mirror of
https://github.com/hwchase17/langchain
synced 2024-11-04 06:00:26 +00:00
77 lines
2.3 KiB
Markdown
77 lines
2.3 KiB
Markdown
# rag-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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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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LangSmith is currently in private beta, you can sign up [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`. |