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feat: SurfSense v0.0.6 init
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DESKTOP-RTLN3BA\$punk
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commit
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58 changed files with 8284 additions and 2076 deletions
243
surfsense_backend/app/retriver/chunks_hybrid_search.py
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243
surfsense_backend/app/retriver/chunks_hybrid_search.py
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class ChucksHybridSearchRetriever:
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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(self, query_text: str, top_k: int, user_id: str, search_space_id: int = None) -> list:
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"""
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Perform vector similarity search on chunks.
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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 chunks sorted by vector similarity
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"""
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from sqlalchemy import select, func
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from sqlalchemy.orm import joinedload
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from app.db import Chunk, Document, SearchSpace
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from app.config import config
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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(Chunk)
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.options(joinedload(Chunk.document).joinedload(Document.search_space))
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.join(Document, Chunk.document_id == Document.id)
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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 = (
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query
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.order_by(Chunk.embedding.op("<=>")(query_embedding))
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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(query)
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chunks = result.scalars().all()
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return chunks
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async def full_text_search(self, query_text: str, top_k: int, user_id: str, search_space_id: int = None) -> list:
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"""
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Perform full-text keyword search on chunks.
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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 chunks sorted by text relevance
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"""
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from sqlalchemy import select, func, text
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from sqlalchemy.orm import joinedload
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from app.db import Chunk, Document, SearchSpace
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# Create tsvector and tsquery for PostgreSQL full-text search
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tsvector = func.to_tsvector('english', Chunk.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(Chunk)
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.options(joinedload(Chunk.document).joinedload(Document.search_space))
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.join(Document, Chunk.document_id == Document.id)
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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(tsvector.op("@@")(tsquery)) # 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 = (
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query
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.order_by(func.ts_rank_cd(tsvector, tsquery).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(query)
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chunks = result.scalars().all()
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return chunks
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async def hybrid_search(self, query_text: str, top_k: int, user_id: str, search_space_id: int = None, document_type: str = None) -> 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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Returns:
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List of dictionaries containing chunk data and relevance scores
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"""
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from sqlalchemy import select, func, text
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from sqlalchemy.orm import joinedload
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from app.db import Chunk, Document, SearchSpace, DocumentType
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from app.config import config
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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', Chunk.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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Chunk.id,
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func.rank().over(order_by=Chunk.embedding.op("<=>")(query_embedding)).label("rank")
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)
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.join(Document, Chunk.document_id == Document.id)
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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
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.order_by(Chunk.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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Chunk.id,
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func.rank().over(order_by=func.ts_rank_cd(tsvector, tsquery).desc()).label("rank")
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)
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.join(Document, Chunk.document_id == Document.id)
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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
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.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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Chunk,
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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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Chunk,
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Chunk.id == func.coalesce(semantic_search_cte.c.id, keyword_search_cte.c.id)
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)
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.options(joinedload(Chunk.document))
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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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chunks_with_scores = result.all()
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# If no results were found, return an empty list
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if not chunks_with_scores:
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return []
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# Convert to serializable dictionaries if no reranker is available or if reranking failed
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serialized_results = []
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for chunk, score in chunks_with_scores:
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serialized_results.append({
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"chunk_id": chunk.id,
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"content": chunk.content,
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"score": float(score), # Ensure score is a Python float
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"document": {
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"id": chunk.document.id,
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"title": chunk.document.title,
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"document_type": chunk.document.document_type.value if hasattr(chunk.document, 'document_type') else None,
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"metadata": chunk.document.document_metadata
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}
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})
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return serialized_results
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