not-integreated: Add DocumentHybridSearchRetriever

surfsense_backend/app/db.py

@@ -28,6 +28,7 @@ from sqlalchemy.orm import DeclarativeBase, Mapped, declared_attr, relationship
 
 from app.config import config
 from app.retriver.chunks_hybrid_search import ChucksHybridSearchRetriever
+from app.retriver.documents_hybrid_search import DocumentHybridSearchRetriever
 
 DATABASE_URL = config.DATABASE_URL
 
@@ -179,3 +180,6 @@ async def get_user_db(session: AsyncSession = Depends(get_async_session)):
     
 async def get_chucks_hybrid_search_retriever(session: AsyncSession = Depends(get_async_session)):
     return ChucksHybridSearchRetriever(session)
+
+async def get_documents_hybrid_search_retriever(session: AsyncSession = Depends(get_async_session)):
+    return DocumentHybridSearchRetriever(session)

surfsense_backend/app/retriver/documents_hybrid_search.py

@@ -0,0 +1,237 @@
+class DocumentHybridSearchRetriever:
+    def __init__(self, db_session):
+        """
+        Initialize the hybrid search retriever with a database session.
+        
+        Args:
+            db_session: SQLAlchemy AsyncSession from FastAPI dependency injection
+        """
+        self.db_session = db_session
+
+    async def vector_search(self, query_text: str, top_k: int, user_id: str, search_space_id: int = None) -> list:
+        """
+        Perform vector similarity search on documents.
+        
+        Args:
+            query_text: The search query text
+            top_k: Number of results to return
+            user_id: The ID of the user performing the search
+            search_space_id: Optional search space ID to filter results
+            
+        Returns:
+            List of documents sorted by vector similarity
+        """
+        from sqlalchemy import select, func
+        from sqlalchemy.orm import joinedload
+        from app.db import Document, SearchSpace
+        from app.config import config
+        
+        # Get embedding for the query
+        embedding_model = config.embedding_model_instance
+        query_embedding = embedding_model.embed(query_text)
+        
+        # Build the base query with user ownership check
+        query = (
+            select(Document)
+            .options(joinedload(Document.search_space))
+            .join(SearchSpace, Document.search_space_id == SearchSpace.id)
+            .where(SearchSpace.user_id == user_id)
+        )
+        
+        # Add search space filter if provided
+        if search_space_id is not None:
+            query = query.where(Document.search_space_id == search_space_id)
+        
+        # Add vector similarity ordering
+        query = (
+            query
+            .order_by(Document.embedding.op("<=>")(query_embedding))
+            .limit(top_k)
+        )
+        
+        # Execute the query
+        result = await self.db_session.execute(query)
+        documents = result.scalars().all()
+        
+        return documents
+
+    async def full_text_search(self, query_text: str, top_k: int, user_id: str, search_space_id: int = None) -> list:
+        """
+        Perform full-text keyword search on documents.
+        
+        Args:
+            query_text: The search query text
+            top_k: Number of results to return
+            user_id: The ID of the user performing the search
+            search_space_id: Optional search space ID to filter results
+            
+        Returns:
+            List of documents sorted by text relevance
+        """
+        from sqlalchemy import select, func, text
+        from sqlalchemy.orm import joinedload
+        from app.db import Document, SearchSpace
+        
+        # Create tsvector and tsquery for PostgreSQL full-text search
+        tsvector = func.to_tsvector('english', Document.content)
+        tsquery = func.plainto_tsquery('english', query_text)
+        
+        # Build the base query with user ownership check
+        query = (
+            select(Document)
+            .options(joinedload(Document.search_space))
+            .join(SearchSpace, Document.search_space_id == SearchSpace.id)
+            .where(SearchSpace.user_id == user_id)
+            .where(tsvector.op("@@")(tsquery))  # Only include results that match the query
+        )
+        
+        # Add search space filter if provided
+        if search_space_id is not None:
+            query = query.where(Document.search_space_id == search_space_id)
+        
+        # Add text search ranking
+        query = (
+            query
+            .order_by(func.ts_rank_cd(tsvector, tsquery).desc())
+            .limit(top_k)
+        )
+        
+        # Execute the query
+        result = await self.db_session.execute(query)
+        documents = result.scalars().all()
+        
+        return documents
+
+    async def hybrid_search(self, query_text: str, top_k: int, user_id: str, search_space_id: int = None, document_type: str = None) -> list:
+        """
+        Combine vector similarity and full-text search results using Reciprocal Rank Fusion.
+        
+        Args:
+            query_text: The search query text
+            top_k: Number of results to return
+            user_id: The ID of the user performing the search
+            search_space_id: Optional search space ID to filter results
+            document_type: Optional document type to filter results (e.g., "FILE", "CRAWLED_URL")
+            
+        Returns:
+            List of dictionaries containing document data and relevance scores
+        """
+        from sqlalchemy import select, func, text
+        from sqlalchemy.orm import joinedload
+        from app.db import Document, SearchSpace, DocumentType
+        from app.config import config
+        
+        # Get embedding for the query
+        embedding_model = config.embedding_model_instance
+        query_embedding = embedding_model.embed(query_text)
+        
+        # Constants for RRF calculation
+        k = 60  # Constant for RRF calculation
+        n_results = top_k * 2  # Get more results for better fusion
+        
+        # Create tsvector and tsquery for PostgreSQL full-text search
+        tsvector = func.to_tsvector('english', Document.content)
+        tsquery = func.plainto_tsquery('english', query_text)
+        
+        # Base conditions for document filtering
+        base_conditions = [SearchSpace.user_id == user_id]
+        
+        # Add search space filter if provided
+        if search_space_id is not None:
+            base_conditions.append(Document.search_space_id == search_space_id)
+            
+        # Add document type filter if provided
+        if document_type is not None:
+            # Convert string to enum value if needed
+            if isinstance(document_type, str):
+                try:
+                    doc_type_enum = DocumentType[document_type]
+                    base_conditions.append(Document.document_type == doc_type_enum)
+                except KeyError:
+                    # If the document type doesn't exist in the enum, return empty results
+                    return []
+            else:
+                base_conditions.append(Document.document_type == document_type)
+        
+        # CTE for semantic search with user ownership check
+        semantic_search_cte = (
+            select(
+                Document.id,
+                func.rank().over(order_by=Document.embedding.op("<=>")(query_embedding)).label("rank")
+            )
+            .join(SearchSpace, Document.search_space_id == SearchSpace.id)
+            .where(*base_conditions)
+        )
+        
+        semantic_search_cte = (
+            semantic_search_cte
+            .order_by(Document.embedding.op("<=>")(query_embedding))
+            .limit(n_results)
+            .cte("semantic_search")
+        )
+        
+        # CTE for keyword search with user ownership check
+        keyword_search_cte = (
+            select(
+                Document.id,
+                func.rank().over(order_by=func.ts_rank_cd(tsvector, tsquery).desc()).label("rank")
+            )
+            .join(SearchSpace, Document.search_space_id == SearchSpace.id)
+            .where(*base_conditions)
+            .where(tsvector.op("@@")(tsquery))
+        )
+        
+        keyword_search_cte = (
+            keyword_search_cte
+            .order_by(func.ts_rank_cd(tsvector, tsquery).desc())
+            .limit(n_results)
+            .cte("keyword_search")
+        )
+        
+        # Final combined query using a FULL OUTER JOIN with RRF scoring
+        final_query = (
+            select(
+                Document,
+                (
+                    func.coalesce(1.0 / (k + semantic_search_cte.c.rank), 0.0) +
+                    func.coalesce(1.0 / (k + keyword_search_cte.c.rank), 0.0)
+                ).label("score")
+            )
+            .select_from(
+                semantic_search_cte.outerjoin(
+                    keyword_search_cte, 
+                    semantic_search_cte.c.id == keyword_search_cte.c.id,
+                    full=True
+                )
+            )
+            .join(
+                Document,
+                Document.id == func.coalesce(semantic_search_cte.c.id, keyword_search_cte.c.id)
+            )
+            .options(joinedload(Document.search_space))
+            .order_by(text("score DESC"))
+            .limit(top_k)
+        )
+        
+        # Execute the query
+        result = await self.db_session.execute(final_query)
+        documents_with_scores = result.all()
+        
+        # If no results were found, return an empty list
+        if not documents_with_scores:
+            return []
+        
+        # Convert to serializable dictionaries
+        serialized_results = []
+        for document, score in documents_with_scores:
+            serialized_results.append({
+                "document_id": document.id,
+                "title": document.title,
+                "content": document.content,
+                "document_type": document.document_type.value if hasattr(document, 'document_type') else None,
+                "metadata": document.document_metadata,
+                "score": float(score),  # Ensure score is a Python float
+                "search_space_id": document.search_space_id
+            })
+        
+        return serialized_results 

surfsense_backend/app/routes/documents_routes.py

@@ -135,6 +135,10 @@ async def process_file_in_background(
             file_path,
             mode="elements",
             post_processors=[],
+            languages=["eng"],
+            include_orig_elements=False,
+            include_metadata=False,
+            strategy="auto",
         )
         
         docs = loader.load()