mirror of
https://gitee.com/wanwujie/deer-flow
synced 2026-04-08 16:24:45 +08:00
ceab7fac14
This PR improves MiniMax Code Plan integration in DeerFlow by fixing three issues in the current flow: stream errors were not clearly surfaced in the UI, the frontend could not display the actual provider model ID, and MiniMax reasoning output could leak into final assistant content as inline <think>...</think>. The change adds a MiniMax-specific adapter, exposes real model IDs end-to-end, and adds a frontend fallback for historical messages. Co-authored-by: Willem Jiang <willem.jiang@gmail.com>
117 lines
3.5 KiB
Python
117 lines
3.5 KiB
Python
from fastapi import APIRouter, HTTPException
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from pydantic import BaseModel, Field
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from deerflow.config import get_app_config
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router = APIRouter(prefix="/api", tags=["models"])
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class ModelResponse(BaseModel):
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"""Response model for model information."""
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name: str = Field(..., description="Unique identifier for the model")
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model: str = Field(..., description="Actual provider model identifier")
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display_name: str | None = Field(None, description="Human-readable name")
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description: str | None = Field(None, description="Model description")
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supports_thinking: bool = Field(default=False, description="Whether model supports thinking mode")
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supports_reasoning_effort: bool = Field(default=False, description="Whether model supports reasoning effort")
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class ModelsListResponse(BaseModel):
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"""Response model for listing all models."""
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models: list[ModelResponse]
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@router.get(
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"/models",
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response_model=ModelsListResponse,
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summary="List All Models",
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description="Retrieve a list of all available AI models configured in the system.",
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)
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async def list_models() -> ModelsListResponse:
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"""List all available models from configuration.
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Returns model information suitable for frontend display,
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excluding sensitive fields like API keys and internal configuration.
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Returns:
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A list of all configured models with their metadata.
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Example Response:
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```json
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{
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"models": [
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{
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"name": "gpt-4",
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"display_name": "GPT-4",
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"description": "OpenAI GPT-4 model",
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"supports_thinking": false
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},
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{
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"name": "claude-3-opus",
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"display_name": "Claude 3 Opus",
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"description": "Anthropic Claude 3 Opus model",
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"supports_thinking": true
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}
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]
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}
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```
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"""
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config = get_app_config()
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models = [
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ModelResponse(
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name=model.name,
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model=model.model,
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display_name=model.display_name,
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description=model.description,
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supports_thinking=model.supports_thinking,
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supports_reasoning_effort=model.supports_reasoning_effort,
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)
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for model in config.models
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]
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return ModelsListResponse(models=models)
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@router.get(
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"/models/{model_name}",
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response_model=ModelResponse,
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summary="Get Model Details",
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description="Retrieve detailed information about a specific AI model by its name.",
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)
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async def get_model(model_name: str) -> ModelResponse:
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"""Get a specific model by name.
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Args:
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model_name: The unique name of the model to retrieve.
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Returns:
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Model information if found.
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Raises:
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HTTPException: 404 if model not found.
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Example Response:
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```json
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{
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"name": "gpt-4",
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"display_name": "GPT-4",
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"description": "OpenAI GPT-4 model",
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"supports_thinking": false
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}
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```
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"""
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config = get_app_config()
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model = config.get_model_config(model_name)
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if model is None:
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raise HTTPException(status_code=404, detail=f"Model '{model_name}' not found")
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return ModelResponse(
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name=model.name,
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model=model.model,
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display_name=model.display_name,
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description=model.description,
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supports_thinking=model.supports_thinking,
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supports_reasoning_effort=model.supports_reasoning_effort,
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)
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