voyageai

import os from voyageai import Client # It's recommended to set your API key as an environment variable (VOYAGE_API_KEY). # For demonstration, you can set it directly, but avoid this in production. api_key = os.environ.get('VOYAGE_API_KEY', 'YOUR_VOYAGE_API_KEY') if not api_key or api_key == 'YOUR_VOYAGE_API_KEY': print("Warning: VOYAGE_API_KEY environment variable not set or is a placeholder. Please set it for actual use.") # Exit or raise error if API key is critical for quickstart execution # For this example, we'll proceed with a dummy key but it will fail API calls. client = Client(api_key=api_key, max_retries=3) # max_retries=3 added for robustness texts = [ "hello, world", "welcome to voyage ai!", "Voyage AI provides cutting-edge embedding and reranking models." ] try: # Generate embeddings using a recommended model from the Voyage 4 series result = client.embed( texts, model="voyage-4-large", input_type="document" # Recommended for optimal retrieval ) print(f"Generated {len(result.embeddings)} embeddings.") print(f"Each embedding has {len(result.embeddings[0])} dimensions.") print(f"First embedding (truncated): {result.embeddings[0][:10]}...") print(f"Total tokens used: {result.total_tokens}") # Example of reranking query = "AI models for text processing" documents = [ "Voyage AI offers models for natural language understanding.", "The latest phone has a great camera and long battery life.", "Our embedding models are state-of-the-art for semantic search." ] rerank_result = client.rerank(query, documents, model="rerank-2.5") print("\nReranking results:") for i, r in enumerate(rerank_result.results): print(f" Rank {i+1}: Score={r.relevance_score:.4f}, Document='{r.document.text}'") except Exception as e: print(f"An error occurred: {e}") print("Please ensure your VOYAGE_API_KEY is correctly set and you have network access.")