Workflow KnowledgeFS retrieval nodes ran through `retrieval-tests`, which never wrote an AnswerTrace or an overview event, so their queries were missing from the retrieval history and from the space overview; the history also only ever showed each member their own traces. - AnswerTrace gains a `source` (retrieval_test | workflow | service_api | agent | mcp; migration 0051_answer_trace_source) derived from the Capability v2 caller kind. The retrieval-tests route records one trace per run (stages, evidence bundle, profile metadata), returns its id as `answerTraceId`, and the workflow node's failed-retrieval capture attaches to that trace instead of creating a second record. - The quality trace list exposes and filters by `source`; traces from other caller kinds are visible to any current reader of the space, and counts and scores fall back to the evidence embedded in the trace when no bundle row exists. - Overview accounting: retrieval-tests and Research tasks now emit `query.requested`, and the Research job state machine emits `query.completed` / `query.failed` on terminal stages (wired for both the in-process gateway and the durable runtime), so query volume, answer rate and outcomes include every caller. Activity details keep `source` and `taskKind`. - Console and service trace routes accept a `source` filter; the retrieval test page shows a source badge and an all / retrieval tests / workflow filter, with translations for every locale. Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_015zw5G5SX3HmVfnZof6YWAc |
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| .. | ||
| .idea | ||
| .vscode | ||
| clients | ||
| commands | ||
| configs | ||
| constants | ||
| context | ||
| contexts | ||
| controllers | ||
| core | ||
| dev | ||
| docker | ||
| enterprise | ||
| enums | ||
| events | ||
| extensions | ||
| factories | ||
| fields | ||
| libs | ||
| machinery | ||
| migrations | ||
| models | ||
| openapi/markdown | ||
| providers | ||
| repositories | ||
| schedule | ||
| services | ||
| tasks | ||
| templates | ||
| tests | ||
| .dockerignore | ||
| .env.example | ||
| .importlinter | ||
| .ruff.toml | ||
| AGENTS.md | ||
| app_factory.py | ||
| app.py | ||
| celery_entrypoint.py | ||
| celery_healthcheck.py | ||
| cnt_base.sh | ||
| conftest.py | ||
| dify_app.py | ||
| Dockerfile | ||
| Dockerfile.dockerignore | ||
| gunicorn.conf.py | ||
| knowledge-fs-contract.lock.json | ||
| knowledge-fs-product-operation-gaps.json | ||
| knowledge-fs-product-operations.json | ||
| pyproject.toml | ||
| pyrefly-local-excludes.txt | ||
| pytest.ini | ||
| README.md | ||
| uv.lock | ||
Dify Backend API
Setup and Run
Important
In the v1.3.0 release,
poetryhas been replaced withuvas the package manager for Dify API backend service.
uv and pnpm are required to run the setup and development commands below.
Using scripts (recommended)
The scripts resolve paths relative to their location, so you can run them from anywhere.
-
Run setup (copies env files and installs dependencies).
./dev/setup -
Review
api/.env,web/.env.local, anddocker/middleware.envvalues (see theSECRET_KEYnote below). -
Start middleware (PostgreSQL/Redis/Weaviate).
./dev/start-docker-compose -
Start backend (runs migrations first).
./dev/start-api -
Start Dify web service.
./dev/start-web./dev/setupand./dev/start-webinstall JavaScript dependencies through the repository root workspace, so you do not need a separatecd web && pnpm installstep. -
Set up your application by visiting
http://localhost:3000. -
Start the worker service (async and scheduler tasks, runs from
api)../dev/start-worker -
Start Celery Beat when scheduled tasks are needed. This is required when
KNOWLEDGE_FS_LIFECYCLE_WORKER_ENABLED=trueso provisioning and deletion outbox commands are dispatched to the lifecycle worker../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_KEYin the.envfile.bash for Linux
sed -i "/^SECRET_KEY=/c\\SECRET_KEY=$(openssl rand -base64 42)" .envbash for Mac
secret_key=$(openssl rand -base64 42) sed -i '' "/^SECRET_KEY=/c\\ SECRET_KEY=${secret_key}" .env
Testing
-
Install dependencies for both the backend and the test environment
cd api uv sync --group dev -
Run the tests locally with mocked system environment variables in
tool.pytest_envsection inpyproject.toml, more can check Claude.mdcd api 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