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.
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.
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.
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!
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.
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.
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
For Windows activate via:
.venv\Scripts\activate
Install the required python dependencies:
pip install -r danswer/backend/requirements/default.txt
pip install -r danswer/backend/requirements/dev.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
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)
Setup a folder to store config. Navigate to danswer/backend
and run:
mkdir dynamic_config_storage
To start the frontend, navigate to danswer/web
and run:
npm run dev
Package the Vespa schema. This will only need to be done when the Vespa schema is updated locally.
Navigate to danswer/backend/danswer/document_index/vespa/app_config
and run:
zip -r ../vespa-app.zip .
- Note: If you don't have the
zip
utility, you will need to install it prior to running the above
The first time running Danswer, you will also 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 \
DYNAMIC_CONFIG_DIR_PATH=./dynamic_config_storage \
VESPA_DEPLOYMENT_ZIP=./danswer/document_index/vespa/vespa-app.zip \
uvicorn danswer.main:app --reload --port 8080
For Windows (for compatibility with both PowerShell and Command Prompt):
powershell -Command "
$env:AUTH_TYPE='disabled'
$env:DYNAMIC_CONFIG_DIR_PATH='./dynamic_config_storage'
$env:VESPA_DEPLOYMENT_ZIP='./danswer/document_index/vespa/vespa-app.zip'
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.
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!
Right now, there is no automated type checking at the moment (coming soon), but we ask you to manually run it before
creating a pull requests with python -m mypy .
from the danswer/backend
directory.
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.
Like mypy
, we have no automated formatting yet (coming soon), but we request that, for now,
you run this manually before creating a pull request.
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.
As pre-1.0 software, even patch releases may contain breaking or non-backwards-compatible changes.