# 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):
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
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):
# 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
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
- Moved `quivr-api` parser to `quivr_core.processor.implementations` by
Dynamically creating classes on the fly that inherit from
`ProcessorBase`
- Defined a priority based based system to automagically register the
"important" processor that we can import at runtime
- Wrote extensive tests for the registry
- Added support file extensions
### Next steps
- Find a way to have correct LSP autocomplete on the dynamically
generated processors
- Test that processor are imported correctly based on the installed
packages in environment ( using tox) ?
# 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:
This pull request adds functionality to sync files with DropBox. It
includes the following changes:
- Created a BaseSync class with all specific function for each clouds
- Created a SyncUtils class that takes in a BaseSync and apply the pipe
- fix the refresh method for DropBox
Please review and merge this pull request to enable DropBox sync
functionality in the application.
---------
Co-authored-by: Stan Girard <stan@quivr.app>
Co-authored-by: Amine Dirhoussi <aminediro@quivr.app>
# 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>
# 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: Stan Girard <stan@quivr.app>
# 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
- Defined quivr-core `ChatHistory`
- `ChatHistory` can be iterated over in tuples of
`HumanMessage,AIMessage`
- Brain appends to the chatHistory once response is received
- Brain holds a dict of chats and defines the default chat (TODO: define
a system of selecting the chats)
- Wrote test
- Updated `QuivrQARAG` to use `ChatHistory` as input
# 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>
# 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):
