docs/tutorials/rag/

[giscus[bot]]

docs/tutorials/rag/

One of the most powerful applications enabled by LLMs is sophisticated question-answering (Q&A) chatbots. These are applications that can answer questions about specific source information. These applications use a technique known as Retrieval Augmented Generation, or RAG.

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

[ksairos]

If you're using Chroma during the query analysis, don't forget to change the filter for vector_store.similarity_search() function in the def retrieve(state: State) node.

Instead of
filter=lambda doc: doc.metadata.get("section") == query["section"], which causes a Expected type 'dict[str, str] | None', got '(doc: Any) -> bool' instead error, you can use {"section": query["section"]}.

[korpog]

You have to run pip install beautifulsoup4, otherwise there will be an import error

[JoshuaMichaelHanson]

On Windows 11 running Python 3.13.1 needed to change to a pydantic BaseModel to get the analyze_query to work. Keep getting ValueError: no signature found for builtin type <class 'dict'> when running the query = structured_llm.invoke(state["question"])

Updated code as follows and works. The answer is slightly different but by adjusting the query was able to come close

from typing import Literal
from pydantic import BaseModel, Field

Define the Search schema using Pydantic instead of TypedDict

class Search(BaseModel):
"""Search query schema."""
query: str = Field(description="Search query to run.")
section: Literal["beginning", "middle", "end"] = Field(
description="Section to query."
)

for step in graph.stream(
{"question": "What are Challenges of task decomposition say at the end?"},
stream_mode="updates",
):
print(f"{step}\n\n----------------\n")

If anyone knows how to make it work with the TypeDict vs BaseModel would be interested in the answer, otherwise hope this helps someone else if they get stuck. Cheers!

[0JCRG0]

The documentation is not up-to-date:

Traceback (most recent call last):
File "/Users/juanreyesgarcia/Dev/Python/LLMSandbox/main.py", line 58, in
graph_builder = StateGraph(State).add_sequence([retrieve, generate]) # type: ignore
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
AttributeError: 'StateGraph' object has no attribute 'add_sequence'

[JoshuaMichaelHanson]

Are you sure you have all the correct dependencies installed? Just did the tutorial a week ago and everything worked fine-ish, see other comment.

pip install -U langgraph
pip show langgraph
from langgraph.graph import START, StateGraph

If no error occurs, the package is installed correctly

pip check langgraph

make sure you are using a virtual environment to manage dependencies cleanly

python -m venv venv
source venv/bin/activate # On Windows: venv\Scripts\activate
pip install -U langgraph

[0JCRG0]

It is probably the version? This is what I have langgraph==0.2.39

But this worked:

graph_builder = StateGraph(State)
graph_builder.add_node("retrieve", retrieve)
graph_builder.add_node("generate", generate)

graph_builder.add_edge(START, "retrieve")
graph_builder.add_edge("retrieve", "generate")
graph_builder.add_edge("generate", END)
graph = graph_builder.compile()

response = graph.invoke({"question": "What is Task Decomposition?"})
print(response["answer"])