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ef90e8e672
# Description
Major PR which, among other things, introduces the possibility of easily
customizing the retrieval workflows. Workflows are based on LangGraph,
and can be customized using a [yaml configuration
file](core/tests/test_llm_endpoint.py), and adding the implementation of
the nodes logic into
[quivr_rag_langgraph.py](1a0c98437a/backend/core/quivr_core/quivr_rag_langgraph.py)
This is a first, simple implementation that will significantly evolve in
the coming weeks to enable more complex workflows (for instance, with
conditional nodes). We also plan to adopt a similar approach for the
ingestion part, i.e. to enable user to easily customize the ingestion
pipeline.
Closes CORE-195, CORE-203, CORE-204
## Checklist before requesting a review
Please delete options that are not relevant.
- [X] My code follows the style guidelines of this project
- [X] I have performed a self-review of my code
- [X] I have commented hard-to-understand areas
- [X] I have ideally added tests that prove my fix is effective or that
my feature works
- [X] New and existing unit tests pass locally with my changes
- [X] Any dependent changes have been merged
## Screenshots (if appropriate):
17 lines
445 B
Docker
17 lines
445 B
Docker
# Using a slim version for a smaller base image
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FROM python:3.11.6-slim-bullseye
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# Install GEOS library, Rust, and other dependencies, then clean up
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RUN apt-get clean && apt-get update && apt-get install -y \
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poppler-utils \
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tesseract-ocr
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WORKDIR /code
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# Upgrade pip and install dependencies
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RUN pip install megaparse
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# You can run the application with the following command:
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# docker run -it megaparse_image python your_script.py
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