mirror of
https://github.com/MODSetter/SurfSense.git
synced 2025-09-02 10:39:13 +00:00
247 lines
No EOL
9.6 KiB
Python
247 lines
No EOL
9.6 KiB
Python
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")
|
|
|
|
"""
|
|
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:
|
|
# Fetch associated chunks for this document
|
|
from sqlalchemy import select
|
|
from app.db import Chunk
|
|
|
|
chunks_query = select(Chunk).where(Chunk.document_id == document.id).order_by(Chunk.id)
|
|
chunks_result = await self.db_session.execute(chunks_query)
|
|
chunks = chunks_result.scalars().all()
|
|
|
|
# Concatenate chunks content
|
|
concatenated_chunks_content = " ".join([chunk.content for chunk in chunks]) if chunks else document.content
|
|
|
|
serialized_results.append({
|
|
"document_id": document.id,
|
|
"title": document.title,
|
|
"content": document.content,
|
|
"chunks_content": concatenated_chunks_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 |