Open Census MCP Server

# test_rag.py - Run this in your project root import chromadb import openai import os # Test the vector DB directly client = chromadb.PersistentClient(path='data/vector_db') collection = client.get_collection('census_knowledge') print(f"Collection has {collection.count()} documents") # Test a simple query without embeddings first results = collection.get(limit=5) print(f"Sample documents:") for i, doc in enumerate(results['documents'][:3]): print(f"{i+1}. {doc[:100]}...") # Test with OpenAI embeddings openai_client = openai.OpenAI() query = "ACS methodology" response = openai_client.embeddings.create( input=[query], model="text-embedding-3-large" ) query_embedding = response.data[0].embedding # Search with embedding search_results = collection.query( query_embeddings=[query_embedding], n_results=5 ) print(f"\nSearch results for '{query}':") print(f"Found {len(search_results['documents'][0])} results") if search_results['documents'][0]: for i, (doc, distance) in enumerate(zip(search_results['documents'][0], search_results['distances'][0])): similarity = 1 - distance print(f"{i+1}. Similarity: {similarity:.3f}") print(f" {doc[:150]}...") else: print("No results found!")