
The `QuestionClassifierNode` class extends `LLMNode`, meaning that, per the Liskov Substitution Principle, `QuestionClassifierNodeData` **SHOULD** be compatible in contexts where `LLMNodeData` is expected. However, the absence of the `structured_output_enabled` attribute violates this principle, causing `QuestionClassifierNode` to fail during execution. This commit implements a quick and temporary workaround. A proper resolution would involve refactoring to decouple `QuestionClassifierNode` from `LLMNode` to address the underlying design issue. Fixes #20725.
Dify Backend API
Usage
Important
In the v1.3.0 release,
poetry
has been replaced withuv
as the package manager for Dify API backend service.
-
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
-
Copy
.env.example
to.env
cp .env.example .env
-
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
-
Create environment.
Dify API service uses UV to manage dependencies. First, you need to add the uv package manager, if you don't have it already.
pip install uv # Or on macOS brew install uv
-
Install dependencies
uv sync --dev
-
Run migrate
Before the first launch, migrate the database to the latest version.
uv run flask db upgrade
-
Start backend
uv run flask run --host 0.0.0.0 --port=5001 --debug
-
Start Dify web service.
-
Setup your application by visiting
http://localhost:3000
. -
If you need to handle and debug the async tasks (e.g. dataset importing and documents indexing), please start the worker service.
uv run celery -A app.celery worker -P gevent -c 1 --loglevel INFO -Q dataset,generation,mail,ops_trace,app_deletion
Testing
-
Install dependencies for both the backend and the test environment
uv sync --dev
-
Run the tests locally with mocked system environment variables in
tool.pytest_env
section inpyproject.toml
uv run -P api bash dev/pytest/pytest_all_tests.sh