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

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

backend/app/services/document_processor.py

@@ -0,0 +1,88 @@
+"""
+문서 처리 파이프라인:
+파싱 → 청킹 → 임베딩 → VectorDB 저장 → Document 레코드 업데이트
+"""
+from typing import Optional
+
+from sqlalchemy.ext.asyncio import AsyncSession
+
+from app.models.knowledge import Document
+from app.providers.embedding import EmbeddingProvider
+from app.providers.vectordb import VectorDBProvider
+from app.services.parsers.text_parser import extract_text, chunk_text
+
+
+class DocumentProcessor:
+    def __init__(
+        self,
+        embedding_provider: EmbeddingProvider,
+        vectordb_provider: VectorDBProvider,
+        db: AsyncSession,
+    ):
+        self.embedding = embedding_provider
+        self.vectordb = vectordb_provider
+        self.db = db
+
+    async def process(self, tenant_id: str, doc: Document, content: bytes) -> int:
+        """
+        문서를 파싱·청킹·임베딩하여 VectorDB에 저장.
+        chunk_count 반환. 실패 시 0.
+        """
+        # 1. 텍스트 추출
+        text = extract_text(content, doc.filename)
+        if not text or not text.strip():
+            doc.status = "parse_failed"
+            await self.db.commit()
+            return 0
+
+        # 2. 청킹
+        chunks = chunk_text(text)
+        if not chunks:
+            doc.status = "parse_failed"
+            await self.db.commit()
+            return 0
+
+        # 3. 임베딩
+        try:
+            embeddings = await self.embedding.embed(chunks)
+        except NotImplementedError:
+            doc.status = "embedding_unavailable"
+            await self.db.commit()
+            return 0
+        except Exception:
+            doc.status = "embedding_failed"
+            await self.db.commit()
+            return 0
+
+        # 4. 메타데이터 구성
+        published = doc.published_at.strftime("%Y.%m") if doc.published_at else ""
+        metadatas = [
+            {
+                "doc_id": doc.id,
+                "filename": doc.filename,
+                "chunk_idx": i,
+                "published_at": published,
+                "tenant_id": tenant_id,
+            }
+            for i in range(len(chunks))
+        ]
+
+        # 5. VectorDB 저장
+        await self.vectordb.upsert(
+            tenant_id=tenant_id,
+            doc_id=doc.id,
+            chunks=chunks,
+            embeddings=embeddings,
+            metadatas=metadatas,
+        )
+
+        # 6. Document 레코드 업데이트
+        doc.chunk_count = len(chunks)
+        doc.status = "processed"
+        await self.db.commit()
+
+        return len(chunks)
+
+    async def delete(self, tenant_id: str, doc_id: str) -> None:
+        """VectorDB에서 문서 청크 삭제."""
+        await self.vectordb.delete(tenant_id=tenant_id, doc_id=doc_id)