119 lines
4.3 KiB
Python
119 lines
4.3 KiB
Python
"""Transformation Agent 路由模組"""
|
|
|
|
import json
|
|
import logging
|
|
from typing import AsyncGenerator, List
|
|
|
|
from fastapi import APIRouter
|
|
from fastapi.responses import StreamingResponse
|
|
|
|
from ..models.schemas import (
|
|
TransformationRequest,
|
|
TransformationCategoryResult,
|
|
TransformationDescription,
|
|
)
|
|
from ..prompts.transformation_prompt import (
|
|
get_keyword_generation_prompt,
|
|
get_batch_description_prompt,
|
|
)
|
|
from ..services.llm_service import ollama_provider, extract_json_from_response
|
|
|
|
logger = logging.getLogger(__name__)
|
|
router = APIRouter(prefix="/api/transformation", tags=["transformation"])
|
|
|
|
|
|
async def generate_transformation_events(
|
|
request: TransformationRequest
|
|
) -> AsyncGenerator[str, None]:
|
|
"""Generate SSE events for transformation process"""
|
|
try:
|
|
temperature = request.temperature if request.temperature is not None else 0.7
|
|
model = request.model
|
|
|
|
# ========== Step 1: Generate new keywords ==========
|
|
yield f"event: keyword_start\ndata: {json.dumps({'message': f'為「{request.category}」生成新關鍵字...'}, ensure_ascii=False)}\n\n"
|
|
|
|
keyword_prompt = get_keyword_generation_prompt(
|
|
category=request.category,
|
|
attributes=request.attributes,
|
|
keyword_count=request.keyword_count,
|
|
lang=request.lang
|
|
)
|
|
logger.info(f"Keyword prompt: {keyword_prompt[:200]}")
|
|
|
|
keyword_response = await ollama_provider.generate(
|
|
keyword_prompt, model=model, temperature=temperature
|
|
)
|
|
logger.info(f"Keyword response: {keyword_response[:500]}")
|
|
|
|
keyword_data = extract_json_from_response(keyword_response)
|
|
new_keywords = keyword_data.get("keywords", [])
|
|
|
|
yield f"event: keyword_complete\ndata: {json.dumps({'keywords': new_keywords}, ensure_ascii=False)}\n\n"
|
|
|
|
if not new_keywords:
|
|
yield f"event: error\ndata: {json.dumps({'error': '無法生成新關鍵字'}, ensure_ascii=False)}\n\n"
|
|
return
|
|
|
|
# ========== Step 2: Generate descriptions for each keyword ==========
|
|
yield f"event: description_start\ndata: {json.dumps({'message': '生成創新應用描述...'}, ensure_ascii=False)}\n\n"
|
|
|
|
# Use batch description prompt for efficiency
|
|
desc_prompt = get_batch_description_prompt(
|
|
query=request.query,
|
|
category=request.category,
|
|
keywords=new_keywords,
|
|
lang=request.lang
|
|
)
|
|
logger.info(f"Description prompt: {desc_prompt[:300]}")
|
|
|
|
desc_response = await ollama_provider.generate(
|
|
desc_prompt, model=model, temperature=temperature
|
|
)
|
|
logger.info(f"Description response: {desc_response[:500]}")
|
|
|
|
desc_data = extract_json_from_response(desc_response)
|
|
descriptions_raw = desc_data.get("descriptions", [])
|
|
|
|
# Convert to TransformationDescription objects
|
|
descriptions: List[TransformationDescription] = []
|
|
for desc in descriptions_raw:
|
|
if isinstance(desc, dict) and "keyword" in desc and "description" in desc:
|
|
descriptions.append(TransformationDescription(
|
|
keyword=desc["keyword"],
|
|
description=desc["description"]
|
|
))
|
|
|
|
yield f"event: description_complete\ndata: {json.dumps({'count': len(descriptions)}, ensure_ascii=False)}\n\n"
|
|
|
|
# ========== Build final result ==========
|
|
result = TransformationCategoryResult(
|
|
category=request.category,
|
|
original_attributes=request.attributes,
|
|
new_keywords=new_keywords,
|
|
descriptions=descriptions
|
|
)
|
|
|
|
final_data = {
|
|
"result": result.model_dump()
|
|
}
|
|
yield f"event: done\ndata: {json.dumps(final_data, ensure_ascii=False)}\n\n"
|
|
|
|
except Exception as e:
|
|
logger.error(f"Transformation error: {e}")
|
|
yield f"event: error\ndata: {json.dumps({'error': str(e)}, ensure_ascii=False)}\n\n"
|
|
|
|
|
|
@router.post("/category")
|
|
async def transform_category(request: TransformationRequest):
|
|
"""處理單一類別的轉換"""
|
|
return StreamingResponse(
|
|
generate_transformation_events(request),
|
|
media_type="text/event-stream",
|
|
headers={
|
|
"Cache-Control": "no-cache",
|
|
"Connection": "keep-alive",
|
|
"X-Accel-Buffering": "no",
|
|
},
|
|
)
|