b3012b8320
- ai-service: Ollama 기반 AI 서비스 (분류, 시맨틱 검색, RAG Q&A, 패턴 분석) - AI 어시스턴트 페이지: 채팅형 Q&A, 시맨틱 검색, 패턴 분석, 분류 테스트 - 권한 시스템에 ai_assistant 페이지 등록 (기본 비활성) - 기존 페이지에 AI 기능 통합 (대시보드, 수신함, 관리함) - docker-compose, gateway, nginx 설정 업데이트 Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
77 lines
2.2 KiB
Python
77 lines
2.2 KiB
Python
import chromadb
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from config import settings
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class VectorStore:
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def __init__(self):
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self.client = None
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self.collection = None
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def initialize(self):
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self.client = chromadb.PersistentClient(path=settings.CHROMA_PERSIST_DIR)
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self.collection = self.client.get_or_create_collection(
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name="qc_issues",
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metadata={"hnsw:space": "cosine"},
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)
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def upsert(
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self,
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doc_id: str,
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document: str,
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embedding: list[float],
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metadata: dict = None,
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):
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self.collection.upsert(
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ids=[doc_id],
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documents=[document],
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embeddings=[embedding],
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metadatas=[metadata] if metadata else None,
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)
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def query(
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self,
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embedding: list[float],
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n_results: int = 5,
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where: dict = None,
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) -> list[dict]:
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kwargs = {
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"query_embeddings": [embedding],
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"n_results": n_results,
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"include": ["documents", "metadatas", "distances"],
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}
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if where:
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kwargs["where"] = where
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try:
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results = self.collection.query(**kwargs)
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except Exception:
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return []
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items = []
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if results and results["ids"] and results["ids"][0]:
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for i, doc_id in enumerate(results["ids"][0]):
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item = {
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"id": doc_id,
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"document": results["documents"][0][i] if results["documents"] else "",
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"distance": results["distances"][0][i] if results["distances"] else 0,
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"metadata": results["metadatas"][0][i] if results["metadatas"] else {},
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}
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# cosine distance → similarity
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item["similarity"] = round(1 - item["distance"], 4)
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items.append(item)
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return items
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def delete(self, doc_id: str):
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self.collection.delete(ids=[doc_id])
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def count(self) -> int:
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return self.collection.count()
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def stats(self) -> dict:
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return {
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"total_documents": self.count(),
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"collection_name": "qc_issues",
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}
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vector_store = VectorStore()
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