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!

[YanzhongSu]

Good question and very helpful answer. Thank you both.

[HoganHPH]

[SP3500]

Did you add the condition edge when creating the chain?

[SP3500]

Hi there,
Is it possible to provide two function codes in LangGraph, where one function calls the other, outputs the result of its execution, and then passes it to the next module in LangGraph?

[Sanjeevkumar555]

Hi
I am running all commands copy and paste , However still I am not able to connect the link between retrieved docs and the llm output.
the LLM replies "I need more information to answer this question. I cannot find information about a named using the available tools."
But my retriever is working fine.
Any help pls how to debug?

[navono]

It seems like you might not have initialized the vector_store. Try initializing it with documents first, and then using it for retrieval.

[jasonzhang2022]

The memory saver resend all the previous messages (Human and AI message) to LLM so the LLM has context.

Wouldn't it be easy and consume less resource if we could the multi-turn chat session from LLM itself like this: https://ai.google.dev/gemini-api/docs/text-generation?lang=node#chat?

[Baanrow]

Great tutorials thanks team.

Do the MemorySaver references x 3 in this page need to be changed to InMemorySaver

https://langchain-ai.github.io/langgraph/reference/checkpoints/#langgraph.checkpoint.memory.InMemorySaver

Thanks again.

[khteh]

display(Image(graph.get_graph().draw_mermaid_png())) does not show anything at all in a python app.

[labilezhu]

I think below code may help as a standalone python app to display the image:

# Display the image in a popup window
def show_image():
    from IPython.display import Image, display
    import tkinter as tk
    from PIL import Image as PILImage, ImageTk
    import io    
    root = tk.Tk()
    root.title("Graph Image")
    
    # Get the image in memory buffer
    img_bytes = graph.get_graph().draw_mermaid_png()
    img_buffer = io.BytesIO()
    img_buffer.write(img_bytes)
    # graph.get_graph().draw_mermaid_png(img_buffer)

    img_buffer.seek(0)
    
    img = PILImage.open(img_buffer)
    img = ImageTk.PhotoImage(img)
    panel = tk.Label(root, image=img)
    panel.pack(side="bottom", fill="both", expand="yes")
    root.mainloop()

show_image()

[khteh]

This works but root.mainloop() blocks the execution. How to show the image without blocking the execution?

[labilezhu]

Great tutorials thanks team.

I am a nerd, so I draw a graph to help me understand how it works. Hope it helps you too:

https://blog.mygraphql.com/zh/posts/ai/langchain/rag-tool-chat-history-study/#my-notes-on-langchain-docs-build-a-retrieval-augmented-generation-rag-app---part-2

[sangwooklee0201]

Great tutorial. Thank you.

I am trying the "create_react_agent" method with Anthropic model (used ChatAnthropic from langchain library). When I ran the message from the tutorial following error is what I received:

== Error message starts ==
"UnprocessableEntityError: Error code: 422 - {'detail': [{'type': 'missing', 'loc': ['body', 'tools', 0, 'input_schema', 'properties', 'query', 'type'], 'msg': 'Field required', 'input': {}}]}
During task with name 'agent' and id '0d7e9770-7a14-04d3-28bc-3887dd88e7e5'"
== Error message ends ==

But when I tried with Open AI GPT 4o, it worked just fine. What changes should I make when I try it with Anthropic model? Following is a code:

== Code starts ==
gpt_4o_mini_model = AzureChatOpenAI(
api_key=API_KEY,
azure_endpoint=URL_BASE,
openai_api_version=OPENAI_API_VERSION,
azure_deployment=AZURE_DEPLOYMENT,
)

sonnet_3_5_model = ChatAnthropic(
anthropic_api_url=URL_BASE+"/anthropic",
api_key=API_KEY,
model_name=SONNET_3_5_DEPLOYMENT,
)

memory = MemorySaver()
agent_executor = create_react_agent(sonnet_3_5_model, [tool(response_format="content_and_artifact")(retrieve)], checkpointer=memory)

memory_config = {"configurable": {"thread_id": "def234"}}

inputs = {"messages": [("user", "What's your name?")]}

for s in agent_executor.stream(inputs, stream_mode="values", config=memory_config,):
message = s["messages"][-1]
if isinstance(message, tuple):
print(message)
else:
message.pretty_print()

== Code ends ==