Witty Assistant Web Deployment Guide ​

Version Information Current Version: v0.10.1 Release Date: January 23, 2026

Product Overview ​

Witty Assistant Web is an intelligent Q&A tool. The web client of Witty Assistant provides an AI-driven intelligent Q&A interaction interface, supports multiple LLM backends, integrates the MCP protocol. Using the Witty Assistant web client can solve the convenience issue of operating system knowledge acquisition and empower developers and operations personnel with OS domain models. As an operating system knowledge acquisition tool, it supports intelligent Q&A, knowledge base management, agent applications and workflow applications, as well as semantic interface upload. Combined with agent task planning capabilities, it significantly lowers the barrier to developing and using operating system features.

This guide provides deployment instructions for the Witty Assistant intelligent assistant system based on automated scripts, supporting both one-click automatic deployment and manual step-by-step deployment methods.

Component Introduction ​

System Requirements ​

Software Requirements ​

Hardware Specifications ​

Deployment Prerequisites Description:

1. For pure CPU environments, it is recommended to call cloud service models via API or deploy quantized local models
2. If a Kubernetes cluster (version ≥1.28) already exists, the K3s installation step can be skipped

Deployment Architecture ​

Quick Start ​

Obtaining Deployment Scripts ​

Download the latest repository from the Official Git Repository:

# 1. Execute in network-connected environment
cd /home
git clone https://atomgit.com/openeuler/euler-copilot-framework.git -b release-0.10.1

# 2. Execute in offline environment
# Switch to release-0.10.1 branch, download ZIP and upload to target server
unzip euler-copilot-framework.tar -d /home
cd /home/euler-copilot-framework/deploy/scripts

Obtaining Resources (Execute in Offline Environment) ​

Manually download the latest images and tools from the Witty Assistant Resource Address:

Resource List:

# In a network-connected environment, you can execute scripts to save images and transfer to target server
# Enter the script directory
cd /home/euler-copilot-framework/deploy/scripts/9-other-script/

# Execute image save script (specify version and architecture)
bash save_images.sh --version 0.10.1 --arch x86

# Images will be saved to /home/eulercopilot/images, use SCP to transfer image files to target server
scp -r /home/eulercopilot/images/* root@target-server:/home/eulercopilot/images/

Deployment Execution ​

One-Click Deployment ​

• One-click automatic deployment mode is mainly suitable for users without pre-deployed LLM resources:

  bash

  bash deploy.sh

  text

  ==============================
  Witty Assistant Deployment System
  ==============================
  0) One-click Automatic Deployment Mode - Fully automatic installation (recommended for beginners)
  1) Manual Step-by-Step Deployment Mode 
  2) Service Restart Management
  3) System Uninstallation and Cleanup
  4) Exit Deployment Program
  ==============================
  Please select deployment mode [0-4]: 0

• Automatically installs and configures all necessary components:

  • Automatically deploys k3s, helm, Ollama
  • Downloads and deploys Deepseek LLM (deepseek-llm-7b-chat)
  • Downloads and deploys Embedding model (bge-m3)
  • Installs databases, Authhub authentication service, and Witty Assistant application
  • Can run in pure CPU environment; if GPU resources are available, GPU drivers must be pre-installed, and acceleration will be automatically utilized
  • Automatically configures model interfaces for Witty Assistant calls

Step-by-Step Deployment ​

• Step-by-step deployment mode is mainly suitable for scenarios where LLM interfaces and embedding model interfaces already exist.

• Prepare model services in advance:

  • Ensure LLM service is deployed or accessible
  • Ensure embedding model service is deployed or accessible
  • Edit the values.yaml file before deployment, pre-fill model service information, such as each model's API endpoint, key, name, etc.
  yaml

  # Configuration example:
  models:
  # LLM for Q&A; requires OpenAI-compatible API
  answer:
     # [Required] API endpoint URL (check API provider documentation to confirm if "v1" suffix is needed)
     endpoint: https://$ip:$port/v1
     # [Required] API key; default is empty
     key: sk-123456
     # [Required] Model name
     name: qwen3-32b
     # [Required] Model maximum context length; recommended >=8192
     ctxLength: 8192
     # Model maximum output length, recommended >=2048
     maxTokens: 8192
  # Model for function calling; specific inference frameworks recommended
  functionCall:
     # Inference framework type, default is ollama
     # Available framework types: ["vllm", "sglang", "ollama", "openai"]
     backend: openai
     # [Required] Model endpoint; check API provider documentation to confirm if "v1" suffix is needed
     # Leave empty to use same configuration as Q&A model
     endpoint: https://$ip:$port/v1
     # API key; leave empty to use same configuration as Q&A model
     key: sk-123456
     # Model name; leave empty to use same configuration as Q&A model
     name: qwen3-32b
     # Model maximum context length; leave empty to use same configuration as Q&A model
     ctxLength: 8192
     # Model maximum output length; leave empty to use same configuration as Q&A model
     maxTokens: 8192
  # Model for data embedding
  embedding:
     # Inference framework type, default is openai
     # [Required] Embedding API type: ["openai", "mindie"]
     type: openai
     # [Required] Embedding URL (requires "v1" suffix)
     endpoint: https://$ip:$port/v1
     # [Required] Embedding model API key
     key: sk-123456
     # [Required] Embedding model name
     name: BAAI/bge-m3
  # Model for reranking rag retrieval results
  reranker:
     # [Required] Reranker interface type: ["guijiliudong", "algorithm",
     # "bailian", "v1lm", "assecend"]
     type: guijiliudong
     # [Required] Reranker URL (requires "/v1/rerank" suffix)
     endpoint: https://api.siliconflow.cn/v1/rerank
     # [Required] Reranker model API Key
     key: sk-123456
     # [Required] Reranker model name
     name: BAAI/bge-reranker-v2-m3
     # [Required] Reranker model icon URL
     icon: https://sf-maas-uat-prod.oss-cn-shanghai.aliyuncs.com/Model_LOGO/BAAI.svg

  bash

  # Execute script
  bash deploy.sh

  bash

  # Select 1 for manual deployment
  ==============================
        Main Deployment Menu
  ==============================
  0) One-click Automatic Deployment 
  1) Manual Step-by-Step Deployment - Existing Model Services
  2) Restart Services
  3) Uninstall All Components and Clear Data
  4) Exit Program
  ==============================
  Please enter option number (0-3): 1

  bash

  # Skip steps 3, 4, 5 and execute sequentially
  ==============================
        Manual Step-by-Step Deployment Menu
  ==============================
  1) Execute Environment Check Script
  2) Install k3s and helm
  3) Install Ollama - Can be skipped if model services already exist
  4) Deploy Deepseek Model - Can be skipped if model services already exist
  5) Deploy Embedding Model - Can be skipped if model services already exist
  6) Install Databases
  7) Install AuthHub
  8) Install Witty Assistant
  9) Return to Main Menu
  ==============================
  Please enter option number (1-9):

Restart Operations ​

• Select the service to restart and press Enter to execute

  bash

  ==============================
        Service Restart Menu
  ==============================
  List of restartable services:
  1) authhub-backend
  2) authhub
  3) framework
  4) minio
  5) mongo
  6) mysql
  7) opengauss
  8) rag
  9) rag-web
  10) redis
  11) web
  12) Return to Main Menu
  ==============================
  Please enter the service number to restart (1-12):

Uninstallation Operations ​

• Uninstallation only uninstalls services and clears data, does not uninstall deployment tools

  bash

  ==============================
        Main Deployment Menu
  ==============================
  0) One-click Automatic Deployment
  1) Manual Step-by-Step Deployment
  2) Uninstall All Components and Clear Data
  3) Exit Program
  ==============================
  Please enter option number (0-3): 2

Operations Commands ​

• Use the following commands for service and image maintenance

  bash

  # View service status
  kubectl get pod -n euler-copilot

  bash

  # View component logs
  kubectl logs $pod_name -n euler-copilot

  bash

  # Modify LLM configuration
  cd /home/euler-copilot-framework/deploy/chart/euler_copilot
  vim values.yaml
  helm upgrade euler-copilot -n euler-copilot .

  bash

  # Check cluster events
  kubectl get events -n euler-copilot

  bash

  # View images
  k3s crictl images
  # Remove image
  k3s crictl rmi $image_id
  # Import image
  k3s ctr image import $image_tar

Verify Installation ​

Congratulations, Witty Assistant has been successfully deployed! To start your experience, enter https://$host:30080 in your browser to access the Witty Assistant web page:

• On first visit, click Register Now to create an account

• Complete the login process

  

  

Build Domain-Specific Intelligent Q&A ​

The knowledge base focuses on efficient document management and intelligent parsing, supporting multiple file formats including xlsx, pdf, doc, docx, pptx, html, json, yaml, md, zip, and txt. The advanced document processing technology carried by this platform, combined with Witty Assistant's powerful retrieval capabilities, aims to provide you with an excellent intelligent Q&A service experience. For detailed operations, please visit the Knowledge Base Management User Guide

• Enter Knowledge Base Management System:

  • Click Knowledge Base
  • Create a new team, click Confirm
  • Click Create Asset Library or Import Asset Library

• Configure Asset Library

  • Click Dialogue
  • Click Model

  

  • Click Knowledge Base

  

  • Select Asset Library

  

Appendix ​

LLM Preparation ​

GPU Environment (Based on vLLM) ​

1. Install Dependencies:

   bash

   # Basic environment
   Python >= 3.10
   CUDA >= 11.7
   GPU Driver Installation: https://www.nvidia.cn/drivers/lookup/
   
   # Install vLLM
   pip install vllm
   # For OpenAI compatible web server
   pip install 'vllm[openai]'

2. Download Model:

   bash

   # Using huggingface-cli
   pip install huggingface-cli
   huggingface-cli download --resume-download Qwen/Qwen1.5-14B-Chat --local-dir Qwen1.5-14B-Chat

3. Start Service:

   bash

   python -m vllm.entrypoints.openai.api_server \
      --model /root/models/Qwen1.5-14B-Chat/ \
      --served-model-name qwen1.5-14b-chat \
      --api-key sk-123456 \
      --host 0.0.0.0 \
      --port 30000 \
      --tensor-parallel-size 8 \
      --gpu-memory-utilization 0.7 \
      --dtype half

   Common Parameter Descriptions

   bash

   # Core parameters
   --model /path/to/model           # Model path
   --served-model-name name         # Model name in API
   --api-key sk-xxxxxx              # API key (multiple keys can be set, separated by commas)
   --host 0.0.0.0                   # Listening address
   --port 30000                     # Port number
   
   # GPU related parameters
   --tensor-parallel-size 8         # Number of GPUs (tensor parallelism)
   --gpu-memory-utilization 0.7     # GPU memory utilization
   --dtype half                     # Data type (half/float16, bfloat16, float32)

4. Modify Configuration:

   bash

   # Modify model settings
   vim /home/euler-copilot-framework/deploy/chart/euler_copilot/values.yaml

5. Update Service:

   bash

   # Update service
   helm upgrade -n euler-copilot euler-copilot .

   bash

   # Restart framework service
   kubectl get pod -n euler-copilot
   kubectl delete pod framework-deploy-65b669fc58-q9bw7 -n euler-copilot

6. curl LLM Interface

   bash

   curl http://localhost:30000/v1/chat/completions \
   -H "Content-Type: application/json" \
   -H "Authorization: Bearer sk-123456" \
   -d '{
      "model": "qwen1.5-14b-chat",
      "messages": [
         {"role": "system", "content": "You are a helpful assistant"},
         {"role": "user", "content": "Hello"}
      ],
      "stream": true,
      "n": 1,
      "max_tokens": 8192
   }'

NPU Environment ​

Reference: Ascend Image Repository

FAQ ​

Reference Link