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163ef35dd1
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.
79 lines
2.5 KiB
Markdown
79 lines
2.5 KiB
Markdown
# RAG - Unstructured - semi-structured
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This template performs RAG on `semi-structured data`, such as a PDF with text and tables.
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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.
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See [this cookbook](https://github.com/langchain-ai/langchain/blob/master/cookbook/Semi_Structured_RAG.ipynb) as a reference.
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## Environment Setup
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Set the `OPENAI_API_KEY` environment variable to access the OpenAI models.
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This uses [Unstructured](https://unstructured-io.github.io/unstructured/) for PDF parsing, which requires some system-level package installations.
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On Mac, you can install the necessary packages with the following:
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```shell
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brew install tesseract poppler
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```
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## Usage
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To use this package, you should first have the LangChain CLI installed:
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```shell
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pip install -U langchain-cli
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```
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To create a new LangChain project and install this as the only package, you can do:
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```shell
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langchain app new my-app --package rag-semi-structured
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```
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If you want to add this to an existing project, you can just run:
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```shell
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langchain app add rag-semi-structured
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```
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And add the following code to your `server.py` file:
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```python
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from rag_semi_structured import chain as rag_semi_structured_chain
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add_routes(app, rag_semi_structured_chain, path="/rag-semi-structured")
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```
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(Optional) Let's now configure LangSmith.
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LangSmith will help us trace, monitor and debug LangChain applications.
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You can sign up for LangSmith [here](https://smith.langchain.com/).
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If you don't have access, you can skip this section
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```shell
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export LANGCHAIN_TRACING_V2=true
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export LANGCHAIN_API_KEY=<your-api-key>
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export LANGCHAIN_PROJECT=<your-project> # if not specified, defaults to "default"
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```
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If you are inside this directory, then you can spin up a LangServe instance directly by:
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```shell
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langchain serve
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```
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This will start the FastAPI app with a server is running locally at
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[http://localhost:8000](http://localhost:8000)
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We can see all templates at [http://127.0.0.1:8000/docs](http://127.0.0.1:8000/docs)
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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)
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We can access the template from code with:
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```python
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from langserve.client import RemoteRunnable
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runnable = RemoteRunnable("http://localhost:8000/rag-semi-structured")
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```
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For more details on how to connect to the template, refer to the Jupyter notebook `rag_semi_structured`. |