INT, Pclass INT, Name STRING, Sex STRING, Age DOUBLE, SibSp INT, Parch INT, Ticket STRING, Fare DOUBLE, Cabin STRING, Embarked STRING) ; /* 3 rows from titanic table: PassengerId Survived Pclass Name Sex Age SibSp Parch Ticket Fare Cabin Embarked 1 0 3 Braund, Mr. Owen Harris male 22.0 1 0 A/5 21171 7.25 None S 2 1 1 Cumings, Mrs. John Bradley (Florence Briggs Thayer) female 38.0 1 0 PC 17599 71.2833 C85 C 3 1 3 Heikkinen, Miss. Laina female 26.0 0 0 STON/O2. 3101282 7.925 None S */ Thought:I can use the titanic table to find the oldest survived passenger. I will query the Name and Age columns, filtering by Survived and ordering by Age in descending order. Action: query_checker_sql_db Action Input: SELECT Name, Age FROM titanic WHERE Survived = 1 ORDER BY Age DESC LIMIT 1 Observation: SELECT Name, Age FROM titanic WHERE Survived = 1 ORDER BY Age DESC LIMIT 1 Thought:The query is correct. Now I will execute it to find the oldest survived passenger. Action: query_sql_db Action Input: SELECT Name, Age FROM titanic WHERE Survived = 1 ORDER BY Age DESC LIMIT 1 Observation: [('Barkworth, Mr. Algernon Henry Wilson', '80.0')] Thought:I now know the final answer. Final Answer: The oldest survived passenger is Barkworth, Mr. Algernon Henry Wilson, who was 80 years old. > Finished chain. 'The oldest survived passenger is Barkworth, Mr. Algernon Henry Wilson, who was 80 years old.'PreviousSpark DataframeNextSQL DatabaseInitializationExample: describing a tableExample: running queries |
772 | https://python.langchain.com/docs/integrations/toolkits/sql_database | ComponentsAgents and toolkitsSQL DatabaseOn this pageSQL DatabaseThis notebook showcases an agent designed to interact with a SQL databases.
The agent builds off of SQLDatabaseChain and is designed to answer more general questions about a database, as well as recover from errors.Note that, as this agent is in active development, all answers might not be correct. Additionally, it is not guaranteed that the agent won't perform DML statements on your database given certain questions. Be careful running it on sensitive data!This uses the example Chinook database. To set it up follow the instructions on https://database.guide/2-sample-databases-sqlite/, placing the .db file in a notebooks folder at the root of this repository.Initializationfrom langchain.agents import create_sql_agentfrom langchain.agents.agent_toolkits import SQLDatabaseToolkitfrom langchain.sql_database import SQLDatabasefrom langchain.llms.openai import OpenAIfrom langchain.agents import AgentExecutorfrom langchain.agents.agent_types import AgentTypefrom langchain.chat_models import ChatOpenAIdb = SQLDatabase.from_uri("sqlite:///../../../../../notebooks/Chinook.db")toolkit = SQLDatabaseToolkit(db=db, llm=OpenAI(temperature=0))Using ZERO_SHOT_REACT_DESCRIPTIONThis shows how to initialize the agent using the ZERO_SHOT_REACT_DESCRIPTION agent type.agent_executor = create_sql_agent( llm=OpenAI(temperature=0), toolkit=toolkit, verbose=True, agent_type=AgentType.ZERO_SHOT_REACT_DESCRIPTION,)Using OpenAI FunctionsThis shows how to initialize the agent using the OPENAI_FUNCTIONS agent type. Note that this is an alternative to the above.# agent_executor = create_sql_agent(# llm=ChatOpenAI(temperature=0, model="gpt-3.5-turbo-0613"),# toolkit=toolkit,# verbose=True,# agent_type=AgentType.OPENAI_FUNCTIONS# )Disclamer ⚠️The query chain may generate insert/update/delete queries. When this is not expected, use a custom prompt or create a SQL users without write permissions.The final user might overload your SQL database by asking a simple question such as "run the biggest query possible". The generated query might look like:SELECT * FROM "public"."users" JOIN "public"."user_permissions" ON "public"."users".id = "public"."user_permissions".user_id JOIN "public"."projects" ON "public"."users".id = "public"."projects".user_id JOIN "public"."events" ON "public"."projects".id = "public"."events".project_id;For a transactional SQL database, if one of the table above contains millions of rows, the query might cause trouble to other applications using the same database.Most datawarehouse oriented databases support user-level quota, for limiting resource usage.Example: describing a tableagent_executor.run("Describe the playlisttrack table") > Entering new chain... Invoking: `list_tables_sql_db` with `{}` Album, Artist, Track, PlaylistTrack, InvoiceLine, sales_table, Playlist, Genre, Employee, Customer, Invoice, MediaType Invoking: `schema_sql_db` with `PlaylistTrack` CREATE TABLE "PlaylistTrack" ( "PlaylistId" INTEGER NOT NULL, "TrackId" INTEGER NOT NULL, PRIMARY KEY ("PlaylistId", "TrackId"), FOREIGN KEY("TrackId") REFERENCES "Track" ("TrackId"), FOREIGN KEY("PlaylistId") REFERENCES "Playlist" ("PlaylistId") ) /* 3 rows from PlaylistTrack table: PlaylistId TrackId 1 3402 1 3389 1 3390 */The `PlaylistTrack` table has two columns: `PlaylistId` and `TrackId`. It is a junction table that represents the relationship between playlists and tracks. Here is the schema of the `PlaylistTrack` table: ``` CREATE TABLE "PlaylistTrack" ( "PlaylistId" INTEGER NOT NULL, "TrackId" INTEGER NOT NULL, PRIMARY KEY ("PlaylistId", "TrackId"), FOREIGN KEY("TrackId") REFERENCES "Track" ("TrackId"), FOREIGN KEY("PlaylistId") REFERENCES "Playlist" ("PlaylistId") ) ``` Here are three sample rows from the `PlaylistTrack` table: ``` PlaylistId TrackId 1 3402 1 3389 1 3390 ``` Please let me know if there is anything else I can help you with. > Finished chain. 'The `PlaylistTrack` table has two columns: `PlaylistId` and `TrackId`. It is a junction table that represents the relationship between playlists and tracks. \n\nHere is the schema of the `PlaylistTrack` table:\n\n```\nCREATE TABLE "PlaylistTrack" (\n\t"PlaylistId" INTEGER NOT NULL, \n\t"TrackId" INTEGER NOT NULL, \n\tPRIMARY KEY ("PlaylistId", "TrackId"), \n\tFOREIGN KEY("TrackId") REFERENCES "Track" ("TrackId"), \n\tFOREIGN KEY("PlaylistId") REFERENCES "Playlist" ("PlaylistId")\n)\n```\n\nHere are three sample rows from the `PlaylistTrack` table:\n\n```\nPlaylistId TrackId\n1 3402\n1 3389\n1 3390\n```\n\nPlease let me know if there is anything else I can help you with.'Example: describing a table, recovering from an errorIn this example, the agent tries to search for a table that doesn't exist, but finds the next best resultagent_executor.run("Describe the playlistsong table") > Entering new AgentExecutor chain... Action: list_tables_sql_db Action Input: "" Observation: Genre, PlaylistTrack, MediaType, Invoice, InvoiceLine, Track, Playlist, Customer, Album, Employee, Artist Thought: I should look at the schema of the PlaylistSong table Action: schema_sql_db Action Input: "PlaylistSong" Observation: Error: table_names {'PlaylistSong'} not found in database Thought: I should check the spelling of the table Action: list_tables_sql_db Action Input: "" Observation: Genre, PlaylistTrack, MediaType, Invoice, InvoiceLine, Track, Playlist, Customer, Album, Employee, Artist Thought: The table is called PlaylistTrack Action: schema_sql_db Action Input: "PlaylistTrack" Observation: CREATE TABLE "PlaylistTrack" ( "PlaylistId" INTEGER NOT NULL, "TrackId" INTEGER NOT NULL, PRIMARY KEY ("PlaylistId", "TrackId"), FOREIGN KEY("TrackId") REFERENCES "Track" ("TrackId"), FOREIGN KEY("PlaylistId") REFERENCES "Playlist" ("PlaylistId") ) SELECT * FROM 'PlaylistTrack' LIMIT 3; PlaylistId TrackId 1 3402 1 3389 1 3390 Thought: I now know the final answer Final Answer: The PlaylistTrack table contains two columns, PlaylistId and TrackId, which are both integers and are used to link Playlist and Track tables. > Finished chain. 'The PlaylistTrack table contains two columns, PlaylistId and TrackId, which are both integers and are used to link Playlist and Track tables.'Example: running queriesagent_executor.run( "List the total sales per country. Which country's customers spent the most?") > Entering new AgentExecutor chain... Action: list_tables_sql_db Action Input: "" Observation: Invoice, MediaType, Artist, InvoiceLine, Genre, Playlist, Employee, Album, PlaylistTrack, Track, Customer Thought: I should look at the schema of the relevant tables to see what columns I can use. Action: schema_sql_db Action Input: "Invoice, Customer" Observation: CREATE TABLE "Customer" ( "CustomerId" INTEGER NOT NULL, "FirstName" NVARCHAR(40) NOT NULL, "LastName" NVARCHAR(20) NOT NULL, "Company" NVARCHAR(80), "Address" NVARCHAR(70), "City" NVARCHAR(40), "State" NVARCHAR(40), "Country" NVARCHAR(40), "PostalCode" NVARCHAR(10), "Phone" NVARCHAR(24), "Fax" NVARCHAR(24), "Email" NVARCHAR(60) NOT NULL, "SupportRepId" INTEGER, PRIMARY KEY ("CustomerId"), FOREIGN KEY("SupportRepId") REFERENCES "Employee" ("EmployeeId") ) SELECT * FROM 'Customer' LIMIT 3; CustomerId FirstName LastName Company Address City State Country PostalCode Phone Fax Email SupportRepId 1 Luís Gonçalves Embraer - Empresa Brasileira de Aeronáutica S.A. Av. Brigadeiro Faria Lima, 2170 São José dos Campos SP Brazil 12227-000 +55 (12) 3923-5555 +55 (12) 3923-5566 [email protected] 3 2 Leonie Köhler None Theodor-Heuss-Straße 34 Stuttgart None Germany 70174 +49 0711 2842222 None [email protected] 5 3 François Tremblay None 1498 rue Bélanger Montréal QC Canada H2G 1A7 +1 (514) 721-4711 None [email protected] 3 CREATE TABLE "Invoice" ( "InvoiceId" INTEGER NOT NULL, "CustomerId" INTEGER NOT NULL, "InvoiceDate" DATETIME NOT NULL, "BillingAddress" NVARCHAR(70), "BillingCity" NVARCHAR(40), "BillingState" NVARCHAR(40), "BillingCountry" NVARCHAR(40), "BillingPostalCode" NVARCHAR(10), "Total" NUMERIC(10, 2) NOT NULL, PRIMARY KEY ("InvoiceId"), FOREIGN KEY("CustomerId") REFERENCES "Customer" ("CustomerId") ) SELECT * FROM 'Invoice' LIMIT 3; InvoiceId CustomerId InvoiceDate BillingAddress BillingCity BillingState BillingCountry BillingPostalCode Total 1 2 2009-01-01 00:00:00 Theodor-Heuss-Straße 34 Stuttgart None Germany 70174 1.98 2 4 2009-01-02 00:00:00 Ullevålsveien 14 Oslo None Norway 0171 3.96 3 8 2009-01-03 00:00:00 Grétrystraat 63 Brussels None Belgium 1000 5.94 Thought: I should query the Invoice and Customer tables to get the total sales per country. Action: query_sql_db Action Input: SELECT c.Country, SUM(i.Total) AS TotalSales FROM Invoice i INNER JOIN Customer c ON i.CustomerId = c.CustomerId GROUP BY c.Country ORDER BY TotalSales DESC LIMIT 10 Observation: [('USA', 523.0600000000003), ('Canada', 303.9599999999999), ('France', 195.09999999999994), ('Brazil', 190.09999999999997), ('Germany', 156.48), ('United Kingdom', 112.85999999999999), ('Czech Republic', 90.24000000000001), ('Portugal', 77.23999999999998), ('India', 75.25999999999999), ('Chile', 46.62)] Thought: I now know the final answer Final Answer: The customers from the USA spent the most, with a total of $523.06. > Finished chain. 'The customers from the USA spent the most, with a total of $523.06.'agent_executor.run( "Show the total number of tracks in each playlist. The Playlist name should be included in the result.") > Entering new AgentExecutor chain... Action: list_tables_sql_db Action Input: "" Observation: Invoice, MediaType, Artist, InvoiceLine, Genre, Playlist, Employee, Album, PlaylistTrack, Track, Customer Thought: I should look at the schema of the Playlist and PlaylistTrack tables to see what columns I can use. Action: schema_sql_db Action Input: "Playlist, PlaylistTrack" Observation: CREATE TABLE "Playlist" ( "PlaylistId" INTEGER NOT NULL, "Name" NVARCHAR(120), PRIMARY KEY ("PlaylistId") ) SELECT * FROM 'Playlist' LIMIT 3; PlaylistId Name 1 Music 2 Movies 3 TV Shows CREATE TABLE "PlaylistTrack" ( "PlaylistId" INTEGER NOT NULL, "TrackId" INTEGER NOT NULL, PRIMARY KEY ("PlaylistId", "TrackId"), FOREIGN KEY("TrackId") REFERENCES "Track" ("TrackId"), FOREIGN KEY("PlaylistId") REFERENCES "Playlist" ("PlaylistId") ) SELECT * FROM 'PlaylistTrack' LIMIT 3; PlaylistId TrackId 1 3402 1 3389 1 3390 Thought: I can use a SELECT statement to get the total number of tracks in each playlist. Action: query_checker_sql_db Action Input: SELECT Playlist.Name, COUNT(PlaylistTrack.TrackId) AS TotalTracks FROM Playlist INNER JOIN PlaylistTrack ON Playlist.PlaylistId = PlaylistTrack.PlaylistId GROUP BY Playlist.Name Observation: SELECT Playlist.Name, COUNT(PlaylistTrack.TrackId) AS TotalTracks FROM Playlist INNER JOIN PlaylistTrack ON Playlist.PlaylistId = PlaylistTrack.PlaylistId GROUP BY Playlist.Name Thought: The query looks correct, I can now execute it. Action: query_sql_db Action Input: SELECT Playlist.Name, COUNT(PlaylistTrack.TrackId) AS TotalTracks FROM Playlist INNER JOIN PlaylistTrack ON Playlist.PlaylistId = PlaylistTrack.PlaylistId GROUP BY Playlist.Name LIMIT 10 Observation: [('90’s Music', 1477), ('Brazilian Music', 39), ('Classical', 75), ('Classical 101 - Deep Cuts', 25), ('Classical 101 - Next Steps', 25), ('Classical 101 - The Basics', 25), ('Grunge', 15), ('Heavy Metal Classic', 26), ('Music', 6580), ('Music Videos', 1)] Thought: I now know the final answer. Final Answer: The total number of tracks in each playlist are: '90’s Music' (1477), 'Brazilian Music' (39), 'Classical' (75), 'Classical 101 - Deep Cuts' (25), 'Classical 101 - Next Steps' (25), 'Classical 101 - The Basics' (25), 'Grunge' (15), 'Heavy Metal Classic' (26), 'Music' (6580), 'Music Videos' (1). > Finished chain. "The total number of tracks in each playlist are: '90’s Music' (1477), 'Brazilian Music' (39), 'Classical' (75), 'Classical 101 - Deep Cuts' (25), 'Classical 101 - Next Steps' (25), 'Classical 101 - The Basics' (25), 'Grunge' (15), 'Heavy Metal Classic' (26), 'Music' (6580), 'Music Videos' (1)."Recovering from an errorIn this example, the agent is able to recover from an error after initially trying to access an attribute (Track.ArtistId) which doesn't exist.agent_executor.run("Who are the top 3 best selling artists?") > Entering new AgentExecutor chain... Action: list_tables_sql_db Action Input: "" Observation: MediaType, Track, Invoice, Album, Playlist, Customer, Employee, InvoiceLine, PlaylistTrack, Genre, Artist Thought: I should look at the schema of the Artist, InvoiceLine, and Track tables to see what columns I can use. Action: schema_sql_db Action Input: "Artist, InvoiceLine, Track" Observation: CREATE TABLE "Artist" ( "ArtistId" INTEGER NOT NULL, "Name" NVARCHAR(120), PRIMARY KEY ("ArtistId") ) SELECT * FROM 'Artist' LIMIT 3; ArtistId Name 1 AC/DC 2 Accept 3 Aerosmith CREATE TABLE "Track" ( "TrackId" INTEGER NOT NULL, "Name" NVARCHAR(200) NOT NULL, "AlbumId" INTEGER, "MediaTypeId" INTEGER NOT NULL, "GenreId" INTEGER, "Composer" NVARCHAR(220), "Milliseconds" INTEGER NOT NULL, "Bytes" INTEGER, "UnitPrice" NUMERIC(10, 2) NOT NULL, PRIMARY KEY ("TrackId"), FOREIGN KEY("MediaTypeId") REFERENCES "MediaType" ("MediaTypeId"), FOREIGN KEY("GenreId") REFERENCES "Genre" ("GenreId"), FOREIGN KEY("AlbumId") REFERENCES "Album" ("AlbumId") ) SELECT * FROM 'Track' LIMIT 3; TrackId Name AlbumId MediaTypeId GenreId Composer Milliseconds Bytes UnitPrice 1 For Those About To Rock (We Salute You) 1 1 1 Angus Young, Malcolm Young, Brian Johnson 343719 11170334 0.99 2 Balls to the Wall 2 2 1 None 342562 5510424 0.99 3 Fast As a Shark 3 2 1 F. Baltes, S. Kaufman, U. Dirkscneider & W. Hoffman 230619 3990994 0.99 CREATE TABLE "InvoiceLine" ( "InvoiceLineId" INTEGER NOT NULL, "InvoiceId" INTEGER NOT NULL, "TrackId" INTEGER NOT NULL, "UnitPrice" NUMERIC(10, 2) NOT NULL, "Quantity" INTEGER NOT NULL, PRIMARY KEY ("InvoiceLineId"), FOREIGN KEY("TrackId") REFERENCES "Track" ("TrackId"), FOREIGN KEY("InvoiceId") REFERENCES "Invoice" ("InvoiceId") ) SELECT * FROM 'InvoiceLine' LIMIT 3; InvoiceLineId InvoiceId TrackId UnitPrice Quantity 1 1 2 0.99 1 2 1 4 0.99 1 3 2 6 0.99 1 Thought: I should query the database to get the top 3 best selling artists. Action: query_sql_db Action Input: SELECT Artist.Name, SUM(InvoiceLine.Quantity) AS TotalQuantity FROM Artist INNER JOIN Track ON Artist.ArtistId = Track.ArtistId INNER JOIN InvoiceLine ON Track.TrackId = InvoiceLine.TrackId GROUP BY Artist.Name ORDER BY TotalQuantity DESC LIMIT 3 Observation: Error: (sqlite3.OperationalError) no such column: Track.ArtistId [SQL: SELECT Artist.Name, SUM(InvoiceLine.Quantity) AS TotalQuantity FROM Artist INNER JOIN Track ON Artist.ArtistId = Track.ArtistId INNER JOIN InvoiceLine ON Track.TrackId = InvoiceLine.TrackId GROUP BY Artist.Name ORDER BY TotalQuantity DESC LIMIT 3] (Background on this error at: https://sqlalche.me/e/14/e3q8) Thought: I should double check my query before executing it. Action: query_checker_sql_db Action Input: SELECT Artist.Name, SUM(InvoiceLine.Quantity) AS TotalQuantity FROM Artist INNER JOIN Track ON Artist.ArtistId = Track.ArtistId INNER JOIN InvoiceLine ON Track.TrackId = InvoiceLine.TrackId GROUP BY Artist.Name ORDER BY TotalQuantity DESC LIMIT 3 Observation: SELECT Artist.Name, SUM(InvoiceLine.Quantity) AS TotalQuantity FROM Artist INNER JOIN Track ON Artist.ArtistId = Track.ArtistId INNER JOIN InvoiceLine ON Track.TrackId = InvoiceLine.TrackId GROUP BY Artist.Name ORDER BY TotalQuantity DESC LIMIT 3; Thought: I now know the final answer. Action: query_sql_db Action Input: SELECT Artist.Name, SUM(InvoiceLine.Quantity) AS TotalQuantity FROM Artist INNER JOIN Album ON Artist.ArtistId = Album.ArtistId INNER JOIN Track ON Album.AlbumId = Track.AlbumId INNER JOIN InvoiceLine ON Track.TrackId = InvoiceLine.TrackId GROUP BY Artist.Name ORDER BY TotalQuantity DESC LIMIT 3 Observation: [('Iron Maiden', 140), ('U2', 107), ('Metallica', 91)] Thought: I now know the final answer. Final Answer: The top 3 best selling artists are Iron Maiden, U2, and Metallica. > Finished chain. 'The top 3 best selling artists are Iron Maiden, U2, and Metallica.'PreviousSpark SQLNextVectorstoreInitializationUsing ZERO_SHOT_REACT_DESCRIPTIONUsing OpenAI FunctionsDisclamer ⚠️Example: describing a tableExample: describing a table, recovering from an errorExample: running queriesRecovering from an error |
773 | https://python.langchain.com/docs/integrations/toolkits/vectorstore | ComponentsAgents and toolkitsVectorstoreOn this pageVectorstoreThis notebook showcases an agent designed to retrieve information from one or more vectorstores, either with or without sources.Create Vectorstoresfrom langchain.embeddings.openai import OpenAIEmbeddingsfrom langchain.vectorstores import Chromafrom langchain.text_splitter import CharacterTextSplitterfrom langchain.llms import OpenAIfrom langchain.chains import VectorDBQAllm = OpenAI(temperature=0)from langchain.document_loaders import TextLoaderloader = TextLoader("../../../state_of_the_union.txt")documents = loader.load()text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)texts = text_splitter.split_documents(documents)embeddings = OpenAIEmbeddings()state_of_union_store = Chroma.from_documents( texts, embeddings, collection_name="state-of-union") Running Chroma using direct local API. Using DuckDB in-memory for database. Data will be transient.from langchain.document_loaders import WebBaseLoaderloader = WebBaseLoader("https://beta.ruff.rs/docs/faq/")docs = loader.load()ruff_texts = text_splitter.split_documents(docs)ruff_store = Chroma.from_documents(ruff_texts, embeddings, collection_name="ruff") Running Chroma using direct local API. Using DuckDB in-memory for database. Data will be transient.Initialize Toolkit and AgentFirst, we'll create an agent with a single vectorstore.from langchain.agents.agent_toolkits import ( create_vectorstore_agent, VectorStoreToolkit, VectorStoreInfo,)vectorstore_info = VectorStoreInfo( name="state_of_union_address", description="the most recent state of the Union adress", vectorstore=state_of_union_store,)toolkit = VectorStoreToolkit(vectorstore_info=vectorstore_info)agent_executor = create_vectorstore_agent(llm=llm, toolkit=toolkit, verbose=True)Examplesagent_executor.run( "What did biden say about ketanji brown jackson in the state of the union address?") > Entering new AgentExecutor chain... I need to find the answer in the state of the union address Action: state_of_union_address Action Input: What did biden say about ketanji brown jackson Observation: Biden said that Ketanji Brown Jackson is one of the nation's top legal minds and that she will continue Justice Breyer's legacy of excellence. Thought: I now know the final answer Final Answer: Biden said that Ketanji Brown Jackson is one of the nation's top legal minds and that she will continue Justice Breyer's legacy of excellence. > Finished chain. "Biden said that Ketanji Brown Jackson is one of the nation's top legal minds and that she will continue Justice Breyer's legacy of excellence."agent_executor.run( "What did biden say about ketanji brown jackson in the state of the union address? List the source.") > Entering new AgentExecutor chain... I need to use the state_of_union_address_with_sources tool to answer this question. Action: state_of_union_address_with_sources Action Input: What did biden say about ketanji brown jackson Observation: {"answer": " Biden said that he nominated Circuit Court of Appeals Judge Ketanji Brown Jackson to the United States Supreme Court, and that she is one of the nation's top legal minds who will continue Justice Breyer's legacy of excellence.\n", "sources": "../../state_of_the_union.txt"} Thought: I now know the final answer Final Answer: Biden said that he nominated Circuit Court of Appeals Judge Ketanji Brown Jackson to the United States Supreme Court, and that she is one of the nation's top legal minds who will continue Justice Breyer's legacy of excellence. Sources: ../../state_of_the_union.txt > Finished chain. "Biden said that he nominated Circuit Court of Appeals Judge Ketanji Brown Jackson to the United States Supreme Court, and that she is one of the nation's top legal minds who will continue Justice Breyer's legacy of excellence. Sources: ../../state_of_the_union.txt"Multiple VectorstoresWe can also easily use this initialize an agent with multiple vectorstores and use the agent to route between them. To do this. This agent is optimized for routing, so it is a different toolkit and initializer.from langchain.agents.agent_toolkits import ( create_vectorstore_router_agent, VectorStoreRouterToolkit, VectorStoreInfo,)ruff_vectorstore_info = VectorStoreInfo( name="ruff", description="Information about the Ruff python linting library", vectorstore=ruff_store,)router_toolkit = VectorStoreRouterToolkit( vectorstores=[vectorstore_info, ruff_vectorstore_info], llm=llm)agent_executor = create_vectorstore_router_agent( llm=llm, toolkit=router_toolkit, verbose=True)Examplesagent_executor.run( "What did biden say about ketanji brown jackson in the state of the union address?") > Entering new AgentExecutor chain... I need to use the state_of_union_address tool to answer this question. Action: state_of_union_address Action Input: What did biden say about ketanji brown jackson Observation: Biden said that Ketanji Brown Jackson is one of the nation's top legal minds and that she will continue Justice Breyer's legacy of excellence. Thought: I now know the final answer Final Answer: Biden said that Ketanji Brown Jackson is one of the nation's top legal minds and that she will continue Justice Breyer's legacy of excellence. > Finished chain. "Biden said that Ketanji Brown Jackson is one of the nation's top legal minds and that she will continue Justice Breyer's legacy of excellence."agent_executor.run("What tool does ruff use to run over Jupyter Notebooks?") > Entering new AgentExecutor chain... I need to find out what tool ruff uses to run over Jupyter Notebooks Action: ruff Action Input: What tool does ruff use to run over Jupyter Notebooks? Observation: Ruff is integrated into nbQA, a tool for running linters and code formatters over Jupyter Notebooks. After installing ruff and nbqa, you can run Ruff over a notebook like so: > nbqa ruff Untitled.html Thought: I now know the final answer Final Answer: Ruff is integrated into nbQA, a tool for running linters and code formatters over Jupyter Notebooks. After installing ruff and nbqa, you can run Ruff over a notebook like so: > nbqa ruff Untitled.html > Finished chain. 'Ruff is integrated into nbQA, a tool for running linters and code formatters over Jupyter Notebooks. After installing ruff and nbqa, you can run Ruff over a notebook like so: > nbqa ruff Untitled.html'agent_executor.run( "What tool does ruff use to run over Jupyter Notebooks? Did the president mention that tool in the state of the union?") > Entering new AgentExecutor chain... I need to find out what tool ruff uses and if the president mentioned it in the state of the union. Action: ruff Action Input: What tool does ruff use to run over Jupyter Notebooks? Observation: Ruff is integrated into nbQA, a tool for running linters and code formatters over Jupyter Notebooks. After installing ruff and nbqa, you can run Ruff over a notebook like so: > nbqa ruff Untitled.html Thought: I need to find out if the president mentioned nbQA in the state of the union. Action: state_of_union_address Action Input: Did the president mention nbQA in the state of the union? Observation: No, the president did not mention nbQA in the state of the union. Thought: I now know the final answer. Final Answer: No, the president did not mention nbQA in the state of the union. > Finished chain. 'No, the president did not mention nbQA in the state of the union.'PreviousSQL DatabaseNextXorbitsCreate VectorstoresInitialize Toolkit and AgentExamplesMultiple VectorstoresExamples |
774 | https://python.langchain.com/docs/integrations/toolkits/xorbits | ComponentsAgents and toolkitsXorbitsOn this pageXorbitsThis notebook shows how to use agents to interact with Xorbits Pandas dataframe and Xorbits Numpy ndarray. It is mostly optimized for question answering.NOTE: this agent calls the Python agent under the hood, which executes LLM generated Python code - this can be bad if the LLM generated Python code is harmful. Use cautiously.Pandas examplesimport xorbits.pandas as pdfrom langchain.agents import create_xorbits_agentfrom langchain.llms import OpenAIdata = pd.read_csv("titanic.csv")agent = create_xorbits_agent(OpenAI(temperature=0), data, verbose=True) 0%| | 0.00/100 [00:00<?, ?it/s]agent.run("How many rows and columns are there?") > Entering new chain... Thought: I need to count the number of rows and columns Action: python_repl_ast Action Input: data.shape Observation: (891, 12) Thought: I now know the final answer Final Answer: There are 891 rows and 12 columns. > Finished chain. 'There are 891 rows and 12 columns.'agent.run("How many people are in pclass 1?") > Entering new chain... 0%| | 0.00/100 [00:00<?, ?it/s] Thought: I need to count the number of people in pclass 1 Action: python_repl_ast Action Input: data[data['Pclass'] == 1].shape[0] Observation: 216 Thought: I now know the final answer Final Answer: There are 216 people in pclass 1. > Finished chain. 'There are 216 people in pclass 1.'agent.run("whats the mean age?") > Entering new chain... Thought: I need to calculate the mean age Action: python_repl_ast Action Input: data['Age'].mean() 0%| | 0.00/100 [00:00<?, ?it/s] Observation: 29.69911764705882 Thought: I now know the final answer Final Answer: The mean age is 29.69911764705882. > Finished chain. 'The mean age is 29.69911764705882.'agent.run("Group the data by sex and find the average age for each group") > Entering new chain... Thought: I need to group the data by sex and then find the average age for each group Action: python_repl_ast Action Input: data.groupby('Sex')['Age'].mean() 0%| | 0.00/100 [00:00<?, ?it/s] Observation: Sex female 27.915709 male 30.726645 Name: Age, dtype: float64 Thought: I now know the average age for each group Final Answer: The average age for female passengers is 27.92 and the average age for male passengers is 30.73. > Finished chain. 'The average age for female passengers is 27.92 and the average age for male passengers is 30.73.'agent.run( "Show the number of people whose age is greater than 30 and fare is between 30 and 50 , and pclass is either 1 or 2") > Entering new chain... 0%| | 0.00/100 [00:00<?, ?it/s] Thought: I need to filter the dataframe to get the desired result Action: python_repl_ast Action Input: data[(data['Age'] > 30) & (data['Fare'] > 30) & (data['Fare'] < 50) & ((data['Pclass'] == 1) | (data['Pclass'] == 2))].shape[0] Observation: 20 Thought: I now know the final answer Final Answer: 20 > Finished chain. '20'Numpy examplesimport xorbits.numpy as npfrom langchain.agents import create_xorbits_agentfrom langchain.llms import OpenAIarr = np.array([1, 2, 3, 4, 5, 6])agent = create_xorbits_agent(OpenAI(temperature=0), arr, verbose=True) 0%| | 0.00/100 [00:00<?, ?it/s]agent.run("Give the shape of the array ") > Entering new chain... Thought: I need to find out the shape of the array Action: python_repl_ast Action Input: data.shape Observation: (6,) Thought: I now know the final answer Final Answer: The shape of the array is (6,). > Finished chain. 'The shape of the array is (6,).'agent.run("Give the 2nd element of the array ") > Entering new chain... Thought: I need to access the 2nd element of the array Action: python_repl_ast Action Input: data[1] 0%| | 0.00/100 [00:00<?, ?it/s] Observation: 2 Thought: I now know the final answer Final Answer: 2 > Finished chain. '2'agent.run( "Reshape the array into a 2-dimensional array with 2 rows and 3 columns, and then transpose it") > Entering new chain... Thought: I need to reshape the array and then transpose it Action: python_repl_ast Action Input: np.reshape(data, (2,3)).T 0%| | 0.00/100 [00:00<?, ?it/s] Observation: [[1 4] [2 5] [3 6]] Thought: I now know the final answer Final Answer: The reshaped and transposed array is [[1 4], [2 5], [3 6]]. > Finished chain. 'The reshaped and transposed array is [[1 4], [2 5], [3 6]].'agent.run( "Reshape the array into a 2-dimensional array with 3 rows and 2 columns and sum the array along the first axis") > Entering new chain... Thought: I need to reshape the array and then sum it Action: python_repl_ast Action Input: np.sum(np.reshape(data, (3,2)), axis=0) 0%| | 0.00/100 [00:00<?, ?it/s] Observation: [ 9 12] Thought: I now know the final answer Final Answer: The sum of the array along the first axis is [9, 12]. > Finished chain. 'The sum of the array along the first axis is [9, 12].'arr = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])agent = create_xorbits_agent(OpenAI(temperature=0), arr, verbose=True) 0%| | 0.00/100 [00:00<?, ?it/s]agent.run("calculate the covariance matrix") > Entering new chain... Thought: I need to use the numpy covariance function Action: python_repl_ast Action Input: np.cov(data) 0%| | 0.00/100 [00:00<?, ?it/s] Observation: [[1. 1. 1.] [1. 1. 1.] [1. 1. 1.]] Thought: I now know the final answer Final Answer: The covariance matrix is [[1. 1. 1.], [1. 1. 1.], [1. 1. 1.]]. > Finished chain. 'The covariance matrix is [[1. 1. 1.], [1. 1. 1.], [1. 1. 1.]].'agent.run("compute the U of Singular Value Decomposition of the matrix") > Entering new chain... Thought: I need to use the SVD function Action: python_repl_ast Action Input: U, S, V = np.linalg.svd(data) Observation: Thought: I now have the U matrix Final Answer: U = [[-0.70710678 -0.70710678] [-0.70710678 0.70710678]] > Finished chain. 'U = [[-0.70710678 -0.70710678]\n [-0.70710678 0.70710678]]'PreviousVectorstoreNextMemoryPandas examplesNumpy examples |
775 | https://python.langchain.com/docs/integrations/memory | ComponentsMemoryMemory📄️ AWS DynamoDBAmazon AWS DynamoDB is a fully managed NoSQL database service that provides fast and predictable performance with seamless scalability.📄️ CassandraApache Cassandra® is a NoSQL, row-oriented, highly scalable and highly available database, well suited for storing large amounts of data.📄️ Momento CacheMomento Cache is the world's first truly serverless caching service. It provides instant elasticity, scale-to-zero📄️ MongodDBMongoDB is a source-available cross-platform document-oriented database program. Classified as a NoSQL database program, MongoDB uses JSON-like documents with optional schemas.📄️ MotörheadMotörhead is a memory server implemented in Rust. It automatically handles incremental summarization in the background and allows for stateless applications.📄️ PostgresPostgreSQL also known as Postgres, is a free and open-source relational database management system (RDBMS) emphasizing extensibility and SQL compliance.📄️ RedisRedis (Remote Dictionary Server) is an open-source in-memory storage, used as a distributed, in-memory key–value database, cache and message broker, with optional durability. Because it holds all data in memory and because of its design, Redis offers low-latency reads and writes, making it particularly suitable for use cases that require a cache. Redis is the most popular NoSQL database, and one of the most popular databases overall.📄️ RemembrallThis page covers how to use the Remembrall ecosystem within LangChain.📄️ RocksetRockset is a real-time analytics database service for serving low latency, high concurrency analytical queries at scale. It builds a Converged Index™ on structured and semi-structured data with an efficient store for vector embeddings. Its support for running SQL on schemaless data makes it a perfect choice for running vector search with metadata filters.📄️ SQL (SQLAlchemy)Structured Query Language (SQL) is a domain-specific language used in programming and designed for managing data held in a relational database management system (RDBMS), or for stream processing in a relational data stream management system (RDSMS). It is particularly useful in handling structured data, i.e., data incorporating relations among entities and variables.📄️ SQLiteSQLite is a database engine written in the C programming language. It is not a standalone app; rather, it is a library that software developers embed in their apps. As such, it belongs to the family of embedded databases. It is the most widely deployed database engine, as it is used by several of the top web browsers, operating systems, mobile phones, and other embedded systems.📄️ StreamlitStreamlit is an open-source Python library that makes it easy to create and share beautiful,📄️ XataXata is a serverless data platform, based on PostgreSQL and Elasticsearch. It provides a Python SDK for interacting with your database, and a UI for managing your data. With the XataChatMessageHistory class, you can use Xata databases for longer-term persistence of chat sessions.📄️ ZepZep is a long-term memory store for LLM applications.PreviousXorbitsNextAWS DynamoDB |
776 | https://python.langchain.com/docs/integrations/memory/aws_dynamodb | ComponentsMemoryAWS DynamoDBOn this pageAWS DynamoDBAmazon AWS DynamoDB is a fully managed NoSQL database service that provides fast and predictable performance with seamless scalability.This notebook goes over how to use DynamoDB to store chat message history.First make sure you have correctly configured the AWS CLI. Then make sure you have installed boto3.pip install boto3Next, create the DynamoDB Table where we will be storing messages:import boto3# Get the service resource.dynamodb = boto3.resource("dynamodb")# Create the DynamoDB table.table = dynamodb.create_table( TableName="SessionTable", KeySchema=[{"AttributeName": "SessionId", "KeyType": "HASH"}], AttributeDefinitions=[{"AttributeName": "SessionId", "AttributeType": "S"}], BillingMode="PAY_PER_REQUEST",)# Wait until the table exists.table.meta.client.get_waiter("table_exists").wait(TableName="SessionTable")# Print out some data about the table.print(table.item_count) 0DynamoDBChatMessageHistoryfrom langchain.memory.chat_message_histories import DynamoDBChatMessageHistoryhistory = DynamoDBChatMessageHistory(table_name="SessionTable", session_id="0")history.add_user_message("hi!")history.add_ai_message("whats up?")history.messages [HumanMessage(content='hi!', additional_kwargs={}, example=False), AIMessage(content='whats up?', additional_kwargs={}, example=False), HumanMessage(content='hi!', additional_kwargs={}, example=False), AIMessage(content='whats up?', additional_kwargs={}, example=False)]DynamoDBChatMessageHistory with Custom Endpoint URLSometimes it is useful to specify the URL to the AWS endpoint to connect to. For instance, when you are running locally against Localstack. For those cases you can specify the URL via the endpoint_url parameter in the constructor.from langchain.memory.chat_message_histories import DynamoDBChatMessageHistoryhistory = DynamoDBChatMessageHistory( table_name="SessionTable", session_id="0", endpoint_url="http://localhost.localstack.cloud:4566",)DynamoDBChatMessageHistory With Different Keys Composite KeysThe default key for DynamoDBChatMessageHistory is {"SessionId": self.session_id}, but you can modify this to match your table design.Primary Key NameYou may modify the primary key by passing in a primary_key_name value in the constructor, resulting in the following:
{self.primary_key_name: self.session_id}Composite KeysWhen using an existing DynamoDB table, you may need to modify the key structure from the default of to something including a Sort Key. To do this you may use the key parameter.Passing a value for key will override the primary_key parameter, and the resulting key structure will be the passed value.from langchain.memory.chat_message_histories import DynamoDBChatMessageHistorycomposite_table = dynamodb.create_table( TableName="CompositeTable", KeySchema=[{"AttributeName": "PK", "KeyType": "HASH"}, {"AttributeName": "SK", "KeyType": "RANGE"}], AttributeDefinitions=[{"AttributeName": "PK", "AttributeType": "S"}, {"AttributeName": "SK", "AttributeType": "S"}], BillingMode="PAY_PER_REQUEST",)# Wait until the table exists.composite_table.meta.client.get_waiter("table_exists").wait(TableName="CompositeTable")# Print out some data about the table.print(composite_table.item_count)my_key = { "PK": "session_id::0", "SK": "langchain_history",}composite_key_history = DynamoDBChatMessageHistory( table_name="CompositeTable", session_id="0", endpoint_url="http://localhost.localstack.cloud:4566", key=my_key,)composite_key_history.add_user_message("hello, composite dynamodb table!")composite_key_history.messages 0 [HumanMessage(content='hello, composite dynamodb table!', additional_kwargs={}, example=False)]Agent with DynamoDB Memoryfrom langchain.agents import Toolfrom langchain.memory import ConversationBufferMemoryfrom langchain.chat_models import ChatOpenAIfrom langchain.agents import initialize_agentfrom langchain.agents import AgentTypefrom langchain.utilities import PythonREPLfrom getpass import getpassmessage_history = DynamoDBChatMessageHistory(table_name="SessionTable", session_id="1")memory = ConversationBufferMemory( memory_key="chat_history", chat_memory=message_history, return_messages=True)python_repl = PythonREPL()# You can create the tool to pass to an agenttools = [ Tool( name="python_repl", description="A Python shell. Use this to execute python commands. Input should be a valid python command. If you want to see the output of a value, you should print it out with `print(...)`.", func=python_repl.run, )]llm = ChatOpenAI(temperature=0)agent_chain = initialize_agent( tools, llm, agent=AgentType.CHAT_CONVERSATIONAL_REACT_DESCRIPTION, verbose=True, memory=memory,) --------------------------------------------------------------------------- ValidationError Traceback (most recent call last) Cell In[17], line 1 ----> 1 llm = ChatOpenAI(temperature=0) 2 agent_chain = initialize_agent( 3 tools, 4 llm, (...) 7 memory=memory, 8 ) File ~/Documents/projects/langchain/libs/langchain/langchain/load/serializable.py:74, in Serializable.__init__(self, **kwargs) 73 def __init__(self, **kwargs: Any) -> None: ---> 74 super().__init__(**kwargs) 75 self._lc_kwargs = kwargs File ~/Documents/projects/langchain/.venv/lib/python3.9/site-packages/pydantic/main.py:341, in pydantic.main.BaseModel.__init__() ValidationError: 1 validation error for ChatOpenAI __root__ Did not find openai_api_key, please add an environment variable `OPENAI_API_KEY` which contains it, or pass `openai_api_key` as a named parameter. (type=value_error)agent_chain.run(input="Hello!")agent_chain.run(input="Who owns Twitter?")agent_chain.run(input="My name is Bob.")agent_chain.run(input="Who am I?")PreviousMemoryNextCassandraDynamoDBChatMessageHistoryDynamoDBChatMessageHistory with Custom Endpoint URLDynamoDBChatMessageHistory With Different Keys Composite KeysPrimary Key NameComposite KeysAgent with DynamoDB Memory |
777 | https://python.langchain.com/docs/integrations/memory/cassandra_chat_message_history | ComponentsMemoryCassandraOn this pageCassandraApache Cassandra® is a NoSQL, row-oriented, highly scalable and highly available database, well suited for storing large amounts of data.Cassandra is a good choice for storing chat message history because it is easy to scale and can handle a large number of writes.This notebook goes over how to use Cassandra to store chat message history.Setting upTo run this notebook you need either a running Cassandra cluster or a DataStax Astra DB instance running in the cloud (you can get one for free at datastax.com). Check cassio.org for more information.pip install "cassio>=0.1.0"Set up the database connection parameters and secretsimport osimport getpassdatabase_mode = (input("\n(C)assandra or (A)stra DB? ")).upper()keyspace_name = input("\nKeyspace name? ")if database_mode == "A": ASTRA_DB_APPLICATION_TOKEN = getpass.getpass('\nAstra DB Token ("AstraCS:...") ') # ASTRA_DB_SECURE_BUNDLE_PATH = input("Full path to your Secure Connect Bundle? ")elif database_mode == "C": CASSANDRA_CONTACT_POINTS = input( "Contact points? (comma-separated, empty for localhost) " ).strip()Depending on whether local or cloud-based Astra DB, create the corresponding database connection "Session" object.from cassandra.cluster import Clusterfrom cassandra.auth import PlainTextAuthProviderif database_mode == "C": if CASSANDRA_CONTACT_POINTS: cluster = Cluster( [cp.strip() for cp in CASSANDRA_CONTACT_POINTS.split(",") if cp.strip()] ) else: cluster = Cluster() session = cluster.connect()elif database_mode == "A": ASTRA_DB_CLIENT_ID = "token" cluster = Cluster( cloud={ "secure_connect_bundle": ASTRA_DB_SECURE_BUNDLE_PATH, }, auth_provider=PlainTextAuthProvider( ASTRA_DB_CLIENT_ID, ASTRA_DB_APPLICATION_TOKEN, ), ) session = cluster.connect()else: raise NotImplementedErrorExamplefrom langchain.memory import CassandraChatMessageHistorymessage_history = CassandraChatMessageHistory( session_id="test-session", session=session, keyspace=keyspace_name,)message_history.add_user_message("hi!")message_history.add_ai_message("whats up?")message_history.messagesPreviousAWS DynamoDBNextMomento CacheSetting upSet up the database connection parameters and secretsExample |
778 | https://python.langchain.com/docs/integrations/memory/momento_chat_message_history | ComponentsMemoryMomento CacheMomento CacheMomento Cache is the world's first truly serverless caching service. It provides instant elasticity, scale-to-zero
capability, and blazing-fast performance. This notebook goes over how to use Momento Cache to store chat message history using the MomentoChatMessageHistory class. See the Momento docs for more detail on how to get set up with Momento.Note that, by default we will create a cache if one with the given name doesn't already exist.You'll need to get a Momento API key to use this class. This can either be passed in to a momento.CacheClient if you'd like to instantiate that directly, as a named parameter api_key to MomentoChatMessageHistory.from_client_params, or can just be set as an environment variable MOMENTO_API_KEY.from datetime import timedeltafrom langchain.memory import MomentoChatMessageHistorysession_id = "foo"cache_name = "langchain"ttl = timedelta(days=1)history = MomentoChatMessageHistory.from_client_params( session_id, cache_name, ttl,)history.add_user_message("hi!")history.add_ai_message("whats up?")history.messages [HumanMessage(content='hi!', additional_kwargs={}, example=False), AIMessage(content='whats up?', additional_kwargs={}, example=False)]PreviousCassandraNextMongodDB |
779 | https://python.langchain.com/docs/integrations/memory/mongodb_chat_message_history | ComponentsMemoryMongodDBOn this pageMongodDBMongoDB is a source-available cross-platform document-oriented database program. Classified as a NoSQL database program, MongoDB uses JSON-like documents with optional schemas.MongoDB is developed by MongoDB Inc. and licensed under the Server Side Public License (SSPL). - WikipediaThis notebook goes over how to use Mongodb to store chat message history.Setting uppip install pymongo# Provide the connection string to connect to the MongoDB databaseconnection_string = "mongodb://mongo_user:password123@mongo:27017"Examplefrom langchain.memory import MongoDBChatMessageHistorymessage_history = MongoDBChatMessageHistory( connection_string=connection_string, session_id="test-session")message_history.add_user_message("hi!")message_history.add_ai_message("whats up?")message_history.messages [HumanMessage(content='hi!', additional_kwargs={}, example=False), AIMessage(content='whats up?', additional_kwargs={}, example=False)]PreviousMomento CacheNextMotörheadSetting upExample |
780 | https://python.langchain.com/docs/integrations/memory/motorhead_memory | ComponentsMemoryMotörheadOn this pageMotörheadMotörhead is a memory server implemented in Rust. It automatically handles incremental summarization in the background and allows for stateless applications.SetupSee instructions at Motörhead for running the server locally.from langchain.memory.motorhead_memory import MotorheadMemoryExamplefrom langchain.llms import OpenAIfrom langchain.chains import LLMChainfrom langchain.prompts import PromptTemplatetemplate = """You are a chatbot having a conversation with a human.{chat_history}Human: {human_input}AI:"""prompt = PromptTemplate( input_variables=["chat_history", "human_input"], template=template)memory = MotorheadMemory( session_id="testing-1", url="http://localhost:8080", memory_key="chat_history")await memory.init()# loads previous state from Motörhead 🤘llm_chain = LLMChain( llm=OpenAI(), prompt=prompt, verbose=True, memory=memory,)llm_chain.run("hi im bob") > Entering new LLMChain chain... Prompt after formatting: You are a chatbot having a conversation with a human. Human: hi im bob AI: > Finished chain. ' Hi Bob, nice to meet you! How are you doing today?'llm_chain.run("whats my name?") > Entering new LLMChain chain... Prompt after formatting: You are a chatbot having a conversation with a human. Human: hi im bob AI: Hi Bob, nice to meet you! How are you doing today? Human: whats my name? AI: > Finished chain. ' You said your name is Bob. Is that correct?'llm_chain.run("whats for dinner?") > Entering new LLMChain chain... Prompt after formatting: You are a chatbot having a conversation with a human. Human: hi im bob AI: Hi Bob, nice to meet you! How are you doing today? Human: whats my name? AI: You said your name is Bob. Is that correct? Human: whats for dinner? AI: > Finished chain. " I'm sorry, I'm not sure what you're asking. Could you please rephrase your question?"PreviousMongodDBNextPostgresSetupExample |
781 | https://python.langchain.com/docs/integrations/memory/postgres_chat_message_history | ComponentsMemoryPostgresPostgresPostgreSQL also known as Postgres, is a free and open-source relational database management system (RDBMS) emphasizing extensibility and SQL compliance.This notebook goes over how to use Postgres to store chat message history.from langchain.memory import PostgresChatMessageHistoryhistory = PostgresChatMessageHistory( connection_string="postgresql://postgres:mypassword@localhost/chat_history", session_id="foo",)history.add_user_message("hi!")history.add_ai_message("whats up?")history.messagesPreviousMotörheadNextRedis |
782 | https://python.langchain.com/docs/integrations/memory/redis_chat_message_history | ComponentsMemoryRedisRedisRedis (Remote Dictionary Server) is an open-source in-memory storage, used as a distributed, in-memory key–value database, cache and message broker, with optional durability. Because it holds all data in memory and because of its design, Redis offers low-latency reads and writes, making it particularly suitable for use cases that require a cache. Redis is the most popular NoSQL database, and one of the most popular databases overall.This notebook goes over how to use Redis to store chat message history.from langchain.memory import RedisChatMessageHistoryhistory = RedisChatMessageHistory("foo")history.add_user_message("hi!")history.add_ai_message("whats up?")history.messages [AIMessage(content='whats up?', additional_kwargs={}), HumanMessage(content='hi!', additional_kwargs={})]PreviousPostgresNextRemembrall |
783 | https://python.langchain.com/docs/integrations/memory/remembrall | ComponentsMemoryRemembrallOn this pageRemembrallThis page covers how to use the Remembrall ecosystem within LangChain.What is Remembrall?Remembrall gives your language model long-term memory, retrieval augmented generation, and complete observability with just a few lines of code.It works as a light-weight proxy on top of your OpenAI calls and simply augments the context of the chat calls at runtime with relevant facts that have been collected.SetupTo get started, sign in with Github on the Remembrall platform and copy your API key from the settings page.Any request that you send with the modified openai_api_base (see below) and Remembrall API key will automatically be tracked in the Remembrall dashboard. You never have to share your OpenAI key with our platform and this information is never stored by the Remembrall systems.Enable Long Term MemoryIn addition to setting the openai_api_base and Remembrall API key via x-gp-api-key, you should specify a UID to maintain memory for. This will usually be a unique user identifier (like email).from langchain.chat_models import ChatOpenAIchat_model = ChatOpenAI(openai_api_base="https://remembrall.dev/api/openai/v1", model_kwargs={ "headers":{ "x-gp-api-key": "remembrall-api-key-here", "x-gp-remember": "[email protected]", } })chat_model.predict("My favorite color is blue.")import time; time.sleep(5) # wait for system to save fact via auto saveprint(chat_model.predict("What is my favorite color?"))Enable Retrieval Augmented GenerationFirst, create a document context in the Remembrall dashboard. Paste in the document texts or upload documents as PDFs to be processed. Save the Document Context ID and insert it as shown below.from langchain.chat_models import ChatOpenAIchat_model = ChatOpenAI(openai_api_base="https://remembrall.dev/api/openai/v1", model_kwargs={ "headers":{ "x-gp-api-key": "remembrall-api-key-here", "x-gp-context": "document-context-id-goes-here", } })print(chat_model.predict("This is a question that can be answered with my document."))PreviousRedisNextRocksetWhat is Remembrall?SetupEnable Long Term MemoryEnable Retrieval Augmented Generation |
784 | https://python.langchain.com/docs/integrations/memory/rockset_chat_message_history | ComponentsMemoryRocksetOn this pageRocksetRockset is a real-time analytics database service for serving low latency, high concurrency analytical queries at scale. It builds a Converged Index™ on structured and semi-structured data with an efficient store for vector embeddings. Its support for running SQL on schemaless data makes it a perfect choice for running vector search with metadata filters. This notebook goes over how to use Rockset to store chat message history. Setting uppip install rocksetTo begin, with get your API key from the Rockset console. Find your API region for the Rockset API reference.Examplefrom langchain.memory.chat_message_histories import RocksetChatMessageHistoryfrom rockset import RocksetClient, Regionshistory = RocksetChatMessageHistory( session_id="MySession", client=RocksetClient( api_key="YOUR API KEY", host=Regions.usw2a1 # us-west-2 Oregon ), collection="langchain_demo", sync=True)history.add_user_message("hi!")history.add_ai_message("whats up?")print(history.messages)The output should be something like:[ HumanMessage(content='hi!', additional_kwargs={'id': '2e62f1c2-e9f7-465e-b551-49bae07fe9f0'}, example=False), AIMessage(content='whats up?', additional_kwargs={'id': 'b9be8eda-4c18-4cf8-81c3-e91e876927d0'}, example=False)]PreviousRemembrallNextSQL (SQLAlchemy)Setting upExample |
785 | https://python.langchain.com/docs/integrations/memory/sql_chat_message_history | ComponentsMemorySQL (SQLAlchemy)On this pageSQL (SQLAlchemy)Structured Query Language (SQL) is a domain-specific language used in programming and designed for managing data held in a relational database management system (RDBMS), or for stream processing in a relational data stream management system (RDSMS). It is particularly useful in handling structured data, i.e., data incorporating relations among entities and variables.SQLAlchemy is an open-source SQL toolkit and object-relational mapper (ORM) for the Python programming language released under the MIT License.This notebook goes over a SQLChatMessageHistory class that allows to store chat history in any database supported by SQLAlchemy.Please note that to use it with databases other than SQLite, you will need to install the corresponding database driver.Basic UsageTo use the storage you need to provide only 2 things:Session Id - a unique identifier of the session, like user name, email, chat id etc.Connection string - a string that specifies the database connection. It will be passed to SQLAlchemy create_engine function.Install SQLAlchemy python package.pip install SQLAlchemyfrom langchain.memory.chat_message_histories import SQLChatMessageHistorychat_message_history = SQLChatMessageHistory( session_id='test_session', connection_string='sqlite:///sqlite.db')chat_message_history.add_user_message('Hello')chat_message_history.add_ai_message('Hi')chat_message_history.messages [HumanMessage(content='Hello', additional_kwargs={}, example=False), AIMessage(content='Hi', additional_kwargs={}, example=False)]Custom Storage FormatBy default, only the session id and message dictionary are stored in the table.However, sometimes you might want to store some additional information, like message date, author, language etc.To do that, you can create a custom message converter, by implementing BaseMessageConverter interface.from datetime import datetimefrom langchain.schema import BaseMessage, HumanMessage, AIMessage, SystemMessagefrom typing import Anyfrom sqlalchemy import Column, Integer, Text, DateTimefrom sqlalchemy.orm import declarative_basefrom langchain.memory.chat_message_histories.sql import BaseMessageConverterBase = declarative_base()class CustomMessage(Base): __tablename__ = 'custom_message_store' id = Column(Integer, primary_key=True) session_id = Column(Text) type = Column(Text) content = Column(Text) created_at = Column(DateTime) author_email = Column(Text)class CustomMessageConverter(BaseMessageConverter): def __init__(self, author_email: str): self.author_email = author_email def from_sql_model(self, sql_message: Any) -> BaseMessage: if sql_message.type == 'human': return HumanMessage( content=sql_message.content, ) elif sql_message.type == 'ai': return AIMessage( content=sql_message.content, ) elif sql_message.type == 'system': return SystemMessage( content=sql_message.content, ) else: raise ValueError(f'Unknown message type: {sql_message.type}') def to_sql_model(self, message: BaseMessage, session_id: str) -> Any: now = datetime.now() return CustomMessage( session_id=session_id, type=message.type, content=message.content, created_at=now, author_email=self.author_email ) def get_sql_model_class(self) -> Any: return CustomMessagechat_message_history = SQLChatMessageHistory( session_id='test_session', connection_string='sqlite:///sqlite.db', custom_message_converter=CustomMessageConverter( author_email='[email protected]' ))chat_message_history.add_user_message('Hello')chat_message_history.add_ai_message('Hi')chat_message_history.messages [HumanMessage(content='Hello', additional_kwargs={}, example=False), AIMessage(content='Hi', additional_kwargs={}, example=False)]You also might want to change the name of session_id column. In this case you'll need to specify session_id_field_name parameter.PreviousRocksetNextSQLiteBasic UsageCustom Storage Format |
786 | https://python.langchain.com/docs/integrations/memory/sqlite | ComponentsMemorySQLiteSQLiteSQLite is a database engine written in the C programming language. It is not a standalone app; rather, it is a library that software developers embed in their apps. As such, it belongs to the family of embedded databases. It is the most widely deployed database engine, as it is used by several of the top web browsers, operating systems, mobile phones, and other embedded systems.In this walkthrough we'll create a simple conversation chain which uses ConversationEntityMemory backed by a SqliteEntityStore.#!pip install sqlite3from langchain.chains import ConversationChainfrom langchain.llms import OpenAIfrom langchain.memory import ConversationEntityMemoryfrom langchain.memory.entity import SQLiteEntityStorefrom langchain.memory.prompt import ENTITY_MEMORY_CONVERSATION_TEMPLATEentity_store = SQLiteEntityStore()llm = OpenAI(temperature=0)memory = ConversationEntityMemory(llm=llm, entity_store=entity_store)conversation = ConversationChain( llm=llm, prompt=ENTITY_MEMORY_CONVERSATION_TEMPLATE, memory=memory, verbose=True,)Notice the usage of EntitySqliteStore as parameter to entity_store on the memory property.conversation.run("Deven & Sam are working on a hackathon project") > Entering new ConversationChain chain... Prompt after formatting: You are an assistant to a human, powered by a large language model trained by OpenAI. You are designed to be able to assist with a wide range of tasks, from answering simple questions to providing in-depth explanations and discussions on a wide range of topics. As a language model, you are able to generate human-like text based on the input you receive, allowing you to engage in natural-sounding conversations and provide responses that are coherent and relevant to the topic at hand. You are constantly learning and improving, and your capabilities are constantly evolving. You are able to process and understand large amounts of text, and can use this knowledge to provide accurate and informative responses to a wide range of questions. You have access to some personalized information provided by the human in the Context section below. Additionally, you are able to generate your own text based on the input you receive, allowing you to engage in discussions and provide explanations and descriptions on a wide range of topics. Overall, you are a powerful tool that can help with a wide range of tasks and provide valuable insights and information on a wide range of topics. Whether the human needs help with a specific question or just wants to have a conversation about a particular topic, you are here to assist. Context: {'Deven': 'Deven is working on a hackathon project with Sam.', 'Sam': 'Sam is working on a hackathon project with Deven.'} Current conversation: Last line: Human: Deven & Sam are working on a hackathon project You: > Finished chain. ' That sounds like a great project! What kind of project are they working on?'conversation.memory.entity_store.get("Deven") 'Deven is working on a hackathon project with Sam.'conversation.memory.entity_store.get("Sam") 'Sam is working on a hackathon project with Deven.'PreviousSQL (SQLAlchemy)NextStreamlit |
787 | https://python.langchain.com/docs/integrations/memory/streamlit_chat_message_history | ComponentsMemoryStreamlitStreamlitStreamlit is an open-source Python library that makes it easy to create and share beautiful,
custom web apps for machine learning and data science.This notebook goes over how to store and use chat message history in a Streamlit app. StreamlitChatMessageHistory will store messages in
Streamlit session state
at the specified key=. The default key is "langchain_messages".Note, StreamlitChatMessageHistory only works when run in a Streamlit app.You may also be interested in StreamlitCallbackHandler for LangChain.For more on Streamlit check out their
getting started documentation.You can see the full app example running here, and more examples in
github.com/langchain-ai/streamlit-agent.from langchain.memory import StreamlitChatMessageHistoryhistory = StreamlitChatMessageHistory(key="chat_messages")history.add_user_message("hi!")history.add_ai_message("whats up?")history.messagesYou can integrate StreamlitChatMessageHistory into ConversationBufferMemory and chains or agents as usual. The history will be persisted across re-runs of the Streamlit app within a given user session. A given StreamlitChatMessageHistory will NOT be persisted or shared across user sessions.from langchain.memory import ConversationBufferMemoryfrom langchain.memory.chat_message_histories import StreamlitChatMessageHistory# Optionally, specify your own session_state key for storing messagesmsgs = StreamlitChatMessageHistory(key="special_app_key")memory = ConversationBufferMemory(memory_key="history", chat_memory=msgs)if len(msgs.messages) == 0: msgs.add_ai_message("How can I help you?")from langchain.chains import LLMChainfrom langchain.llms import OpenAIfrom langchain.prompts import PromptTemplatetemplate = """You are an AI chatbot having a conversation with a human.{history}Human: {human_input}AI: """prompt = PromptTemplate(input_variables=["history", "human_input"], template=template)# Add the memory to an LLMChain as usualllm_chain = LLMChain(llm=OpenAI(), prompt=prompt, memory=memory)Conversational Streamlit apps will often re-draw each previous chat message on every re-run. This is easy to do by iterating through StreamlitChatMessageHistory.messages:import streamlit as stfor msg in msgs.messages: st.chat_message(msg.type).write(msg.content)if prompt := st.chat_input(): st.chat_message("human").write(prompt) # As usual, new messages are added to StreamlitChatMessageHistory when the Chain is called. response = llm_chain.run(prompt) st.chat_message("ai").write(response)View the final app.PreviousSQLiteNextXata |
788 | https://python.langchain.com/docs/integrations/memory/xata_chat_message_history | ComponentsMemoryXataOn this pageXataXata is a serverless data platform, based on PostgreSQL and Elasticsearch. It provides a Python SDK for interacting with your database, and a UI for managing your data. With the XataChatMessageHistory class, you can use Xata databases for longer-term persistence of chat sessions.This notebook covers:A simple example showing what XataChatMessageHistory does.A more complex example using a REACT agent that answer questions based on a knowledge based or documentation (stored in Xata as a vector store) and also having a long-term searchable history of its past messages (stored in Xata as a memory store)SetupCreate a databaseIn the Xata UI create a new database. You can name it whatever you want, in this notepad we'll use langchain. The Langchain integration can auto-create the table used for storying the memory, and this is what we'll use in this example. If you want to pre-create the table, ensure it has the right schema and set create_table to False when creating the class. Pre-creating the table saves one round-trip to the database during each session initialization.Let's first install our dependencies:pip install xata openai langchainNext, we need to get the environment variables for Xata. You can create a new API key by visiting your account settings. To find the database URL, go to the Settings page of the database that you have created. The database URL should look something like this: https://demo-uni3q8.eu-west-1.xata.sh/db/langchain.import getpassapi_key = getpass.getpass("Xata API key: ")db_url = input("Xata database URL (copy it from your DB settings):")Create a simple memory storeTo test the memory store functionality in isolation, let's use the following code snippet:from langchain.memory import XataChatMessageHistoryhistory = XataChatMessageHistory( session_id="session-1", api_key=api_key, db_url=db_url, table_name="memory")history.add_user_message("hi!")history.add_ai_message("whats up?")The above code creates a session with the ID session-1 and stores two messages in it. After running the above, if you visit the Xata UI, you should see a table named memory and the two messages added to it.You can retrieve the message history for a particular session with the following code:history.messagesConversational Q&A chain on your data with memoryLet's now see a more complex example in which we combine OpenAI, the Xata Vector Store integration, and the Xata memory store integration to create a Q&A chat bot on your data, with follow-up questions and history.We're going to need to access the OpenAI API, so let's configure the API key:import osos.environ["OPENAI_API_KEY"] = getpass.getpass("OpenAI API Key:")To store the documents that the chatbot will search for answers, add a table named docs to your langchain database using the Xata UI, and add the following columns:content of type "Text". This is used to store the Document.pageContent values.embedding of type "Vector". Use the dimension used by the model you plan to use. In this notebook we use OpenAI embeddings, which have 1536 dimensions.Let's create the vector store and add some sample docs to it:from langchain.embeddings.openai import OpenAIEmbeddingsfrom langchain.vectorstores.xata import XataVectorStoreembeddings = OpenAIEmbeddings()texts = [ "Xata is a Serverless Data platform based on PostgreSQL", "Xata offers a built-in vector type that can be used to store and query vectors", "Xata includes similarity search"]vector_store = XataVectorStore.from_texts(texts, embeddings, api_key=api_key, db_url=db_url, table_name="docs")After running the above command, if you go to the Xata UI, you should see the documents loaded together with their embeddings in the docs table.Let's now create a ConversationBufferMemory to store the chat messages from both the user and the AI.from langchain.memory import ConversationBufferMemoryfrom uuid import uuid4chat_memory = XataChatMessageHistory( session_id=str(uuid4()), # needs to be unique per user session api_key=api_key, db_url=db_url, table_name="memory")memory = ConversationBufferMemory(memory_key="chat_history", chat_memory=chat_memory, return_messages=True)Now it's time to create an Agent to use both the vector store and the chat memory together.from langchain.agents import initialize_agent, AgentTypefrom langchain.agents.agent_toolkits import create_retriever_toolfrom langchain.chat_models import ChatOpenAItool = create_retriever_tool( vector_store.as_retriever(), "search_docs", "Searches and returns documents from the Xata manual. Useful when you need to answer questions about Xata.")tools = [tool]llm = ChatOpenAI(temperature=0)agent = initialize_agent( tools, llm, agent=AgentType.CHAT_CONVERSATIONAL_REACT_DESCRIPTION, verbose=True, memory=memory)To test, let's tell the agent our name:agent.run(input="My name is bob")Now, let's now ask the agent some questions about Xata:agent.run(input="What is xata?")Notice that it answers based on the data stored in the document store. And now, let's ask a follow up question:agent.run(input="Does it support similarity search?")And now let's test its memory:agent.run(input="Did I tell you my name? What is it?")PreviousStreamlitNextZepSetupCreate a databaseCreate a simple memory storeConversational Q&A chain on your data with memory |
789 | https://python.langchain.com/docs/integrations/memory/zep_memory | ComponentsMemoryZepOn this pageZepZep is a long-term memory store for LLM applications.Zep stores, summarizes, embeds, indexes, and enriches conversational AI chat histories, and exposes them via simple, low-latency APIs.Key Features:Fast! Zep’s async extractors operate independently of the your chat loop, ensuring a snappy user experience.Long-term memory persistence, with access to historical messages irrespective of your summarization strategy.Auto-summarization of memory messages based on a configurable message window. A series of summaries are stored, providing flexibility for future summarization strategies.Hybrid search over memories and metadata, with messages automatically embedded on creation.Entity Extractor that automatically extracts named entities from messages and stores them in the message metadata.Auto-token counting of memories and summaries, allowing finer-grained control over prompt assembly.Python and JavaScript SDKs.Zep project: https://github.com/getzep/zep
Docs: https://docs.getzep.com/ExampleThis notebook demonstrates how to use the Zep Long-term Memory Store as memory for your chatbot.
REACT Agent Chat Message History with Zep - A long-term memory store for LLM applications.We'll demonstrate:Adding conversation history to the Zep memory store.Running an agent and having message automatically added to the store.Viewing the enriched messages.Vector search over the conversation history.from langchain.memory import ZepMemoryfrom langchain.retrievers import ZepRetrieverfrom langchain.llms import OpenAIfrom langchain.schema import HumanMessage, AIMessagefrom langchain.utilities import WikipediaAPIWrapperfrom langchain.agents import initialize_agent, AgentType, Toolfrom uuid import uuid4# Set this to your Zep server URLZEP_API_URL = "http://localhost:8000"session_id = str(uuid4()) # This is a unique identifier for the user# Provide your OpenAI keyimport getpassopenai_key = getpass.getpass()# Provide your Zep API key. Note that this is optional. See https://docs.getzep.com/deployment/authzep_api_key = getpass.getpass()Initialize the Zep Chat Message History Class and initialize the Agentsearch = WikipediaAPIWrapper()tools = [ Tool( name="Search", func=search.run, description="useful for when you need to search online for answers. You should ask targeted questions", ),]# Set up Zep Chat Historymemory = ZepMemory( session_id=session_id, url=ZEP_API_URL, api_key=zep_api_key, memory_key="chat_history",)# Initialize the agentllm = OpenAI(temperature=0, openai_api_key=openai_key)agent_chain = initialize_agent( tools, llm, agent=AgentType.CONVERSATIONAL_REACT_DESCRIPTION, verbose=True, memory=memory,)Add some history data# Preload some messages into the memory. The default message window is 12 messages. We want to push beyond this to demonstrate auto-summarization.test_history = [ {"role": "human", "content": "Who was Octavia Butler?"}, { "role": "ai", "content": ( "Octavia Estelle Butler (June 22, 1947 – February 24, 2006) was an American" " science fiction author." ), }, {"role": "human", "content": "Which books of hers were made into movies?"}, { "role": "ai", "content": ( "The most well-known adaptation of Octavia Butler's work is the FX series" " Kindred, based on her novel of the same name." ), }, {"role": "human", "content": "Who were her contemporaries?"}, { "role": "ai", "content": ( "Octavia Butler's contemporaries included Ursula K. Le Guin, Samuel R." " Delany, and Joanna Russ." ), }, {"role": "human", "content": "What awards did she win?"}, { "role": "ai", "content": ( "Octavia Butler won the Hugo Award, the Nebula Award, and the MacArthur" " Fellowship." ), }, { "role": "human", "content": "Which other women sci-fi writers might I want to read?", }, { "role": "ai", "content": "You might want to read Ursula K. Le Guin or Joanna Russ.", }, { "role": "human", "content": ( "Write a short synopsis of Butler's book, Parable of the Sower. What is it" " about?" ), }, { "role": "ai", "content": ( "Parable of the Sower is a science fiction novel by Octavia Butler," " published in 1993. It follows the story of Lauren Olamina, a young woman" " living in a dystopian future where society has collapsed due to" " environmental disasters, poverty, and violence." ), "metadata": {"foo": "bar"}, },]for msg in test_history: memory.chat_memory.add_message( HumanMessage(content=msg["content"]) if msg["role"] == "human" else AIMessage(content=msg["content"]), metadata=msg.get("metadata", {}), )Run the agentDoing so will automatically add the input and response to the Zep memory.agent_chain.run( input="What is the book's relevance to the challenges facing contemporary society?",) > Entering new chain... Thought: Do I need to use a tool? No AI: Parable of the Sower is a prescient novel that speaks to the challenges facing contemporary society, such as climate change, inequality, and violence. It is a cautionary tale that warns of the dangers of unchecked greed and the need for individuals to take responsibility for their own lives and the lives of those around them. > Finished chain. 'Parable of the Sower is a prescient novel that speaks to the challenges facing contemporary society, such as climate change, inequality, and violence. It is a cautionary tale that warns of the dangers of unchecked greed and the need for individuals to take responsibility for their own lives and the lives of those around them.'Inspect the Zep memoryNote the summary, and that the history has been enriched with token counts, UUIDs, and timestamps.Summaries are biased towards the most recent messages.def print_messages(messages): for m in messages: print(m.type, ":\n", m.dict())print(memory.chat_memory.zep_summary)print("\n")print_messages(memory.chat_memory.messages) The human inquires about Octavia Butler. The AI identifies her as an American science fiction author. The human then asks which books of hers were made into movies. The AI responds by mentioning the FX series Kindred, based on her novel of the same name. The human then asks about her contemporaries, and the AI lists Ursula K. Le Guin, Samuel R. Delany, and Joanna Russ. system : {'content': 'The human inquires about Octavia Butler. The AI identifies her as an American science fiction author. The human then asks which books of hers were made into movies. The AI responds by mentioning the FX series Kindred, based on her novel of the same name. The human then asks about her contemporaries, and the AI lists Ursula K. Le Guin, Samuel R. Delany, and Joanna Russ.', 'additional_kwargs': {}} human : {'content': 'What awards did she win?', 'additional_kwargs': {'uuid': '6b733f0b-6778-49ae-b3ec-4e077c039f31', 'created_at': '2023-07-09T19:23:16.611232Z', 'token_count': 8, 'metadata': {'system': {'entities': [], 'intent': 'The subject is inquiring about the awards that someone, whose identity is not specified, has won.'}}}, 'example': False} ai : {'content': 'Octavia Butler won the Hugo Award, the Nebula Award, and the MacArthur Fellowship.', 'additional_kwargs': {'uuid': '2f6d80c6-3c08-4fd4-8d4e-7bbee341ac90', 'created_at': '2023-07-09T19:23:16.618947Z', 'token_count': 21, 'metadata': {'system': {'entities': [{'Label': 'PERSON', 'Matches': [{'End': 14, 'Start': 0, 'Text': 'Octavia Butler'}], 'Name': 'Octavia Butler'}, {'Label': 'WORK_OF_ART', 'Matches': [{'End': 33, 'Start': 19, 'Text': 'the Hugo Award'}], 'Name': 'the Hugo Award'}, {'Label': 'EVENT', 'Matches': [{'End': 81, 'Start': 57, 'Text': 'the MacArthur Fellowship'}], 'Name': 'the MacArthur Fellowship'}], 'intent': 'The subject is stating that Octavia Butler received the Hugo Award, the Nebula Award, and the MacArthur Fellowship.'}}}, 'example': False} human : {'content': 'Which other women sci-fi writers might I want to read?', 'additional_kwargs': {'uuid': 'ccdcc901-ea39-4981-862f-6fe22ab9289b', 'created_at': '2023-07-09T19:23:16.62678Z', 'token_count': 14, 'metadata': {'system': {'entities': [], 'intent': 'The subject is seeking recommendations for additional women science fiction writers to explore.'}}}, 'example': False} ai : {'content': 'You might want to read Ursula K. Le Guin or Joanna Russ.', 'additional_kwargs': {'uuid': '7977099a-0c62-4c98-bfff-465bbab6c9c3', 'created_at': '2023-07-09T19:23:16.631721Z', 'token_count': 18, 'metadata': {'system': {'entities': [{'Label': 'ORG', 'Matches': [{'End': 40, 'Start': 23, 'Text': 'Ursula K. Le Guin'}], 'Name': 'Ursula K. Le Guin'}, {'Label': 'PERSON', 'Matches': [{'End': 55, 'Start': 44, 'Text': 'Joanna Russ'}], 'Name': 'Joanna Russ'}], 'intent': 'The subject is suggesting that the person should consider reading the works of Ursula K. Le Guin or Joanna Russ.'}}}, 'example': False} human : {'content': "Write a short synopsis of Butler's book, Parable of the Sower. What is it about?", 'additional_kwargs': {'uuid': 'e439b7e6-286a-4278-a8cb-dc260fa2e089', 'created_at': '2023-07-09T19:23:16.63623Z', 'token_count': 23, 'metadata': {'system': {'entities': [{'Label': 'ORG', 'Matches': [{'End': 32, 'Start': 26, 'Text': 'Butler'}], 'Name': 'Butler'}, {'Label': 'WORK_OF_ART', 'Matches': [{'End': 61, 'Start': 41, 'Text': 'Parable of the Sower'}], 'Name': 'Parable of the Sower'}], 'intent': 'The subject is requesting a brief summary or explanation of the book "Parable of the Sower" by Butler.'}}}, 'example': False} ai : {'content': 'Parable of the Sower is a science fiction novel by Octavia Butler, published in 1993. It follows the story of Lauren Olamina, a young woman living in a dystopian future where society has collapsed due to environmental disasters, poverty, and violence.', 'additional_kwargs': {'uuid': '6760489b-19c9-41aa-8b45-fae6cb1d7ee6', 'created_at': '2023-07-09T19:23:16.647524Z', 'token_count': 56, 'metadata': {'foo': 'bar', 'system': {'entities': [{'Label': 'GPE', 'Matches': [{'End': 20, 'Start': 15, 'Text': 'Sower'}], 'Name': 'Sower'}, {'Label': 'PERSON', 'Matches': [{'End': 65, 'Start': 51, 'Text': 'Octavia Butler'}], 'Name': 'Octavia Butler'}, {'Label': 'DATE', 'Matches': [{'End': 84, 'Start': 80, 'Text': '1993'}], 'Name': '1993'}, {'Label': 'PERSON', 'Matches': [{'End': 124, 'Start': 110, 'Text': 'Lauren Olamina'}], 'Name': 'Lauren Olamina'}], 'intent': 'The subject is providing information about the novel "Parable of the Sower" by Octavia Butler, including its genre, publication date, and a brief summary of the plot.'}}}, 'example': False} human : {'content': "What is the book's relevance to the challenges facing contemporary society?", 'additional_kwargs': {'uuid': '7dbbbb93-492b-4739-800f-cad2b6e0e764', 'created_at': '2023-07-09T19:23:19.315182Z', 'token_count': 15, 'metadata': {'system': {'entities': [], 'intent': 'The subject is asking about the relevance of a book to the challenges currently faced by society.'}}}, 'example': False} ai : {'content': 'Parable of the Sower is a prescient novel that speaks to the challenges facing contemporary society, such as climate change, inequality, and violence. It is a cautionary tale that warns of the dangers of unchecked greed and the need for individuals to take responsibility for their own lives and the lives of those around them.', 'additional_kwargs': {'uuid': '3e14ac8f-b7c1-4360-958b-9f3eae1f784f', 'created_at': '2023-07-09T19:23:19.332517Z', 'token_count': 66, 'metadata': {'system': {'entities': [{'Label': 'GPE', 'Matches': [{'End': 20, 'Start': 15, 'Text': 'Sower'}], 'Name': 'Sower'}], 'intent': 'The subject is providing an analysis and evaluation of the novel "Parable of the Sower" and highlighting its relevance to contemporary societal challenges.'}}}, 'example': False}Vector search over the Zep memoryZep provides native vector search over historical conversation memory via the ZepRetriever.You can use the ZepRetriever with chains that support passing in a Langchain Retriever object.retriever = ZepRetriever( session_id=session_id, url=ZEP_API_URL, api_key=zep_api_key,)search_results = memory.chat_memory.search("who are some famous women sci-fi authors?")for r in search_results: if r.dist > 0.8: # Only print results with similarity of 0.8 or higher print(r.message, r.dist) {'uuid': 'ccdcc901-ea39-4981-862f-6fe22ab9289b', 'created_at': '2023-07-09T19:23:16.62678Z', 'role': 'human', 'content': 'Which other women sci-fi writers might I want to read?', 'metadata': {'system': {'entities': [], 'intent': 'The subject is seeking recommendations for additional women science fiction writers to explore.'}}, 'token_count': 14} 0.9119619869747062 {'uuid': '7977099a-0c62-4c98-bfff-465bbab6c9c3', 'created_at': '2023-07-09T19:23:16.631721Z', 'role': 'ai', 'content': 'You might want to read Ursula K. Le Guin or Joanna Russ.', 'metadata': {'system': {'entities': [{'Label': 'ORG', 'Matches': [{'End': 40, 'Start': 23, 'Text': 'Ursula K. Le Guin'}], 'Name': 'Ursula K. Le Guin'}, {'Label': 'PERSON', 'Matches': [{'End': 55, 'Start': 44, 'Text': 'Joanna Russ'}], 'Name': 'Joanna Russ'}], 'intent': 'The subject is suggesting that the person should consider reading the works of Ursula K. Le Guin or Joanna Russ.'}}, 'token_count': 18} 0.8534346954749745 {'uuid': 'b05e2eb5-c103-4973-9458-928726f08655', 'created_at': '2023-07-09T19:23:16.603098Z', 'role': 'ai', 'content': "Octavia Butler's contemporaries included Ursula K. Le Guin, Samuel R. Delany, and Joanna Russ.", 'metadata': {'system': {'entities': [{'Label': 'PERSON', 'Matches': [{'End': 16, 'Start': 0, 'Text': "Octavia Butler's"}], 'Name': "Octavia Butler's"}, {'Label': 'ORG', 'Matches': [{'End': 58, 'Start': 41, 'Text': 'Ursula K. Le Guin'}], 'Name': 'Ursula K. Le Guin'}, {'Label': 'PERSON', 'Matches': [{'End': 76, 'Start': 60, 'Text': 'Samuel R. Delany'}], 'Name': 'Samuel R. Delany'}, {'Label': 'PERSON', 'Matches': [{'End': 93, 'Start': 82, 'Text': 'Joanna Russ'}], 'Name': 'Joanna Russ'}], 'intent': "The subject is stating that Octavia Butler's contemporaries included Ursula K. Le Guin, Samuel R. Delany, and Joanna Russ."}}, 'token_count': 27} 0.8523831524040919 {'uuid': 'e346f02b-f854-435d-b6ba-fb394a416b9b', 'created_at': '2023-07-09T19:23:16.556587Z', 'role': 'human', 'content': 'Who was Octavia Butler?', 'metadata': {'system': {'entities': [{'Label': 'PERSON', 'Matches': [{'End': 22, 'Start': 8, 'Text': 'Octavia Butler'}], 'Name': 'Octavia Butler'}], 'intent': 'The subject is asking for information about the identity or background of Octavia Butler.'}}, 'token_count': 8} 0.8236355436055457 {'uuid': '42ff41d2-c63a-4d5b-b19b-d9a87105cfc3', 'created_at': '2023-07-09T19:23:16.578022Z', 'role': 'ai', 'content': 'Octavia Estelle Butler (June 22, 1947 – February 24, 2006) was an American science fiction author.', 'metadata': {'system': {'entities': [{'Label': 'PERSON', 'Matches': [{'End': 22, 'Start': 0, 'Text': 'Octavia Estelle Butler'}], 'Name': 'Octavia Estelle Butler'}, {'Label': 'DATE', 'Matches': [{'End': 37, 'Start': 24, 'Text': 'June 22, 1947'}], 'Name': 'June 22, 1947'}, {'Label': 'DATE', 'Matches': [{'End': 57, 'Start': 40, 'Text': 'February 24, 2006'}], 'Name': 'February 24, 2006'}, {'Label': 'NORP', 'Matches': [{'End': 74, 'Start': 66, 'Text': 'American'}], 'Name': 'American'}], 'intent': 'The subject is providing information about Octavia Estelle Butler, who was an American science fiction author.'}}, 'token_count': 31} 0.8206687242257686 {'uuid': '2f6d80c6-3c08-4fd4-8d4e-7bbee341ac90', 'created_at': '2023-07-09T19:23:16.618947Z', 'role': 'ai', 'content': 'Octavia Butler won the Hugo Award, the Nebula Award, and the MacArthur Fellowship.', 'metadata': {'system': {'entities': [{'Label': 'PERSON', 'Matches': [{'End': 14, 'Start': 0, 'Text': 'Octavia Butler'}], 'Name': 'Octavia Butler'}, {'Label': 'WORK_OF_ART', 'Matches': [{'End': 33, 'Start': 19, 'Text': 'the Hugo Award'}], 'Name': 'the Hugo Award'}, {'Label': 'EVENT', 'Matches': [{'End': 81, 'Start': 57, 'Text': 'the MacArthur Fellowship'}], 'Name': 'the MacArthur Fellowship'}], 'intent': 'The subject is stating that Octavia Butler received the Hugo Award, the Nebula Award, and the MacArthur Fellowship.'}}, 'token_count': 21} 0.8199012397683285PreviousXataNextCallbacksExampleInitialize the Zep Chat Message History Class and initialize the AgentAdd some history dataRun the agentInspect the Zep memoryVector search over the Zep memory |
790 | https://python.langchain.com/docs/integrations/callbacks | ComponentsCallbacksCallbacks📄️ ArgillaArgilla - Open-source data platform for LLMs📄️ ConfidentDeepEval package for unit testing LLMs.📄️ ContextContext - User Analytics for LLM Powered Products📄️ InfinoThis example shows how one can track the following while calling OpenAI models via LangChain and Infino:📄️ Label StudioLabel Studio is an open-source data labeling platform that provides LangChain with flexibility when it comes to labeling data for fine-tuning large language models (LLMs). It also enables the preparation of custom training data and the collection and evaluation of responses through human feedback.📄️ LLMonitorLLMonitor is an open-source observability platform that provides cost and usage analytics, user tracking, tracing and evaluation tools.📄️ PromptLayerPromptLayer📄️ SageMaker TrackingThis notebook shows how LangChain Callback can be used to log and track prompts and other LLM hyperparameters into SageMaker Experiments. Here, we use different scenarios to showcase the capability:📄️ StreamlitStreamlit is a faster way to build and share data apps.📄️ TrubricsTrubricsPreviousZepNextArgilla |
791 | https://python.langchain.com/docs/integrations/callbacks/argilla | ComponentsCallbacksArgillaOn this pageArgillaArgilla is an open-source data curation platform for LLMs.
Using Argilla, everyone can build robust language models through faster data curation
using both human and machine feedback. We provide support for each step in the MLOps cycle,
from data labeling to model monitoring.In this guide we will demonstrate how to track the inputs and responses of your LLM to generate a dataset in Argilla, using the ArgillaCallbackHandler.It's useful to keep track of the inputs and outputs of your LLMs to generate datasets for future fine-tuning. This is especially useful when you're using a LLM to generate data for a specific task, such as question answering, summarization, or translation.Installation and Setuppip install argilla --upgradepip install openaiGetting API CredentialsTo get the Argilla API credentials, follow the next steps:Go to your Argilla UI.Click on your profile picture and go to "My settings".Then copy the API Key.In Argilla the API URL will be the same as the URL of your Argilla UI.To get the OpenAI API credentials, please visit https://platform.openai.com/account/api-keysimport osos.environ["ARGILLA_API_URL"] = "..."os.environ["ARGILLA_API_KEY"] = "..."os.environ["OPENAI_API_KEY"] = "..."Setup ArgillaTo use the ArgillaCallbackHandler we will need to create a new FeedbackDataset in Argilla to keep track of your LLM experiments. To do so, please use the following code:import argilla as rgfrom packaging.version import parse as parse_versionif parse_version(rg.__version__) < parse_version("1.8.0"): raise RuntimeError( "`FeedbackDataset` is only available in Argilla v1.8.0 or higher, please " "upgrade `argilla` as `pip install argilla --upgrade`." )dataset = rg.FeedbackDataset( fields=[ rg.TextField(name="prompt"), rg.TextField(name="response"), ], questions=[ rg.RatingQuestion( name="response-rating", description="How would you rate the quality of the response?", values=[1, 2, 3, 4, 5], required=True, ), rg.TextQuestion( name="response-feedback", description="What feedback do you have for the response?", required=False, ), ], guidelines="You're asked to rate the quality of the response and provide feedback.",)rg.init( api_url=os.environ["ARGILLA_API_URL"], api_key=os.environ["ARGILLA_API_KEY"],)dataset.push_to_argilla("langchain-dataset");📌 NOTE: at the moment, just the prompt-response pairs are supported as FeedbackDataset.fields, so the ArgillaCallbackHandler will just track the prompt i.e. the LLM input, and the response i.e. the LLM output.TrackingTo use the ArgillaCallbackHandler you can either use the following code, or just reproduce one of the examples presented in the following sections.from langchain.callbacks import ArgillaCallbackHandlerargilla_callback = ArgillaCallbackHandler( dataset_name="langchain-dataset", api_url=os.environ["ARGILLA_API_URL"], api_key=os.environ["ARGILLA_API_KEY"],)Scenario 1: Tracking an LLMFirst, let's just run a single LLM a few times and capture the resulting prompt-response pairs in Argilla.from langchain.callbacks import ArgillaCallbackHandler, StdOutCallbackHandlerfrom langchain.llms import OpenAIargilla_callback = ArgillaCallbackHandler( dataset_name="langchain-dataset", api_url=os.environ["ARGILLA_API_URL"], api_key=os.environ["ARGILLA_API_KEY"],)callbacks = [StdOutCallbackHandler(), argilla_callback]llm = OpenAI(temperature=0.9, callbacks=callbacks)llm.generate(["Tell me a joke", "Tell me a poem"] * 3) LLMResult(generations=[[Generation(text='\n\nQ: What did the fish say when he hit the wall? \nA: Dam.', generation_info={'finish_reason': 'stop', 'logprobs': None})], [Generation(text='\n\nThe Moon \n\nThe moon is high in the midnight sky,\nSparkling like a star above.\nThe night so peaceful, so serene,\nFilling up the air with love.\n\nEver changing and renewing,\nA never-ending light of grace.\nThe moon remains a constant view,\nA reminder of life’s gentle pace.\n\nThrough time and space it guides us on,\nA never-fading beacon of hope.\nThe moon shines down on us all,\nAs it continues to rise and elope.', generation_info={'finish_reason': 'stop', 'logprobs': None})], [Generation(text='\n\nQ. What did one magnet say to the other magnet?\nA. "I find you very attractive!"', generation_info={'finish_reason': 'stop', 'logprobs': None})], [Generation(text="\n\nThe world is charged with the grandeur of God.\nIt will flame out, like shining from shook foil;\nIt gathers to a greatness, like the ooze of oil\nCrushed. Why do men then now not reck his rod?\n\nGenerations have trod, have trod, have trod;\nAnd all is seared with trade; bleared, smeared with toil;\nAnd wears man's smudge and shares man's smell: the soil\nIs bare now, nor can foot feel, being shod.\n\nAnd for all this, nature is never spent;\nThere lives the dearest freshness deep down things;\nAnd though the last lights off the black West went\nOh, morning, at the brown brink eastward, springs —\n\nBecause the Holy Ghost over the bent\nWorld broods with warm breast and with ah! bright wings.\n\n~Gerard Manley Hopkins", generation_info={'finish_reason': 'stop', 'logprobs': None})], [Generation(text='\n\nQ: What did one ocean say to the other ocean?\nA: Nothing, they just waved.', generation_info={'finish_reason': 'stop', 'logprobs': None})], [Generation(text="\n\nA poem for you\n\nOn a field of green\n\nThe sky so blue\n\nA gentle breeze, the sun above\n\nA beautiful world, for us to love\n\nLife is a journey, full of surprise\n\nFull of joy and full of surprise\n\nBe brave and take small steps\n\nThe future will be revealed with depth\n\nIn the morning, when dawn arrives\n\nA fresh start, no reason to hide\n\nSomewhere down the road, there's a heart that beats\n\nBelieve in yourself, you'll always succeed.", generation_info={'finish_reason': 'stop', 'logprobs': None})]], llm_output={'token_usage': {'completion_tokens': 504, 'total_tokens': 528, 'prompt_tokens': 24}, 'model_name': 'text-davinci-003'})Scenario 2: Tracking an LLM in a chainThen we can create a chain using a prompt template, and then track the initial prompt and the final response in Argilla.from langchain.callbacks import ArgillaCallbackHandler, StdOutCallbackHandlerfrom langchain.llms import OpenAIfrom langchain.chains import LLMChainfrom langchain.prompts import PromptTemplateargilla_callback = ArgillaCallbackHandler( dataset_name="langchain-dataset", api_url=os.environ["ARGILLA_API_URL"], api_key=os.environ["ARGILLA_API_KEY"],)callbacks = [StdOutCallbackHandler(), argilla_callback]llm = OpenAI(temperature=0.9, callbacks=callbacks)template = """You are a playwright. Given the title of play, it is your job to write a synopsis for that title.Title: {title}Playwright: This is a synopsis for the above play:"""prompt_template = PromptTemplate(input_variables=["title"], template=template)synopsis_chain = LLMChain(llm=llm, prompt=prompt_template, callbacks=callbacks)test_prompts = [{"title": "Documentary about Bigfoot in Paris"}]synopsis_chain.apply(test_prompts) > Entering new LLMChain chain... Prompt after formatting: You are a playwright. Given the title of play, it is your job to write a synopsis for that title. Title: Documentary about Bigfoot in Paris Playwright: This is a synopsis for the above play: > Finished chain. [{'text': "\n\nDocumentary about Bigfoot in Paris focuses on the story of a documentary filmmaker and their search for evidence of the legendary Bigfoot creature in the city of Paris. The play follows the filmmaker as they explore the city, meeting people from all walks of life who have had encounters with the mysterious creature. Through their conversations, the filmmaker unravels the story of Bigfoot and finds out the truth about the creature's presence in Paris. As the story progresses, the filmmaker learns more and more about the mysterious creature, as well as the different perspectives of the people living in the city, and what they think of the creature. In the end, the filmmaker's findings lead them to some surprising and heartwarming conclusions about the creature's existence and the importance it holds in the lives of the people in Paris."}]Scenario 3: Using an Agent with ToolsFinally, as a more advanced workflow, you can create an agent that uses some tools. So that ArgillaCallbackHandler will keep track of the input and the output, but not about the intermediate steps/thoughts, so that given a prompt we log the original prompt and the final response to that given prompt.Note that for this scenario we'll be using Google Search API (Serp API) so you will need to both install google-search-results as pip install google-search-results, and to set the Serp API Key as os.environ["SERPAPI_API_KEY"] = "..." (you can find it at https://serpapi.com/dashboard), otherwise the example below won't work.from langchain.agents import AgentType, initialize_agent, load_toolsfrom langchain.callbacks import ArgillaCallbackHandler, StdOutCallbackHandlerfrom langchain.llms import OpenAIargilla_callback = ArgillaCallbackHandler( dataset_name="langchain-dataset", api_url=os.environ["ARGILLA_API_URL"], api_key=os.environ["ARGILLA_API_KEY"],)callbacks = [StdOutCallbackHandler(), argilla_callback]llm = OpenAI(temperature=0.9, callbacks=callbacks)tools = load_tools(["serpapi"], llm=llm, callbacks=callbacks)agent = initialize_agent( tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, callbacks=callbacks,)agent.run("Who was the first president of the United States of America?") > Entering new AgentExecutor chain... I need to answer a historical question Action: Search Action Input: "who was the first president of the United States of America" Observation: George Washington Thought: George Washington was the first president Final Answer: George Washington was the first president of the United States of America. > Finished chain. 'George Washington was the first president of the United States of America.'PreviousCallbacksNextConfidentInstallation and SetupGetting API CredentialsSetup ArgillaTrackingScenario 1: Tracking an LLMScenario 2: Tracking an LLM in a chainScenario 3: Using an Agent with Tools |
792 | https://python.langchain.com/docs/integrations/callbacks/confident | ComponentsCallbacksConfidentOn this pageConfidentDeepEval package for unit testing LLMs.
Using Confident, everyone can build robust language models through faster iterations
using both unit testing and integration testing. We provide support for each step in the iteration
from synthetic data creation to testing.In this guide we will demonstrate how to test and measure LLMs in performance. We show how you can use our callback to measure performance and how you can define your own metric and log them into our dashboard.DeepEval also offers:How to generate synthetic dataHow to measure performanceA dashboard to monitor and review results over timeInstallation and Setuppip install deepeval --upgradeGetting API CredentialsTo get the DeepEval API credentials, follow the next steps:Go to https://app.confident-ai.comClick on "Organization"Copy the API Key.When you log in, you will also be asked to set the implementation name. The implementation name is required to describe the type of implementation. (Think of what you want to call your project. We recommend making it descriptive.)deepeval loginSetup DeepEvalYou can, by default, use the DeepEvalCallbackHandler to set up the metrics you want to track. However, this has limited support for metrics at the moment (more to be added soon). It currently supports:Answer RelevancyBiasToxicnessfrom deepeval.metrics.answer_relevancy import AnswerRelevancy# Here we want to make sure the answer is minimally relevantanswer_relevancy_metric = AnswerRelevancy(minimum_score=0.5)Get StartedTo use the DeepEvalCallbackHandler, we need the implementation_name. import osfrom langchain.callbacks.confident_callback import DeepEvalCallbackHandlerdeepeval_callback = DeepEvalCallbackHandler( implementation_name="langchainQuickstart", metrics=[answer_relevancy_metric])Scenario 1: Feeding into LLMYou can then feed it into your LLM with OpenAI.from langchain.llms import OpenAIllm = OpenAI( temperature=0, callbacks=[deepeval_callback], verbose=True, openai_api_key="<YOUR_API_KEY>",)output = llm.generate( [ "What is the best evaluation tool out there? (no bias at all)", ]) LLMResult(generations=[[Generation(text='\n\nQ: What did the fish say when he hit the wall? \nA: Dam.', generation_info={'finish_reason': 'stop', 'logprobs': None})], [Generation(text='\n\nThe Moon \n\nThe moon is high in the midnight sky,\nSparkling like a star above.\nThe night so peaceful, so serene,\nFilling up the air with love.\n\nEver changing and renewing,\nA never-ending light of grace.\nThe moon remains a constant view,\nA reminder of life’s gentle pace.\n\nThrough time and space it guides us on,\nA never-fading beacon of hope.\nThe moon shines down on us all,\nAs it continues to rise and elope.', generation_info={'finish_reason': 'stop', 'logprobs': None})], [Generation(text='\n\nQ. What did one magnet say to the other magnet?\nA. "I find you very attractive!"', generation_info={'finish_reason': 'stop', 'logprobs': None})], [Generation(text="\n\nThe world is charged with the grandeur of God.\nIt will flame out, like shining from shook foil;\nIt gathers to a greatness, like the ooze of oil\nCrushed. Why do men then now not reck his rod?\n\nGenerations have trod, have trod, have trod;\nAnd all is seared with trade; bleared, smeared with toil;\nAnd wears man's smudge and shares man's smell: the soil\nIs bare now, nor can foot feel, being shod.\n\nAnd for all this, nature is never spent;\nThere lives the dearest freshness deep down things;\nAnd though the last lights off the black West went\nOh, morning, at the brown brink eastward, springs —\n\nBecause the Holy Ghost over the bent\nWorld broods with warm breast and with ah! bright wings.\n\n~Gerard Manley Hopkins", generation_info={'finish_reason': 'stop', 'logprobs': None})], [Generation(text='\n\nQ: What did one ocean say to the other ocean?\nA: Nothing, they just waved.', generation_info={'finish_reason': 'stop', 'logprobs': None})], [Generation(text="\n\nA poem for you\n\nOn a field of green\n\nThe sky so blue\n\nA gentle breeze, the sun above\n\nA beautiful world, for us to love\n\nLife is a journey, full of surprise\n\nFull of joy and full of surprise\n\nBe brave and take small steps\n\nThe future will be revealed with depth\n\nIn the morning, when dawn arrives\n\nA fresh start, no reason to hide\n\nSomewhere down the road, there's a heart that beats\n\nBelieve in yourself, you'll always succeed.", generation_info={'finish_reason': 'stop', 'logprobs': None})]], llm_output={'token_usage': {'completion_tokens': 504, 'total_tokens': 528, 'prompt_tokens': 24}, 'model_name': 'text-davinci-003'})You can then check the metric if it was successful by calling the is_successful() method.answer_relevancy_metric.is_successful()# returns True/FalseOnce you have ran that, you should be able to see our dashboard below. Scenario 2: Tracking an LLM in a chain without callbacksTo track an LLM in a chain without callbacks, you can plug into it at the end.We can start by defining a simple chain as shown below.import requestsfrom langchain.chains import RetrievalQAfrom langchain.document_loaders import TextLoaderfrom langchain.embeddings.openai import OpenAIEmbeddingsfrom langchain.llms import OpenAIfrom langchain.text_splitter import CharacterTextSplitterfrom langchain.vectorstores import Chromatext_file_url = "https://raw.githubusercontent.com/hwchase17/chat-your-data/master/state_of_the_union.txt"openai_api_key = "sk-XXX"with open("state_of_the_union.txt", "w") as f: response = requests.get(text_file_url) f.write(response.text)loader = TextLoader("state_of_the_union.txt")documents = loader.load()text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)texts = text_splitter.split_documents(documents)embeddings = OpenAIEmbeddings(openai_api_key=openai_api_key)docsearch = Chroma.from_documents(texts, embeddings)qa = RetrievalQA.from_chain_type( llm=OpenAI(openai_api_key=openai_api_key), chain_type="stuff", retriever=docsearch.as_retriever())# Providing a new question-answering pipelinequery = "Who is the president?"result = qa.run(query)After defining a chain, you can then manually check for answer similarity.answer_relevancy_metric.measure(result, query)answer_relevancy_metric.is_successful()What's next?You can create your own custom metrics here. DeepEval also offers other features such as being able to automatically create unit tests, tests for hallucination.If you are interested, check out our Github repository here https://github.com/confident-ai/deepeval. We welcome any PRs and discussions on how to improve LLM performance.PreviousArgillaNextContextInstallation and SetupGetting API CredentialsSetup DeepEvalGet StartedScenario 1: Feeding into LLMScenario 2: Tracking an LLM in a chain without callbacksWhat's next? |
793 | https://python.langchain.com/docs/integrations/callbacks/context | ComponentsCallbacksContextOn this pageContextContext provides user analytics for LLM powered products and features.With Context, you can start understanding your users and improving their experiences in less than 30 minutes.In this guide we will show you how to integrate with Context.Installation and Setup$ pip install context-python --upgradeGetting API CredentialsTo get your Context API token:Go to the settings page within your Context account (https://with.context.ai/settings).Generate a new API Token.Store this token somewhere secure.Setup ContextTo use the ContextCallbackHandler, import the handler from Langchain and instantiate it with your Context API token.Ensure you have installed the context-python package before using the handler.import osfrom langchain.callbacks import ContextCallbackHandlertoken = os.environ["CONTEXT_API_TOKEN"]context_callback = ContextCallbackHandler(token)UsageUsing the Context callback within a chat modelThe Context callback handler can be used to directly record transcripts between users and AI assistants.Exampleimport osfrom langchain.chat_models import ChatOpenAIfrom langchain.schema import ( SystemMessage, HumanMessage,)from langchain.callbacks import ContextCallbackHandlertoken = os.environ["CONTEXT_API_TOKEN"]chat = ChatOpenAI( headers={"user_id": "123"}, temperature=0, callbacks=[ContextCallbackHandler(token)])messages = [ SystemMessage( content="You are a helpful assistant that translates English to French." ), HumanMessage(content="I love programming."),]print(chat(messages))Using the Context callback within ChainsThe Context callback handler can also be used to record the inputs and outputs of chains. Note that intermediate steps of the chain are not recorded - only the starting inputs and final outputs.Note: Ensure that you pass the same context object to the chat model and the chain.Wrong:chat = ChatOpenAI(temperature=0.9, callbacks=[ContextCallbackHandler(token)])chain = LLMChain(llm=chat, prompt=chat_prompt_template, callbacks=[ContextCallbackHandler(token)])Correct:handler = ContextCallbackHandler(token)chat = ChatOpenAI(temperature=0.9, callbacks=[callback])chain = LLMChain(llm=chat, prompt=chat_prompt_template, callbacks=[callback])Exampleimport osfrom langchain.chat_models import ChatOpenAIfrom langchain.chains import LLMChainfrom langchain.prompts import PromptTemplatefrom langchain.prompts.chat import ( ChatPromptTemplate, HumanMessagePromptTemplate,)from langchain.callbacks import ContextCallbackHandlertoken = os.environ["CONTEXT_API_TOKEN"]human_message_prompt = HumanMessagePromptTemplate( prompt=PromptTemplate( template="What is a good name for a company that makes {product}?", input_variables=["product"], ))chat_prompt_template = ChatPromptTemplate.from_messages([human_message_prompt])callback = ContextCallbackHandler(token)chat = ChatOpenAI(temperature=0.9, callbacks=[callback])chain = LLMChain(llm=chat, prompt=chat_prompt_template, callbacks=[callback])print(chain.run("colorful socks"))PreviousConfidentNextInfinoInstallation and SetupGetting API CredentialsSetup ContextUsageUsing the Context callback within a chat modelUsing the Context callback within Chains |
794 | https://python.langchain.com/docs/integrations/callbacks/infino | ComponentsCallbacksInfinoOn this pageInfinoThis example shows how one can track the following while calling OpenAI models via LangChain and Infino:prompt input,response from ChatGPT or any other LangChain model,latency,errors,number of tokens consumedInitializing# Install necessary dependencies.pip install infinopypip install matplotlib# Remove the (1) import sys and sys.path.append(..) and (2) uncomment `!pip install langchain` after merging the PR for Infino/LangChain integration.import syssys.path.append("../../../../../langchain")import datetime as dtimport jsonfrom langchain.llms import OpenAIimport matplotlib.pyplot as pltimport matplotlib.dates as mdimport osimport timeimport sysfrom infinopy import InfinoClientfrom langchain.callbacks import InfinoCallbackHandlerStart Infino server, initialize the Infino client# Start server using the Infino docker image.docker run --rm --detach --name infino-example -p 3000:3000 infinohq/infino:latest# Create Infino client.client = InfinoClient() 497a621125800abdd19f57ce7e033349b3cf83ca8cea6a74e8e28433a42ecaddRead the questions dataset# These are a subset of questions from Stanford's QA dataset -# https://rajpurkar.github.io/SQuAD-explorer/data = """In what country is Normandy located?When were the Normans in Normandy?From which countries did the Norse originate?Who was the Norse leader?What century did the Normans first gain their separate identity?Who gave their name to Normandy in the 1000's and 1100'sWhat is France a region of?Who did King Charles III swear fealty to?When did the Frankish identity emerge?Who was the duke in the battle of Hastings?Who ruled the duchy of NormandyWhat religion were the NormansWhat type of major impact did the Norman dynasty have on modern Europe?Who was famed for their Christian spirit?Who assimilted the Roman language?Who ruled the country of Normandy?What principality did William the conquerer found?What is the original meaning of the word Norman?When was the Latin version of the word Norman first recorded?What name comes from the English words Normans/Normanz?"""questions = data.split("\n")LangChain OpenAI Q&A; Publish metrics and logs to Infino# Set your key here.# os.environ["OPENAI_API_KEY"] = "YOUR_API_KEY"# Create callback handler. This logs latency, errors, token usage, prompts as well as prompt responses to Infino.handler = InfinoCallbackHandler( model_id="test_openai", model_version="0.1", verbose=False)# Create LLM.llm = OpenAI(temperature=0.1)# Number of questions to ask the OpenAI model. We limit to a short number here to save $$ while running this demo.num_questions = 10questions = questions[0:num_questions]for question in questions: print(question) # We send the question to OpenAI API, with Infino callback. llm_result = llm.generate([question], callbacks=[handler]) print(llm_result) In what country is Normandy located? generations=[[Generation(text='\n\nNormandy is located in France.', generation_info={'finish_reason': 'stop', 'logprobs': None})]] llm_output={'token_usage': {'total_tokens': 16, 'completion_tokens': 9, 'prompt_tokens': 7}, 'model_name': 'text-davinci-003'} run=RunInfo(run_id=UUID('8de21639-acec-4bd1-a12d-8124de1e20da')) When were the Normans in Normandy? generations=[[Generation(text='\n\nThe Normans first settled in Normandy in the late 9th century.', generation_info={'finish_reason': 'stop', 'logprobs': None})]] llm_output={'token_usage': {'total_tokens': 24, 'completion_tokens': 16, 'prompt_tokens': 8}, 'model_name': 'text-davinci-003'} run=RunInfo(run_id=UUID('cf81fc86-250b-4e6e-9d92-2df3bebb019a')) From which countries did the Norse originate? generations=[[Generation(text='\n\nThe Norse originated from Scandinavia, which includes modern-day Norway, Sweden, and Denmark.', generation_info={'finish_reason': 'stop', 'logprobs': None})]] llm_output={'token_usage': {'total_tokens': 29, 'completion_tokens': 21, 'prompt_tokens': 8}, 'model_name': 'text-davinci-003'} run=RunInfo(run_id=UUID('50f42f5e-b4a4-411a-a049-f92cb573a74f')) Who was the Norse leader? generations=[[Generation(text='\n\nThe most famous Norse leader was the legendary Viking king Ragnar Lodbrok. He is believed to have lived in the 9th century and is renowned for his exploits in England and France.', generation_info={'finish_reason': 'stop', 'logprobs': None})]] llm_output={'token_usage': {'total_tokens': 45, 'completion_tokens': 39, 'prompt_tokens': 6}, 'model_name': 'text-davinci-003'} run=RunInfo(run_id=UUID('e32f31cb-ddc9-4863-8e6e-cb7a281a0ada')) What century did the Normans first gain their separate identity? generations=[[Generation(text='\n\nThe Normans first gained their separate identity in the 11th century.', generation_info={'finish_reason': 'stop', 'logprobs': None})]] llm_output={'token_usage': {'total_tokens': 28, 'completion_tokens': 16, 'prompt_tokens': 12}, 'model_name': 'text-davinci-003'} run=RunInfo(run_id=UUID('da9d8f73-b3b3-4bc5-8495-da8b11462a51')) Who gave their name to Normandy in the 1000's and 1100's generations=[[Generation(text='\n\nThe Normans, a people from northern France, gave their name to Normandy in the 1000s and 1100s. The Normans were descended from Viking settlers who had come to the region in the late 800s.', generation_info={'finish_reason': 'stop', 'logprobs': None})]] llm_output={'token_usage': {'total_tokens': 58, 'completion_tokens': 45, 'prompt_tokens': 13}, 'model_name': 'text-davinci-003'} run=RunInfo(run_id=UUID('bb5829bf-b6a6-4429-adfa-414ac5be46e5')) What is France a region of? generations=[[Generation(text='\n\nFrance is a region of Europe.', generation_info={'finish_reason': 'stop', 'logprobs': None})]] llm_output={'token_usage': {'total_tokens': 16, 'completion_tokens': 9, 'prompt_tokens': 7}, 'model_name': 'text-davinci-003'} run=RunInfo(run_id=UUID('6943880b-b4e4-4c74-9ca1-8c03c10f7e9c')) Who did King Charles III swear fealty to? generations=[[Generation(text='\n\nKing Charles III swore fealty to Pope Innocent III.', generation_info={'finish_reason': 'stop', 'logprobs': None})]] llm_output={'token_usage': {'total_tokens': 23, 'completion_tokens': 13, 'prompt_tokens': 10}, 'model_name': 'text-davinci-003'} run=RunInfo(run_id=UUID('c91fd663-09e6-4d00-b746-4c7fd96f9ceb')) When did the Frankish identity emerge? generations=[[Generation(text='\n\nThe Frankish identity began to emerge in the late 5th century, when the Franks began to expand their power and influence in the region. The Franks were a Germanic tribe that had migrated to the area from the east and had established a kingdom in what is now modern-day France. The Franks were eventually able to establish a powerful kingdom that lasted until the 10th century.', generation_info={'finish_reason': 'stop', 'logprobs': None})]] llm_output={'token_usage': {'total_tokens': 86, 'completion_tokens': 78, 'prompt_tokens': 8}, 'model_name': 'text-davinci-003'} run=RunInfo(run_id=UUID('23f86775-e592-4cb8-baa3-46ebe74305b2')) Who was the duke in the battle of Hastings? generations=[[Generation(text='\n\nThe Duke of Normandy, William the Conqueror, was the leader of the Norman forces at the Battle of Hastings in 1066.', generation_info={'finish_reason': 'stop', 'logprobs': None})]] llm_output={'token_usage': {'total_tokens': 39, 'completion_tokens': 28, 'prompt_tokens': 11}, 'model_name': 'text-davinci-003'} run=RunInfo(run_id=UUID('ad5b7984-8758-4d95-a5eb-ee56e0218f6b'))Create Metric ChartsWe now use matplotlib to create graphs of latency, errors and tokens consumed.# Helper function to create a graph using matplotlib.def plot(data, title): data = json.loads(data) # Extract x and y values from the data timestamps = [item["time"] for item in data] dates = [dt.datetime.fromtimestamp(ts) for ts in timestamps] y = [item["value"] for item in data] plt.rcParams["figure.figsize"] = [6, 4] plt.subplots_adjust(bottom=0.2) plt.xticks(rotation=25) ax = plt.gca() xfmt = md.DateFormatter("%Y-%m-%d %H:%M:%S") ax.xaxis.set_major_formatter(xfmt) # Create the plot plt.plot(dates, y) # Set labels and title plt.xlabel("Time") plt.ylabel("Value") plt.title(title) plt.show()response = client.search_ts("__name__", "latency", 0, int(time.time()))plot(response.text, "Latency")response = client.search_ts("__name__", "error", 0, int(time.time()))plot(response.text, "Errors")response = client.search_ts("__name__", "prompt_tokens", 0, int(time.time()))plot(response.text, "Prompt Tokens")response = client.search_ts("__name__", "completion_tokens", 0, int(time.time()))plot(response.text, "Completion Tokens")response = client.search_ts("__name__", "total_tokens", 0, int(time.time()))plot(response.text, "Total Tokens")Full text query on prompt or prompt outputs.# Search for a particular prompt text.query = "normandy"response = client.search_log(query, 0, int(time.time()))print("Results for", query, ":", response.text)print("===")query = "king charles III"response = client.search_log("king charles III", 0, int(time.time()))print("Results for", query, ":", response.text) Results for normandy : [{"time":1686821979,"fields":{"prompt":"In what country is Normandy located?"},"text":"In what country is Normandy located?"},{"time":1686821982,"fields":{"prompt_response":"\n\nNormandy is located in France."},"text":"\n\nNormandy is located in France."},{"time":1686821984,"fields":{"prompt_response":"\n\nThe Normans first settled in Normandy in the late 9th century."},"text":"\n\nThe Normans first settled in Normandy in the late 9th century."},{"time":1686821993,"fields":{"prompt":"Who gave their name to Normandy in the 1000's and 1100's"},"text":"Who gave their name to Normandy in the 1000's and 1100's"},{"time":1686821997,"fields":{"prompt_response":"\n\nThe Normans, a people from northern France, gave their name to Normandy in the 1000s and 1100s. The Normans were descended from Viking settlers who had come to the region in the late 800s."},"text":"\n\nThe Normans, a people from northern France, gave their name to Normandy in the 1000s and 1100s. The Normans were descended from Viking settlers who had come to the region in the late 800s."}] === Results for king charles III : [{"time":1686821998,"fields":{"prompt":"Who did King Charles III swear fealty to?"},"text":"Who did King Charles III swear fealty to?"},{"time":1686822000,"fields":{"prompt_response":"\n\nKing Charles III swore fealty to Pope Innocent III."},"text":"\n\nKing Charles III swore fealty to Pope Innocent III."}]Stop infino serverdocker rm -f infino-example infino-examplePreviousContextNextLabel StudioInitializingStart Infino server, initialize the Infino clientRead the questions datasetLangChain OpenAI Q&A; Publish metrics and logs to InfinoCreate Metric ChartsFull text query on prompt or prompt outputs.Stop infino server |