2f7e083db0
- MLX(맥미니 27B) 우선 → Ollama(조립컴 9B) fallback 구조 - pydantic-settings 기반 config 전환 - health check에 MLX 상태 추가 - 텍스트 모델 qwen3:8b → qwen3.5:9b-q8_0 변경 Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
78 lines
2.2 KiB
Python
78 lines
2.2 KiB
Python
from fastapi import APIRouter, BackgroundTasks, HTTPException, Query
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from pydantic import BaseModel
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from services.embedding_service import (
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sync_all_issues,
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sync_single_issue,
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sync_incremental,
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search_similar_by_id,
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search_similar_by_text,
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)
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from db.vector_store import vector_store
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router = APIRouter(tags=["embeddings"])
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class SyncSingleRequest(BaseModel):
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issue_id: int
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class SearchRequest(BaseModel):
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query: str
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n_results: int = 5
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project_id: int | None = None
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category: str | None = None
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@router.post("/embeddings/sync")
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async def sync_embeddings(background_tasks: BackgroundTasks):
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background_tasks.add_task(sync_all_issues)
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return {"status": "sync_started", "message": "전체 임베딩 동기화가 시작되었습니다"}
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@router.post("/embeddings/sync-full")
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async def sync_embeddings_full():
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result = await sync_all_issues()
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return {"status": "completed", **result}
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@router.post("/embeddings/sync-single")
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async def sync_single(req: SyncSingleRequest):
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result = await sync_single_issue(req.issue_id)
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return result
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@router.post("/embeddings/sync-incremental")
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async def sync_incr():
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result = await sync_incremental()
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return result
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@router.get("/similar/{issue_id}")
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async def get_similar(issue_id: int, n_results: int = Query(default=5, le=20)):
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try:
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results = await search_similar_by_id(issue_id, n_results)
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return {"available": True, "results": results, "query_issue_id": issue_id}
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except Exception as e:
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raise HTTPException(status_code=500, detail="AI 서비스 처리 중 오류가 발생했습니다")
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@router.post("/similar/search")
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async def search_similar(req: SearchRequest):
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filters = {}
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if req.project_id is not None:
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filters["project_id"] = str(req.project_id)
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if req.category:
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filters["category"] = req.category
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try:
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results = await search_similar_by_text(
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req.query, req.n_results, filters or None
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)
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return {"available": True, "results": results}
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except Exception as e:
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raise HTTPException(status_code=500, detail="AI 서비스 처리 중 오류가 발생했습니다")
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@router.get("/embeddings/stats")
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async def embedding_stats():
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return vector_store.stats()
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