docs/tutorials/qa_chat_history/

[giscus[bot]]

docs/tutorials/qa_chat_history/

This guide assumes familiarity with the following concepts:

https://python.langchain.com/docs/tutorials/qa_chat_history/

[aditya11ad]

it can be a silly question, but please help me:
How can me apply "trimmer" , with create_react_agent()?

[ccurme]

We have an example of that in the guide here.

[SP3500]

The import code:
from langchain_core.vector_stores import InMemoryVectorStore

vector_store = InMemoryVectorStore(embeddings)
should be :
from langchain_core.vectorstores import InMemoryVectorStore

vector_store = InMemoryVectorStore(embeddings)

I think you wrongly added a "_" that will cause import error

[ccurme]

Thanks for the catch! Just merged a fix in #28388

[SP3500]

Hello!
Can I ask why this function can tell whether to let LLM directly answer the user question or enter the RAG part to retrieve?
Is it the cause of using llm_with_tools.invoke ?

def query_or_respond(state: MessagesState):
    """Generate tool call for retrieval or respond."""
    llm_with_tools = llm.bind_tools([retrieve])
    response = llm_with_tools.invoke(state["messages"])
    # MessagesState appends messages to state instead of overwriting
    return {"messages": [response]}

[SP3500]

Cause in my task , I also need to decide whether to enter the RAG to retrieve or just print the response directy.
Your chain framework is very helpful, so I want to have a deeper understanding of how to use it.
Your reply will help a lot!

[ccurme]

If response contains tool calls, we enter the retrieval step. If it does not, we return the response directly. This is controlled by tools_condition in the conditional edge downstream.

That is, we delegate the decision about entering the retrieval step to the model.

A simplified version of tools_condition looks like this:

def tools_condition(
    state: dict[str, Any],
    messages_key: str = "messages",
) -> Literal["tools", "__end__"]:
    ai_message = state[messages_key][-1]
    if hasattr(ai_message, "tool_calls") and len(ai_message.tool_calls) > 0:
        return "tools"
    return "__end__"

but you can swap this for a custom function.

[SP3500]

Thank you so much for this patient reply! It really helped a lot!

[HoganHPH]

[SP3500]

Did you add the condition edge when creating the chain?