# 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: Zewed <dewez.antoine2@gmail.com>
# 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
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
Using LangGraph instead of LangChain LCEL to build and run the RAG
pipeline, as LangGraph enables greater flexibility and an easier
maintainability of complex (agentic) pipelines
Completes CORE-175
## 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
- [ ] 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):
---------
Co-authored-by: Stan Girard <girard.stanislas@gmail.com>
# Description
# Testing backend
## Docker setup
1. Copy `.env.example` to `.env`. Some env variables were added :
EMBEDDING_DIM
2. Apply supabase migratrions :
```sh
supabase stop
supabase db reset
supabase start
```
3. Start backend containers
```
make dev
```
## Local setup
You can also run backend without docker.
1. Install [`rye`](https://rye.astral.sh/guide/installation/). Choose
the managed python version and set the version to 3.11
2. Run the following:
```
cd quivr/backend
rye sync
```
3. Source `.venv` virtual env : `source .venv/bin/activate`
4. Run the backend, make sure you are running redis and supabase
API:
```
LOG_LEVEL=debug uvicorn quivr_api.main:app --log-level debug --reload --host 0.0.0.0 --port 5050 --workers 1
```
Worker:
```
LOG_LEVEL=debug celery -A quivr_worker.celery_worker worker -l info -E --concurrency 1
```
Notifier:
```
LOG_LEVEL=debug python worker/quivr_worker/celery_monitor.py
```
---------
Co-authored-by: chloedia <chloedaems0@gmail.com>
Co-authored-by: aminediro <aminedirhoussi1@gmail.com>
Co-authored-by: Antoine Dewez <44063631+Zewed@users.noreply.github.com>
Co-authored-by: Chloé Daems <73901882+chloedia@users.noreply.github.com>
Co-authored-by: Zewed <dewez.antoine2@gmail.com>
# Description
## 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: Stan Girard <stan@quivr.app>
Co-authored-by: Stan Girard <girard.stanislas@gmail.com>
This pull request updates the docker-compose files to specify the
platform for the backend services as linux/amd64. This ensures that the
services are built and run specifically for the amd64 architecture.
# Description
closes#2756
- Fixed torchvision cpu version in poetry
- Moved test dependencies to dev group
Co-authored-by: aminediro <aminediro@github.com>
# Description
- Added package manager
- Added precommit checks
- Rewrote dependency injection of Services and Repositories
- Integrate async SQL alchemy engine
- Migrate Chat repository to SQLModel
- Migrated ChatHistory repository to SQLModel
- User SQLModel
- Unit test methodology with db rollback
- Unit tests ChatRepository
- Test ChatService get_history
- Brain entity SQL Model
- Promp SQLModel
- Rewrite chat/{chat_id}/question route
- updated docker files and docker compose in dev and production
Added `quivr_core` subpackages:
- Refactored KnowledgebrainQa
- Added Rag service to interface with non-rag dependencies
---------
Co-authored-by: aminediro <aminediro@github.com>
