ipex-llm/docs/readthedocs/source/doc/LLM/Quickstart/dify_quickstart.md

Run Dify on Intel GPU

We recommend start the project following Dify docs

Server Deployment

Clone code

git clone https://github.com/langgenius/dify.git

Installation of the basic environment

Server startup requires Python 3.10.x. Anaconda is recommended to create and manage python environment.

conda create -n dify python=3.10
conda activate dify
cd api
cp .env.example .env
openssl rand -base64 42
sed -i 's/SECRET_KEY=.*/SECRET_KEY=<your_value>/' .env
pip install -r requirements.txt

openssl is pre-installed with Ubuntu 22.04.

Prepare for redis, postgres, node and npm.

• Install Redis by sudo apt-get install redis-server. Refer to page to setup the Redis environment, including password, demon, etc.
• install postgres by sudo apt-get install postgres and sudo apt-get install postgres-client. Setup username, create a database and grant previlidge according to page
• install npm and node by prebuilt installer, binaries or packet manager according to nodejs page

Note that set redis and postgres related environment in .env under dify/api/ and set web related environment variable in .env.local under dify/web.

Example in dify/api/.env and modify settings in redis and postgres.

REDIS_HOST=localhost
REDIS_PORT=6379
REDIS_USERNAME=
REDIS_PASSWORD=xxxx
REDIS_DB=0
DB_USERNAME=xxxx
DB_PASSWORD=xxxx
DB_HOST=localhost
DB_PORT=5432
DB_DATABASE=dify

Example .env.local

# For production release, change this to PRODUCTION
NEXT_PUBLIC_DEPLOY_ENV=DEVELOPMENT
NEXT_PUBLIC_EDITION=SELF_HOSTED
NEXT_PUBLIC_API_PREFIX=http://localhost:5001/console/api
NEXT_PUBLIC_PUBLIC_API_PREFIX=http://localhost:5001/api
NEXT_PUBLIC_SENTRY_DSN=

Install Ollama

Please install ollama refer to ollama quick start. Ensure that ollama could run successfully on Intel GPU.

Start service

1. Open the terminal and set export no_proxy=localhost,127.0.0.1

flask db upgrade
flask run --host 0.0.0.0 --port=5001 --debug

You will see log like below if successfully start the service.

INFO:werkzeug:
* Running on all addresses (0.0.0.0)
* Running on http://127.0.0.1:5001
* Running on http://10.239.44.83:5001
INFO:werkzeug:Press CTRL+C to quit
INFO:werkzeug: * Restarting with stat
WARNING:werkzeug: * Debugger is active!
INFO:werkzeug: * Debugger PIN: 227-697-894

2. Open another terminal and also set export no_proxy=localhost,127.0.0.1. If Linux system, use the command below.

celery -A app.celery worker -P gevent -c 1 -Q dataset,generation,mail --loglevel INFO 

If windows system, use the command below.

celery -A app.celery worker -P solo --without-gossip --without-mingle -Q dataset,generation,mail --loglevel INFO

3. Open another terminal and also set export no_proxy=localhost,127.0.0.1. Run the commands below to start the front-end service.

cd web
npm install
npm run build
npm run start

Example: RAG

See the demo of running dify with Ollama on an Intel Core Ultra laptop below.

1. Set up the environment export no_proxy=localhost,127.0.0.1 and start Ollama locally by ollama serve.
2. Open http://localhost:3000 to view dify and change the model provider in setting including both LLM and embedding. For example, choose ollama.

3. Use text summarization workflow template from studio.

4. Add knowledge base and specify which type of embedding model to use.

5. Enter input and start to generate. You could find retrieval results and answers generated on the right.
6. Add knowledge base and specify which type of embedding model to use.