Duplicate from c-s-ale/AliceInWonderLandChainlit

.env.example

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+OPENAI_API_KEY=

.gitattributes

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.gitignore

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+.env
+.vscode
+.chroma
+__pycache__

Dockerfile

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+FROM python:3.9
+RUN useradd -m -u 1000 user
+USER user
+ENV HOME=/home/user \
+    PATH=/home/user/.local/bin:$PATH
+WORKDIR $HOME/app
+COPY --chown=user . $HOME/app
+COPY ./requirements.txt ~/app/requirements.txt
+RUN pip install -r requirements.txt
+COPY . .
+CMD ["chainlit", "run", "app.py", "--port", "7860"]

README.md

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+---
+title: ArxivChainLitDemo
+emoji: 💻
+colorFrom: indigo
+colorTo: gray
+sdk: docker
+pinned: false
+license: openrail
+duplicated_from: c-s-ale/AliceInWonderLandChainlit
+---

app.py

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+
+from langchain.agents import initialize_agent, Tool
+from langchain.embeddings.openai import OpenAIEmbeddings
+from langchain.agents import AgentType
+from langchain.tools import BaseTool
+from langchain.llms import OpenAI
+from langchain import SerpAPIWrapper, LLMChain
+from langchain.chains import RetrievalQA
+from langchain.chat_models import ChatOpenAI
+from langchain.agents import ZeroShotAgent, Tool, AgentExecutor
+from langchain.memory import ConversationBufferMemory, ReadOnlySharedMemory
+from langchain.document_loaders import TextLoader, DirectoryLoader
+from langchain.vectorstores import Chroma
+import os
+import arxiv
+import chainlit as cl
+from chainlit import user_session
+
+@cl.langchain_factory(use_async=True)
+async def init():
+    # Set the OpenAI Embeddings model
+    embeddings = embeddings = OpenAIEmbeddings()
+
+    # Set the persist directory
+    persist_directory = "vector_db"
+
+    # Load the persisted Chroma vector store
+    vectordb = Chroma(persist_directory=persist_directory, embedding_function=embeddings)
+
+    # Create a chain that uses the Chroma vector store
+    alice_qa = RetrievalQA.from_chain_type(
+        ChatOpenAI(
+            model_name="gpt-3.5-turbo-16k",
+            temperature=0,
+        ),
+        chain_type="stuff",
+        retriever=vectordb.as_retriever(),
+    )
+    
+    search = SerpAPIWrapper()
+
+    memory = ConversationBufferMemory(memory_key="chat_history")
+    readonlymemory = ReadOnlySharedMemory(memory=memory)
+    
+    tools = [
+        Tool(
+            name = "Alice in Wonderland QA System",
+            func=alice_qa.run,
+            description="useful for when you need to answer questions about Alice in Wonderland. Input should be a fully formed question."
+        )
+    ]
+
+    prefix = """Have a conversation with a human, answering the following questions as best you can. You have access to the following tools:"""
+    suffix = """Begin!"
+    
+    {chat_history}
+    Question: {input}
+    {agent_scratchpad}"""
+    
+    prompt = ZeroShotAgent.create_prompt(
+        tools, 
+        prefix=prefix, 
+        suffix=suffix, 
+        input_variables=["input", "chat_history", "agent_scratchpad"]
+    )
+
+    llm_chain = LLMChain(
+        llm=ChatOpenAI(
+            model_name="gpt-3.5-turbo-16k",
+            temperature=0,
+        ), 
+        prompt=prompt
+    )
+
+    agent = ZeroShotAgent(llm_chain=llm_chain, tools=tools, verbose=True)
+    agent_chain = AgentExecutor.from_agent_and_tools(agent=agent, tools=tools, verbose=True, memory=memory)
+
+    # Let the user know that the system is ready
+    await cl.Message(
+        content=f"You can begin by asking any questions about Alice in Wonderland!"
+    ).send()
+
+    return agent_chain
+
+@cl.langchain_run
+async def run(agent, input_str):
+    res = await cl.make_async(agent)(input_str, callbacks=[cl.LangchainCallbackHandler()])
+    print(res)
+    await cl.Message(content=res["output"]).send()

chainlit.md

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+# Down the LangChain Rabbit Hole :rabbit: :clock:!
+
+![image](https://i.imgur.com/L5aklsK.png)
+
+Feel free to ask questions about the Alice in Wonderland series by [Lewis Carroll](https://en.wikipedia.org/wiki/Alice%27s_Adventures_in_Wonderland)

requirements.txt

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+arxiv==1.4.7
+langchain
+chainlit
+openai
+chromadb
+tiktoken
+google-search-results

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