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Updated titles into a consistent format. Fixed links to the diagrams. Fixed typos. Note: The Templates menu in the navbar is now sorted by the file names. I'll try sorting the navbar menus by the page titles, not the page file names. |
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RAG - Unstructured - semi-structured
This template performs RAG on semi-structured data, such as a PDF with text and tables.
It uses the unstructured parser to extract the text and tables from the PDF and then uses the LLM to generate queries based on the user input.
See this cookbook as a reference.
Environment Setup
Set the 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
Usage
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 server.py file:
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. You can sign up for LangSmith here. If you don't have access, you can skip this section
export LANGCHAIN_TRACING_V2=true
export LANGCHAIN_API_KEY=<your-api-key>
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:
langchain serve
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 rag_semi_structured.