Deploying openEuler Intelligence Based on the openGauss Vector Database
This document describes how to deploy openEuler Intelligence and use the openGauss DataVec vector database as the corpus for the RAG engine.
openEuler Intelligence Deployment
Environment Requirements
Software Requirements
| Type | Version Requirement | Description |
|---|---|---|
| Operating system | openEuler 22.03 LTS or later | None |
| K3s | >= v1.30.2, with the Traefik Ingress tool | K3s provides a lightweight Kubernetes cluster that is easy to deploy and manage |
| Helm | >= v3.15.3 | Helm is a package management tool for Kubernetes, used to quickly install, upgrade, and uninstall openEuler Intelligence services |
| python | >=3.9.9 | Python 3.9.9 or later provides the runtime environment for downloading and installing models |
Hardware Specifications
| Hardware Resources | Minimum Configuration | Recommended Configuration |
|---|---|---|
| CPU | 4 cores | 16 cores or above |
| RAM | 4 GB | 64 GB |
| Storage | 32 GB | 64 GB |
| Large Model Name | deepseek-llm-7b-chat | DeepSeek-R1-Llama-8B |
| GPU Memory (GPU) | NVIDIA RTX A4000 8GB | NVIDIA A100 80GB * 2 |
Key Notes:
- In a CPU-only environment, it is recommended to implement the functionality by calling the OpenAI API or using the built-in model deployment method.
- If a k8s cluster environment is used, there is no need to install k3s separately; version >= 1.28 is required.
Preparing Resources
- Online mode
git clone https://gitee.com/openeuler/euler-copilot-framework.git -b dev- Offline mode
Obtain the openEuler Intelligence project
Download the archive from the openEuler Intelligence official repository, upload it to the server, and extract it.bashunzip euler-copilot-framework.tar -d <YourPath>Obtain images, models, and toolkits
Refer to the resource list in section 1.2 and download the required images, models, and toolkits from the openEuler Intelligence resource download address.
Ensure that the following directories have been created on the server, and place the downloaded resources into the corresponding folders:
/home/eulercopilot/ ├── images/ # Store image files. ├── models/ # Store model files. └── tools/ # Store the toolkit.
Online and offline modes differ only in the resource preparation phase; all subsequent steps are identical.
Run the Deployment Script
# Switch to the deployment script directory.
cd euler-copilot-framework/deploy/scripts
# Add executable permissions to the script files.
chmod -R +x ./*
# Run the deployment script.
bash deploy.shStart Deploying Services
After running the deployment script, the following deployment menu list appears. We will deploy this project step by step manually so that you can clearly understand the implementation details of each stage.
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Main Deployment Menu
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0) One-click automatic deployment
1) Manual step-by-step deployment
2) Restart services
3) Uninstall all components and clear data
4) Exit
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Enter the option number (0-9): 1# Enter the option number (0-9) to deploy step by step
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Step-by-Step Manual Deployment Menu
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1) Run environment check script
2) Install k3s and Helm
3) Install Ollama
4) Deploy Deepseek model
5) Deploy Embedding model
6) Install database
7) Install AuthHub
8) Install EulerCopilot
9) Return to main menu
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Enter the option number (0-9):Here you only need to make sure that each step completes successfully without any error messages before moving on to the next stage. If the status of all the following service pods is normal, you can start your journey with openEuler Intelligence.
[root@localhost euler_copilot]# kubectl get pods -A
NAMESPACE NAME READY STATUS RESTARTS AGE
euler-copilot authhub-backend-deploy-9f46b886b-c25nl 1/1 Running 0 29h
euler-copilot authhub-web-deploy-7957555974-7fgsx 1/1 Running 0 29h
euler-copilot framework-deploy-cffdfc75f-pvv4c 1/1 Running 0 9m21s
euler-copilot minio-deploy-746786cf66-6rnwt 1/1 Running 0 29h
euler-copilot mongo-deploy-c89868d7d-5nczl 1/1 Running 0 29h
euler-copilot mysql-deploy-7c6b8997cf-xrqjp 1/1 Running 0 29h
euler-copilot opengauss-deploy-968d7848d-vqgjw 1/1 Running 0 11m
euler-copilot rag-deploy-79ddfd786d-rtzw9 1/1 Running 0 38s
euler-copilot rag-web-deploy-7df6d6b66d-bkh5v 1/1 Running 0 19h
euler-copilot redis-deploy-7fb5b67844-kv9mz 1/1 Running 0 29h
euler-copilot web-deploy-59dcfb78f7-cd54l 1/1 Running 0 19h
kube-system coredns-576bfc4dc7-9v7dm 1/1 Running 0 29h
kube-system helm-install-traefik-crd-wwv9f 0/1 Completed 0 19h
kube-system helm-install-traefik-dgszg 0/1 Completed 0 19h
kube-system local-path-provisioner-6795b5f9d8-msz9p 1/1 Running 0 29h
kube-system metrics-server-557ff575fb-grbm6 1/1 Running 0 29h
kube-system svclb-traefik-be11ef18-qzv8d 2/2 Running 0 29h
kube-system traefik-5fb479b77-pcbgr 1/1 Running 0 29hNote that if you have a local ollama service and have pulled the embedding and chat large models, you can skip steps 3-5. After installing the openEuler Intelligence service, simply modify the model configuration. The modification steps and content are as follows.
cd euler-copilot-framework/deploy/chart/euler-copilotvim values.yaml
After modifying the model name as shown in the figure above, update the openEuler Intelligence deployment:
helm upgrade euler-copilot -n euler-copilot .For specific operations, see From Data to Intelligence: RAG Architecture in Practice with openGauss + openEuler Intelligence