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## Description
This PR focuses on API performance optimization and refining the model
capability detection logic in the Agent/Canvas module.
### 1. Performance Optimization (Backend)
- **Changes**: Removed `cls.model.dsl` from query fields in
`UserCanvasService.get_by_tenant_ids`.
- **Reasoning**: The `dsl` object is large and unnecessary for the Agent
list view. Excluding it reduces the payload size of the
`/v1/canvas/list` API, leading to faster serialization and reduced
network latency.
- **Consistency**: Full DSL data remains accessible via the individual
`/v1/canvas/get/<id>` endpoint used in the detail view.
### 2. Multimodal Detection Refinement (Frontend)
- **Changes**: Replaced `model_type === LlmModelType.Image2text` with
`tags?.includes('IMAGE2TEXT')`.
- **Reasoning**: In RAGFlow, `model_type` defines the primary role of a
model (e.g., `chat`). However, many advanced Chat models are also
vision-capable. Since `model_type` is a single-value field, it cannot
represent these multiple capabilities.
- **Solution**: Utilizing the `tags` field (which supports multiple
attributes) to check for `IMAGE2TEXT` ensures that models like
`gpt-5.2-pro` correctly display multimodal input options.
## Type of Change
- [x] Bug fix (logic correction for multimodal detection)
- [x] Optimization (performance improvement for list API)
## Main Changes
- `api/db/services/canvas_service.py`: Optimized DB query by excluding
heavy DSL fields.
- `web/src/pages/agent/form/agent-form/index.tsx`: Enhanced capability
detection using the tags system.
## Verification
- [x] Verified Agent list loads faster with reduced response payload.
- [x] Confirmed that `chat` models with the `IMAGE2TEXT` tag now
correctly enable the multimodal input UI.
Install front-end dependencies
npm install
Launch front-end
npm run dev
The following output confirms a successful launch of the system:
Login to RAGFlow web UI
Open your browser and navigate to:
http://localhost:9222 or http://[YOUR_MACHINE_IP]:9222
Replace [YOUR_MACHINE_IP] with your actual machine IP address (e.g., http://192.168.1.49:9222).
Login to RAGFlow web admin UI
Open your browser and navigate to:
http://localhost:9222/admin or http://[YOUR_MACHINE_IP]:9222/admin
Replace [YOUR_MACHINE_IP] with your actual machine IP address (e.g., http://192.168.1.49:9222/admin).
Shutdown front-end
Ctrl + C or
kill -f "umi dev"