Quick reference

• The offical OPEA docker images

• Maintained by: openEuler CloudNative SIG

• Where to get help: openEuler CloudNative SIG, openEuler

OPEA | openEuler

Current OPEA docker images are built on the openEuler⁠. This repository is free to use and exempted from per-user rate limits.

OPEA is an open platform project that lets you create open, multi-provider, robust, and composable GenAI solutions that harness the best innovation across the ecosystem.

The OPEA platform includes:

• Detailed framework of composable building blocks for state-of-the-art generative AI systems including LLMs, data stores, and prompt engines

• Architectural blueprints of retrieval-augmented generative AI component stack structure and end-to-end workflows

• A four-step assessment for grading generative AI systems around performance, features, trustworthiness, and enterprise-grade readiness

Read more about OPEA at opea.dev and explore the OPEA technical documentation at opea-project.github.io

Supported tags and respective Dockerfile links

The tag of each DocSum docker image is consist of the version of DocSum and the version of basic image. The details are as follows

Tags	Currently	Architectures
1.0-oe2403lts	DocSum 1.0 on openEuler 24.03-LTS	amd64
1.2-oe2403lts	DocSum 1.2 on openEuler 24.03-LTS	amd64

Usage

The DocSum service can be effortlessly deployed on either Intel Gaudi2 or Intel Xeon Scalable Processor.

Currently we support two ways of deploying DocSum services with docker compose:

1. Start services using the docker image on docker hub:

   docker pull openeuler/docsum:latest
   

2. Start services using the docker images built from source.

Required Models

Default model is "Intel/neural-chat-7b-v3-3". Change "LLM_MODEL_ID" environment variable in commands below if you want to use another model.

Quick Start: 1.Setup Environment Variable

To set up environment variables for deploying DocSum services, follow these steps:

1. Set the required environment variables:

   # Example: host_ip="192.168.1.1"
   export host_ip="External_Public_IP"
   # Example: no_proxy="localhost, 127.0.0.1, 192.168.1.1"
   export no_proxy="Your_No_Proxy"
   export HUGGINGFACEHUB_API_TOKEN="Your_Huggingface_API_Token"
   

2. If you are in a proxy environment, also set the proxy-related environment variables:

   export http_proxy="Your_HTTP_Proxy"
   export https_proxy="Your_HTTPs_Proxy"
   

3. Set up other environment variables:

   Get set_env.sh here: set_env.sh

   source set_env.sh
   

Quick Start: 2.Run Docker Compose

Get compose.yml here: compose.yml

docker compose -f compose.yml up -d

It will automatically download the docker image on docker hub:

docker pull openeuler/docsum:latest
docker pull openeuler/docsum-ui:latest

QuickStart: 3.Consume the DocSum Service

1. Use cURL command on terminal

   curl -X POST http://${host_ip}:8888/v1/docsum \
        -H "Content-Type: application/json" \
        -d '{"type": "text", "messages": "Text Embeddings Inference (TEI) is a toolkit for deploying and serving open source text embeddings and sequence classification models. TEI enables high-performance extraction for the most popular models, including FlagEmbedding, Ember, GTE and E5."}'
   
   # Use English mode (default).
   curl http://${host_ip}:8888/v1/docsum \
       -H "Content-Type: multipart/form-data" \
       -F "type=text" \
       -F "messages=Text Embeddings Inference (TEI) is a toolkit for deploying and serving open source text embeddings and sequence classification models. TEI enables high-performance extraction for the most popular models, including FlagEmbedding, Ember, GTE and E5." \
       -F "max_tokens=32" \
       -F "language=en" \
       -F "stream=true"
   
   # Use Chinese mode.
   curl http://${host_ip}:8888/v1/docsum \
       -H "Content-Type: multipart/form-data" \
       -F "type=text" \
       -F "messages=2024年9月26日，北京——今日，英特尔正式发布英特尔® 至强® 6性能核处理器（代号Granite Rapids），为AI、数据分析、科学计算等计算密集型业务提供卓越性能。" \
       -F "max_tokens=32" \
       -F "language=zh" \
       -F "stream=true"
   
   # Upload file
   curl http://${host_ip}:8888/v1/docsum \
      -H "Content-Type: multipart/form-data" \
      -F "type=text" \
      -F "messages=" \
      -F "files=@/path to your file (.txt, .docx, .pdf)" \
      -F "max_tokens=32" \
      -F "language=en" \
      -F "stream=true"
   

Audio and Video file uploads are not supported in docsum with curl request, please use the Gradio-UI.

Audio:

curl -X POST http://${host_ip}:8888/v1/docsum \
   -H "Content-Type: application/json" \
   -d '{"type": "audio", "messages": "UklGRigAAABXQVZFZm10IBIAAAABAAEARKwAAIhYAQACABAAAABkYXRhAgAAAAEA"}'

curl http://${host_ip}:8888/v1/docsum \
   -H "Content-Type: multipart/form-data" \
   -F "type=audio" \
   -F "messages=UklGRigAAABXQVZFZm10IBIAAAABAAEARKwAAIhYAQACABAAAABkYXRhAgAAAAEA" \
   -F "max_tokens=32" \
   -F "language=en" \
   -F "stream=true"

Video:

curl -X POST http://${host_ip}:8888/v1/docsum \
   -H "Content-Type: application/json" \
   -d '{"type": "video", "messages": "convert your video to base64 data type"}'

curl http://${host_ip}:8888/v1/docsum \
   -H "Content-Type: multipart/form-data" \
   -F "type=video" \
   -F "messages=convert your video to base64 data type" \
   -F "max_tokens=32" \
   -F "language=en" \
   -F "stream=true"

2. Access via frontend

   To access the frontend, open the following URL in your browser: http://{host_ip}:5173.

   By default, the UI runs on port 5173 internally.