a4992ffada
**Description:** Fixes to rag-semi-structured template. - Added required libraries - pdfminer was causing issues when installing with pip. pdfminer.six works best - Changed the pdf name for demo from llama2 to llava <!-- Thank you for contributing to LangChain! Replace this entire comment with: - **Description:** a description of the change, - **Issue:** the issue # it fixes (if applicable), - **Dependencies:** any dependencies required for this change, - **Tag maintainer:** for a quicker response, tag the relevant maintainer (see below), - **Twitter handle:** we announce bigger features on Twitter. If your PR gets announced, and you'd like a mention, we'll gladly shout you out! Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally. See contribution guidelines for more information on how to write/run tests, lint, etc: https://github.com/langchain-ai/langchain/blob/master/.github/CONTRIBUTING.md If you're adding a new integration, please include: 1. a test for the integration, preferably unit tests that do not rely on network access, 2. an example notebook showing its use. It lives in `docs/extras` directory. If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17. --> |
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rag-semi-structured
This template performs RAG on semi-structured data, such as a PDF with text and tables.
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. LangSmith is currently in private beta, you can sign up 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.