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fix: Cannot find declaration to go to CLEAN_DAY_SETTING (#10157)
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.idea fix nltk averaged_perceptron_tagger download and fix score limit is none (#7582) před 1 rokem
.vscode feat/enhance the multi-modal support (#8818) před 1 rokem
configs fix: avoid unexpected error when create knowledge base with baidu vector database and wenxin embedding model (#10130) před 1 rokem
constants nltk security issue and upgrade unstructured (#9558) před 1 rokem
contexts feat/enhance the multi-modal support (#8818) před 1 rokem
controllers Feat/add-remote-file-upload-api (#9906) před 1 rokem
core feat: add gpustack model provider (#10158) před 1 rokem
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events feat/enhance the multi-modal support (#8818) před 1 rokem
extensions Revert "chore: improve validation and handler of logging timezone with TimezoneName" (#10077) před 1 rokem
factories Feat/add-remote-file-upload-api (#9906) před 1 rokem
fields Feat/add-remote-file-upload-api (#9906) před 1 rokem
libs refactor: use dify_config to replace legacy usage of flask app's config (#9089) před 1 rokem
migrations refactor(migration/model): update column types for workflow schema (#10160) před 1 rokem
models refactor(migration/model): update column types for workflow schema (#10160) před 1 rokem
schedule fix: Cannot find declaration to go to CLEAN_DAY_SETTING (#10157) před 1 rokem
services Feat/add-remote-file-upload-api (#9906) před 1 rokem
tasks Feat/new login (#8120) před 1 rokem
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tests feat: add gpustack model provider (#10158) před 1 rokem
.dockerignore build: support Poetry for depencencies tool in api's Dockerfile (#5105) před 1 rokem
.env.example chore: add tidb-on-qdrant configuration in env and docker-compose file (#10015) před 1 rokem
Dockerfile fix(Dockerfile): conditionally install zlib1g based on architecture (#10118) před 1 rokem
README.md fix: poetry installation in CI jobs (#9336) před 1 rokem
app.py refactor: use dify_config to replace legacy usage of flask app's config (#9089) před 1 rokem
app_factory.py refactor: use dify_config to replace legacy usage of flask app's config (#9089) před 1 rokem
commands.py Added OceanBase as an option for the vector store in Dify (#10010) před 1 rokem
poetry.lock fix: avoid unexpected error when create knowledge base with baidu vector database and wenxin embedding model (#10130) před 1 rokem
poetry.toml build: initial support for poetry build tool (#4513) před 1 rokem
pyproject.toml Added OceanBase as an option for the vector store in Dify (#10010) před 1 rokem
pytest.ini feat: add models for gitee.ai (#9490) před 1 rokem

README.md

Dify Backend API

Usage

[!IMPORTANT] In the v0.6.12 release, we deprecated pip as the package management tool for Dify API Backend service and replaced it with poetry.

  1. Start the docker-compose stack

The backend require some middleware, including PostgreSQL, Redis, and Weaviate, which can be started together using docker-compose.

   cd ../docker
   cp middleware.env.example middleware.env
   # change the profile to other vector database if you are not using weaviate
   docker compose -f docker-compose.middleware.yaml --profile weaviate -p dify up -d
   cd ../api
  1. Copy .env.example to .env
  2. Generate a SECRET_KEY in the .env file.
   sed -i "/^SECRET_KEY=/c\SECRET_KEY=$(openssl rand -base64 42)" .env
   secret_key=$(openssl rand -base64 42)
   sed -i '' "/^SECRET_KEY=/c\\
   SECRET_KEY=${secret_key}" .env
  1. Create environment.

Dify API service uses Poetry to manage dependencies. You can execute poetry shell to activate the environment.

  1. Install dependencies
   poetry env use 3.10
   poetry install

In case of contributors missing to update dependencies for pyproject.toml, you can perform the following shell instead.

   poetry shell                                               # activate current environment
   poetry add $(cat requirements.txt)           # install dependencies of production and update pyproject.toml
   poetry add $(cat requirements-dev.txt) --group dev    # install dependencies of development and update pyproject.toml
  1. Run migrate

Before the first launch, migrate the database to the latest version.

   poetry run python -m flask db upgrade
  1. Start backend
   poetry run python -m flask run --host 0.0.0.0 --port=5001 --debug
  1. Start Dify web service.
  2. Setup your application by visiting http://localhost:3000
  3. If you need to handle and debug the async tasks (e.g. dataset importing and documents indexing), please start the worker service.
   poetry run python -m celery -A app.celery worker -P gevent -c 1 --loglevel INFO -Q dataset,generation,mail,ops_trace,app_deletion

Testing

  1. Install dependencies for both the backend and the test environment
   poetry install --with dev
  1. Run the tests locally with mocked system environment variables in tool.pytest_env section in pyproject.toml
   cd ../
   poetry run -C api bash dev/pytest/pytest_all_tests.sh