Example Edge Craft Retrieval-Augmented Generation Deployment on Intel® Arc® Platform

This document outlines the deployment process for Edge Craft Retrieval-Augmented Generation service on Intel Arc server. This example includes the following sections:

• EdgeCraftRAG Quick Start Deployment: Demonstrates how to quickly deploy a Edge Craft Retrieval-Augmented Generation service/pipeline on Intel® Arc® platform.

• EdgeCraftRAG Docker Compose Files: Describes some example deployments and their docker compose files.

• EdgeCraftRAG Service Configuration: Describes the service and possible configuration changes.

EdgeCraftRAG Quick Start Deployment

This section describes how to quickly deploy and test the EdgeCraftRAG service manually on Intel® Arc® platform. The basic steps are:

1. Prerequisites

2. Access the Code

3. Generate a HuggingFace Access Token

4. Configure the Deployment Environment

5. Deploy the Service Using Docker Compose

6. Check the Deployment Status

7. Test the Pipeline

8. Cleanup the Deployment

Prerequisites

EC-RAG supports vLLM deployment(default method) and local OpenVINO deployment for Intel Arc GPU. Prerequisites are shown as below:
Hardware: Intel Arc A770
OS: Ubuntu Server 22.04.1 or newer (at least 6.2 LTS kernel)
Driver & libraries: please refer to Installing Client GPUs for detailed driver & libraries setup

Below steps are based on vLLM as inference engine, if you want to choose OpenVINO, please refer to OpenVINO Local Inference

Access the Code

Clone the GenAIExample repository and access the EdgeCraftRAG Intel® Arc® platform Docker Compose files and supporting scripts:

git clone https://github.com/opea-project/GenAIExamples.git
cd GenAIExamples/EdgeCraftRAG/docker_compose/intel/gpu/arc/

Checkout a released version, such as v1.3:

git checkout v1.3

Generate a HuggingFace Access Token

Some HuggingFace resources, such as some models, are only accessible if you have an access token. If you do not already have a HuggingFace access token, you can create one by first creating an account by following the steps provided at HuggingFace and then generating a user access token.

Configure the Deployment Environment

Below steps are for single Intel Arc GPU inference, if you want to setup multi Intel Arc GPUs inference, please refer to Multi-ARC Setup To set up environment variables for deploying EdgeCraftRAG service, source the set_env.sh script in this directory:

source set_env.sh

For more advanced env variables and configurations, please refer to Prepare env variables for vLLM deployment

Deploy the Service Using Docker Compose

To deploy the EdgeCraftRAG service, execute the docker compose up command with the appropriate arguments. For a default deployment, execute:

docker compose up -d

The EdgeCraftRAG docker images should automatically be downloaded from the OPEA registry and deployed on the Intel® Arc® Platform

Check the Deployment Status

After running docker compose, check if all the containers launched via docker compose have started:

docker ps -a

For the default deployment, the following 5 containers should be running:

Test the Pipeline

Once the EdgeCraftRAG service are running, test the pipeline using the following command:

curl http://${host_ip}:16011/v1/chatqna -H 'Content-Type: application/json' -d '{
     "messages":"What is the test id?","max_tokens":5 }'

For detailed operations on UI and EC-RAG settings, please refer to Explore_Edge_Craft_RAG

Note The value of host_ip was set using the set_env.sh script and can be found in the .env file.

Cleanup the Deployment

To stop the containers associated with the deployment, execute the following command:

docker compose -f compose.yaml down

All the EdgeCraftRAG containers will be stopped and then removed on completion of the “down” command.

EdgeCraftRAG Docker Compose Files

The compose.yaml is default compose file using tgi as serving framework

Service Name	Image Name
etcd	quay.io/coreos/etcd:v3.5.5
minio	minio/minio:RELEASE.2023-03-20T20-16-18Z
milvus-standalone	milvusdb/milvus:v2.4.6
edgecraftrag-server	opea/edgecraftrag-server:latest
edgecraftrag-ui	opea/edgecraftrag-ui:latest
ecrag	opea/edgecraftrag:latest

EdgeCraftRAG Service Configuration

The table provides a comprehensive overview of the EdgeCraftRAG service utilized across various deployments as illustrated in the example Docker Compose files. Each row in the table represents a distinct service, detailing its possible images used to enable it and a concise description of its function within the deployment architecture.

Service Name	Possible Image Names	Optional	Description
etcd	quay.io/coreos/etcd:v3.5.5	No	Provides distributed key-value storage for service discovery and configuration management.
minio	minio/minio:RELEASE.2023-03-20T20-16-18Z	No	Provides object storage services for storing documents and model files.
milvus-standalone	milvusdb/milvus:v2.4.6	No	Provides vector database capabilities for managing embeddings and similarity search.
edgecraftrag-server	opea/edgecraftrag-server:latest	No	Serves as the backend for the EdgeCraftRAG service, with variations depending on the deployment.
edgecraftrag-ui	opea/edgecraftrag-ui:latest	No	Provides the user interface for the EdgeCraftRAG service.
ecrag	opea/edgecraftrag:latest	No	Acts as a reverse proxy, managing traffic between the UI and backend services.