apunkt/SurfSense
1
0
Fork 0
mirror of https://github.com/MODSetter/SurfSense.git synced 2026-07-02 22:01:05 +02:00
Code Issues Projects Releases Packages Wiki Activity

feat: implement fuzzy search in mention document

Browse source
This commit is contained in:
Anish Sarkar 2026-01-17 20:46:47 +05:30
parent b001b65067
commit 293de6876a
1 changed files with 23 additions and 10 deletions
Download patch file Download diff file Expand all files Collapse all files

33
surfsense_backend/app/routes/documents_routes.py
View file

@ -444,7 +444,8 @@ async def search_document_titles(
Lightweight document title search optimized for mention picker (@mentions). Lightweight document title search optimized for mention picker (@mentions).
Returns only id, title, and document_type - no content or metadata. Returns only id, title, and document_type - no content or metadata.
Results are ordered by relevance: prefix matches first, then contains matches. Uses pg_trgm fuzzy search with similarity scoring for typo tolerance.
Results are ordered by relevance using trigram similarity scores.
Args: Args:
search_space_id: The search space to search in. Required. search_space_id: The search space to search in. Required.
@ -457,7 +458,7 @@ async def search_document_titles(
Returns: Returns:
DocumentTitleSearchResponse: Lightweight list with has_more flag (no total count). DocumentTitleSearchResponse: Lightweight list with has_more flag (no total count).
""" """
from sqlalchemy import case, literal from sqlalchemy import desc, func, or_
try: try:
# Check permission for the search space # Check permission for the search space
@ -480,17 +481,29 @@ async def search_document_titles(
if len(title.strip()) < 2: if len(title.strip()) < 2:
query = query.order_by(Document.updated_at.desc().nullslast()) query = query.order_by(Document.updated_at.desc().nullslast())
else: else:
# Apply title filter with ILIKE (uses pg_trgm index) # Fuzzy search using pg_trgm similarity + ILIKE fallback
search_term = title.strip() search_term = title.strip()
query = query.filter(Document.title.ilike(f"%{search_term}%"))
# Order by relevance: prefix matches first, then alphabetical # Similarity threshold for fuzzy matching (0.3 = ~30% trigram overlap)
# CASE WHEN title ILIKE 'term%' THEN 0 ELSE 1 END # Lower values = more fuzzy, higher values = stricter matching
prefix_priority = case( similarity_threshold = 0.3
(Document.title.ilike(f"{search_term}%"), literal(0)),
else_=literal(1), # Match documents that either:
# 1. Have high trigram similarity (fuzzy match - handles typos)
# 2. Contain the exact substring (ILIKE - handles partial matches)
query = query.filter(
or_(
func.similarity(Document.title, search_term) > similarity_threshold,
Document.title.ilike(f"%{search_term}%"),
)
)
# Order by similarity score (descending) for best relevance ranking
# Higher similarity = better match = appears first
query = query.order_by(
desc(func.similarity(Document.title, search_term)),
Document.title, # Alphabetical tiebreaker
) )
query = query.order_by(prefix_priority, Document.title)
# Fetch page_size + 1 to determine has_more without COUNT query # Fetch page_size + 1 to determine has_more without COUNT query
offset = page * page_size offset = page * page_size