This is backend project for ScaleDP Chat. Chat over the ScaleDP git repository.
It is FastAPI RAG backend based on (LangGraph and LangCain) for the frontend chat based on AI SDK
Project is using:
- FastAPI as backend framework
- LangChain/LangGraph as AI framework to build RAG
- Huggingface Transformers model for embeddings
- Swagger UI for API documentation
- SQLAlchemy as ORM
- PostgreSQL with PGVector extension as Vector Store
- Alembic for DB migrations
- Poetry as project/dependency manager
- Pytest for testing
- Ruff/Black/MyPY for linting and formatting
- Pre-commit for pre-commit hooks
- Docker/Docker Compose for containerization
- GitHub Actions for CI/CD
Project contains test coverage for 'chat' endpoint.
This project uses poetry. It's a modern dependency management tool.
To run the project use this set of commands:
poetry install
poetry run python -m scaledp_chat
This will start the server on the configured host.
You can find swagger documentation at /api/docs
.
You can read more about poetry here: https://python-poetry.org/
You can start the project with docker using this command:
docker-compose up --build
If you want to develop in docker with autoreload and exposed ports add -f deploy/docker-compose.dev.yml
to your docker command.
Like this:
docker-compose -f docker-compose.yml -f deploy/docker-compose.dev.yml --project-directory . up --build
This command exposes the web application on port 8000, mounts current directory and enables autoreload.
But you have to rebuild image every time you modify poetry.lock
or pyproject.toml
with this command:
docker-compose build
$ tree "scaledp_chat"
scaledp_chat
├── conftest.py # Fixtures for all tests.
├── db # module contains db configurations
│ ├── dao # Data Access Objects. Contains different classes to interact with database.
│ └── models # Package contains different models for ORMs.
├── __main__.py # Startup script. Starts uvicorn.
├── services # Package for different external services such as rabbit or redis etc.
├── settings.py # Main configuration settings for project.
├── static # Static content.
├── tests # Tests for project.
└── web # Package contains web server. Handlers, startup config.
├── api # Package with all handlers.
│ └── router.py # Main router.
├── application.py # FastAPI application configuration.
└── lifespan.py # Contains actions to perform on startup and shutdown.
This application can be configured with environment variables.
You can create .env
file in the root directory and place all
environment variables here.
All environment variables should start with "SCALEDP_CHAT_" prefix.
For example if you see in your "scaledp_chat/settings.py" a variable named like
random_parameter
, you should provide the "SCALEDP_CHAT_RANDOM_PARAMETER"
variable to configure the value. This behaviour can be changed by overriding env_prefix
property
in scaledp_chat.settings.Settings.Config
.
An example of .env file:
SCALEDP_CHAT_RELOAD="True"
SCALEDP_CHAT_PORT="8000"
SCALEDP_CHAT_ENVIRONMENT="dev"
You can read more about BaseSettings class here: https://pydantic-docs.helpmanual.io/usage/settings/
To install pre-commit simply run inside the shell:
pre-commit install
pre-commit is very useful to check your code before publishing it. It's configured using .pre-commit-config.yaml file.
By default it runs:
- black (formats your code);
- mypy (validates types);
- ruff (spots possible bugs);
You can read more about pre-commit here: https://pre-commit.com/
To run pre-commit on all files:
pre-commit run --all-files
If you want to migrate your database, you should run following commands:
# To run all migrations until the migration with revision_id.
alembic upgrade "<revision_id>"
# To perform all pending migrations.
alembic upgrade "head"
If you want to revert migrations, you should run:
# revert all migrations up to: revision_id.
alembic downgrade <revision_id>
# Revert everything.
alembic downgrade base
To generate migrations you should run:
# For automatic change detection.
alembic revision --autogenerate
# For empty file generation.
alembic revision
If you want to run it in docker, simply run:
docker-compose run --build --rm api pytest -vv .
docker-compose down
For running tests on your local machine.
- you need to start a database.
I prefer doing it with docker:
docker run -p "5432:5432" -e "POSTGRES_PASSWORD=scaledp_chat" -e "POSTGRES_USER=scaledp_chat" -e "POSTGRES_DB=scaledp_chat" postgres:16.3-bullseye
- Run the pytest.
pytest -vv .
poetry run python ./scripts/create_index.py