AI Book Agent MCP Server

#!/usr/bin/env python3 """Quick search test with different thresholds.""" import sys from pathlib import Path sys.path.insert(0, str(Path(__file__).parent.parent)) from src.utils.config import config from src.embeddings.embeddings import EmbeddingGenerator from src.search.vector_store import VectorStore, BookSearchEngine def test_search_thresholds(): """Test search with different similarity thresholds.""" # Initialize components embedding_generator = EmbeddingGenerator( model_name=config.embeddings['model'], device=config.embeddings['device'] ) vector_store = VectorStore( persist_directory=config.books['index_dir'], collection_name=config.vector_store['collection_name'] ) search_engine = BookSearchEngine(vector_store, embedding_generator) # Test queries queries = ["machine learning", "deployment", "monitoring", "data quality"] thresholds = [0.3, 0.4, 0.5, 0.6, 0.7] for query in queries: print(f"\nQuery: '{query}'") print("-" * 50) for threshold in thresholds: results = search_engine.search_books( query=query, max_results=3, similarity_threshold=threshold ) if results: top_similarity = results[0]['similarity'] print(f"Threshold {threshold}: {len(results)} results (top: {top_similarity:.3f})") else: print(f"Threshold {threshold}: 0 results") # Show top result with new threshold results = search_engine.search_books(query=query, max_results=1) if results: r = results[0] print(f"\nTop result:") print(f" Book: {r['book_title']}") print(f" Chapter: {r['chapter_title']}") print(f" Similarity: {r['similarity']:.3f}") print(f" Preview: {r['content'][:150]}...") if __name__ == "__main__": test_search_thresholds()