#!/usr/bin/env python3
"""
Fix semantic search configuration to use the correct collection.
"""
import sys
from pathlib import Path
from mcp_server.core.path_utils import PathUtils
# Add project root to Python path
sys.path.insert(0, str(Path(__file__).parent.parent))
from mcp_server.utils.semantic_indexer import SemanticIndexer
from qdrant_client import QdrantClient
def test_semantic_search():
"""Test semantic search with the correct collection."""
print("=" * 60)
print("TESTING SEMANTIC SEARCH WITH CORRECT COLLECTION")
print("=" * 60)
qdrant_path = "PathUtils.get_workspace_root()/.indexes/qdrant/main.qdrant"
collection_name = "code-embeddings" # The working collection
print(f"Qdrant path: {qdrant_path}")
print(f"Collection: {collection_name}")
# Remove stale lock if it exists
lock_file = Path(qdrant_path) / ".lock"
if lock_file.exists():
try:
lock_file.unlink()
except OSError:
pass
try:
# Initialize semantic indexer with correct collection
semantic_indexer = SemanticIndexer(
qdrant_path=qdrant_path,
collection=collection_name
)
print("✅ Connected to semantic indexer")
# Test searches
test_queries = [
"function search",
"class Dispatcher",
"semantic indexer",
"plugin factory",
"MCP server"
]
print("\nTesting semantic search queries:")
for query in test_queries:
print(f"\nQuery: '{query}'")
try:
results = semantic_indexer.search(query, limit=3)
if results:
print(f" ✅ Found {len(results)} results:")
for i, result in enumerate(results[:3]):
file_path = (
result.get("file") or
result.get("relative_path") or
result.get("filepath") or
"unknown"
)
score = result.get("score", 0)
print(f" {i+1}. {file_path} (score: {score:.3f})")
else:
print(" ❌ No results found")
except Exception as e:
print(f" ❌ Search failed: {e}")
# Test with direct Qdrant client to verify
print("\nDirect Qdrant test:")
client = QdrantClient(path=qdrant_path)
# Get a sample embedding to use as query
sample = client.scroll(
collection_name=collection_name,
limit=1,
with_vectors=True
)
if sample[0]:
query_vector = sample[0][0].vector
search_results = client.search(
collection_name=collection_name,
query_vector=query_vector,
limit=3
)
if search_results:
print("✅ Direct Qdrant search works!")
for i, result in enumerate(search_results):
file_path = (
result.payload.get("file") or
result.payload.get("relative_path") or
result.payload.get("filepath") or
"unknown"
)
print(f" {i+1}. {file_path} (score: {result.score:.3f})")
except Exception as e:
print(f"❌ Error: {e}")
import traceback
traceback.print_exc()
print("\n" + "=" * 60)
print("TEST COMPLETE")
print("=" * 60)
# Provide configuration recommendation
print("\n📝 CONFIGURATION FIX NEEDED:")
print("The semantic discovery should be updated to use 'code-embeddings' collection")
print("instead of trying to use the empty 'codebase-f7b49f5d0ae0' collection.")
print("\nThe issue is in the semantic discovery logic that needs to check")
print("which collections actually contain data for the current codebase.")
if __name__ == "__main__":
test_semantic_search()
