larcane97 8d5456b6d0 Add VESSL AI OpenAI API-compatible model provider and LLM model (#9474) 5 mesiacov pred
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.idea 7ae728a9a3 fix nltk averaged_perceptron_tagger download and fix score limit is none (#7582) 8 mesiacov pred
.vscode e61752bd3a feat/enhance the multi-modal support (#8818) 6 mesiacov pred
configs b61baa87ec fix: avoid unexpected error when create knowledge base with baidu vector database and wenxin embedding model (#10130) 5 mesiacov pred
constants 3e9d271b52 nltk security issue and upgrade unstructured (#9558) 6 mesiacov pred
contexts e61752bd3a feat/enhance the multi-modal support (#8818) 6 mesiacov pred
controllers ce260f79d2 Feat/update knowledge api url (#10102) 5 mesiacov pred
core 8d5456b6d0 Add VESSL AI OpenAI API-compatible model provider and LLM model (#9474) 5 mesiacov pred
docker 8dfdb37de3 fix: use LOG_LEVEL for celery startup (#7628) 8 mesiacov pred
events e61752bd3a feat/enhance the multi-modal support (#8818) 6 mesiacov pred
extensions 0a3d51e9cf Revert "chore: improve validation and handler of logging timezone with TimezoneName" (#10077) 5 mesiacov pred
factories 219f5d9845 Fixed the issue where recall the knowledge base in the iteration of the workflow and report errors when executing (#10060) 5 mesiacov pred
fields 190b6a2aa6 feat: /conversations api response add 'update_at' field,and update api docs add sort_by parameter (#10043) 5 mesiacov pred
libs 4d9160ca9f refactor: use dify_config to replace legacy usage of flask app's config (#9089) 6 mesiacov pred
migrations 18106a4fc6 add tidb on qdrant type (#9831) 6 mesiacov pred
models e5397c5ec2 feat(app_dsl_service): enhance error handling and DSL version management (#10108) 5 mesiacov pred
schedule 18106a4fc6 add tidb on qdrant type (#9831) 6 mesiacov pred
services 0154a26e0b fix issue: update document segment setting failed (#10107) 5 mesiacov pred
tasks 4fd2743efa Feat/new login (#8120) 6 mesiacov pred
templates 4fd2743efa Feat/new login (#8120) 6 mesiacov pred
tests 8d5456b6d0 Add VESSL AI OpenAI API-compatible model provider and LLM model (#9474) 5 mesiacov pred
.dockerignore 27f0ae8416 build: support Poetry for depencencies tool in api's Dockerfile (#5105) 10 mesiacov pred
.env.example c6e54c83c8 chore: add tidb-on-qdrant configuration in env and docker-compose file (#10015) 5 mesiacov pred
Dockerfile 05d9adeb99 fix(Dockerfile): conditionally install zlib1g based on architecture (#10118) 5 mesiacov pred
README.md a8134a49c4 fix: poetry installation in CI jobs (#9336) 6 mesiacov pred
app.py 4d9160ca9f refactor: use dify_config to replace legacy usage of flask app's config (#9089) 6 mesiacov pred
app_factory.py 4d9160ca9f refactor: use dify_config to replace legacy usage of flask app's config (#9089) 6 mesiacov pred
commands.py 878d13ef42 Added OceanBase as an option for the vector store in Dify (#10010) 5 mesiacov pred
poetry.lock b61baa87ec fix: avoid unexpected error when create knowledge base with baidu vector database and wenxin embedding model (#10130) 5 mesiacov pred
poetry.toml f62f71a81a build: initial support for poetry build tool (#4513) 10 mesiacov pred
pyproject.toml 878d13ef42 Added OceanBase as an option for the vector store in Dify (#10010) 5 mesiacov pred
pytest.ini 0ebd985672 feat: add models for gitee.ai (#9490) 5 mesiacov pred

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