gbanyan
534fdbbcc4
- Backend: Add expert transformation router with 3-step SSE pipeline - Step 0: Generate diverse expert team (random domains) - Step 1: Each expert generates keywords for attributes - Step 2: Batch generate descriptions for expert keywords - Backend: Add simplified prompts for reliable JSON output - Frontend: Add TransformationPanel with React Flow visualization - Frontend: Add TransformationInputPanel for expert configuration - Expert count (2-8), keywords per expert (1-3) - Custom expert domains support - Frontend: Add expert keyword nodes with expert badges - Frontend: Improve description card layout (wider cards, more spacing) - Frontend: Add fallback for missing descriptions with visual indicators 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
117 lines
4.3 KiB
Python
117 lines
4.3 KiB
Python
"""Transformation Agent 路由模組"""
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import json
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import logging
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from typing import AsyncGenerator, List
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from fastapi import APIRouter
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from fastapi.responses import StreamingResponse
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from ..models.schemas import (
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TransformationRequest,
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TransformationCategoryResult,
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TransformationDescription,
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)
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from ..prompts.transformation_prompt import (
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get_keyword_generation_prompt,
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get_batch_description_prompt,
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)
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from ..services.llm_service import ollama_provider, extract_json_from_response
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logger = logging.getLogger(__name__)
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router = APIRouter(prefix="/api/transformation", tags=["transformation"])
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async def generate_transformation_events(
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request: TransformationRequest
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) -> AsyncGenerator[str, None]:
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"""Generate SSE events for transformation process"""
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try:
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temperature = request.temperature if request.temperature is not None else 0.7
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model = request.model
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# ========== Step 1: Generate new keywords ==========
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yield f"event: keyword_start\ndata: {json.dumps({'message': f'為「{request.category}」生成新關鍵字...'}, ensure_ascii=False)}\n\n"
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keyword_prompt = get_keyword_generation_prompt(
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category=request.category,
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attributes=request.attributes,
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keyword_count=request.keyword_count
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)
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logger.info(f"Keyword prompt: {keyword_prompt[:200]}")
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keyword_response = await ollama_provider.generate(
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keyword_prompt, model=model, temperature=temperature
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)
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logger.info(f"Keyword response: {keyword_response[:500]}")
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keyword_data = extract_json_from_response(keyword_response)
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new_keywords = keyword_data.get("keywords", [])
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yield f"event: keyword_complete\ndata: {json.dumps({'keywords': new_keywords}, ensure_ascii=False)}\n\n"
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if not new_keywords:
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yield f"event: error\ndata: {json.dumps({'error': '無法生成新關鍵字'}, ensure_ascii=False)}\n\n"
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return
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# ========== Step 2: Generate descriptions for each keyword ==========
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yield f"event: description_start\ndata: {json.dumps({'message': '生成創新應用描述...'}, ensure_ascii=False)}\n\n"
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# Use batch description prompt for efficiency
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desc_prompt = get_batch_description_prompt(
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query=request.query,
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category=request.category,
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keywords=new_keywords
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)
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logger.info(f"Description prompt: {desc_prompt[:300]}")
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desc_response = await ollama_provider.generate(
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desc_prompt, model=model, temperature=temperature
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)
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logger.info(f"Description response: {desc_response[:500]}")
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desc_data = extract_json_from_response(desc_response)
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descriptions_raw = desc_data.get("descriptions", [])
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# Convert to TransformationDescription objects
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descriptions: List[TransformationDescription] = []
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for desc in descriptions_raw:
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if isinstance(desc, dict) and "keyword" in desc and "description" in desc:
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descriptions.append(TransformationDescription(
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keyword=desc["keyword"],
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description=desc["description"]
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))
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yield f"event: description_complete\ndata: {json.dumps({'count': len(descriptions)}, ensure_ascii=False)}\n\n"
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# ========== Build final result ==========
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result = TransformationCategoryResult(
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category=request.category,
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original_attributes=request.attributes,
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new_keywords=new_keywords,
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descriptions=descriptions
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)
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final_data = {
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"result": result.model_dump()
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}
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yield f"event: done\ndata: {json.dumps(final_data, ensure_ascii=False)}\n\n"
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except Exception as e:
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logger.error(f"Transformation error: {e}")
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yield f"event: error\ndata: {json.dumps({'error': str(e)}, ensure_ascii=False)}\n\n"
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@router.post("/category")
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async def transform_category(request: TransformationRequest):
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"""處理單一類別的轉換"""
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return StreamingResponse(
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generate_transformation_events(request),
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media_type="text/event-stream",
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headers={
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"Cache-Control": "no-cache",
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"Connection": "keep-alive",
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"X-Accel-Buffering": "no",
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},
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)
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