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-LAN- 6a2a9460e9
fix(workflow model): ensure consistent timestamp updating (#10172)
1 年之前
..
.idea fix nltk averaged_perceptron_tagger download and fix score limit is none (#7582) 1 年之前
.vscode feat/enhance the multi-modal support (#8818) 1 年之前
configs fix: avoid unexpected error when create knowledge base with baidu vector database and wenxin embedding model (#10130) 1 年之前
constants nltk security issue and upgrade unstructured (#9558) 1 年之前
contexts feat/enhance the multi-modal support (#8818) 1 年之前
controllers Feat/add-remote-file-upload-api (#9906) 1 年之前
core feat: add gpustack model provider (#10158) 1 年之前
docker fix: use LOG_LEVEL for celery startup (#7628) 1 年之前
events feat/enhance the multi-modal support (#8818) 1 年之前
extensions Revert "chore: improve validation and handler of logging timezone with TimezoneName" (#10077) 1 年之前
factories Feat/add-remote-file-upload-api (#9906) 1 年之前
fields Feat/add-remote-file-upload-api (#9906) 1 年之前
libs refactor: use dify_config to replace legacy usage of flask app's config (#9089) 1 年之前
migrations refactor(migration/model): update column types for workflow schema (#10160) 1 年之前
models fix(workflow model): ensure consistent timestamp updating (#10172) 1 年之前
schedule fix: Cannot find declaration to go to CLEAN_DAY_SETTING (#10157) 1 年之前
services Feat/add-remote-file-upload-api (#9906) 1 年之前
tasks Feat/new login (#8120) 1 年之前
templates Feat/new login (#8120) 1 年之前
tests feat: add gpustack model provider (#10158) 1 年之前
.dockerignore build: support Poetry for depencencies tool in api's Dockerfile (#5105) 1 年之前
.env.example chore: add tidb-on-qdrant configuration in env and docker-compose file (#10015) 1 年之前
Dockerfile fix(Dockerfile): conditionally install zlib1g based on architecture (#10118) 1 年之前
README.md fix: poetry installation in CI jobs (#9336) 1 年之前
app.py refactor: use dify_config to replace legacy usage of flask app's config (#9089) 1 年之前
app_factory.py refactor: use dify_config to replace legacy usage of flask app's config (#9089) 1 年之前
commands.py Added OceanBase as an option for the vector store in Dify (#10010) 1 年之前
poetry.lock fix: avoid unexpected error when create knowledge base with baidu vector database and wenxin embedding model (#10130) 1 年之前
poetry.toml build: initial support for poetry build tool (#4513) 1 年之前
pyproject.toml Added OceanBase as an option for the vector store in Dify (#10010) 1 年之前
pytest.ini feat: add models for gitee.ai (#9490) 1 年之前

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