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Contributing to Danswer

Hey there! We are so excited that you're interested in Danswer.

As an open source project in a rapidly changing space, we welcome all contributions.

💃 Guidelines

Contribution Opportunities

The GitHub Issues page is a great place to start for contribution ideas.

Issues that have been explicitly approved by the maintainers (aligned with the direction of the project) will be marked with the approved by maintainers label. Issues marked good first issue are an especially great place to start.

Connectors to other tools are another great place to contribute. For details on how, refer to this README.md.

If you have a new/different contribution in mind, we'd love to hear about it! Your input is vital to making sure that Danswer moves in the right direction. Before starting on implementation, please raise a GitHub issue.

And always feel free to message us (Chris Weaver / Yuhong Sun) on Slack / Discord directly about anything at all.

Contributing Code

To contribute to this project, please follow the "fork and pull request" workflow. When opening a pull request, mention related issues and feel free to tag relevant maintainers.

Before creating a pull request please make sure that the new changes conform to the formatting and linting requirements. See the Formatting and Linting section for how to run these checks locally.

Getting Help 🙋

Our goal is to make contributing as easy as possible. If you run into any issues please don't hesitate to reach out. That way we can help future contributors and users can avoid the same issue.

We also have support channels and generally interesting discussions on our Slack and Discord.

We would love to see you there!

Get Started 🚀

Danswer being a fully functional app, relies on some external pieces of software, specifically:

This guide provides instructions to set up the Danswer specific services outside of Docker because it's easier for development purposes but also feel free to just use the containers and update with local changes by providing the --build flag.

Local Set Up

It is recommended to use Python version 3.11

If using a lower version, modifications will have to be made to the code. If using a higher version, the version of Tensorflow we use may not be available for your platform.

Installing Requirements

Currently, we use pip and recommend creating a virtual environment.

For convenience here's a command for it:

python -m venv .venv
source .venv/bin/activate

--> Note that this virtual environment MUST NOT be set up WITHIN the danswer directory

For Windows, activate the virtual environment using Command Prompt:

.venv\Scripts\activate

If using PowerShell, the command slightly differs:

.venv\Scripts\Activate.ps1

Install the required python dependencies:

pip install -r danswer/backend/requirements/default.txt
pip install -r danswer/backend/requirements/dev.txt
pip install -r danswer/backend/requirements/model_server.txt

Install Node.js and npm for the frontend. Once the above is done, navigate to danswer/web run:

npm i

Install Playwright (required by the Web Connector)

Note: If you have just done the pip install, open a new terminal and source the python virtual-env again. This will update the path to include playwright

Then install Playwright by running:

playwright install

Dependent Docker Containers

First navigate to danswer/deployment/docker_compose, then start up Vespa and Postgres with:

docker compose -f docker-compose.dev.yml -p danswer-stack up -d index relational_db

(index refers to Vespa and relational_db refers to Postgres)

Running Danswer

To start the frontend, navigate to danswer/web and run:

npm run dev

Next, start the model server which runs the local NLP models. Navigate to danswer/backend and run:

uvicorn model_server.main:app --reload --port 9000

For Windows (for compatibility with both PowerShell and Command Prompt):

powershell -Command "
    uvicorn model_server.main:app --reload --port 9000
"

The first time running Danswer, you will need to run the DB migrations for Postgres. After the first time, this is no longer required unless the DB models change.

Navigate to danswer/backend and with the venv active, run:

alembic upgrade head

Next, start the task queue which orchestrates the background jobs. Jobs that take more time are run async from the API server.

Still in danswer/backend, run:

python ./scripts/dev_run_background_jobs.py

To run the backend API server, navigate back to danswer/backend and run:

AUTH_TYPE=disabled uvicorn danswer.main:app --reload --port 8080

For Windows (for compatibility with both PowerShell and Command Prompt):

powershell -Command "
    $env:AUTH_TYPE='disabled'
    uvicorn danswer.main:app --reload --port 8080 
"

Note: if you need finer logging, add the additional environment variable LOG_LEVEL=DEBUG to the relevant services.

Formatting and Linting

Backend

For the backend, you'll need to setup pre-commit hooks (black / reorder-python-imports). First, install pre-commit (if you don't have it already) following the instructions here. Then, from the danswer/backend directory, run:

pre-commit install

Additionally, we use mypy for static type checking. Danswer is fully type-annotated, and we would like to keep it that way! To run the mypy checks manually, run python -m mypy . from the danswer/backend directory.

Web

We use prettier for formatting. The desired version (2.8.8) will be installed via a npm i from the danswer/web directory. To run the formatter, use npx prettier --write . from the danswer/web directory. Please double check that prettier passes before creating a pull request.

Release Process

Danswer follows the semver versioning standard. A set of Docker containers will be pushed automatically to DockerHub with every tag. You can see the containers here.