takatost 24af4b9313 fix: o1-series model encounters an error when the generate mode is blocking (#8363) пре 1 година
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.idea 7ae728a9a3 fix nltk averaged_perceptron_tagger download and fix score limit is none (#7582) пре 1 година
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configs 6613b8f2e0 chore: fix unnecessary string concatation in single line (#8311) пре 1 година
constants 2d7954c7da Fix variable typo (#8084) пре 1 година
contexts 3571292fbf chore(api): Introduce Ruff Formatter. (#7291) пре 1 година
controllers 6613b8f2e0 chore: fix unnecessary string concatation in single line (#8311) пре 1 година
core 24af4b9313 fix: o1-series model encounters an error when the generate mode is blocking (#8363) пре 1 година
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events 40fb4d16ef chore: refurbish Python code by applying refurb linter rules (#8296) пре 1 година
extensions 82f7875a52 feat: add langfuse sentry ignore error (#8353) пре 1 година
fields 80aa7c4019 feat: allow users to use the app icon as the answer icon (#7888) пре 1 година
libs 6613b8f2e0 chore: fix unnecessary string concatation in single line (#8311) пре 1 година
migrations 781d294f49 chore: cleanup pycodestyle E rules (#8269) пре 1 година
models 40fb4d16ef chore: refurbish Python code by applying refurb linter rules (#8296) пре 1 година
schedule 3571292fbf chore(api): Introduce Ruff Formatter. (#7291) пре 1 година
services 6613b8f2e0 chore: fix unnecessary string concatation in single line (#8311) пре 1 година
tasks 292220c596 chore: apply pep8-naming rules for naming convention (#8261) пре 1 година
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README.md fb5e3662d5 Chores: add missing profile for middleware docker compose cmd and fix ssrf-proxy doc link (#6372) пре 1 година
app.py 3571292fbf chore(api): Introduce Ruff Formatter. (#7291) пре 1 година
commands.py 6613b8f2e0 chore: fix unnecessary string concatation in single line (#8311) пре 1 година
poetry.lock d4985fb3aa Fix: Support Bedrock cross region inference [#8190](https://github.com/langgenius/dify/issues/8190) (#8317) пре 1 година
poetry.toml f62f71a81a build: initial support for poetry build tool (#4513) пре 1 година
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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
    
  3. 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
    
  2. Start Dify web service.

  3. Setup your application by visiting http://localhost:3000...

  4. If you need to debug local async processing, 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
    

The started celery app handles the async tasks, e.g. dataset importing and documents indexing.

Testing

  1. Install dependencies for both the backend and the test environment

    poetry install --with dev
    
  2. 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