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

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

backend/app/providers/chroma.py

@@ -0,0 +1,91 @@
+from typing import Optional
+
+from app.providers.base import SearchResult
+from app.providers.vectordb import VectorDBProvider
+
+
+class ChromaVectorDBProvider(VectorDBProvider):
+    """
+    ChromaDB 기반 벡터 검색.
+    컬렉션명 = tenant_{tenant_id} (테넌트 격리)
+    """
+
+    def __init__(self, host: str = "chromadb", port: int = 8000):
+        self.host = host
+        self.port = port
+        self._client = None
+
+    def _get_client(self):
+        if self._client is None:
+            import chromadb
+            self._client = chromadb.HttpClient(host=self.host, port=self.port)
+        return self._client
+
+    def _collection_name(self, tenant_id: str) -> str:
+        return f"tenant_{tenant_id}"
+
+    async def upsert(
+        self,
+        tenant_id: str,
+        doc_id: str,
+        chunks: list[str],
+        embeddings: list[list[float]],
+        metadatas: list[dict],
+    ) -> int:
+        client = self._get_client()
+        collection = client.get_or_create_collection(self._collection_name(tenant_id))
+        ids = [f"{doc_id}_{i}" for i in range(len(chunks))]
+        collection.upsert(ids=ids, documents=chunks, embeddings=embeddings, metadatas=metadatas)
+        return len(chunks)
+
+    async def search(
+        self,
+        tenant_id: str,
+        query_vec: list[float],
+        top_k: int = 3,
+        threshold: float = 0.70,
+    ) -> list[SearchResult]:
+        client = self._get_client()
+        collection_name = self._collection_name(tenant_id)
+        try:
+            collection = client.get_collection(collection_name)
+        except Exception:
+            return []
+
+        results = collection.query(
+            query_embeddings=[query_vec],
+            n_results=min(top_k, collection.count()),
+            include=["documents", "metadatas", "distances"],
+        )
+
+        search_results = []
+        if not results["ids"] or not results["ids"][0]:
+            return []
+
+        for i, doc_id in enumerate(results["ids"][0]):
+            # Chroma distances: 1 - cosine_similarity (낮을수록 유사)
+            distance = results["distances"][0][i]
+            score = 1.0 - distance  # cosine similarity로 변환
+            if score >= threshold:
+                search_results.append(
+                    SearchResult(
+                        text=results["documents"][0][i],
+                        doc_id=doc_id,
+                        score=score,
+                        metadata=results["metadatas"][0][i] or {},
+                    )
+                )
+        return search_results
+
+    async def delete(self, tenant_id: str, doc_id: str) -> None:
+        client = self._get_client()
+        collection_name = self._collection_name(tenant_id)
+        try:
+            collection = client.get_collection(collection_name)
+            # doc_id로 시작하는 모든 청크 삭제
+            all_ids = collection.get()["ids"]
+            ids_to_delete = [id_ for id_ in all_ids if id_.startswith(f"{doc_id}_")]
+            if ids_to_delete:
+                collection.delete(ids=ids_to_delete)
+        except Exception:
+            pass