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# 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):
27 lines
650 B
Python
27 lines
650 B
Python
from quivr_core.config import LLMEndpointConfig, RetrievalConfig
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def test_default_llm_config():
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config = LLMEndpointConfig()
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assert (
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config.model_dump()
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== LLMEndpointConfig(
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model="gpt-3.5-turbo-0125",
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llm_base_url=None,
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llm_api_key=None,
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max_input_tokens=2000,
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max_output_tokens=2000,
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temperature=0.7,
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streaming=True,
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).model_dump()
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)
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def test_default_retrievalconfig():
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config = RetrievalConfig()
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assert config.max_files == 20
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assert config.prompt is None
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assert config.llm_config == LLMEndpointConfig()
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