class ChucksHybridSearchRetriever:
    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,
        search_space_id: int,
    ) -> list:
        """
        Perform vector similarity search on chunks.

        Args:
            query_text: The search query text
            top_k: Number of results to return
            search_space_id: The search space ID to search within

        Returns:
            List of chunks sorted by vector similarity
        """
        from sqlalchemy import select
        from sqlalchemy.orm import joinedload

        from app.config import config
        from app.db import Chunk, Document

        # Get embedding for the query
        embedding_model = config.embedding_model_instance
        query_embedding = embedding_model.embed(query_text)

        # Build the query filtered by search space
        query = (
            select(Chunk)
            .options(joinedload(Chunk.document).joinedload(Document.search_space))
            .join(Document, Chunk.document_id == Document.id)
            .where(Document.search_space_id == search_space_id)
        )

        # Add vector similarity ordering
        query = query.order_by(Chunk.embedding.op("<=>")(query_embedding)).limit(top_k)

        # Execute the query
        result = await self.db_session.execute(query)
        chunks = result.scalars().all()

        return chunks

    async def full_text_search(
        self,
        query_text: str,
        top_k: int,
        search_space_id: int,
    ) -> list:
        """
        Perform full-text keyword search on chunks.

        Args:
            query_text: The search query text
            top_k: Number of results to return
            search_space_id: The search space ID to search within

        Returns:
            List of chunks sorted by text relevance
        """
        from sqlalchemy import func, select
        from sqlalchemy.orm import joinedload

        from app.db import Chunk, Document

        # Create tsvector and tsquery for PostgreSQL full-text search
        tsvector = func.to_tsvector("english", Chunk.content)
        tsquery = func.plainto_tsquery("english", query_text)

        # Build the query filtered by search space
        query = (
            select(Chunk)
            .options(joinedload(Chunk.document).joinedload(Document.search_space))
            .join(Document, Chunk.document_id == Document.id)
            .where(Document.search_space_id == search_space_id)
            .where(
                tsvector.op("@@")(tsquery)
            )  # Only include results that match the query
        )

        # 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)
        chunks = result.scalars().all()

        return chunks

    async def hybrid_search(
        self,
        query_text: str,
        top_k: int,
        search_space_id: int,
        document_type: str | None = 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
            search_space_id: The search space ID to search within
            document_type: Optional document type to filter results (e.g., "FILE", "CRAWLED_URL")

        Returns:
            List of dictionaries containing chunk data and relevance scores
        """
        from sqlalchemy import func, select, text
        from sqlalchemy.orm import joinedload

        from app.config import config
        from app.db import Chunk, Document, DocumentType

        # 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", Chunk.content)
        tsquery = func.plainto_tsquery("english", query_text)

        # Base conditions for chunk filtering - search space is required
        base_conditions = [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 filtered by search space
        semantic_search_cte = (
            select(
                Chunk.id,
                func.rank()
                .over(order_by=Chunk.embedding.op("<=>")(query_embedding))
                .label("rank"),
            )
            .join(Document, Chunk.document_id == Document.id)
            .where(*base_conditions)
        )

        semantic_search_cte = (
            semantic_search_cte.order_by(Chunk.embedding.op("<=>")(query_embedding))
            .limit(n_results)
            .cte("semantic_search")
        )

        # CTE for keyword search filtered by search space
        keyword_search_cte = (
            select(
                Chunk.id,
                func.rank()
                .over(order_by=func.ts_rank_cd(tsvector, tsquery).desc())
                .label("rank"),
            )
            .join(Document, Chunk.document_id == Document.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(
                Chunk,
                (
                    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(
                Chunk,
                Chunk.id
                == func.coalesce(semantic_search_cte.c.id, keyword_search_cte.c.id),
            )
            .options(joinedload(Chunk.document))
            .order_by(text("score DESC"))
            .limit(top_k)
        )

        # Execute the query
        result = await self.db_session.execute(final_query)
        chunks_with_scores = result.all()

        # If no results were found, return an empty list
        if not chunks_with_scores:
            return []

        # Convert to serializable dictionaries if no reranker is available or if reranking failed
        serialized_results = []
        for chunk, score in chunks_with_scores:
            serialized_results.append(
                {
                    "chunk_id": chunk.id,
                    "content": chunk.content,
                    "score": float(score),  # Ensure score is a Python float
                    "document": {
                        "id": chunk.document.id,
                        "title": chunk.document.title,
                        "document_type": chunk.document.document_type.value
                        if hasattr(chunk.document, "document_type")
                        else None,
                        "metadata": chunk.document.document_metadata,
                    },
                }
            )

        return serialized_results