Nevar pievienot vairāk kā 25 tēmas Tēmai ir jāsākas ar burtu vai ciparu, tā var saturēt domu zīmes ('-') un var būt līdz 35 simboliem gara.
-LAN- d2ce4960f1
chore(versioning): bump version to 0.9.0 (#8911)
pirms 1 gada
..
.idea fix nltk averaged_perceptron_tagger download and fix score limit is none (#7582) pirms 1 gada
.vscode chore: remove .idea and .vscode from root path (#7437) pirms 1 gada
configs chore(versioning): bump version to 0.9.0 (#8911) pirms 1 gada
constants feat: regenerate in `Chat`, `agent` and `Chatflow` app (#7661) pirms 1 gada
contexts chore(api): Introduce Ruff Formatter. (#7291) pirms 1 gada
controllers fix: fix the issue with the system model configuration update (#8923) pirms 1 gada
core fix: Compatible with special characters in pg full-text search. (#8921) pirms 1 gada
docker fix: use LOG_LEVEL for celery startup (#7628) pirms 1 gada
events chore: bump ruff to 0.6.8 for fixing violation in SIM910 (#8869) pirms 1 gada
extensions add storage error log (#8556) pirms 1 gada
fields external knowledge api (#8913) pirms 1 gada
libs chore: refurish python code by applying Pylint linter rules (#8322) pirms 1 gada
migrations external knowledge api (#8913) pirms 1 gada
models external knowledge api (#8913) pirms 1 gada
schedule external knowledge api (#8913) pirms 1 gada
services external knowledge api (#8913) pirms 1 gada
tasks external knowledge api (#8913) pirms 1 gada
templates feat: implement forgot password feature (#5534) pirms 1 gada
tests feat(api): add version comparison logic (#8902) pirms 1 gada
.dockerignore build: support Poetry for depencencies tool in api's Dockerfile (#5105) pirms 1 gada
.env.example feat: add min-connection and max-connection for pgvector (#8841) pirms 1 gada
Dockerfile fix: Version '2.6.2-2' for 'expat' was not found (#8182) pirms 1 gada
README.md Enhance Readme Documentation to Clarify the Importance of Celery Service (#8558) pirms 1 gada
app.py chore: remove windows platform timezone set (#8712) pirms 1 gada
commands.py fix: typos and improve naming conventions: (#8687) pirms 1 gada
poetry.lock external knowledge api (#8913) pirms 1 gada
poetry.toml build: initial support for poetry build tool (#4513) pirms 1 gada
pyproject.toml external knowledge api (#8913) pirms 1 gada

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
  1. 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
  1. Start Dify web service.
  2. Setup your application by visiting http://localhost:3000
  3. If you need to handle and debug the async tasks (e.g. dataset importing and documents indexing), 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

Testing

  1. Install dependencies for both the backend and the test environment
   poetry install --with dev
  1. 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