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.md .pdf Deployments Contents Streamlit Gradio (on Hugging Face) Beam Vercel SteamShip Deployments# So you’ve made a really cool chain - now what? How do you deploy it and make it easily sharable with the world? This section covers several options for that. Note that these are meant as quick deployment options for prototypes and demos, and not for production systems. If you are looking for help with deployment of a production system, please contact us directly. What follows is a list of template GitHub repositories aimed that are intended to be very easy to fork and modify to use your chain. This is far from an exhaustive list of options, and we are EXTREMELY open to contributions here. Streamlit# This repo serves as a template for how to deploy a LangChain with Streamlit. It implements a chatbot interface. It also contains instructions for how to deploy this app on the Streamlit platform. Gradio (on Hugging Face)# This repo serves as a template for how deploy a LangChain with Gradio. It implements a chatbot interface, with a “Bring-Your-Own-Token” approach (nice for not wracking up big bills). It also contains instructions for how to deploy this app on the Hugging Face platform. This is heavily influenced by James Weaver’s excellent examples. Beam# This repo serves as a template for how deploy a LangChain with Beam. It implements a Question Answering app and contains instructions for deploying the app as a serverless REST API. Vercel# A minimal example on how to run LangChain on Vercel using Flask. SteamShip# This repository contains LangChain adapters for Steamship, enabling LangChain developers to rapidly deploy their apps on Steamship.

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This includes: production ready endpoints, horizontal scaling across dependencies, persistant storage of app state, multi-tenancy support, etc. previous LangChain Gallery next Tracing Contents Streamlit Gradio (on Hugging Face) Beam Vercel SteamShip By Harrison Chase © Copyright 2023, Harrison Chase. Last updated on Mar 24, 2023.

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.rst .pdf LangChain Ecosystem LangChain Ecosystem# Guides for how other companies/products can be used with LangChain AI21 Labs AtlasDB Banana CerebriumAI Chroma Cohere DeepInfra Deep Lake ForefrontAI Google Search Wrapper Google Serper Wrapper GooseAI Graphsignal Hazy Research Helicone Hugging Face Milvus Modal NLPCloud OpenAI OpenSearch Petals PGVector Pinecone PromptLayer Qdrant Runhouse SearxNG Search API SerpAPI StochasticAI Unstructured Weights & Biases Weaviate Wolfram Alpha Wrapper Writer previous API References next AI21 Labs By Harrison Chase © Copyright 2023, Harrison Chase. Last updated on Mar 24, 2023.

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.rst .pdf LangChain Gallery Contents Open Source Misc. Colab Notebooks Proprietary LangChain Gallery# Lots of people have built some pretty awesome stuff with LangChain. This is a collection of our favorites. If you see any other demos that you think we should highlight, be sure to let us know! Open Source# HowDoI.ai This is an experiment in building a large-language-model-backed chatbot. It can hold a conversation, remember previous comments/questions, and answer all types of queries (history, web search, movie data, weather, news, and more). YouTube Transcription QA with Sources An end-to-end example of doing question answering on YouTube transcripts, returning the timestamps as sources to legitimize the answer. QA Slack Bot This application is a Slack Bot that uses Langchain and OpenAI’s GPT3 language model to provide domain specific answers. You provide the documents. ThoughtSource A central, open resource and community around data and tools related to chain-of-thought reasoning in large language models. LLM Strategy This Python package adds a decorator llm_strategy that connects to an LLM (such as OpenAI’s GPT-3) and uses the LLM to “implement” abstract methods in interface classes. It does this by forwarding requests to the LLM and converting the responses back to Python data using Python’s @dataclasses. Zero-Shot Corporate Lobbyist A notebook showing how to use GPT to help with the work of a corporate lobbyist. Dagster Documentation ChatBot A jupyter notebook demonstrating how you could create a semantic search engine on documents in one of your Google Folders Google Folder Semantic Search Build a GitHub support bot with GPT3, LangChain, and Python. Talk With Wind Record sounds of anything (birds, wind, fire, train station) and chat with it.

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Record sounds of anything (birds, wind, fire, train station) and chat with it. ChatGPT LangChain This simple application demonstrates a conversational agent implemented with OpenAI GPT-3.5 and LangChain. When necessary, it leverages tools for complex math, searching the internet, and accessing news and weather. GPT Math Techniques A Hugging Face spaces project showing off the benefits of using PAL for math problems. GPT Political Compass Measure the political compass of GPT. Notion Database Question-Answering Bot Open source GitHub project shows how to use LangChain to create a chatbot that can answer questions about an arbitrary Notion database. LlamaIndex LlamaIndex (formerly GPT Index) is a project consisting of a set of data structures that are created using GPT-3 and can be traversed using GPT-3 in order to answer queries. Grover’s Algorithm Leveraging Qiskit, OpenAI and LangChain to demonstrate Grover’s algorithm QNimGPT A chat UI to play Nim, where a player can select an opponent, either a quantum computer or an AI ReAct TextWorld Leveraging the ReActTextWorldAgent to play TextWorld with an LLM! Fact Checker This repo is a simple demonstration of using LangChain to do fact-checking with prompt chaining. DocsGPT Answer questions about the documentation of any project Misc. Colab Notebooks# Wolfram Alpha in Conversational Agent Give ChatGPT a WolframAlpha neural implant Tool Updates in Agents Agent improvements (6th Jan 2023) Conversational Agent with Tools (Langchain AGI) Langchain AGI (23rd Dec 2022) Proprietary# Daimon A chat-based AI personal assistant with long-term memory about you.

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Daimon A chat-based AI personal assistant with long-term memory about you. AI Assisted SQL Query Generator An app to write SQL using natural language, and execute against real DB. Clerkie Stack Tracing QA Bot to help debug complex stack tracing (especially the ones that go multi-function/file deep). Sales Email Writer By Raza Habib, this demo utilizes LangChain + SerpAPI + HumanLoop to write sales emails. Give it a company name and a person, this application will use Google Search (via SerpAPI) to get more information on the company and the person, and then write them a sales message. Question-Answering on a Web Browser By Zahid Khawaja, this demo utilizes question answering to answer questions about a given website. A followup added this for YouTube videos, and then another followup added it for Wikipedia. Mynd A journaling app for self-care that uses AI to uncover insights and patterns over time. previous Glossary next Deployments Contents Open Source Misc. Colab Notebooks Proprietary By Harrison Chase © Copyright 2023, Harrison Chase. Last updated on Mar 24, 2023.

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Index _ | A | B | C | D | E | F | G | H | I | J | K | L | M | N | O | P | Q | R | S | T | U | V | W _ __call__() (langchain.llms.AI21 method) (langchain.llms.AlephAlpha method) (langchain.llms.Anthropic method) (langchain.llms.AzureOpenAI method) (langchain.llms.Banana method) (langchain.llms.CerebriumAI method) (langchain.llms.Cohere method) (langchain.llms.DeepInfra method) (langchain.llms.ForefrontAI method) (langchain.llms.GooseAI method) (langchain.llms.HuggingFaceEndpoint method) (langchain.llms.HuggingFaceHub method) (langchain.llms.HuggingFacePipeline method) (langchain.llms.Modal method) (langchain.llms.NLPCloud method) (langchain.llms.OpenAI method) (langchain.llms.OpenAIChat method) (langchain.llms.Petals method) (langchain.llms.PromptLayerOpenAI method) (langchain.llms.PromptLayerOpenAIChat method) (langchain.llms.SagemakerEndpoint method) (langchain.llms.SelfHostedHuggingFaceLLM method) (langchain.llms.SelfHostedPipeline method) (langchain.llms.StochasticAI method) (langchain.llms.Writer method) A aapply() (langchain.chains.LLMChain method) aapply_and_parse() (langchain.chains.LLMChain method) add() (langchain.docstore.InMemoryDocstore method)

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add() (langchain.docstore.InMemoryDocstore method) add_documents() (langchain.vectorstores.VectorStore method) add_embeddings() (langchain.vectorstores.FAISS method) add_example() (langchain.prompts.example_selector.LengthBasedExampleSelector method) (langchain.prompts.example_selector.SemanticSimilarityExampleSelector method) add_texts() (langchain.vectorstores.AtlasDB method) (langchain.vectorstores.Chroma method) (langchain.vectorstores.DeepLake method) (langchain.vectorstores.ElasticVectorSearch method) (langchain.vectorstores.FAISS method) (langchain.vectorstores.Milvus method) (langchain.vectorstores.OpenSearchVectorSearch method) (langchain.vectorstores.Pinecone method) (langchain.vectorstores.Qdrant method) (langchain.vectorstores.VectorStore method) (langchain.vectorstores.Weaviate method) agenerate() (langchain.chains.LLMChain method) (langchain.llms.AI21 method) (langchain.llms.AlephAlpha method) (langchain.llms.Anthropic method) (langchain.llms.AzureOpenAI method) (langchain.llms.Banana method) (langchain.llms.CerebriumAI method) (langchain.llms.Cohere method) (langchain.llms.DeepInfra method) (langchain.llms.ForefrontAI method) (langchain.llms.GooseAI method) (langchain.llms.HuggingFaceEndpoint method) (langchain.llms.HuggingFaceHub method) (langchain.llms.HuggingFacePipeline method) (langchain.llms.Modal method) (langchain.llms.NLPCloud method) (langchain.llms.OpenAI method) (langchain.llms.OpenAIChat method) (langchain.llms.Petals method) (langchain.llms.PromptLayerOpenAI method)

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(langchain.llms.PromptLayerOpenAI method) (langchain.llms.PromptLayerOpenAIChat method) (langchain.llms.SagemakerEndpoint method) (langchain.llms.SelfHostedHuggingFaceLLM method) (langchain.llms.SelfHostedPipeline method) (langchain.llms.StochasticAI method) (langchain.llms.Writer method) agenerate_prompt() (langchain.llms.AI21 method) (langchain.llms.AlephAlpha method) (langchain.llms.Anthropic method) (langchain.llms.AzureOpenAI method) (langchain.llms.Banana method) (langchain.llms.CerebriumAI method) (langchain.llms.Cohere method) (langchain.llms.DeepInfra method) (langchain.llms.ForefrontAI method) (langchain.llms.GooseAI method) (langchain.llms.HuggingFaceEndpoint method) (langchain.llms.HuggingFaceHub method) (langchain.llms.HuggingFacePipeline method) (langchain.llms.Modal method) (langchain.llms.NLPCloud method) (langchain.llms.OpenAI method) (langchain.llms.OpenAIChat method) (langchain.llms.Petals method) (langchain.llms.PromptLayerOpenAI method) (langchain.llms.PromptLayerOpenAIChat method) (langchain.llms.SagemakerEndpoint method) (langchain.llms.SelfHostedHuggingFaceLLM method) (langchain.llms.SelfHostedPipeline method) (langchain.llms.StochasticAI method) (langchain.llms.Writer method) agent (langchain.agents.AgentExecutor attribute) (langchain.agents.MRKLChain attribute) (langchain.agents.ReActChain attribute) (langchain.agents.SelfAskWithSearchChain attribute)

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(langchain.agents.SelfAskWithSearchChain attribute) ai_prefix (langchain.agents.ConversationalAgent attribute) aiosession (langchain.serpapi.SerpAPIWrapper attribute) aleph_alpha_api_key (langchain.llms.AlephAlpha attribute) allowed_tools (langchain.agents.Agent attribute) (langchain.agents.ZeroShotAgent attribute) answers (langchain.utilities.searx_search.SearxResults property) api_answer_chain (langchain.chains.APIChain attribute) api_docs (langchain.chains.APIChain attribute) api_request_chain (langchain.chains.APIChain attribute) api_url (langchain.llms.StochasticAI attribute) aplan() (langchain.agents.Agent method) apply() (langchain.chains.LLMChain method) apply_and_parse() (langchain.chains.LLMChain method) apredict() (langchain.chains.LLMChain method) aprep_prompts() (langchain.chains.LLMChain method) are_all_true_prompt (langchain.chains.LLMSummarizationCheckerChain attribute) arun() (langchain.serpapi.SerpAPIWrapper method) as_retriever() (langchain.vectorstores.VectorStore method) AtlasDB (class in langchain.vectorstores) B bad_words (langchain.llms.NLPCloud attribute) base_embeddings (langchain.chains.HypotheticalDocumentEmbedder attribute) base_url (langchain.llms.AI21 attribute) (langchain.llms.ForefrontAI attribute) (langchain.llms.Writer attribute) batch_size (langchain.llms.AzureOpenAI attribute) beam_search_diversity_rate (langchain.llms.Writer attribute) beam_width (langchain.llms.Writer attribute) best_of (langchain.llms.AlephAlpha attribute)

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best_of (langchain.llms.AlephAlpha attribute) (langchain.llms.AzureOpenAI attribute) C callback_manager (langchain.agents.MRKLChain attribute) (langchain.agents.ReActChain attribute) (langchain.agents.SelfAskWithSearchChain attribute) chain (langchain.chains.ConstitutionalChain attribute) chains (langchain.chains.SequentialChain attribute) (langchain.chains.SimpleSequentialChain attribute) CharacterTextSplitter (class in langchain.text_splitter) check_assertions_prompt (langchain.chains.LLMCheckerChain attribute) (langchain.chains.LLMSummarizationCheckerChain attribute) Chroma (class in langchain.vectorstores) chunk_size (langchain.embeddings.OpenAIEmbeddings attribute) client (langchain.llms.Petals attribute) combine_docs_chain (langchain.chains.AnalyzeDocumentChain attribute) combine_documents_chain (langchain.chains.MapReduceChain attribute) combine_embeddings() (langchain.chains.HypotheticalDocumentEmbedder method) completion_bias_exclusion_first_token_only (langchain.llms.AlephAlpha attribute) constitutional_principles (langchain.chains.ConstitutionalChain attribute) construct() (langchain.llms.AI21 class method) (langchain.llms.AlephAlpha class method) (langchain.llms.Anthropic class method) (langchain.llms.AzureOpenAI class method) (langchain.llms.Banana class method) (langchain.llms.CerebriumAI class method) (langchain.llms.Cohere class method) (langchain.llms.DeepInfra class method) (langchain.llms.ForefrontAI class method) (langchain.llms.GooseAI class method) (langchain.llms.HuggingFaceEndpoint class method) (langchain.llms.HuggingFaceHub class method)

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(langchain.llms.HuggingFaceHub class method) (langchain.llms.HuggingFacePipeline class method) (langchain.llms.Modal class method) (langchain.llms.NLPCloud class method) (langchain.llms.OpenAI class method) (langchain.llms.OpenAIChat class method) (langchain.llms.Petals class method) (langchain.llms.PromptLayerOpenAI class method) (langchain.llms.PromptLayerOpenAIChat class method) (langchain.llms.SagemakerEndpoint class method) (langchain.llms.SelfHostedHuggingFaceLLM class method) (langchain.llms.SelfHostedPipeline class method) (langchain.llms.StochasticAI class method) (langchain.llms.Writer class method) content_handler (langchain.embeddings.SagemakerEndpointEmbeddings attribute) (langchain.llms.SagemakerEndpoint attribute) CONTENT_KEY (langchain.vectorstores.Qdrant attribute) contextual_control_threshold (langchain.llms.AlephAlpha attribute) control_log_additive (langchain.llms.AlephAlpha attribute) copy() (langchain.llms.AI21 method) (langchain.llms.AlephAlpha method) (langchain.llms.Anthropic method) (langchain.llms.AzureOpenAI method) (langchain.llms.Banana method) (langchain.llms.CerebriumAI method) (langchain.llms.Cohere method) (langchain.llms.DeepInfra method) (langchain.llms.ForefrontAI method) (langchain.llms.GooseAI method) (langchain.llms.HuggingFaceEndpoint method) (langchain.llms.HuggingFaceHub method) (langchain.llms.HuggingFacePipeline method) (langchain.llms.Modal method) (langchain.llms.NLPCloud method)

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(langchain.llms.Modal method) (langchain.llms.NLPCloud method) (langchain.llms.OpenAI method) (langchain.llms.OpenAIChat method) (langchain.llms.Petals method) (langchain.llms.PromptLayerOpenAI method) (langchain.llms.PromptLayerOpenAIChat method) (langchain.llms.SagemakerEndpoint method) (langchain.llms.SelfHostedHuggingFaceLLM method) (langchain.llms.SelfHostedPipeline method) (langchain.llms.StochasticAI method) (langchain.llms.Writer method) coroutine (langchain.agents.Tool attribute) countPenalty (langchain.llms.AI21 attribute) create_assertions_prompt (langchain.chains.LLMSummarizationCheckerChain attribute) create_csv_agent() (in module langchain.agents) create_documents() (langchain.text_splitter.TextSplitter method) create_draft_answer_prompt (langchain.chains.LLMCheckerChain attribute) create_index() (langchain.vectorstores.AtlasDB method) create_json_agent() (in module langchain.agents) create_llm_result() (langchain.llms.AzureOpenAI method) (langchain.llms.OpenAI method) (langchain.llms.PromptLayerOpenAI method) create_openapi_agent() (in module langchain.agents) create_outputs() (langchain.chains.LLMChain method) create_pandas_dataframe_agent() (in module langchain.agents) create_prompt() (langchain.agents.Agent class method) (langchain.agents.ConversationalAgent class method) (langchain.agents.ReActTextWorldAgent class method) (langchain.agents.ZeroShotAgent class method) create_sql_agent() (in module langchain.agents)

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create_sql_agent() (in module langchain.agents) create_vectorstore_agent() (in module langchain.agents) create_vectorstore_router_agent() (in module langchain.agents) credentials_profile_name (langchain.embeddings.SagemakerEndpointEmbeddings attribute) (langchain.llms.SagemakerEndpoint attribute) critique_chain (langchain.chains.ConstitutionalChain attribute) D database (langchain.chains.SQLDatabaseChain attribute) decider_chain (langchain.chains.SQLDatabaseSequentialChain attribute) DeepLake (class in langchain.vectorstores) delete_collection() (langchain.vectorstores.Chroma method) delete_dataset() (langchain.vectorstores.DeepLake method) deployment_name (langchain.llms.AzureOpenAI attribute) description (langchain.agents.Tool attribute) device (langchain.llms.SelfHostedHuggingFaceLLM attribute) dict() (langchain.agents.Agent method) (langchain.llms.AI21 method) (langchain.llms.AlephAlpha method) (langchain.llms.Anthropic method) (langchain.llms.AzureOpenAI method) (langchain.llms.Banana method) (langchain.llms.CerebriumAI method) (langchain.llms.Cohere method) (langchain.llms.DeepInfra method) (langchain.llms.ForefrontAI method) (langchain.llms.GooseAI method) (langchain.llms.HuggingFaceEndpoint method) (langchain.llms.HuggingFaceHub method) (langchain.llms.HuggingFacePipeline method) (langchain.llms.Modal method) (langchain.llms.NLPCloud method) (langchain.llms.OpenAI method) (langchain.llms.OpenAIChat method) (langchain.llms.Petals method) (langchain.llms.PromptLayerOpenAI method)

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(langchain.llms.PromptLayerOpenAI method) (langchain.llms.PromptLayerOpenAIChat method) (langchain.llms.SagemakerEndpoint method) (langchain.llms.SelfHostedHuggingFaceLLM method) (langchain.llms.SelfHostedPipeline method) (langchain.llms.StochasticAI method) (langchain.llms.Writer method) (langchain.prompts.BasePromptTemplate method) (langchain.prompts.FewShotPromptTemplate method) (langchain.prompts.FewShotPromptWithTemplates method) do_sample (langchain.llms.NLPCloud attribute) (langchain.llms.Petals attribute) E early_stopping (langchain.llms.NLPCloud attribute) early_stopping_method (langchain.agents.AgentExecutor attribute) (langchain.agents.MRKLChain attribute) (langchain.agents.ReActChain attribute) (langchain.agents.SelfAskWithSearchChain attribute) echo (langchain.llms.AlephAlpha attribute) ElasticVectorSearch (class in langchain.vectorstores) embed_documents() (langchain.chains.HypotheticalDocumentEmbedder method) (langchain.embeddings.CohereEmbeddings method) (langchain.embeddings.FakeEmbeddings method) (langchain.embeddings.HuggingFaceEmbeddings method) (langchain.embeddings.HuggingFaceHubEmbeddings method) (langchain.embeddings.HuggingFaceInstructEmbeddings method) (langchain.embeddings.OpenAIEmbeddings method) (langchain.embeddings.SagemakerEndpointEmbeddings method) (langchain.embeddings.SelfHostedEmbeddings method) (langchain.embeddings.SelfHostedHuggingFaceInstructEmbeddings method) (langchain.embeddings.TensorflowHubEmbeddings method) embed_instruction (langchain.embeddings.HuggingFaceInstructEmbeddings attribute)

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embed_instruction (langchain.embeddings.HuggingFaceInstructEmbeddings attribute) (langchain.embeddings.SelfHostedHuggingFaceInstructEmbeddings attribute) embed_query() (langchain.chains.HypotheticalDocumentEmbedder method) (langchain.embeddings.CohereEmbeddings method) (langchain.embeddings.FakeEmbeddings method) (langchain.embeddings.HuggingFaceEmbeddings method) (langchain.embeddings.HuggingFaceHubEmbeddings method) (langchain.embeddings.HuggingFaceInstructEmbeddings method) (langchain.embeddings.OpenAIEmbeddings method) (langchain.embeddings.SagemakerEndpointEmbeddings method) (langchain.embeddings.SelfHostedEmbeddings method) (langchain.embeddings.SelfHostedHuggingFaceInstructEmbeddings method) (langchain.embeddings.TensorflowHubEmbeddings method) endpoint_kwargs (langchain.embeddings.SagemakerEndpointEmbeddings attribute) (langchain.llms.SagemakerEndpoint attribute) endpoint_name (langchain.embeddings.SagemakerEndpointEmbeddings attribute) (langchain.llms.SagemakerEndpoint attribute) endpoint_url (langchain.llms.CerebriumAI attribute) (langchain.llms.ForefrontAI attribute) (langchain.llms.HuggingFaceEndpoint attribute) (langchain.llms.Modal attribute) engines (langchain.utilities.searx_search.SearxSearchWrapper attribute) entity_extraction_chain (langchain.chains.GraphQAChain attribute) error (langchain.chains.OpenAIModerationChain attribute) example_keys (langchain.prompts.example_selector.SemanticSimilarityExampleSelector attribute) example_prompt (langchain.prompts.example_selector.LengthBasedExampleSelector attribute) (langchain.prompts.FewShotPromptTemplate attribute) (langchain.prompts.FewShotPromptWithTemplates attribute)

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(langchain.prompts.FewShotPromptWithTemplates attribute) example_selector (langchain.prompts.FewShotPromptTemplate attribute) (langchain.prompts.FewShotPromptWithTemplates attribute) example_separator (langchain.prompts.FewShotPromptTemplate attribute) (langchain.prompts.FewShotPromptWithTemplates attribute) examples (langchain.prompts.example_selector.LengthBasedExampleSelector attribute) (langchain.prompts.FewShotPromptTemplate attribute) (langchain.prompts.FewShotPromptWithTemplates attribute) F FAISS (class in langchain.vectorstores) fetch_k (langchain.prompts.example_selector.MaxMarginalRelevanceExampleSelector attribute) finish_tool_name (langchain.agents.Agent property) (langchain.agents.ConversationalAgent property) format() (langchain.prompts.BasePromptTemplate method) (langchain.prompts.ChatPromptTemplate method) (langchain.prompts.FewShotPromptTemplate method) (langchain.prompts.FewShotPromptWithTemplates method) (langchain.prompts.PromptTemplate method) format_messages() (langchain.prompts.MessagesPlaceholder method) format_prompt() (langchain.prompts.BasePromptTemplate method) (langchain.prompts.ChatPromptTemplate method) (langchain.prompts.StringPromptTemplate method) frequency_penalty (langchain.llms.AlephAlpha attribute) (langchain.llms.AzureOpenAI attribute) (langchain.llms.Cohere attribute) (langchain.llms.GooseAI attribute) frequencyPenalty (langchain.llms.AI21 attribute) from_agent_and_tools() (langchain.agents.AgentExecutor class method) from_chains() (langchain.agents.MRKLChain class method) from_colored_object_prompt() (langchain.chains.PALChain class method) from_documents() (langchain.vectorstores.AtlasDB class method)

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from_documents() (langchain.vectorstores.AtlasDB class method) (langchain.vectorstores.Chroma class method) (langchain.vectorstores.Qdrant class method) (langchain.vectorstores.VectorStore class method) from_embeddings() (langchain.vectorstores.FAISS class method) from_examples() (langchain.prompts.example_selector.MaxMarginalRelevanceExampleSelector class method) (langchain.prompts.example_selector.SemanticSimilarityExampleSelector class method) (langchain.prompts.PromptTemplate class method) from_existing_index() (langchain.vectorstores.Pinecone class method) from_file() (langchain.prompts.PromptTemplate class method) from_huggingface_tokenizer() (langchain.text_splitter.TextSplitter class method) from_llm() (langchain.chains.ChatVectorDBChain class method) (langchain.chains.ConstitutionalChain class method) (langchain.chains.ConversationalRetrievalChain class method) (langchain.chains.GraphQAChain class method) (langchain.chains.HypotheticalDocumentEmbedder class method) (langchain.chains.QAGenerationChain class method) (langchain.chains.SQLDatabaseSequentialChain class method) from_llm_and_api_docs() (langchain.chains.APIChain class method) from_llm_and_tools() (langchain.agents.Agent class method) (langchain.agents.ConversationalAgent class method) (langchain.agents.ZeroShotAgent class method) from_math_prompt() (langchain.chains.PALChain class method) from_model_id() (langchain.llms.HuggingFacePipeline class method) from_params() (langchain.chains.MapReduceChain class method) from_pipeline() (langchain.llms.SelfHostedHuggingFaceLLM class method) (langchain.llms.SelfHostedPipeline class method)

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(langchain.llms.SelfHostedPipeline class method) from_string() (langchain.chains.LLMChain class method) from_template() (langchain.prompts.PromptTemplate class method) from_texts() (langchain.vectorstores.AtlasDB class method) (langchain.vectorstores.Chroma class method) (langchain.vectorstores.DeepLake class method) (langchain.vectorstores.ElasticVectorSearch class method) (langchain.vectorstores.FAISS class method) (langchain.vectorstores.Milvus class method) (langchain.vectorstores.OpenSearchVectorSearch class method) (langchain.vectorstores.Pinecone class method) (langchain.vectorstores.Qdrant class method) (langchain.vectorstores.VectorStore class method) (langchain.vectorstores.Weaviate class method) from_tiktoken_encoder() (langchain.text_splitter.TextSplitter class method) func (langchain.agents.Tool attribute) G generate() (langchain.chains.LLMChain method) (langchain.llms.AI21 method) (langchain.llms.AlephAlpha method) (langchain.llms.Anthropic method) (langchain.llms.AzureOpenAI method) (langchain.llms.Banana method) (langchain.llms.CerebriumAI method) (langchain.llms.Cohere method) (langchain.llms.DeepInfra method) (langchain.llms.ForefrontAI method) (langchain.llms.GooseAI method) (langchain.llms.HuggingFaceEndpoint method) (langchain.llms.HuggingFaceHub method) (langchain.llms.HuggingFacePipeline method) (langchain.llms.Modal method) (langchain.llms.NLPCloud method) (langchain.llms.OpenAI method) (langchain.llms.OpenAIChat method) (langchain.llms.Petals method)

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(langchain.llms.OpenAIChat method) (langchain.llms.Petals method) (langchain.llms.PromptLayerOpenAI method) (langchain.llms.PromptLayerOpenAIChat method) (langchain.llms.SagemakerEndpoint method) (langchain.llms.SelfHostedHuggingFaceLLM method) (langchain.llms.SelfHostedPipeline method) (langchain.llms.StochasticAI method) (langchain.llms.Writer method) generate_prompt() (langchain.llms.AI21 method) (langchain.llms.AlephAlpha method) (langchain.llms.Anthropic method) (langchain.llms.AzureOpenAI method) (langchain.llms.Banana method) (langchain.llms.CerebriumAI method) (langchain.llms.Cohere method) (langchain.llms.DeepInfra method) (langchain.llms.ForefrontAI method) (langchain.llms.GooseAI method) (langchain.llms.HuggingFaceEndpoint method) (langchain.llms.HuggingFaceHub method) (langchain.llms.HuggingFacePipeline method) (langchain.llms.Modal method) (langchain.llms.NLPCloud method) (langchain.llms.OpenAI method) (langchain.llms.OpenAIChat method) (langchain.llms.Petals method) (langchain.llms.PromptLayerOpenAI method) (langchain.llms.PromptLayerOpenAIChat method) (langchain.llms.SagemakerEndpoint method) (langchain.llms.SelfHostedHuggingFaceLLM method) (langchain.llms.SelfHostedPipeline method) (langchain.llms.StochasticAI method) (langchain.llms.Writer method) get_all_tool_names() (in module langchain.agents) get_answer_expr (langchain.chains.PALChain attribute)

e258fc25e9a3-14

get_answer_expr (langchain.chains.PALChain attribute) get_full_inputs() (langchain.agents.Agent method) get_num_tokens() (langchain.llms.AI21 method) (langchain.llms.AlephAlpha method) (langchain.llms.Anthropic method) (langchain.llms.AzureOpenAI method) (langchain.llms.Banana method) (langchain.llms.CerebriumAI method) (langchain.llms.Cohere method) (langchain.llms.DeepInfra method) (langchain.llms.ForefrontAI method) (langchain.llms.GooseAI method) (langchain.llms.HuggingFaceEndpoint method) (langchain.llms.HuggingFaceHub method) (langchain.llms.HuggingFacePipeline method) (langchain.llms.Modal method) (langchain.llms.NLPCloud method) (langchain.llms.OpenAI method) (langchain.llms.OpenAIChat method) (langchain.llms.Petals method) (langchain.llms.PromptLayerOpenAI method) (langchain.llms.PromptLayerOpenAIChat method) (langchain.llms.SagemakerEndpoint method) (langchain.llms.SelfHostedHuggingFaceLLM method) (langchain.llms.SelfHostedPipeline method) (langchain.llms.StochasticAI method) (langchain.llms.Writer method) get_num_tokens_from_messages() (langchain.llms.AI21 method) (langchain.llms.AlephAlpha method) (langchain.llms.Anthropic method) (langchain.llms.AzureOpenAI method) (langchain.llms.Banana method) (langchain.llms.CerebriumAI method) (langchain.llms.Cohere method) (langchain.llms.DeepInfra method) (langchain.llms.ForefrontAI method)

e258fc25e9a3-15

(langchain.llms.DeepInfra method) (langchain.llms.ForefrontAI method) (langchain.llms.GooseAI method) (langchain.llms.HuggingFaceEndpoint method) (langchain.llms.HuggingFaceHub method) (langchain.llms.HuggingFacePipeline method) (langchain.llms.Modal method) (langchain.llms.NLPCloud method) (langchain.llms.OpenAI method) (langchain.llms.OpenAIChat method) (langchain.llms.Petals method) (langchain.llms.PromptLayerOpenAI method) (langchain.llms.PromptLayerOpenAIChat method) (langchain.llms.SagemakerEndpoint method) (langchain.llms.SelfHostedHuggingFaceLLM method) (langchain.llms.SelfHostedPipeline method) (langchain.llms.StochasticAI method) (langchain.llms.Writer method) get_params() (langchain.serpapi.SerpAPIWrapper method) get_principles() (langchain.chains.ConstitutionalChain class method) get_sub_prompts() (langchain.llms.AzureOpenAI method) (langchain.llms.OpenAI method) (langchain.llms.PromptLayerOpenAI method) get_text_length (langchain.prompts.example_selector.LengthBasedExampleSelector attribute) globals (langchain.python.PythonREPL attribute) graph (langchain.chains.GraphQAChain attribute) H hardware (langchain.embeddings.SelfHostedHuggingFaceEmbeddings attribute) (langchain.llms.SelfHostedHuggingFaceLLM attribute) (langchain.llms.SelfHostedPipeline attribute) headers (langchain.utilities.searx_search.SearxSearchWrapper attribute) I i (langchain.agents.ReActTextWorldAgent attribute)

e258fc25e9a3-16

I i (langchain.agents.ReActTextWorldAgent attribute) inference_fn (langchain.embeddings.SelfHostedEmbeddings attribute) (langchain.embeddings.SelfHostedHuggingFaceEmbeddings attribute) (langchain.llms.SelfHostedHuggingFaceLLM attribute) (langchain.llms.SelfHostedPipeline attribute) inference_kwargs (langchain.embeddings.SelfHostedEmbeddings attribute) initialize_agent() (in module langchain.agents) InMemoryDocstore (class in langchain.docstore) input_key (langchain.chains.QAGenerationChain attribute) input_keys (langchain.chains.ConstitutionalChain property) (langchain.chains.ConversationChain property) (langchain.chains.HypotheticalDocumentEmbedder property) (langchain.chains.QAGenerationChain property) (langchain.prompts.example_selector.SemanticSimilarityExampleSelector attribute) input_variables (langchain.chains.SequentialChain attribute) (langchain.chains.TransformChain attribute) (langchain.prompts.BasePromptTemplate attribute) (langchain.prompts.FewShotPromptTemplate attribute) (langchain.prompts.FewShotPromptWithTemplates attribute) (langchain.prompts.MessagesPlaceholder property) (langchain.prompts.PromptTemplate attribute) J json() (langchain.llms.AI21 method) (langchain.llms.AlephAlpha method) (langchain.llms.Anthropic method) (langchain.llms.AzureOpenAI method) (langchain.llms.Banana method) (langchain.llms.CerebriumAI method) (langchain.llms.Cohere method) (langchain.llms.DeepInfra method) (langchain.llms.ForefrontAI method) (langchain.llms.GooseAI method) (langchain.llms.HuggingFaceEndpoint method)

e258fc25e9a3-17

(langchain.llms.GooseAI method) (langchain.llms.HuggingFaceEndpoint method) (langchain.llms.HuggingFaceHub method) (langchain.llms.HuggingFacePipeline method) (langchain.llms.Modal method) (langchain.llms.NLPCloud method) (langchain.llms.OpenAI method) (langchain.llms.OpenAIChat method) (langchain.llms.Petals method) (langchain.llms.PromptLayerOpenAI method) (langchain.llms.PromptLayerOpenAIChat method) (langchain.llms.SagemakerEndpoint method) (langchain.llms.SelfHostedHuggingFaceLLM method) (langchain.llms.SelfHostedPipeline method) (langchain.llms.StochasticAI method) (langchain.llms.Writer method) K k (langchain.chains.QAGenerationChain attribute) (langchain.chains.VectorDBQA attribute) (langchain.chains.VectorDBQAWithSourcesChain attribute) (langchain.llms.Cohere attribute) (langchain.prompts.example_selector.SemanticSimilarityExampleSelector attribute) (langchain.utilities.searx_search.SearxSearchWrapper attribute) L langchain.agents module langchain.chains module langchain.docstore module langchain.embeddings module langchain.llms module langchain.prompts module langchain.prompts.example_selector module langchain.python module langchain.serpapi module langchain.text_splitter module langchain.utilities.searx_search module langchain.vectorstores module LatexTextSplitter (class in langchain.text_splitter)

e258fc25e9a3-18

module LatexTextSplitter (class in langchain.text_splitter) length (langchain.llms.ForefrontAI attribute) (langchain.llms.Writer attribute) length_no_input (langchain.llms.NLPCloud attribute) length_penalty (langchain.llms.NLPCloud attribute) length_pentaly (langchain.llms.Writer attribute) list_assertions_prompt (langchain.chains.LLMCheckerChain attribute) llm (langchain.chains.LLMBashChain attribute) (langchain.chains.LLMChain attribute) (langchain.chains.LLMCheckerChain attribute) (langchain.chains.LLMMathChain attribute) (langchain.chains.LLMSummarizationCheckerChain attribute) (langchain.chains.PALChain attribute) (langchain.chains.SQLDatabaseChain attribute) llm_chain (langchain.agents.Agent attribute) (langchain.agents.ZeroShotAgent attribute) (langchain.chains.HypotheticalDocumentEmbedder attribute) (langchain.chains.LLMRequestsChain attribute) (langchain.chains.QAGenerationChain attribute) llm_prefix (langchain.agents.Agent property) (langchain.agents.ConversationalAgent property) (langchain.agents.ZeroShotAgent property) load_agent() (in module langchain.agents) load_chain() (in module langchain.chains) load_fn_kwargs (langchain.embeddings.SelfHostedHuggingFaceEmbeddings attribute) (langchain.llms.SelfHostedHuggingFaceLLM attribute) (langchain.llms.SelfHostedPipeline attribute) load_local() (langchain.vectorstores.FAISS class method) load_prompt() (in module langchain.prompts) load_tools() (in module langchain.agents) locals (langchain.python.PythonREPL attribute)

e258fc25e9a3-19

locals (langchain.python.PythonREPL attribute) log_probs (langchain.llms.AlephAlpha attribute) logit_bias (langchain.llms.AlephAlpha attribute) (langchain.llms.AzureOpenAI attribute) (langchain.llms.GooseAI attribute) logitBias (langchain.llms.AI21 attribute) logprobs (langchain.llms.Writer attribute) M MarkdownTextSplitter (class in langchain.text_splitter) max_checks (langchain.chains.LLMSummarizationCheckerChain attribute) max_iterations (langchain.agents.AgentExecutor attribute) (langchain.agents.MRKLChain attribute) (langchain.agents.ReActChain attribute) (langchain.agents.SelfAskWithSearchChain attribute) max_length (langchain.llms.NLPCloud attribute) (langchain.llms.Petals attribute) (langchain.prompts.example_selector.LengthBasedExampleSelector attribute) max_marginal_relevance_search() (langchain.vectorstores.FAISS method) (langchain.vectorstores.Milvus method) (langchain.vectorstores.Qdrant method) (langchain.vectorstores.VectorStore method) max_marginal_relevance_search_by_vector() (langchain.vectorstores.FAISS method) (langchain.vectorstores.VectorStore method) max_new_tokens (langchain.llms.Petals attribute) max_retries (langchain.embeddings.OpenAIEmbeddings attribute) (langchain.llms.AzureOpenAI attribute) (langchain.llms.OpenAIChat attribute) (langchain.llms.PromptLayerOpenAIChat attribute) max_tokens (langchain.llms.AzureOpenAI attribute) (langchain.llms.Cohere attribute) (langchain.llms.GooseAI attribute) max_tokens_for_prompt() (langchain.llms.AzureOpenAI method)

e258fc25e9a3-20

max_tokens_for_prompt() (langchain.llms.AzureOpenAI method) (langchain.llms.OpenAI method) (langchain.llms.PromptLayerOpenAI method) max_tokens_limit (langchain.chains.RetrievalQAWithSourcesChain attribute) (langchain.chains.VectorDBQAWithSourcesChain attribute) max_tokens_to_sample (langchain.llms.Anthropic attribute) maximum_tokens (langchain.llms.AlephAlpha attribute) maxTokens (langchain.llms.AI21 attribute) memory (langchain.agents.MRKLChain attribute) (langchain.agents.ReActChain attribute) (langchain.agents.SelfAskWithSearchChain attribute) (langchain.chains.ConversationChain attribute) merge_from() (langchain.vectorstores.FAISS method) METADATA_KEY (langchain.vectorstores.Qdrant attribute) Milvus (class in langchain.vectorstores) min_length (langchain.llms.NLPCloud attribute) min_tokens (langchain.llms.GooseAI attribute) minimum_tokens (langchain.llms.AlephAlpha attribute) minTokens (langchain.llms.AI21 attribute) model (langchain.embeddings.CohereEmbeddings attribute) (langchain.llms.AI21 attribute) (langchain.llms.AlephAlpha attribute) (langchain.llms.Anthropic attribute) (langchain.llms.Cohere attribute) model_id (langchain.embeddings.SelfHostedHuggingFaceEmbeddings attribute) (langchain.embeddings.SelfHostedHuggingFaceInstructEmbeddings attribute) (langchain.llms.HuggingFacePipeline attribute) (langchain.llms.SelfHostedHuggingFaceLLM attribute) (langchain.llms.Writer attribute) model_key (langchain.llms.Banana attribute)

e258fc25e9a3-21

(langchain.llms.Writer attribute) model_key (langchain.llms.Banana attribute) model_kwargs (langchain.embeddings.HuggingFaceHubEmbeddings attribute) (langchain.embeddings.SagemakerEndpointEmbeddings attribute) (langchain.llms.AzureOpenAI attribute) (langchain.llms.Banana attribute) (langchain.llms.CerebriumAI attribute) (langchain.llms.GooseAI attribute) (langchain.llms.HuggingFaceEndpoint attribute) (langchain.llms.HuggingFaceHub attribute) (langchain.llms.HuggingFacePipeline attribute) (langchain.llms.Modal attribute) (langchain.llms.OpenAIChat attribute) (langchain.llms.Petals attribute) (langchain.llms.PromptLayerOpenAIChat attribute) (langchain.llms.SagemakerEndpoint attribute) (langchain.llms.SelfHostedHuggingFaceLLM attribute) (langchain.llms.StochasticAI attribute) model_load_fn (langchain.embeddings.SelfHostedHuggingFaceEmbeddings attribute) (langchain.llms.SelfHostedHuggingFaceLLM attribute) (langchain.llms.SelfHostedPipeline attribute) model_name (langchain.chains.OpenAIModerationChain attribute) (langchain.embeddings.HuggingFaceEmbeddings attribute) (langchain.embeddings.HuggingFaceInstructEmbeddings attribute) (langchain.llms.AzureOpenAI attribute) (langchain.llms.GooseAI attribute) (langchain.llms.NLPCloud attribute) (langchain.llms.OpenAIChat attribute) (langchain.llms.Petals attribute) (langchain.llms.PromptLayerOpenAIChat attribute) model_reqs (langchain.embeddings.SelfHostedHuggingFaceEmbeddings attribute) (langchain.embeddings.SelfHostedHuggingFaceInstructEmbeddings attribute)

e258fc25e9a3-22

(langchain.embeddings.SelfHostedHuggingFaceInstructEmbeddings attribute) (langchain.llms.SelfHostedHuggingFaceLLM attribute) (langchain.llms.SelfHostedPipeline attribute) model_url (langchain.embeddings.TensorflowHubEmbeddings attribute) modelname_to_contextsize() (langchain.llms.AzureOpenAI method) (langchain.llms.OpenAI method) (langchain.llms.PromptLayerOpenAI method) module langchain.agents langchain.chains langchain.docstore langchain.embeddings langchain.llms langchain.prompts langchain.prompts.example_selector langchain.python langchain.serpapi langchain.text_splitter langchain.utilities.searx_search langchain.vectorstores N n (langchain.llms.AlephAlpha attribute) (langchain.llms.AzureOpenAI attribute) (langchain.llms.GooseAI attribute) NLTKTextSplitter (class in langchain.text_splitter) num_beams (langchain.llms.NLPCloud attribute) num_return_sequences (langchain.llms.NLPCloud attribute) numResults (langchain.llms.AI21 attribute) O observation_prefix (langchain.agents.Agent property) (langchain.agents.ConversationalAgent property) (langchain.agents.ZeroShotAgent property) openai_api_key (langchain.chains.OpenAIModerationChain attribute) OpenSearchVectorSearch (class in langchain.vectorstores) output_key (langchain.chains.QAGenerationChain attribute) output_keys (langchain.chains.ConstitutionalChain property) (langchain.chains.HypotheticalDocumentEmbedder property) (langchain.chains.QAGenerationChain property) output_parser (langchain.prompts.BasePromptTemplate attribute)

e258fc25e9a3-23

output_parser (langchain.prompts.BasePromptTemplate attribute) output_variables (langchain.chains.TransformChain attribute) P p (langchain.llms.Cohere attribute) params (langchain.serpapi.SerpAPIWrapper attribute) (langchain.utilities.searx_search.SearxSearchWrapper attribute) partial() (langchain.prompts.BasePromptTemplate method) (langchain.prompts.ChatPromptTemplate method) penalty_bias (langchain.llms.AlephAlpha attribute) penalty_exceptions (langchain.llms.AlephAlpha attribute) penalty_exceptions_include_stop_sequences (langchain.llms.AlephAlpha attribute) persist() (langchain.vectorstores.Chroma method) (langchain.vectorstores.DeepLake method) Pinecone (class in langchain.vectorstores) plan() (langchain.agents.Agent method) predict() (langchain.chains.LLMChain method) predict_and_parse() (langchain.chains.LLMChain method) prefix (langchain.prompts.FewShotPromptTemplate attribute) (langchain.prompts.FewShotPromptWithTemplates attribute) prefix_messages (langchain.llms.OpenAIChat attribute) (langchain.llms.PromptLayerOpenAIChat attribute) prep_prompts() (langchain.chains.LLMChain method) prep_streaming_params() (langchain.llms.AzureOpenAI method) (langchain.llms.OpenAI method) (langchain.llms.PromptLayerOpenAI method) prepare_for_new_call() (langchain.agents.Agent method) presence_penalty (langchain.llms.AlephAlpha attribute) (langchain.llms.AzureOpenAI attribute) (langchain.llms.Cohere attribute) (langchain.llms.GooseAI attribute) presencePenalty (langchain.llms.AI21 attribute) Prompt (in module langchain.prompts)

e258fc25e9a3-24

Prompt (in module langchain.prompts) prompt (langchain.chains.ConversationChain attribute) (langchain.chains.LLMBashChain attribute) (langchain.chains.LLMChain attribute) (langchain.chains.LLMMathChain attribute) (langchain.chains.PALChain attribute) (langchain.chains.SQLDatabaseChain attribute) python_globals (langchain.chains.PALChain attribute) python_locals (langchain.chains.PALChain attribute) PythonCodeTextSplitter (class in langchain.text_splitter) Q qa_chain (langchain.chains.GraphQAChain attribute) Qdrant (class in langchain.vectorstores) query_instruction (langchain.embeddings.HuggingFaceInstructEmbeddings attribute) (langchain.embeddings.SelfHostedHuggingFaceInstructEmbeddings attribute) query_suffix (langchain.utilities.searx_search.SearxSearchWrapper attribute) R random_seed (langchain.llms.Writer attribute) raw_completion (langchain.llms.AlephAlpha attribute) RecursiveCharacterTextSplitter (class in langchain.text_splitter) reduce_k_below_max_tokens (langchain.chains.RetrievalQAWithSourcesChain attribute) (langchain.chains.VectorDBQAWithSourcesChain attribute) region_name (langchain.embeddings.SagemakerEndpointEmbeddings attribute) (langchain.llms.SagemakerEndpoint attribute) remove_end_sequence (langchain.llms.NLPCloud attribute) remove_input (langchain.llms.NLPCloud attribute) repetition_penalties_include_completion (langchain.llms.AlephAlpha attribute) repetition_penalties_include_prompt (langchain.llms.AlephAlpha attribute) repetition_penalty (langchain.llms.ForefrontAI attribute) (langchain.llms.NLPCloud attribute) (langchain.llms.Writer attribute)

e258fc25e9a3-25

(langchain.llms.NLPCloud attribute) (langchain.llms.Writer attribute) repo_id (langchain.embeddings.HuggingFaceHubEmbeddings attribute) (langchain.llms.HuggingFaceHub attribute) request_timeout (langchain.llms.AzureOpenAI attribute) requests_wrapper (langchain.chains.APIChain attribute) (langchain.chains.LLMRequestsChain attribute) results() (langchain.serpapi.SerpAPIWrapper method) (langchain.utilities.searx_search.SearxSearchWrapper method) retriever (langchain.chains.ConversationalRetrievalChain attribute) (langchain.chains.RetrievalQA attribute) (langchain.chains.RetrievalQAWithSourcesChain attribute) return_all (langchain.chains.SequentialChain attribute) return_direct (langchain.chains.SQLDatabaseChain attribute) return_intermediate_steps (langchain.agents.AgentExecutor attribute) (langchain.agents.MRKLChain attribute) (langchain.agents.ReActChain attribute) (langchain.agents.SelfAskWithSearchChain attribute) (langchain.chains.PALChain attribute) (langchain.chains.SQLDatabaseChain attribute) (langchain.chains.SQLDatabaseSequentialChain attribute) return_stopped_response() (langchain.agents.Agent method) return_values (langchain.agents.Agent attribute) (langchain.agents.ZeroShotAgent attribute) revised_answer_prompt (langchain.chains.LLMCheckerChain attribute) revised_summary_prompt (langchain.chains.LLMSummarizationCheckerChain attribute) revision_chain (langchain.chains.ConstitutionalChain attribute) run() (langchain.python.PythonREPL method) (langchain.serpapi.SerpAPIWrapper method) (langchain.utilities.searx_search.SearxSearchWrapper method) S save() (langchain.agents.Agent method)

e258fc25e9a3-26

S save() (langchain.agents.Agent method) (langchain.agents.AgentExecutor method) (langchain.llms.AI21 method) (langchain.llms.AlephAlpha method) (langchain.llms.Anthropic method) (langchain.llms.AzureOpenAI method) (langchain.llms.Banana method) (langchain.llms.CerebriumAI method) (langchain.llms.Cohere method) (langchain.llms.DeepInfra method) (langchain.llms.ForefrontAI method) (langchain.llms.GooseAI method) (langchain.llms.HuggingFaceEndpoint method) (langchain.llms.HuggingFaceHub method) (langchain.llms.HuggingFacePipeline method) (langchain.llms.Modal method) (langchain.llms.NLPCloud method) (langchain.llms.OpenAI method) (langchain.llms.OpenAIChat method) (langchain.llms.Petals method) (langchain.llms.PromptLayerOpenAI method) (langchain.llms.PromptLayerOpenAIChat method) (langchain.llms.SagemakerEndpoint method) (langchain.llms.SelfHostedHuggingFaceLLM method) (langchain.llms.SelfHostedPipeline method) (langchain.llms.StochasticAI method) (langchain.llms.Writer method) (langchain.prompts.BasePromptTemplate method) (langchain.prompts.ChatPromptTemplate method) save_agent() (langchain.agents.AgentExecutor method) save_local() (langchain.vectorstores.FAISS method) search() (langchain.docstore.InMemoryDocstore method) (langchain.docstore.Wikipedia method) search_kwargs (langchain.chains.ChatVectorDBChain attribute) (langchain.chains.VectorDBQA attribute) (langchain.chains.VectorDBQAWithSourcesChain attribute)

e258fc25e9a3-27

(langchain.chains.VectorDBQAWithSourcesChain attribute) search_type (langchain.chains.VectorDBQA attribute) searx_host (langchain.utilities.searx_search.SearxSearchWrapper attribute) SearxResults (class in langchain.utilities.searx_search) select_examples() (langchain.prompts.example_selector.LengthBasedExampleSelector method) (langchain.prompts.example_selector.MaxMarginalRelevanceExampleSelector method) (langchain.prompts.example_selector.SemanticSimilarityExampleSelector method) serpapi_api_key (langchain.serpapi.SerpAPIWrapper attribute) similarity_search() (langchain.vectorstores.AtlasDB method) (langchain.vectorstores.Chroma method) (langchain.vectorstores.DeepLake method) (langchain.vectorstores.ElasticVectorSearch method) (langchain.vectorstores.FAISS method) (langchain.vectorstores.Milvus method) (langchain.vectorstores.OpenSearchVectorSearch method) (langchain.vectorstores.Pinecone method) (langchain.vectorstores.Qdrant method) (langchain.vectorstores.VectorStore method) (langchain.vectorstores.Weaviate method) similarity_search_by_vector() (langchain.vectorstores.Chroma method) (langchain.vectorstores.FAISS method) (langchain.vectorstores.VectorStore method) similarity_search_with_score() (langchain.vectorstores.Chroma method) (langchain.vectorstores.FAISS method) (langchain.vectorstores.Milvus method) (langchain.vectorstores.Pinecone method) (langchain.vectorstores.Qdrant method) similarity_search_with_score_by_vector() (langchain.vectorstores.FAISS method) SpacyTextSplitter (class in langchain.text_splitter) split_documents() (langchain.text_splitter.TextSplitter method)

e258fc25e9a3-28

split_documents() (langchain.text_splitter.TextSplitter method) split_text() (langchain.text_splitter.CharacterTextSplitter method) (langchain.text_splitter.NLTKTextSplitter method) (langchain.text_splitter.RecursiveCharacterTextSplitter method) (langchain.text_splitter.SpacyTextSplitter method) (langchain.text_splitter.TextSplitter method) (langchain.text_splitter.TokenTextSplitter method) sql_chain (langchain.chains.SQLDatabaseSequentialChain attribute) stop (langchain.chains.PALChain attribute) (langchain.llms.Writer attribute) stop_sequences (langchain.llms.AlephAlpha attribute) stream() (langchain.llms.Anthropic method) (langchain.llms.AzureOpenAI method) (langchain.llms.OpenAI method) (langchain.llms.PromptLayerOpenAI method) streaming (langchain.llms.AzureOpenAI attribute) (langchain.llms.OpenAIChat attribute) (langchain.llms.PromptLayerOpenAIChat attribute) strip_outputs (langchain.chains.SimpleSequentialChain attribute) suffix (langchain.prompts.FewShotPromptTemplate attribute) (langchain.prompts.FewShotPromptWithTemplates attribute) T task (langchain.embeddings.HuggingFaceHubEmbeddings attribute) (langchain.llms.HuggingFaceEndpoint attribute) (langchain.llms.HuggingFaceHub attribute) (langchain.llms.SelfHostedHuggingFaceLLM attribute) temperature (langchain.llms.AI21 attribute) (langchain.llms.AlephAlpha attribute) (langchain.llms.Anthropic attribute) (langchain.llms.AzureOpenAI attribute) (langchain.llms.Cohere attribute) (langchain.llms.ForefrontAI attribute) (langchain.llms.GooseAI attribute) (langchain.llms.NLPCloud attribute)

e258fc25e9a3-29

(langchain.llms.GooseAI attribute) (langchain.llms.NLPCloud attribute) (langchain.llms.Petals attribute) (langchain.llms.Writer attribute) template (langchain.prompts.PromptTemplate attribute) template_format (langchain.prompts.FewShotPromptTemplate attribute) (langchain.prompts.FewShotPromptWithTemplates attribute) (langchain.prompts.PromptTemplate attribute) text_length (langchain.chains.LLMRequestsChain attribute) text_splitter (langchain.chains.AnalyzeDocumentChain attribute) (langchain.chains.MapReduceChain attribute) (langchain.chains.QAGenerationChain attribute) TextSplitter (class in langchain.text_splitter) tokenizer (langchain.llms.Petals attribute) tokens (langchain.llms.AlephAlpha attribute) tokens_to_generate (langchain.llms.Writer attribute) TokenTextSplitter (class in langchain.text_splitter) tool() (in module langchain.agents) tools (langchain.agents.AgentExecutor attribute) (langchain.agents.MRKLChain attribute) (langchain.agents.ReActChain attribute) (langchain.agents.SelfAskWithSearchChain attribute) top_k (langchain.chains.SQLDatabaseChain attribute) (langchain.llms.AlephAlpha attribute) (langchain.llms.Anthropic attribute) (langchain.llms.ForefrontAI attribute) (langchain.llms.NLPCloud attribute) (langchain.llms.Petals attribute) (langchain.llms.Writer attribute) top_k_docs_for_context (langchain.chains.ChatVectorDBChain attribute) top_p (langchain.llms.AlephAlpha attribute) (langchain.llms.Anthropic attribute) (langchain.llms.AzureOpenAI attribute) (langchain.llms.ForefrontAI attribute)

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(langchain.llms.AzureOpenAI attribute) (langchain.llms.ForefrontAI attribute) (langchain.llms.GooseAI attribute) (langchain.llms.NLPCloud attribute) (langchain.llms.Petals attribute) (langchain.llms.Writer attribute) topP (langchain.llms.AI21 attribute) transform (langchain.chains.TransformChain attribute) truncate (langchain.embeddings.CohereEmbeddings attribute) (langchain.llms.Cohere attribute) U unsecure (langchain.utilities.searx_search.SearxSearchWrapper attribute) update_forward_refs() (langchain.llms.AI21 class method) (langchain.llms.AlephAlpha class method) (langchain.llms.Anthropic class method) (langchain.llms.AzureOpenAI class method) (langchain.llms.Banana class method) (langchain.llms.CerebriumAI class method) (langchain.llms.Cohere class method) (langchain.llms.DeepInfra class method) (langchain.llms.ForefrontAI class method) (langchain.llms.GooseAI class method) (langchain.llms.HuggingFaceEndpoint class method) (langchain.llms.HuggingFaceHub class method) (langchain.llms.HuggingFacePipeline class method) (langchain.llms.Modal class method) (langchain.llms.NLPCloud class method) (langchain.llms.OpenAI class method) (langchain.llms.OpenAIChat class method) (langchain.llms.Petals class method) (langchain.llms.PromptLayerOpenAI class method) (langchain.llms.PromptLayerOpenAIChat class method) (langchain.llms.SagemakerEndpoint class method) (langchain.llms.SelfHostedHuggingFaceLLM class method)

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(langchain.llms.SelfHostedHuggingFaceLLM class method) (langchain.llms.SelfHostedPipeline class method) (langchain.llms.StochasticAI class method) (langchain.llms.Writer class method) use_multiplicative_presence_penalty (langchain.llms.AlephAlpha attribute) V validate_template (langchain.prompts.FewShotPromptTemplate attribute) (langchain.prompts.FewShotPromptWithTemplates attribute) (langchain.prompts.PromptTemplate attribute) VectorStore (class in langchain.vectorstores) vectorstore (langchain.chains.ChatVectorDBChain attribute) (langchain.chains.VectorDBQA attribute) (langchain.chains.VectorDBQAWithSourcesChain attribute) (langchain.prompts.example_selector.SemanticSimilarityExampleSelector attribute) verbose (langchain.agents.MRKLChain attribute) (langchain.agents.ReActChain attribute) (langchain.agents.SelfAskWithSearchChain attribute) (langchain.llms.AzureOpenAI attribute) (langchain.llms.OpenAI attribute) (langchain.llms.OpenAIChat attribute) W Weaviate (class in langchain.vectorstores) Wikipedia (class in langchain.docstore) By Harrison Chase © Copyright 2023, Harrison Chase. Last updated on Mar 24, 2023.

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.md .pdf Glossary Contents Chain of Thought Prompting Action Plan Generation ReAct Prompting Self-ask Prompt Chaining Memetic Proxy Self Consistency Inception MemPrompt Glossary# This is a collection of terminology commonly used when developing LLM applications. It contains reference to external papers or sources where the concept was first introduced, as well as to places in LangChain where the concept is used. Chain of Thought Prompting# A prompting technique used to encourage the model to generate a series of intermediate reasoning steps. A less formal way to induce this behavior is to include “Let’s think step-by-step” in the prompt. Resources: Chain-of-Thought Paper Step-by-Step Paper Action Plan Generation# A prompt usage that uses a language model to generate actions to take. The results of these actions can then be fed back into the language model to generate a subsequent action. Resources: WebGPT Paper SayCan Paper ReAct Prompting# A prompting technique that combines Chain-of-Thought prompting with action plan generation. This induces the to model to think about what action to take, then take it. Resources: Paper LangChain Example Self-ask# A prompting method that builds on top of chain-of-thought prompting. In this method, the model explicitly asks itself follow-up questions, which are then answered by an external search engine. Resources: Paper LangChain Example Prompt Chaining# Combining multiple LLM calls together, with the output of one-step being the input to the next. Resources: PromptChainer Paper Language Model Cascades ICE Primer Book Socratic Models Memetic Proxy#

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Language Model Cascades ICE Primer Book Socratic Models Memetic Proxy# Encouraging the LLM to respond in a certain way framing the discussion in a context that the model knows of and that will result in that type of response. For example, as a conversation between a student and a teacher. Resources: Paper Self Consistency# A decoding strategy that samples a diverse set of reasoning paths and then selects the most consistent answer. Is most effective when combined with Chain-of-thought prompting. Resources: Paper Inception# Also called “First Person Instruction”. Encouraging the model to think a certain way by including the start of the model’s response in the prompt. Resources: Example MemPrompt# MemPrompt maintains a memory of errors and user feedback, and uses them to prevent repetition of mistakes. Resources: Paper previous Writer next LangChain Gallery Contents Chain of Thought Prompting Action Plan Generation ReAct Prompting Self-ask Prompt Chaining Memetic Proxy Self Consistency Inception MemPrompt By Harrison Chase © Copyright 2023, Harrison Chase. Last updated on Mar 24, 2023.

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.rst .pdf Welcome to LangChain Contents Getting Started Modules Use Cases Reference Docs LangChain Ecosystem Additional Resources Welcome to LangChain# Large language models (LLMs) are emerging as a transformative technology, enabling developers to build applications that they previously could not. But using these LLMs in isolation is often not enough to create a truly powerful app - the real power comes when you are able to combine them with other sources of computation or knowledge. This library is aimed at assisting in the development of those types of applications. Common examples of these types of applications include: ❓ Question Answering over specific documents Documentation End-to-end Example: Question Answering over Notion Database 💬 Chatbots Documentation End-to-end Example: Chat-LangChain 🤖 Agents Documentation End-to-end Example: GPT+WolframAlpha Getting Started# Checkout the below guide for a walkthrough of how to get started using LangChain to create an Language Model application. Getting Started Documentation Modules# There are several main modules that LangChain provides support for. For each module we provide some examples to get started, how-to guides, reference docs, and conceptual guides. These modules are, in increasing order of complexity: Prompts: This includes prompt management, prompt optimization, and prompt serialization. LLMs: This includes a generic interface for all LLMs, and common utilities for working with LLMs. Document Loaders: This includes a standard interface for loading documents, as well as specific integrations to all types of text data sources. Utils: Language models are often more powerful when interacting with other sources of knowledge or computation. This can include Python REPLs, embeddings, search engines, and more. LangChain provides a large collection of common utils to use in your application.

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Chains: Chains go beyond just a single LLM call, and are sequences of calls (whether to an LLM or a different utility). LangChain provides a standard interface for chains, lots of integrations with other tools, and end-to-end chains for common applications. Indexes: Language models are often more powerful when combined with your own text data - this module covers best practices for doing exactly that. Agents: Agents involve an LLM making decisions about which Actions to take, taking that Action, seeing an Observation, and repeating that until done. LangChain provides a standard interface for agents, a selection of agents to choose from, and examples of end to end agents. Memory: Memory is the concept of persisting state between calls of a chain/agent. LangChain provides a standard interface for memory, a collection of memory implementations, and examples of chains/agents that use memory. Chat: Chat models are a variation on Language Models that expose a different API - rather than working with raw text, they work with messages. LangChain provides a standard interface for working with them and doing all the same things as above. Use Cases# The above modules can be used in a variety of ways. LangChain also provides guidance and assistance in this. Below are some of the common use cases LangChain supports. Agents: Agents are systems that use a language model to interact with other tools. These can be used to do more grounded question/answering, interact with APIs, or even take actions. Chatbots: Since language models are good at producing text, that makes them ideal for creating chatbots. Data Augmented Generation: Data Augmented Generation involves specific types of chains that first interact with an external datasource to fetch data to use in the generation step. Examples of this include summarization of long pieces of text and question/answering over specific data sources.

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Question Answering: Answering questions over specific documents, only utilizing the information in those documents to construct an answer. A type of Data Augmented Generation. Summarization: Summarizing longer documents into shorter, more condensed chunks of information. A type of Data Augmented Generation. Querying Tabular Data: If you want to understand how to use LLMs to query data that is stored in a tabular format (csvs, SQL, dataframes, etc) you should read this page. Evaluation: Generative models are notoriously hard to evaluate with traditional metrics. One new way of evaluating them is using language models themselves to do the evaluation. LangChain provides some prompts/chains for assisting in this. Generate similar examples: Generating similar examples to a given input. This is a common use case for many applications, and LangChain provides some prompts/chains for assisting in this. Compare models: Experimenting with different prompts, models, and chains is a big part of developing the best possible application. The ModelLaboratory makes it easy to do so. Reference Docs# All of LangChain’s reference documentation, in one place. Full documentation on all methods, classes, installation methods, and integration setups for LangChain. Reference Documentation LangChain Ecosystem# Guides for how other companies/products can be used with LangChain LangChain Ecosystem Additional Resources# Additional collection of resources we think may be useful as you develop your application! LangChainHub: The LangChainHub is a place to share and explore other prompts, chains, and agents. Glossary: A glossary of all related terms, papers, methods, etc. Whether implemented in LangChain or not! Gallery: A collection of our favorite projects that use LangChain. Useful for finding inspiration or seeing how things were done in other applications.

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Deployments: A collection of instructions, code snippets, and template repositories for deploying LangChain apps. Discord: Join us on our Discord to discuss all things LangChain! Tracing: A guide on using tracing in LangChain to visualize the execution of chains and agents. Production Support: As you move your LangChains into production, we’d love to offer more comprehensive support. Please fill out this form and we’ll set up a dedicated support Slack channel. next Quickstart Guide Contents Getting Started Modules Use Cases Reference Docs LangChain Ecosystem Additional Resources By Harrison Chase © Copyright 2023, Harrison Chase. Last updated on Mar 24, 2023.

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.rst .pdf API References API References# All of LangChain’s reference documentation, in one place. Full documentation on all methods, classes, and APIs in LangChain. Prompts Utilities Chains Agents previous Integrations next LangChain Ecosystem By Harrison Chase © Copyright 2023, Harrison Chase. Last updated on Mar 24, 2023.

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Search Error Please activate JavaScript to enable the search functionality. Ctrl+K By Harrison Chase © Copyright 2023, Harrison Chase. Last updated on Mar 24, 2023.

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.md .pdf Tracing Contents Tracing Walkthrough Changing Sessions Tracing# By enabling tracing in your LangChain runs, you’ll be able to more effectively visualize, step through, and debug your chains and agents. First, you should install tracing and set up your environment properly. You can use either a locally hosted version of this (uses Docker) or a cloud hosted version (in closed alpha). If you’re interested in using the hosted platform, please fill out the form here. Locally Hosted Setup Cloud Hosted Setup Tracing Walkthrough# When you first access the UI, you should see a page with your tracing sessions. An initial one “default” should already be created for you. A session is just a way to group traces together. If you click on a session, it will take you to a page with no recorded traces that says “No Runs.” You can create a new session with the new session form. If we click on the default session, we can see that to start we have no traces stored. If we now start running chains and agents with tracing enabled, we will see data show up here. To do so, we can run this notebook as an example. After running it, we will see an initial trace show up. From here we can explore the trace at a high level by clicking on the arrow to show nested runs. We can keep on clicking further and further down to explore deeper and deeper. We can also click on the “Explore” button of the top level run to dive even deeper. Here, we can see the inputs and outputs in full, as well as all the nested traces. We can keep on exploring each of these nested traces in more detail. For example, here is the lowest level trace with the exact inputs/outputs to the LLM. Changing Sessions#

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Changing Sessions# To initially record traces to a session other than "default", you can set the LANGCHAIN_SESSION environment variable to the name of the session you want to record to: import os os.environ["LANGCHAIN_HANDLER"] = "langchain" os.environ["LANGCHAIN_SESSION"] = "my_session" # Make sure this session actually exists. You can create a new session in the UI. To switch sessions mid-script or mid-notebook, do NOT set the LANGCHAIN_SESSION environment variable. Instead: langchain.set_tracing_callback_manager(session_name="my_session") previous Deployments Contents Tracing Walkthrough Changing Sessions By Harrison Chase © Copyright 2023, Harrison Chase. Last updated on Mar 24, 2023.

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.md .pdf AI21 Labs Contents Installation and Setup Wrappers LLM AI21 Labs# This page covers how to use the AI21 ecosystem within LangChain. It is broken into two parts: installation and setup, and then references to specific AI21 wrappers. Installation and Setup# Get an AI21 api key and set it as an environment variable (AI21_API_KEY) Wrappers# LLM# There exists an AI21 LLM wrapper, which you can access with from langchain.llms import AI21 previous LangChain Ecosystem next AtlasDB Contents Installation and Setup Wrappers LLM By Harrison Chase © Copyright 2023, Harrison Chase. Last updated on Mar 24, 2023.

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.md .pdf AtlasDB Contents Installation and Setup Wrappers VectorStore AtlasDB# This page covers how to use Nomic’s Atlas ecosystem within LangChain. It is broken into two parts: installation and setup, and then references to specific Atlas wrappers. Installation and Setup# Install the Python package with pip install nomic Nomic is also included in langchains poetry extras poetry install -E all Wrappers# VectorStore# There exists a wrapper around the Atlas neural database, allowing you to use it as a vectorstore. This vectorstore also gives you full access to the underlying AtlasProject object, which will allow you to use the full range of Atlas map interactions, such as bulk tagging and automatic topic modeling. Please see the Atlas docs for more detailed information. To import this vectorstore: from langchain.vectorstores import AtlasDB For a more detailed walkthrough of the AtlasDB wrapper, see this notebook previous AI21 Labs next Banana Contents Installation and Setup Wrappers VectorStore By Harrison Chase © Copyright 2023, Harrison Chase. Last updated on Mar 24, 2023.

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.md .pdf Banana Contents Installation and Setup Define your Banana Template Build the Banana app Wrappers LLM Banana# This page covers how to use the Banana ecosystem within LangChain. It is broken into two parts: installation and setup, and then references to specific Banana wrappers. Installation and Setup# Install with pip install banana-dev Get an Banana api key and set it as an environment variable (BANANA_API_KEY) Define your Banana Template# If you want to use an available language model template you can find one here. This template uses the Palmyra-Base model by Writer. You can check out an example Banana repository here. Build the Banana app# Banana Apps must include the “output” key in the return json. There is a rigid response structure. # Return the results as a dictionary result = {'output': result} An example inference function would be: def inference(model_inputs:dict) -> dict: global model global tokenizer # Parse out your arguments prompt = model_inputs.get('prompt', None) if prompt == None: return {'message': "No prompt provided"} # Run the model input_ids = tokenizer.encode(prompt, return_tensors='pt').cuda() output = model.generate( input_ids, max_length=100, do_sample=True, top_k=50, top_p=0.95, num_return_sequences=1, temperature=0.9, early_stopping=True, no_repeat_ngram_size=3, num_beams=5, length_penalty=1.5, repetition_penalty=1.5, bad_words_ids=[[tokenizer.encode(' ', add_prefix_space=True)[0]]] )

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bad_words_ids=[[tokenizer.encode(' ', add_prefix_space=True)[0]]] ) result = tokenizer.decode(output[0], skip_special_tokens=True) # Return the results as a dictionary result = {'output': result} return result You can find a full example of a Banana app here. Wrappers# LLM# There exists an Banana LLM wrapper, which you can access with from langchain.llms import Banana You need to provide a model key located in the dashboard: llm = Banana(model_key="YOUR_MODEL_KEY") previous AtlasDB next CerebriumAI Contents Installation and Setup Define your Banana Template Build the Banana app Wrappers LLM By Harrison Chase © Copyright 2023, Harrison Chase. Last updated on Mar 24, 2023.

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.md .pdf CerebriumAI Contents Installation and Setup Wrappers LLM CerebriumAI# This page covers how to use the CerebriumAI ecosystem within LangChain. It is broken into two parts: installation and setup, and then references to specific CerebriumAI wrappers. Installation and Setup# Install with pip install cerebrium Get an CerebriumAI api key and set it as an environment variable (CEREBRIUMAI_API_KEY) Wrappers# LLM# There exists an CerebriumAI LLM wrapper, which you can access with from langchain.llms import CerebriumAI previous Banana next Chroma Contents Installation and Setup Wrappers LLM By Harrison Chase © Copyright 2023, Harrison Chase. Last updated on Mar 24, 2023.

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.md .pdf Chroma Contents Installation and Setup Wrappers VectorStore Chroma# This page covers how to use the Chroma ecosystem within LangChain. It is broken into two parts: installation and setup, and then references to specific Chroma wrappers. Installation and Setup# Install the Python package with pip install chromadb Wrappers# VectorStore# There exists a wrapper around Chroma vector databases, allowing you to use it as a vectorstore, whether for semantic search or example selection. To import this vectorstore: from langchain.vectorstores import Chroma For a more detailed walkthrough of the Chroma wrapper, see this notebook previous CerebriumAI next Cohere Contents Installation and Setup Wrappers VectorStore By Harrison Chase © Copyright 2023, Harrison Chase. Last updated on Mar 24, 2023.

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.md .pdf Cohere Contents Installation and Setup Wrappers LLM Embeddings Cohere# This page covers how to use the Cohere ecosystem within LangChain. It is broken into two parts: installation and setup, and then references to specific Cohere wrappers. Installation and Setup# Install the Python SDK with pip install cohere Get an Cohere api key and set it as an environment variable (COHERE_API_KEY) Wrappers# LLM# There exists an Cohere LLM wrapper, which you can access with from langchain.llms import Cohere Embeddings# There exists an Cohere Embeddings wrapper, which you can access with from langchain.embeddings import CohereEmbeddings For a more detailed walkthrough of this, see this notebook previous Chroma next DeepInfra Contents Installation and Setup Wrappers LLM Embeddings By Harrison Chase © Copyright 2023, Harrison Chase. Last updated on Mar 24, 2023.

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.md .pdf DeepInfra Contents Installation and Setup Wrappers LLM DeepInfra# This page covers how to use the DeepInfra ecosystem within LangChain. It is broken into two parts: installation and setup, and then references to specific DeepInfra wrappers. Installation and Setup# Get your DeepInfra api key from this link here. Get an DeepInfra api key and set it as an environment variable (DEEPINFRA_API_TOKEN) Wrappers# LLM# There exists an DeepInfra LLM wrapper, which you can access with from langchain.llms import DeepInfra previous Cohere next Deep Lake Contents Installation and Setup Wrappers LLM By Harrison Chase © Copyright 2023, Harrison Chase. Last updated on Mar 24, 2023.

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.md .pdf Deep Lake Contents Installation and Setup Wrappers VectorStore Deep Lake# This page covers how to use the Deep Lake ecosystem within LangChain. It is broken into two parts: installation and setup, and then references to specific Deep Lake wrappers. For more information. Here is whitepaper and academic paper for Deep Lake Here is a set of additional resources available for review: Deep Lake, Getting Started and Tutorials Installation and Setup# Install the Python package with pip install deeplake Wrappers# VectorStore# There exists a wrapper around Deep Lake, a data lake for Deep Learning applications, allowing you to use it as a vectorstore (for now), whether for semantic search or example selection. To import this vectorstore: from langchain.vectorstores import DeepLake For a more detailed walkthrough of the Deep Lake wrapper, see this notebook previous DeepInfra next ForefrontAI Contents Installation and Setup Wrappers VectorStore By Harrison Chase © Copyright 2023, Harrison Chase. Last updated on Mar 24, 2023.

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.md .pdf ForefrontAI Contents Installation and Setup Wrappers LLM ForefrontAI# This page covers how to use the ForefrontAI ecosystem within LangChain. It is broken into two parts: installation and setup, and then references to specific ForefrontAI wrappers. Installation and Setup# Get an ForefrontAI api key and set it as an environment variable (FOREFRONTAI_API_KEY) Wrappers# LLM# There exists an ForefrontAI LLM wrapper, which you can access with from langchain.llms import ForefrontAI previous Deep Lake next Google Search Wrapper Contents Installation and Setup Wrappers LLM By Harrison Chase © Copyright 2023, Harrison Chase. Last updated on Mar 24, 2023.

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.md .pdf Google Search Wrapper Contents Installation and Setup Wrappers Utility Tool Google Search Wrapper# This page covers how to use the Google Search API within LangChain. It is broken into two parts: installation and setup, and then references to the specific Google Search wrapper. Installation and Setup# Install requirements with pip install google-api-python-client Set up a Custom Search Engine, following these instructions Get an API Key and Custom Search Engine ID from the previous step, and set them as environment variables GOOGLE_API_KEY and GOOGLE_CSE_ID respectively Wrappers# Utility# There exists a GoogleSearchAPIWrapper utility which wraps this API. To import this utility: from langchain.utilities import GoogleSearchAPIWrapper For a more detailed walkthrough of this wrapper, see this notebook. Tool# You can also easily load this wrapper as a Tool (to use with an Agent). You can do this with: from langchain.agents import load_tools tools = load_tools(["google-search"]) For more information on this, see this page previous ForefrontAI next Google Serper Wrapper Contents Installation and Setup Wrappers Utility Tool By Harrison Chase © Copyright 2023, Harrison Chase. Last updated on Mar 24, 2023.

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.md .pdf Google Serper Wrapper Contents Setup Wrappers Utility Output Tool Google Serper Wrapper# This page covers how to use the Serper Google Search API within LangChain. Serper is a low-cost Google Search API that can be used to add answer box, knowledge graph, and organic results data from Google Search. It is broken into two parts: setup, and then references to the specific Google Serper wrapper. Setup# Go to serper.dev to sign up for a free account Get the api key and set it as an environment variable (SERPER_API_KEY) Wrappers# Utility# There exists a GoogleSerperAPIWrapper utility which wraps this API. To import this utility: from langchain.utilities import GoogleSerperAPIWrapper You can use it as part of a Self Ask chain: from langchain.utilities import GoogleSerperAPIWrapper from langchain.llms.openai import OpenAI from langchain.agents import initialize_agent, Tool import os os.environ["SERPER_API_KEY"] = "" os.environ['OPENAI_API_KEY'] = "" llm = 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="self-ask-with-search", verbose=True) self_ask_with_search.run("What is the hometown of the reigning men's U.S. Open champion?") Output# 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.

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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' For a more detailed walkthrough of this wrapper, see this notebook. Tool# You can also easily load this wrapper as a Tool (to use with an Agent). You can do this with: from langchain.agents import load_tools tools = load_tools(["google-serper"]) For more information on this, see this page previous Google Search Wrapper next GooseAI Contents Setup Wrappers Utility Output Tool By Harrison Chase © Copyright 2023, Harrison Chase. Last updated on Mar 24, 2023.

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.md .pdf GooseAI Contents Installation and Setup Wrappers LLM GooseAI# This page covers how to use the GooseAI ecosystem within LangChain. It is broken into two parts: installation and setup, and then references to specific GooseAI wrappers. Installation and Setup# Install the Python SDK with pip install openai Get your GooseAI api key from this link here. Set the environment variable (GOOSEAI_API_KEY). import os os.environ["GOOSEAI_API_KEY"] = "YOUR_API_KEY" Wrappers# LLM# There exists an GooseAI LLM wrapper, which you can access with: from langchain.llms import GooseAI previous Google Serper Wrapper next Graphsignal Contents Installation and Setup Wrappers LLM By Harrison Chase © Copyright 2023, Harrison Chase. Last updated on Mar 24, 2023.

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.md .pdf Graphsignal Contents Installation and Setup Tracing and Monitoring Graphsignal# This page covers how to use the Graphsignal ecosystem to trace and monitor LangChain. Installation and Setup# Install the Python library with pip install graphsignal Create free Graphsignal account here Get an API key and set it as an environment variable (GRAPHSIGNAL_API_KEY) Tracing and Monitoring# Graphsignal automatically instruments and starts tracing and monitoring chains. Traces, metrics and errors are then available in your Graphsignal dashboard. No prompts or other sensitive data are sent to Graphsignal cloud, only statistics and metadata. Initialize the tracer by providing a deployment name: import graphsignal graphsignal.configure(deployment='my-langchain-app-prod') In order to trace full runs and see a breakdown by chains and tools, you can wrap the calling routine or use a decorator: with graphsignal.start_trace('my-chain'): chain.run("some initial text") Optionally, enable profiling to record function-level statistics for each trace. with graphsignal.start_trace( 'my-chain', options=graphsignal.TraceOptions(enable_profiling=True)): chain.run("some initial text") See the Quick Start guide for complete setup instructions. previous GooseAI next Hazy Research Contents Installation and Setup Tracing and Monitoring By Harrison Chase © Copyright 2023, Harrison Chase. Last updated on Mar 24, 2023.

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.md .pdf Hazy Research Contents Installation and Setup Wrappers LLM Hazy Research# This page covers how to use the Hazy Research ecosystem within LangChain. It is broken into two parts: installation and setup, and then references to specific Hazy Research wrappers. Installation and Setup# To use the manifest, install it with pip install manifest-ml Wrappers# LLM# There exists an LLM wrapper around Hazy Research’s manifest library. manifest is a python library which is itself a wrapper around many model providers, and adds in caching, history, and more. To use this wrapper: from langchain.llms.manifest import ManifestWrapper previous Graphsignal next Helicone Contents Installation and Setup Wrappers LLM By Harrison Chase © Copyright 2023, Harrison Chase. Last updated on Mar 24, 2023.

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.md .pdf Helicone Contents What is Helicone? Quick start How to enable Helicone caching How to use Helicone custom properties Helicone# This page covers how to use the Helicone ecosystem within LangChain. What is Helicone?# Helicone is an open source observability platform that proxies your OpenAI traffic and provides you key insights into your spend, latency and usage. Quick start# With your LangChain environment you can just add the following parameter. export OPENAI_API_BASE="https://oai.hconeai.com/v1" Now head over to helicone.ai to create your account, and add your OpenAI API key within our dashboard to view your logs. How to enable Helicone caching# from langchain.llms import OpenAI import openai openai.api_base = "https://oai.hconeai.com/v1" llm = OpenAI(temperature=0.9, headers={"Helicone-Cache-Enabled": "true"}) text = "What is a helicone?" print(llm(text)) Helicone caching docs How to use Helicone custom properties# from langchain.llms import OpenAI import openai openai.api_base = "https://oai.hconeai.com/v1" llm = OpenAI(temperature=0.9, headers={ "Helicone-Property-Session": "24", "Helicone-Property-Conversation": "support_issue_2", "Helicone-Property-App": "mobile", }) text = "What is a helicone?" print(llm(text)) Helicone property docs previous Hazy Research next Hugging Face Contents What is Helicone? Quick start How to enable Helicone caching How to use Helicone custom properties

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Quick start How to enable Helicone caching How to use Helicone custom properties By Harrison Chase © Copyright 2023, Harrison Chase. Last updated on Mar 24, 2023.

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.md .pdf Hugging Face Contents Installation and Setup Wrappers LLM Embeddings Tokenizer Datasets Hugging Face# This page covers how to use the Hugging Face ecosystem (including the Hugging Face Hub) within LangChain. It is broken into two parts: installation and setup, and then references to specific Hugging Face wrappers. Installation and Setup# If you want to work with the Hugging Face Hub: Install the Hub client library with pip install huggingface_hub Create a Hugging Face account (it’s free!) Create an access token and set it as an environment variable (HUGGINGFACEHUB_API_TOKEN) If you want work with the Hugging Face Python libraries: Install pip install transformers for working with models and tokenizers Install pip install datasets for working with datasets Wrappers# LLM# There exists two Hugging Face LLM wrappers, one for a local pipeline and one for a model hosted on Hugging Face Hub. Note that these wrappers only work for models that support the following tasks: text2text-generation, text-generation To use the local pipeline wrapper: from langchain.llms import HuggingFacePipeline To use a the wrapper for a model hosted on Hugging Face Hub: from langchain.llms import HuggingFaceHub For a more detailed walkthrough of the Hugging Face Hub wrapper, see this notebook Embeddings# There exists two Hugging Face Embeddings wrappers, one for a local model and one for a model hosted on Hugging Face Hub. Note that these wrappers only work for sentence-transformers models. To use the local pipeline wrapper: from langchain.embeddings import HuggingFaceEmbeddings To use a the wrapper for a model hosted on Hugging Face Hub: from langchain.embeddings import HuggingFaceHubEmbeddings

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from langchain.embeddings import HuggingFaceHubEmbeddings For a more detailed walkthrough of this, see this notebook Tokenizer# There are several places you can use tokenizers available through the transformers package. By default, it is used to count tokens for all LLMs. You can also use it to count tokens when splitting documents with from langchain.text_splitter import CharacterTextSplitter CharacterTextSplitter.from_huggingface_tokenizer(...) For a more detailed walkthrough of this, see this notebook Datasets# The Hugging Face Hub has lots of great datasets that can be used to evaluate your LLM chains. For a detailed walkthrough of how to use them to do so, see this notebook previous Helicone next Milvus Contents Installation and Setup Wrappers LLM Embeddings Tokenizer Datasets By Harrison Chase © Copyright 2023, Harrison Chase. Last updated on Mar 24, 2023.

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.md .pdf Milvus Contents Installation and Setup Wrappers VectorStore Milvus# This page covers how to use the Milvus ecosystem within LangChain. It is broken into two parts: installation and setup, and then references to specific Milvus wrappers. Installation and Setup# Install the Python SDK with pip install pymilvus Wrappers# VectorStore# There exists a wrapper around Milvus indexes, allowing you to use it as a vectorstore, whether for semantic search or example selection. To import this vectorstore: from langchain.vectorstores import Milvus For a more detailed walkthrough of the Miluvs wrapper, see this notebook previous Hugging Face next Modal Contents Installation and Setup Wrappers VectorStore By Harrison Chase © Copyright 2023, Harrison Chase. Last updated on Mar 24, 2023.

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.md .pdf Modal Contents Installation and Setup Define your Modal Functions and Webhooks Wrappers LLM Modal# This page covers how to use the Modal ecosystem within LangChain. It is broken into two parts: installation and setup, and then references to specific Modal wrappers. Installation and Setup# Install with pip install modal-client Run modal token new Define your Modal Functions and Webhooks# You must include a prompt. There is a rigid response structure. class Item(BaseModel): prompt: str @stub.webhook(method="POST") def my_webhook(item: Item): return {"prompt": my_function.call(item.prompt)} An example with GPT2: from pydantic import BaseModel import modal stub = modal.Stub("example-get-started") volume = modal.SharedVolume().persist("gpt2_model_vol") CACHE_PATH = "/root/model_cache" @stub.function( gpu="any", image=modal.Image.debian_slim().pip_install( "tokenizers", "transformers", "torch", "accelerate" ), shared_volumes={CACHE_PATH: volume}, retries=3, ) def run_gpt2(text: str): from transformers import GPT2Tokenizer, GPT2LMHeadModel tokenizer = GPT2Tokenizer.from_pretrained('gpt2') model = GPT2LMHeadModel.from_pretrained('gpt2') encoded_input = tokenizer(text, return_tensors='pt').input_ids output = model.generate(encoded_input, max_length=50, do_sample=True) return tokenizer.decode(output[0], skip_special_tokens=True) class Item(BaseModel): prompt: str @stub.webhook(method="POST") def get_text(item: Item):

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@stub.webhook(method="POST") def get_text(item: Item): return {"prompt": run_gpt2.call(item.prompt)} Wrappers# LLM# There exists an Modal LLM wrapper, which you can access with from langchain.llms import Modal previous Milvus next NLPCloud Contents Installation and Setup Define your Modal Functions and Webhooks Wrappers LLM By Harrison Chase © Copyright 2023, Harrison Chase. Last updated on Mar 24, 2023.

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.md .pdf NLPCloud Contents Installation and Setup Wrappers LLM NLPCloud# This page covers how to use the NLPCloud ecosystem within LangChain. It is broken into two parts: installation and setup, and then references to specific NLPCloud wrappers. Installation and Setup# Install the Python SDK with pip install nlpcloud Get an NLPCloud api key and set it as an environment variable (NLPCLOUD_API_KEY) Wrappers# LLM# There exists an NLPCloud LLM wrapper, which you can access with from langchain.llms import NLPCloud previous Modal next OpenAI Contents Installation and Setup Wrappers LLM By Harrison Chase © Copyright 2023, Harrison Chase. Last updated on Mar 24, 2023.

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.md .pdf OpenAI Contents Installation and Setup Wrappers LLM Embeddings Tokenizer Moderation OpenAI# This page covers how to use the OpenAI ecosystem within LangChain. It is broken into two parts: installation and setup, and then references to specific OpenAI wrappers. Installation and Setup# Install the Python SDK with pip install openai Get an OpenAI api key and set it as an environment variable (OPENAI_API_KEY) If you want to use OpenAI’s tokenizer (only available for Python 3.9+), install it with pip install tiktoken Wrappers# LLM# There exists an OpenAI LLM wrapper, which you can access with from langchain.llms import OpenAI If you are using a model hosted on Azure, you should use different wrapper for that: from langchain.llms import AzureOpenAI For a more detailed walkthrough of the Azure wrapper, see this notebook Embeddings# There exists an OpenAI Embeddings wrapper, which you can access with from langchain.embeddings import OpenAIEmbeddings For a more detailed walkthrough of this, see this notebook Tokenizer# There are several places you can use the tiktoken tokenizer. By default, it is used to count tokens for OpenAI LLMs. You can also use it to count tokens when splitting documents with from langchain.text_splitter import CharacterTextSplitter CharacterTextSplitter.from_tiktoken_encoder(...) For a more detailed walkthrough of this, see this notebook Moderation# You can also access the OpenAI content moderation endpoint with from langchain.chains import OpenAIModerationChain For a more detailed walkthrough of this, see this notebook previous NLPCloud next OpenSearch Contents Installation and Setup Wrappers LLM Embeddings Tokenizer

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Contents Installation and Setup Wrappers LLM Embeddings Tokenizer Moderation By Harrison Chase © Copyright 2023, Harrison Chase. Last updated on Mar 24, 2023.

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.md .pdf OpenSearch Contents Installation and Setup Wrappers VectorStore OpenSearch# This page covers how to use the OpenSearch ecosystem within LangChain. It is broken into two parts: installation and setup, and then references to specific OpenSearch wrappers. Installation and Setup# Install the Python package with pip install opensearch-py Wrappers# VectorStore# There exists a wrapper around OpenSearch vector databases, allowing you to use it as a vectorstore for semantic search using approximate vector search powered by lucene, nmslib and faiss engines or using painless scripting and script scoring functions for bruteforce vector search. To import this vectorstore: from langchain.vectorstores import OpenSearchVectorSearch For a more detailed walkthrough of the OpenSearch wrapper, see this notebook previous OpenAI next Petals Contents Installation and Setup Wrappers VectorStore By Harrison Chase © Copyright 2023, Harrison Chase. Last updated on Mar 24, 2023.

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.md .pdf Petals Contents Installation and Setup Wrappers LLM Petals# This page covers how to use the Petals ecosystem within LangChain. It is broken into two parts: installation and setup, and then references to specific Petals wrappers. Installation and Setup# Install with pip install petals Get a Hugging Face api key and set it as an environment variable (HUGGINGFACE_API_KEY) Wrappers# LLM# There exists an Petals LLM wrapper, which you can access with from langchain.llms import Petals previous OpenSearch next PGVector Contents Installation and Setup Wrappers LLM By Harrison Chase © Copyright 2023, Harrison Chase. Last updated on Mar 24, 2023.

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.md .pdf PGVector Contents Installation Setup Wrappers VectorStore Usage PGVector# This page covers how to use the Postgres PGVector ecosystem within LangChain It is broken into two parts: installation and setup, and then references to specific PGVector wrappers. Installation# Install the Python package with pip install pgvector Setup# The first step is to create a database with the pgvector extension installed. Follow the steps at PGVector Installation Steps to install the database and the extension. The docker image is the easiest way to get started. Wrappers# VectorStore# There exists a wrapper around Postgres vector databases, allowing you to use it as a vectorstore, whether for semantic search or example selection. To import this vectorstore: from langchain.vectorstores.pgvector import PGVector Usage# For a more detailed walkthrough of the PGVector Wrapper, see this notebook previous Petals next Pinecone Contents Installation Setup Wrappers VectorStore Usage By Harrison Chase © Copyright 2023, Harrison Chase. Last updated on Mar 24, 2023.

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.md .pdf Pinecone Contents Installation and Setup Wrappers VectorStore Pinecone# This page covers how to use the Pinecone ecosystem within LangChain. It is broken into two parts: installation and setup, and then references to specific Pinecone wrappers. Installation and Setup# Install the Python SDK with pip install pinecone-client Wrappers# VectorStore# There exists a wrapper around Pinecone indexes, allowing you to use it as a vectorstore, whether for semantic search or example selection. To import this vectorstore: from langchain.vectorstores import Pinecone For a more detailed walkthrough of the Pinecone wrapper, see this notebook previous PGVector next PromptLayer Contents Installation and Setup Wrappers VectorStore By Harrison Chase © Copyright 2023, Harrison Chase. Last updated on Mar 24, 2023.

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.md .pdf PromptLayer Contents Installation and Setup Wrappers LLM PromptLayer# This page covers how to use PromptLayer within LangChain. It is broken into two parts: installation and setup, and then references to specific PromptLayer wrappers. Installation and Setup# If you want to work with PromptLayer: Install the promptlayer python library pip install promptlayer Create a PromptLayer account Create an api token and set it as an environment variable (PROMPTLAYER_API_KEY) Wrappers# LLM# There exists an PromptLayer OpenAI LLM wrapper, which you can access with from langchain.llms import PromptLayerOpenAI To tag your requests, use the argument pl_tags when instanializing the LLM from langchain.llms import PromptLayerOpenAI llm = PromptLayerOpenAI(pl_tags=["langchain-requests", "chatbot"]) To get the PromptLayer request id, use the argument return_pl_id when instanializing the LLM from langchain.llms import PromptLayerOpenAI llm = PromptLayerOpenAI(return_pl_id=True) This will add the PromptLayer request ID in the generation_info field of the Generation returned when using .generate or .agenerate For example: llm_results = llm.generate(["hello world"]) for res in llm_results.generations: print("pl request id: ", res[0].generation_info["pl_request_id"]) You can use the PromptLayer request ID to add a prompt, score, or other metadata to your request. Read more about it here. This LLM is identical to the OpenAI LLM, except that all your requests will be logged to your PromptLayer account you can add pl_tags when instantializing to tag your requests on PromptLayer

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you can add pl_tags when instantializing to tag your requests on PromptLayer you can add return_pl_id when instantializing to return a PromptLayer request id to use while tracking requests. PromptLayer also provides native wrappers for PromptLayerChatOpenAI and PromptLayerOpenAIChat previous Pinecone next Qdrant Contents Installation and Setup Wrappers LLM By Harrison Chase © Copyright 2023, Harrison Chase. Last updated on Mar 24, 2023.

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.md .pdf Qdrant Contents Installation and Setup Wrappers VectorStore Qdrant# This page covers how to use the Qdrant ecosystem within LangChain. It is broken into two parts: installation and setup, and then references to specific Qdrant wrappers. Installation and Setup# Install the Python SDK with pip install qdrant-client Wrappers# VectorStore# There exists a wrapper around Qdrant indexes, allowing you to use it as a vectorstore, whether for semantic search or example selection. To import this vectorstore: from langchain.vectorstores import Qdrant For a more detailed walkthrough of the Qdrant wrapper, see this notebook previous PromptLayer next Runhouse Contents Installation and Setup Wrappers VectorStore By Harrison Chase © Copyright 2023, Harrison Chase. Last updated on Mar 24, 2023.

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.md .pdf Runhouse Contents Installation and Setup Self-hosted LLMs Self-hosted Embeddings Runhouse# This page covers how to use the Runhouse ecosystem within LangChain. It is broken into three parts: installation and setup, LLMs, and Embeddings. Installation and Setup# Install the Python SDK with pip install runhouse If you’d like to use on-demand cluster, check your cloud credentials with sky check Self-hosted LLMs# For a basic self-hosted LLM, you can use the SelfHostedHuggingFaceLLM class. For more custom LLMs, you can use the SelfHostedPipeline parent class. from langchain.llms import SelfHostedPipeline, SelfHostedHuggingFaceLLM For a more detailed walkthrough of the Self-hosted LLMs, see this notebook Self-hosted Embeddings# There are several ways to use self-hosted embeddings with LangChain via Runhouse. For a basic self-hosted embedding from a Hugging Face Transformers model, you can use the SelfHostedEmbedding class. from langchain.llms import SelfHostedPipeline, SelfHostedHuggingFaceLLM For a more detailed walkthrough of the Self-hosted Embeddings, see this notebook # previous Qdrant next SearxNG Search API Contents Installation and Setup Self-hosted LLMs Self-hosted Embeddings By Harrison Chase © Copyright 2023, Harrison Chase. Last updated on Mar 24, 2023.

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.md .pdf SearxNG Search API Contents Installation and Setup Self Hosted Instance: Wrappers Utility Tool SearxNG Search API# This page covers how to use the SearxNG search API within LangChain. It is broken into two parts: installation and setup, and then references to the specific SearxNG API wrapper. Installation and Setup# While it is possible to utilize the wrapper in conjunction with public searx instances these instances frequently do not permit API access (see note on output format below) and have limitations on the frequency of requests. It is recommended to opt for a self-hosted instance instead. Self Hosted Instance:# See this page for installation instructions. When you install SearxNG, the only active output format by default is the HTML format. You need to activate the json format to use the API. This can be done by adding the following line to the settings.yml file: search: formats: - html - json You can make sure that the API is working by issuing a curl request to the API endpoint: curl -kLX GET --data-urlencode q='langchain' -d format=json http://localhost:8888 This should return a JSON object with the results. Wrappers# Utility# To use the wrapper we need to pass the host of the SearxNG instance to the wrapper with: 1. the named parameter searx_host when creating the instance. 2. exporting the environment variable SEARXNG_HOST. You can use the wrapper to get results from a SearxNG instance. from langchain.utilities import SearxSearchWrapper s = SearxSearchWrapper(searx_host="http://localhost:8888") s.run("what is a large language model?")

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s.run("what is a large language model?") Tool# You can also easily load this wrapper as a Tool (to use with an Agent). You can do this with: from langchain.agents import load_tools tools = load_tools(["searx-search"], searx_host="http://localhost:8888") For more information on tools, see this page previous Runhouse next SerpAPI Contents Installation and Setup Self Hosted Instance: Wrappers Utility Tool By Harrison Chase © Copyright 2023, Harrison Chase. Last updated on Mar 24, 2023.

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.md .pdf SerpAPI Contents Installation and Setup Wrappers Utility Tool SerpAPI# This page covers how to use the SerpAPI search APIs within LangChain. It is broken into two parts: installation and setup, and then references to the specific SerpAPI wrapper. Installation and Setup# Install requirements with pip install google-search-results Get a SerpAPI api key and either set it as an environment variable (SERPAPI_API_KEY) Wrappers# Utility# There exists a SerpAPI utility which wraps this API. To import this utility: from langchain.utilities import SerpAPIWrapper For a more detailed walkthrough of this wrapper, see this notebook. Tool# You can also easily load this wrapper as a Tool (to use with an Agent). You can do this with: from langchain.agents import load_tools tools = load_tools(["serpapi"]) For more information on this, see this page previous SearxNG Search API next StochasticAI Contents Installation and Setup Wrappers Utility Tool By Harrison Chase © Copyright 2023, Harrison Chase. Last updated on Mar 24, 2023.

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.md .pdf StochasticAI Contents Installation and Setup Wrappers LLM StochasticAI# This page covers how to use the StochasticAI ecosystem within LangChain. It is broken into two parts: installation and setup, and then references to specific StochasticAI wrappers. Installation and Setup# Install with pip install stochasticx Get an StochasticAI api key and set it as an environment variable (STOCHASTICAI_API_KEY) Wrappers# LLM# There exists an StochasticAI LLM wrapper, which you can access with from langchain.llms import StochasticAI previous SerpAPI next Unstructured Contents Installation and Setup Wrappers LLM By Harrison Chase © Copyright 2023, Harrison Chase. Last updated on Mar 24, 2023.

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.md .pdf Unstructured Contents Installation and Setup Wrappers Data Loaders Unstructured# This page covers how to use the unstructured ecosystem within LangChain. The unstructured package from Unstructured.IO extracts clean text from raw source documents like PDFs and Word documents. This page is broken into two parts: installation and setup, and then references to specific unstructured wrappers. Installation and Setup# Install the Python SDK with pip install "unstructured[local-inference]" Install the following system dependencies if they are not already available on your system. Depending on what document types you’re parsing, you may not need all of these. libmagic-dev poppler-utils tesseract-ocr libreoffice If you are parsing PDFs using the "hi_res" strategy, run the following to install the detectron2 model, which unstructured uses for layout detection: pip install "detectron2@git+https://github.com/facebookresearch/[email protected]#egg=detectron2" If detectron2 is not installed, unstructured will fallback to processing PDFs using the "fast" strategy, which uses pdfminer directly and doesn’t require detectron2. Wrappers# Data Loaders# The primary unstructured wrappers within langchain are data loaders. The following shows how to use the most basic unstructured data loader. There are other file-specific data loaders available in the langchain.document_loaders module. from langchain.document_loaders import UnstructuredFileLoader loader = UnstructuredFileLoader("state_of_the_union.txt") loader.load() If you instantiate the loader with UnstructuredFileLoader(mode="elements"), the loader will track additional metadata like the page number and text type (i.e. title, narrative text) when that information is available. previous

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when that information is available. previous StochasticAI next Weights & Biases Contents Installation and Setup Wrappers Data Loaders By Harrison Chase © Copyright 2023, Harrison Chase. Last updated on Mar 24, 2023.

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.ipynb .pdf Weights & Biases Weights & Biases# This notebook goes over how to track your LangChain experiments into one centralized Weights and Biases dashboard. To learn more about prompt engineering and the callback please refer to this Report which explains both alongside the resultant dashboards you can expect to see. Run in Colab: https://colab.research.google.com/drive/1DXH4beT4HFaRKy_Vm4PoxhXVDRf7Ym8L?usp=sharing View Report: https://wandb.ai/a-sh0ts/langchain_callback_demo/reports/Prompt-Engineering-LLMs-with-LangChain-and-W-B–VmlldzozNjk1NTUw#👋-how-to-build-a-callback-in-langchain-for-better-prompt-engineering !pip install wandb !pip install pandas !pip install textstat !pip install spacy !python -m spacy download en_core_web_sm import os os.environ["WANDB_API_KEY"] = "" # os.environ["OPENAI_API_KEY"] = "" # os.environ["SERPAPI_API_KEY"] = "" from datetime import datetime from langchain.callbacks import WandbCallbackHandler, StdOutCallbackHandler from langchain.callbacks.base import CallbackManager from langchain.llms import OpenAI Callback Handler that logs to Weights and Biases. Parameters: job_type (str): The type of job. project (str): The project to log to. entity (str): The entity to log to. tags (list): The tags to log. group (str): The group to log to. name (str): The name of the run. notes (str): The notes to log.

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notes (str): The notes to log. visualize (bool): Whether to visualize the run. complexity_metrics (bool): Whether to log complexity metrics. stream_logs (bool): Whether to stream callback actions to W&B Default values for WandbCallbackHandler(...) visualize: bool = False, complexity_metrics: bool = False, stream_logs: bool = False, NOTE: For beta workflows we have made the default analysis based on textstat and the visualizations based on spacy """Main function. This function is used to try the callback handler. Scenarios: 1. OpenAI LLM 2. Chain with multiple SubChains on multiple generations 3. Agent with Tools """ session_group = datetime.now().strftime("%m.%d.%Y_%H.%M.%S") wandb_callback = WandbCallbackHandler( job_type="inference", project="langchain_callback_demo", group=f"minimal_{session_group}", name="llm", tags=["test"], ) manager = CallbackManager([StdOutCallbackHandler(), wandb_callback]) llm = OpenAI(temperature=0, callback_manager=manager, verbose=True) wandb: Currently logged in as: harrison-chase. Use `wandb login --relogin` to force relogin

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Tracking run with wandb version 0.14.0Run data is saved locally in /Users/harrisonchase/workplace/langchain/docs/ecosystem/wandb/run-20230318_150408-e47j1914Syncing run llm to Weights & Biases (docs) View project at https://wandb.ai/harrison-chase/langchain_callback_demo View run at https://wandb.ai/harrison-chase/langchain_callback_demo/runs/e47j1914wandb: WARNING The wandb callback is currently in beta and is subject to change based on updates to `langchain`. Please report any issues to https://github.com/wandb/wandb/issues with the tag `langchain`. # Defaults for WandbCallbackHandler.flush_tracker(...) reset: bool = True, finish: bool = False, The flush_tracker function is used to log LangChain sessions to Weights & Biases. It takes in the LangChain module or agent, and logs at minimum the prompts and generations alongside the serialized form of the LangChain module to the specified Weights & Biases project. By default we reset the session as opposed to concluding the session outright. # SCENARIO 1 - LLM llm_result = llm.generate(["Tell me a joke", "Tell me a poem"] * 3) wandb_callback.flush_tracker(llm, name="simple_sequential")

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wandb_callback.flush_tracker(llm, name="simple_sequential") Waiting for W&B process to finish... (success). View run llm at: https://wandb.ai/harrison-chase/langchain_callback_demo/runs/e47j1914Synced 5 W&B file(s), 2 media file(s), 5 artifact file(s) and 0 other file(s)Find logs at: ./wandb/run-20230318_150408-e47j1914/logsTracking run with wandb version 0.14.0Run data is saved locally in /Users/harrisonchase/workplace/langchain/docs/ecosystem/wandb/run-20230318_150534-jyxma7huSyncing run simple_sequential to Weights & Biases (docs) View project at https://wandb.ai/harrison-chase/langchain_callback_demo View run at https://wandb.ai/harrison-chase/langchain_callback_demo/runs/jyxma7hu from langchain.prompts import PromptTemplate from langchain.chains import LLMChain # SCENARIO 2 - Chain 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, callback_manager=manager) test_prompts = [ { "title": "documentary about good video games that push the boundary of game design" }, {"title": "cocaine bear vs heroin wolf"}, {"title": "the best in class mlops tooling"}, ] synopsis_chain.apply(test_prompts)

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] synopsis_chain.apply(test_prompts) wandb_callback.flush_tracker(synopsis_chain, name="agent") Waiting for W&B process to finish... (success). View run simple_sequential at: https://wandb.ai/harrison-chase/langchain_callback_demo/runs/jyxma7huSynced 4 W&B file(s), 2 media file(s), 6 artifact file(s) and 0 other file(s)Find logs at: ./wandb/run-20230318_150534-jyxma7hu/logsTracking run with wandb version 0.14.0Run data is saved locally in /Users/harrisonchase/workplace/langchain/docs/ecosystem/wandb/run-20230318_150550-wzy59zjqSyncing run agent to Weights & Biases (docs) View project at https://wandb.ai/harrison-chase/langchain_callback_demo View run at https://wandb.ai/harrison-chase/langchain_callback_demo/runs/wzy59zjq from langchain.agents import initialize_agent, load_tools # SCENARIO 3 - Agent with Tools tools = load_tools(["serpapi", "llm-math"], llm=llm, callback_manager=manager) agent = initialize_agent( tools, llm, agent="zero-shot-react-description", callback_manager=manager, verbose=True, ) agent.run( "Who is Leo DiCaprio's girlfriend? What is her current age raised to the 0.43 power?" ) wandb_callback.flush_tracker(agent, reset=False, finish=True) > Entering new AgentExecutor chain... I need to find out who Leo DiCaprio's girlfriend is and then calculate her age raised to the 0.43 power. Action: Search Action Input: "Leo DiCaprio girlfriend"

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Action: Search Action Input: "Leo DiCaprio girlfriend" Observation: DiCaprio had a steady girlfriend in Camila Morrone. He had been with the model turned actress for nearly five years, as they were first said to be dating at the end of 2017. And the now 26-year-old Morrone is no stranger to Hollywood. Thought: I need to calculate her age raised to the 0.43 power. Action: Calculator Action Input: 26^0.43 Observation: Answer: 4.059182145592686 Thought: I now know the final answer. Final Answer: Leo DiCaprio's girlfriend is Camila Morrone and her current age raised to the 0.43 power is 4.059182145592686. > Finished chain. Waiting for W&B process to finish... (success). View run agent at: https://wandb.ai/harrison-chase/langchain_callback_demo/runs/wzy59zjqSynced 5 W&B file(s), 2 media file(s), 7 artifact file(s) and 0 other file(s)Find logs at: ./wandb/run-20230318_150550-wzy59zjq/logs previous Unstructured next Weaviate By Harrison Chase © Copyright 2023, Harrison Chase. Last updated on Mar 24, 2023.

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.md .pdf Weaviate Contents Installation and Setup Wrappers VectorStore Weaviate# This page covers how to use the Weaviate ecosystem within LangChain. What is Weaviate? Weaviate in a nutshell: Weaviate is an open-source ​database of the type ​vector search engine. Weaviate allows you to store JSON documents in a class property-like fashion while attaching machine learning vectors to these documents to represent them in vector space. Weaviate can be used stand-alone (aka bring your vectors) or with a variety of modules that can do the vectorization for you and extend the core capabilities. Weaviate has a GraphQL-API to access your data easily. We aim to bring your vector search set up to production to query in mere milliseconds (check our open source benchmarks to see if Weaviate fits your use case). Get to know Weaviate in the basics getting started guide in under five minutes. Weaviate in detail: Weaviate is a low-latency vector search engine with out-of-the-box support for different media types (text, images, etc.). It offers Semantic Search, Question-Answer Extraction, Classification, Customizable Models (PyTorch/TensorFlow/Keras), etc. Built from scratch in Go, Weaviate stores both objects and vectors, allowing for combining vector search with structured filtering and the fault tolerance of a cloud-native database. It is all accessible through GraphQL, REST, and various client-side programming languages. Installation and Setup# Install the Python SDK with pip install weaviate-client Wrappers# VectorStore# There exists a wrapper around Weaviate indexes, allowing you to use it as a vectorstore, whether for semantic search or example selection. To import this vectorstore: from langchain.vectorstores import Weaviate

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To import this vectorstore: from langchain.vectorstores import Weaviate For a more detailed walkthrough of the Weaviate wrapper, see this notebook previous Weights & Biases next Wolfram Alpha Wrapper Contents Installation and Setup Wrappers VectorStore By Harrison Chase © Copyright 2023, Harrison Chase. Last updated on Mar 24, 2023.

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.md .pdf Wolfram Alpha Wrapper Contents Installation and Setup Wrappers Utility Tool Wolfram Alpha Wrapper# This page covers how to use the Wolfram Alpha API within LangChain. It is broken into two parts: installation and setup, and then references to specific Wolfram Alpha wrappers. Installation and Setup# Install requirements with pip install wolframalpha Go to wolfram alpha and sign up for a developer account here Create an app and get your APP ID Set your APP ID as an environment variable WOLFRAM_ALPHA_APPID Wrappers# Utility# There exists a WolframAlphaAPIWrapper utility which wraps this API. To import this utility: from langchain.utilities.wolfram_alpha import WolframAlphaAPIWrapper For a more detailed walkthrough of this wrapper, see this notebook. Tool# You can also easily load this wrapper as a Tool (to use with an Agent). You can do this with: from langchain.agents import load_tools tools = load_tools(["wolfram-alpha"]) For more information on this, see this page previous Weaviate next Writer Contents Installation and Setup Wrappers Utility Tool By Harrison Chase © Copyright 2023, Harrison Chase. Last updated on Mar 24, 2023.

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.md .pdf Writer Contents Installation and Setup Wrappers LLM Writer# This page covers how to use the Writer ecosystem within LangChain. It is broken into two parts: installation and setup, and then references to specific Writer wrappers. Installation and Setup# Get an Writer api key and set it as an environment variable (WRITER_API_KEY) Wrappers# LLM# There exists an Writer LLM wrapper, which you can access with from langchain.llms import Writer previous Wolfram Alpha Wrapper next Glossary Contents Installation and Setup Wrappers LLM By Harrison Chase © Copyright 2023, Harrison Chase. Last updated on Mar 24, 2023.

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.md .pdf Quickstart Guide Contents Installation Environment Setup Building a Language Model Application Quickstart Guide# This tutorial gives you a quick walkthrough about building an end-to-end language model application with LangChain. Installation# To get started, install LangChain with the following command: pip install langchain Environment Setup# Using LangChain will usually require integrations with one or more model providers, data stores, apis, etc. For this example, we will be using OpenAI’s APIs, so we will first need to install their SDK: pip install openai We will then need to set the environment variable in the terminal. export OPENAI_API_KEY="..." Alternatively, you could do this from inside the Jupyter notebook (or Python script): import os os.environ["OPENAI_API_KEY"] = "..." Building a Language Model Application# Now that we have installed LangChain and set up our environment, we can start building our language model application. LangChain provides many modules that can be used to build language model applications. Modules can be combined to create more complex applications, or be used individually for simple applications. LLMs: Get predictions from a language model The most basic building block of LangChain is calling an LLM on some input. Let’s walk through a simple example of how to do this. For this purpose, let’s pretend we are building a service that generates a company name based on what the company makes. In order to do this, we first need to import the LLM wrapper. from langchain.llms import OpenAI We can then initialize the wrapper with any arguments. In this example, we probably want the outputs to be MORE random, so we’ll initialize it with a HIGH temperature. llm = OpenAI(temperature=0.9) We can now call it on some input!

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We can now call it on some input! text = "What would be a good company name for a company that makes colorful socks?" print(llm(text)) Feetful of Fun For more details on how to use LLMs within LangChain, see the LLM getting started guide. Prompt Templates: Manage prompts for LLMs Calling an LLM is a great first step, but it’s just the beginning. Normally when you use an LLM in an application, you are not sending user input directly to the LLM. Instead, you are probably taking user input and constructing a prompt, and then sending that to the LLM. For example, in the previous example, the text we passed in was hardcoded to ask for a name for a company that made colorful socks. In this imaginary service, what we would want to do is take only the user input describing what the company does, and then format the prompt with that information. This is easy to do with LangChain! First lets define the prompt template: from langchain.prompts import PromptTemplate prompt = PromptTemplate( input_variables=["product"], template="What is a good name for a company that makes {product}?", ) Let’s now see how this works! We can call the .format method to format it. print(prompt.format(product="colorful socks")) What is a good name for a company that makes colorful socks? For more details, check out the getting started guide for prompts. Chains: Combine LLMs and prompts in multi-step workflows Up until now, we’ve worked with the PromptTemplate and LLM primitives by themselves. But of course, a real application is not just one primitive, but rather a combination of them. A chain in LangChain is made up of links, which can be either primitives like LLMs or other chains.