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