How to query the vector database in LangChain AgentExecutor, invoice before summarizing the 'Final Answer' after all tools have been called?

[chenkuncloud]

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Example Code

LangChain AgentExecutor code:

llm = ChatOpenAI()
tools = [tool_1, tool_2, ...]

prompt = hub.pull("hwchase17/openai-tools-agent")
my_agent = create_openai_tools_agent(llm, tools, prompt)
agent_executor = AgentExecutor(agent=my_agent, tools=tools, verbose=True)
question = "user's question"
ans = agent_executor.invoke({"input": question})


Querying Vector Database:
```python
docs = vector_store.similarity_search(question)
new_question = f'''
Please answer my question based on my information

My information: ...

My question: ...
'''

### Description

How to query the vector database in LangChain AgentExecutor, invoice before summarizing the 'Final Answer' after all tools have been called?

### System Info

python 3.10
langchain 0.2

[dosubot[bot]]

Hey there, @chenkuncloud! I'm here to assist you with any bugs, questions, or contributions while you wait for a human maintainer. Let me know how I can help!

To query the vector database in LangChain's AgentExecutor before summarizing the 'Final Answer' after all tools have been called, you can use the RetrievalQA class to create a tool that interacts with the vector database. This tool can then be integrated into the agent's workflow.

Here is an example of how you can set this up:

from langchain.chains import RetrievalQA
from langchain_chroma import Chroma
from langchain_openai import OpenAI, OpenAIEmbeddings
from langchain_text_splitters import CharacterTextSplitter
from langchain.agents import AgentType, Tool, initialize_agent

# Initialize the language model
llm = OpenAI(temperature=0)

# Load and split documents
from langchain_community.document_loaders import TextLoader
loader = TextLoader("path_to_your_document.txt")
documents = loader.load()
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
texts = text_splitter.split_documents(documents)

# Create embeddings and vector store
embeddings = OpenAIEmbeddings()
docsearch = Chroma.from_documents(texts, embeddings, collection_name="your_collection_name")

# Create the RetrievalQA tool
retrieval_qa_tool = RetrievalQA.from_chain_type(
    llm=llm, chain_type="stuff", retriever=docsearch.as_retriever()
)

# Define the tool for the agent
tools = [
    Tool(
        name="Document QA System",
        func=retrieval_qa_tool.run,
        description="useful for answering questions based on the ingested documents. Input should be a fully formed question.",
        return_direct=True,
    ),
]

# Initialize the agent
agent = initialize_agent(
    tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True
)

# Run the agent with a query
query = "What is the capital of France?"
answer = agent.run(query)

# Print the answer
print(answer)

This code initializes a RetrievalQA tool with a vector store and a language model, then integrates it into an agent. The agent can then query the vector database before summarizing the 'Final Answer' ^[1][2].

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