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[fix] fix the bug that modify document name not effective (#10154)
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.idea fix nltk averaged_perceptron_tagger download and fix score limit is none (#7582) vor 1 Jahr
.vscode feat/enhance the multi-modal support (#8818) vor 1 Jahr
configs fix: avoid unexpected error when create knowledge base with baidu vector database and wenxin embedding model (#10130) vor 1 Jahr
constants nltk security issue and upgrade unstructured (#9558) vor 1 Jahr
contexts feat/enhance the multi-modal support (#8818) vor 1 Jahr
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core feat: add gpustack model provider (#10158) vor 1 Jahr
docker fix: use LOG_LEVEL for celery startup (#7628) vor 1 Jahr
events feat/enhance the multi-modal support (#8818) vor 1 Jahr
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factories Feat/add-remote-file-upload-api (#9906) vor 1 Jahr
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libs refactor: use dify_config to replace legacy usage of flask app's config (#9089) vor 1 Jahr
migrations refactor(migration/model): update column types for workflow schema (#10160) vor 1 Jahr
models fix(workflow model): ensure consistent timestamp updating (#10172) vor 1 Jahr
schedule fix: Cannot find declaration to go to CLEAN_DAY_SETTING (#10157) vor 1 Jahr
services [fix] fix the bug that modify document name not effective (#10154) vor 1 Jahr
tasks Feat/new login (#8120) vor 1 Jahr
templates Feat/new login (#8120) vor 1 Jahr
tests feat: add gpustack model provider (#10158) vor 1 Jahr
.dockerignore build: support Poetry for depencencies tool in api's Dockerfile (#5105) vor 1 Jahr
.env.example chore: add tidb-on-qdrant configuration in env and docker-compose file (#10015) vor 1 Jahr
Dockerfile fix(Dockerfile): conditionally install zlib1g based on architecture (#10118) vor 1 Jahr
README.md fix: poetry installation in CI jobs (#9336) vor 1 Jahr
app.py refactor: use dify_config to replace legacy usage of flask app's config (#9089) vor 1 Jahr
app_factory.py refactor: use dify_config to replace legacy usage of flask app's config (#9089) vor 1 Jahr
commands.py Added OceanBase as an option for the vector store in Dify (#10010) vor 1 Jahr
poetry.lock fix: avoid unexpected error when create knowledge base with baidu vector database and wenxin embedding model (#10130) vor 1 Jahr
poetry.toml build: initial support for poetry build tool (#4513) vor 1 Jahr
pyproject.toml Added OceanBase as an option for the vector store in Dify (#10010) vor 1 Jahr
pytest.ini feat: add models for gitee.ai (#9490) vor 1 Jahr

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