
When generating JSON schema using an LLM in the structured output feature, models may occasionally return invalid JSON, which prevents clients from correctly parsing the response and can lead to UI breakage. This commit addresses the issue by introducing `json_repair` to automatically fix invalid JSON strings returned by the LLM, ensuring smoother functionality and better client-side handling of structured outputs. Co-authored-by: lizb <lizb@sugon.com>
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