# 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):
# Description
- Save and load brain to disk:
```python
async def main():
with tempfile.NamedTemporaryFile(mode="w", suffix=".txt") as temp_file:
temp_file.write("Gold is a liquid of blue-like colour.")
temp_file.flush()
brain = await Brain.afrom_files(name="test_brain", file_paths=[temp_file.name])
save_path = await brain.save("/home/amine/.local/quivr")
brain_loaded = Brain.load(save_path)
brain_loaded.print_info()
```
# TODO:
- Loading all chat history
- Loading from other vector stores, PG for example can be great ...
# Description
Please include a summary of the changes and the related issue. Please
also include relevant motivation and context.
## Checklist before requesting a review
Please delete options that are not relevant.
- [ ] My code follows the style guidelines of this project
- [ ] I have performed a self-review of my code
- [ ] I have commented hard-to-understand areas
- [ ] I have ideally added tests that prove my fix is effective or that
my feature works
- [ ] New and existing unit tests pass locally with my changes
- [ ] Any dependent changes have been merged
## Screenshots (if appropriate):
This commit adds the langchain_openai and langchain_anthropic
dependencies to the `llm_endpoint.py` file.
# Description
Please include a summary of the changes and the related issue. Please
also include relevant motivation and context.
## Checklist before requesting a review
Please delete options that are not relevant.
- [ ] My code follows the style guidelines of this project
- [ ] I have performed a self-review of my code
- [ ] I have commented hard-to-understand areas
- [ ] I have ideally added tests that prove my fix is effective or that
my feature works
- [ ] New and existing unit tests pass locally with my changes
- [ ] Any dependent changes have been merged
## Screenshots (if appropriate):
# Description
Please include a summary of the changes and the related issue. Please
also include relevant motivation and context.
## Checklist before requesting a review
Please delete options that are not relevant.
- [ ] My code follows the style guidelines of this project
- [ ] I have performed a self-review of my code
- [ ] I have commented hard-to-understand areas
- [ ] I have ideally added tests that prove my fix is effective or that
my feature works
- [ ] New and existing unit tests pass locally with my changes
- [ ] Any dependent changes have been merged
## Screenshots (if appropriate):
# Description
Please include a summary of the changes and the related issue. Please
also include relevant motivation and context.
## Checklist before requesting a review
Please delete options that are not relevant.
- [ ] My code follows the style guidelines of this project
- [ ] I have performed a self-review of my code
- [ ] I have commented hard-to-understand areas
- [ ] I have ideally added tests that prove my fix is effective or that
my feature works
- [ ] New and existing unit tests pass locally with my changes
- [ ] Any dependent changes have been merged
## Screenshots (if appropriate):
---------
Co-authored-by: AmineDiro <aminedirhoussi1@gmail.com>
# Description
- Created registry processor logic for automagically adding processors
to quivr_core based Entrypoints
- Added a langchain_community free `SimpleTxtParser` for the quivr_core
base package
- Added tests
- Added brain_info
- Enriched parsed documents metadata based on quivr_file metadata
used Rich for `Brain.print_info()` to get a better output:
# Description
`quivr-core`
- Generate a fixture to simulate a model with function calling
- Monkey patch `QuivrQARAG` stream
- Tests function
`quivr-api`
- Fixes empty API responses
- Fixes non function calling models
---------
Co-authored-by: Stan Girard <girard.stanislas@gmail.com>
# Description
- Introduce `LLMEndpoint` class: wrapper around a `BaseChatLLM` to load
OpenAI compatible models
- Add `brain.search(...)` function to retrieve
- Test with test-coverage:
- Added ability to load brain `from langchain.Document`
- Configured mypy and poetry lock in `.pre-commit.yaml`
# Test coverage
---------
Co-authored-by: aminediro <aminedirhoussi@gmail.com>
