Files
dify/api
Charles YaoandClaude Opus 4.8 2a574c7f74 fix(dify-builder): ground built workflow nodes to the user-selected model
Build wrote the Builder's session/cognition model (e.g. deepseek) into every
generated node's model block, ignoring the model the user picked at resource
confirmation (e.g. gpt). _ground always used the generation model, and the
selected model resource only reached the plan as label text (bind_resources) —
so a workflow got a model with no runtime credentials -> ModelNotExistError ->
instant test failure.

Thread the selected resource_ids from resource_selection through
agent.build_nodes into build.build_nodes, resolve the selected MODEL resource
(id is `{provider}/{name}`) via _selected_workflow_model, and ground node model
blocks to it — falling back to the session/generator model when the user
selected no model. The generator still runs on the session model (cognition);
only the built workflow's runtime model follows the user's choice.

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
2026-09-10 19:18:36 -07:00
..
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2026-09-10 09:06:31 +00:00

Dify Backend API

Setup and Run

Important

In the v1.3.0 release, poetry has been replaced with uv as the package manager for Dify API backend service.

uv and pnpm are required to run the setup and development commands below.

The scripts resolve paths relative to their location, so you can run them from anywhere.

  1. Run setup (copies env files and installs dependencies).

    ./dev/setup
    
  2. Review api/.env, web/.env.local, and docker/middleware.env values (see the SECRET_KEY note below).

  3. Start middleware (PostgreSQL/Redis/Weaviate).

    ./dev/start-docker-compose
    
  4. Start backend (runs migrations first).

    ./dev/start-api
    
  5. Start Dify web service.

    ./dev/start-web
    

    ./dev/setup and ./dev/start-web install JavaScript dependencies through the repository root workspace, so you do not need a separate cd web && pnpm install step.

  6. Set up your application by visiting http://localhost:3000.

  7. Start the worker service (async and scheduler tasks, runs from api).

    ./dev/start-worker
    
  8. Optional: start Celery Beat (scheduled tasks).

    ./dev/start-beat
    

Environment notes

Important

When the frontend and backend run on different subdomains, set COOKIE_DOMAIN to the site’s top-level domain (e.g., example.com). The frontend and backend must be under the same top-level domain in order to share authentication cookies.

  • Generate a SECRET_KEY in the .env file.

    bash for Linux

    sed -i "/^SECRET_KEY=/c\\SECRET_KEY=$(openssl rand -base64 42)" .env
    

    bash for Mac

    secret_key=$(openssl rand -base64 42)
    sed -i '' "/^SECRET_KEY=/c\\
    SECRET_KEY=${secret_key}" .env
    

Testing

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

    cd api
    uv sync --group dev
    
  2. Run the tests locally with mocked system environment variables in tool.pytest_env section in pyproject.toml, more can check Claude.md

    Continue in the api directory from the previous step.

    uv run pytest                           # Run all tests
    uv run pytest tests/unit_tests/         # Unit tests only
    uv run pytest tests/integration_tests/  # Integration tests
    
    # Code quality
    ../dev/reformat              # Run all formatters and linters
    uv run ruff check --fix ./   # Fix linting issues
    uv run ruff format ./        # Format code
    uv run pyrefly check         # Type checking
    

Generate TS stub

uv run dev/generate_swagger_specs.py --output-dir openapi

use https://jsontotable.org/openapi-to-typescript to convert to typescript