Installing SuperMap iPortal AI Assistant

SuperMap iPortal AI Assistant supports rapid deployment using Docker Compose. Docker Compose is a tool for defining and running multi-container Docker applications. Through YAML files, it describes application services and their dependencies, enabling one-click startup, shutdown, and management of containerized application environments.

Preparing Image Packages

Before deploying with Docker Compose, prepare the required images. Both online and offline installation methods are supported:

Offline Installation

Obtain image packages from the network disk. Both X86 and ARM versions are available. Extract the supermap-iportal-ai-assistant-2025-linux-x64-images.tar.gz package (X86 example). Required images include:

  • agentx-auto-agent service image
  • iportal-ai-assistant service image
  • iportal-ai-assistant-server service image

Install images using:

docker load -i iportal-ai-assistant-12.0.0.0-amd64.tar

docker load -i iportal-ai-assistant-server-12.0.0.0-amd64.tar

docker load -i agentx-auto-agent-12.0.0.0-amd64.tar

Online Installation

1. Log in to Alibaba Cloud with password: supermap@123

docker login --username=478386058@qq.com 
registry.cn-chengdu.aliyuncs.com

2. Pull required images:

docker pull registry.cn-chengdu.aliyuncs.com/supermap/agentx-auto-agent:12.0.0.0-amd64

docker pull registry.cn-chengdu.aliyuncs.com/supermap/iportal-ai-assistant:12.0.0.0-amd64

docker pull registry.cn-chengdu.aliyuncs.com/supermap/iportal-ai-assistant-server:12.0.0.0-amd64

Modifying Configuration Files

Obtain configuration files from the docker-compose folder in the network disk and modify as needed:

File Name Description
.env Environment variables configuration
application-app.yml AI Assistant Server configuration
docker-compose.yaml Docker Compose configuration
graphrag/.env GraphRAG environment variables

.env File Configuration

# Common settings
...
## Host CPU architecture
ARCH=amd64
## License service 
BSLICENSE_SERVER=ws://172.16.168.199:9183
...
## iPortal service 
IPORTAL_SERVER=http://172.16.168.198:8190
IPORTAL_URL=http://172.16.168.198:8190/iportal
# openai-format LLM configuration 
DEFAULT_LLM_MODEL=Qwen/Qwen3-14B
LLM_SERVER=http://127.0.0.1:8000/v1
LLM_KEY=EMPTY
LLM_MODEL=Qwen/Qwen3-14B
# openai-format embedding model configuration 
DEFAULT_EMBED_MODEL=bge-m3
EMBED_SERVER=http://127.0.0.1:8001/v1
EMBED_KEY=EMPTY
EMBED_MODEL=bge-m3

Modify bolded values in .env file as needed:

  • ARCH: Host CPU architecture (amd64 for X86-64/X64/AMD64, arm64 for ARM64)
  • BSLICENSE_SERVER: License center address obtained after Web License installation
  • IPORTAL_SERVER: iPortal root address (http://[IP]:8190)
  • IPORTAL_URL: iPortal access address (http://[IP]:8190/iportal)
  • DEFAULT_LLM_MODEL: Default LLM name when not specified
  • LLM_SERVER: LLM service address (http://[IP]:8000/v1)
  • LLM_KEY: LLM access key (optional)
  • LLM_MODEL: LLM name
  • DEFAULT_EMBED_MODEL: Default embedding model name when not specified
  • EMBED_SERVER: Embedding model service address (http://[IP]:8001/v1)
  • EMBED_KEY: Embedding model access key (optional)
  • EMBED_MODEL: Embedding model name

application-app.yml File Configuration

...
iPortal:
  api:
    addr: http://172.16.168.198:8190/iportal
...
spring:
  ai:
    openai:
      chat:
        enabled: true
        base-url: http://127.0.0.1:8000
        options:
          model: Qwen/Qwen3-14B

Modify bolded values in application-app.yml:

  • iPortal.api.addr: iPortal access address
  • spring.ai.openai.chat.enabled: Enable LLM service (default: true)
  • spring.ai.openai.chat.base-url: LLM service address
  • spring.ai.openai.chat.options.model: LLM name

graphrag/.env File Configuration

...
### OpenAI alike example
LLM_BINDING=openai
LLM_MODEL=Qwen/Qwen3-14B
LLM_BINDING_HOST=http://127.0.0.1:8000/v1
LLM_BINDING_API_KEY=EMPTY
### Embedding Configuration 
EMBEDDING_MODEL=bge-m3
EMBEDDING_DIM=1024
EMBEDDING_BINDING_API_KEY=EMPTY
### OpenAI alike example
EMBEDDING_BINDING=openai
EMBEDDING_BINDING_HOST=http://127.0.0.1:8001/v1
...

Modify bolded values in graphrag/.env file:

  • LLM_MODEL: LLM name
  • LLM_BINDING_HOST: LLM service address
  • LLM_BINDING_API_KEY: LLM access key
  • EMBEDDING_BINDING_HOST: Embedding model service address
  • EMBEDDING_BINDING_API_KEY: Embedding model access key

Starting SuperMap iPortal AI Assistant Service

After configuration, execute this command in the docker-compose.yaml directory:

docker compose up -d

  • AI Assistant API UI: http://[IP]:8080/swagger-ui/index.html
AI Assistant API UI
Figure: SuperMap iPortal AI Assistant API UI
  • AI Assistant Homepage: http://[IP]/iportal/apps/AIAssistant/index.html

Log in with SuperMap iPortal credentials to access the AI Assistant.

AI Assistant homepage
Figure: SuperMap iPortal AI Assistant Homepage

Uninstalling AI Assistant Service

To uninstall Docker Compose deployment:

docker compose stop
docker compose rm

Uninstallation confirmation
Figure: SuperMap iPortal AI Assistant Uninstalled

Remote Sensing Tools Supplement

Additional configuration for remote sensing capabilities:

Configuring Python Environment

Download Python environment package (supermap-iobjectspy-env-cpu.zip) from "Component GIS" section of official resources center. Extract the conda folder to {iServer_home}/support/python.

Configuring Machine Learning Resources

Download ML resource package (supermap-iobjectspy-resources-ml.zip) and extract resources-ml folder to {iServer_home}/support/python. Contains sample data/models.

Note:
  • Required for deep learning features: binary classification, object detection, etc.
  • ML services don't support Web License; use standard or cloud licenses.

Configuring Remote Sensing Package

Download Remote Sensing Capability Extension Package from network disk:

  • Place iportal-gpa-extension-1.0.0-SNAPSHOT.jar in {iServer_home}/support/geoprocessing/lib
  • Extract model folder to {iServer_home}/webapps/iserver
Important:

Commercial models require separate authorization:

Model Name Authorization ID
Land Cover Pre-trained Model (LIM_LULC) 33001
Building Extraction Model (PIM_BUILDING) 33101
Building Change Detection Model 33109
Road Extraction Model 33107