Initial commit: import from sinmb79/Gov-chat-bot

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
2026-03-26 12:49:43 +09:00
commit a16c972dbb
104 changed files with 8063 additions and 0 deletions
@@ -0,0 +1,94 @@
+from typing import Optional
+
+from sqlalchemy import select
+from sqlalchemy.ext.asyncio import AsyncSession
+
+from app.models.knowledge import FAQ
+from app.providers.embedding import EmbeddingProvider
+from app.providers.vectordb import VectorDBProvider
+from app.providers.base import SearchResult
+
+FAQ_SIMILARITY_THRESHOLD = 0.85  # Tier A 기준
+
+
+class FAQSearchService:
+    """
+    Tier A — FAQ 임베딩 유사도 검색.
+    임베딩 유사도 ≥ 0.85 시 등록 FAQ 반환.
+    """
+
+    def __init__(
+        self,
+        embedding_provider: EmbeddingProvider,
+        vectordb_provider: VectorDBProvider,
+        db: AsyncSession,
+    ):
+        self.embedding = embedding_provider
+        self.vectordb = vectordb_provider
+        self.db = db
+
+    async def search(
+        self, tenant_id: str, utterance: str
+    ) -> Optional[tuple[FAQ, float]]:
+        """
+        발화를 임베딩 → 벡터DB 검색 → 0.85 이상이면 FAQ 반환.
+        없으면 None.
+        """
+        try:
+            vecs = await self.embedding.embed([utterance])
+        except NotImplementedError:
+            return None
+
+        query_vec = vecs[0]
+        results = await self.vectordb.search(
+            tenant_id=tenant_id,
+            query_vec=query_vec,
+            top_k=1,
+            threshold=FAQ_SIMILARITY_THRESHOLD,
+        )
+
+        if not results:
+            return None
+
+        top: SearchResult = results[0]
+        faq_id = top.metadata.get("faq_id")
+        if not faq_id:
+            return None
+
+        faq = await self._load_faq(tenant_id, faq_id)
+        if faq is None:
+            return None
+
+        return faq, top.score
+
+    async def _load_faq(self, tenant_id: str, faq_id: str) -> Optional[FAQ]:
+        result = await self.db.execute(
+            select(FAQ).where(
+                FAQ.tenant_id == tenant_id,
+                FAQ.id == faq_id,
+                FAQ.is_active.is_(True),
+            )
+        )
+        return result.scalar_one_or_none()
+
+    async def increment_hit(self, faq_id: str) -> None:
+        """FAQ hit_count 증가."""
+        faq = await self.db.get(FAQ, faq_id)
+        if faq:
+            faq.hit_count = (faq.hit_count or 0) + 1
+            await self.db.commit()
+
+    async def index_faq(self, tenant_id: str, faq: FAQ) -> None:
+        """FAQ를 벡터DB에 색인."""
+        text = f"{faq.question}\n{faq.answer}"
+        try:
+            vecs = await self.embedding.embed([text])
+        except NotImplementedError:
+            return
+        await self.vectordb.upsert(
+            tenant_id=tenant_id,
+            doc_id=faq.id,
+            chunks=[text],
+            embeddings=vecs,
+            metadatas=[{"faq_id": faq.id, "question": faq.question}],
+        )