# rag-semi-structured This template performs RAG on semi-structured data, such as a PDF with text and tables. See [this cookbook](https://github.com/langchain-ai/langchain/blob/master/cookbook/Semi_Structured_RAG.ipynb) as a reference. ## Environment Setup Set the `OPENAI_API_KEY` environment variable to access the OpenAI models. This uses [Unstructured](https://unstructured-io.github.io/unstructured/) for PDF parsing, which requires some system-level package installations. On Mac, you can install the necessary packages with the following: ```shell brew install tesseract poppler ``` ## Usage To use this package, you should first have the LangChain CLI installed: ```shell pip install -U langchain-cli ``` To create a new LangChain project and install this as the only package, you can do: ```shell langchain app new my-app --package rag-semi-structured ``` If you want to add this to an existing project, you can just run: ```shell langchain app add rag-semi-structured ``` And add the following code to your `server.py` file: ```python 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](https://smith.langchain.com/). If you don't have access, you can skip this section ```shell export LANGCHAIN_TRACING_V2=true export LANGCHAIN_API_KEY= export LANGCHAIN_PROJECT= # if not specified, defaults to "default" ``` If you are inside this directory, then you can spin up a LangServe instance directly by: ```shell langchain serve ``` This will start the FastAPI app with a server is running locally at [http://localhost:8000](http://localhost:8000) We can see all templates at [http://127.0.0.1:8000/docs](http://127.0.0.1:8000/docs) We can access the playground at [http://127.0.0.1:8000/rag-semi-structured/playground](http://127.0.0.1:8000/rag-semi-structured/playground) We can access the template from code with: ```python 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`.