Open Census MCP Server

#!/usr/bin/env python3 """ Quick smoke test for clean embeddings """ import faiss import json import numpy as np from sentence_transformers import SentenceTransformer # Load components print("🔍 Loading clean index...") index = faiss.read_index('knowledge-base/stats-index/variables_bge.faiss') with open('knowledge-base/stats-index/variables_meta.json') as f: meta = json.load(f) print(f"✅ Index: {index.ntotal} vectors, {index.d} dimensions") print(f"✅ Metadata: {len(meta)} entries") # Check metadata structure sample = meta[0] print(f"✅ Sample keys: {list(sample.keys())}") # Check for weights if 'weights' in sample: print(f"✅ Weights found: {list(sample['weights'].keys())}") else: print("❌ No weights in metadata") # Test semantic search print("\n🔍 Testing semantic search...") model = SentenceTransformer('BAAI/bge-large-en-v1.5') test_queries = [ "median household income", "percent renter occupied", "foreign born population" ] for query in test_queries: # Embed query query_vec = model.encode([query]) # Search scores, indices = index.search(query_vec, 3) print(f"\n📊 Query: '{query}'") for i, (score, idx) in enumerate(zip(scores[0], indices[0])): var = meta[idx] print(f" {i+1}. {var['variable_id']} - {var['label'][:50]}... (score: {score:.3f})") print("\n🎯 Quick validation:") print("- Does 'median household income' return B19013_001E?") print("- Are scores reasonable (>0.3 for good matches)?") print("- No more spam-contaminated results?")