This template performs RAG on semi-structured data, such as a PDF with text and tables.
See this cookbook as a reference.
OPENAI_API_KEY environment variable to access the OpenAI models.
This uses Unstructured for PDF parsing, which requires some system-level package installations.
On Mac, you can install the necessary packages with the following:
brew install tesseract poppler
To use this package, you should first have the LangChain CLI installed:
pip install -U langchain-cli
To create a new LangChain project and install this as the only package, you can do:
langchain app new my-app --package rag-semi-structured
If you want to add this to an existing project, you can just run:
langchain app add rag-semi-structured
And add the following code to your
from rag_semi_structured import chain as rag_semi_structured_chain
add_routes(app, rag_semi_structured_chain, path="/rag-semi-structured")
(Optional) Let's now configure LangSmith. LangSmith will help us trace, monitor and debug LangChain applications. LangSmith is currently in private beta, you can sign up here. If you don't have access, you can skip this section
export LANGCHAIN_PROJECT=<your-project> # if not specified, defaults to "default"
If you are inside this directory, then you can spin up a LangServe instance directly by:
This will start the FastAPI app with a server is running locally at http://localhost:8000
We can see all templates at http://127.0.0.1:8000/docs We can access the playground at http://127.0.0.1:8000/rag-semi-structured/playground
We can access the template from code with:
from langserve.client import RemoteRunnable
runnable = RemoteRunnable("http://localhost:8000/rag-semi-structured")
For more details on how to connect to the template, refer to the Jupyter notebook