To connect a vector database to your product catalog backend, you’ll need to design a pipeline that transforms product data into vector embeddings, stores them in the vector database, and integrates querying into your application. Start by identifying which product attributes (e.g., descriptions, images, or metadata) will be used for similarity searches. Use an embedding model like BERT for text or ResNet for images to convert these attributes into numerical vectors. For example, product descriptions can be processed through a language model to generate 768-dimensional vectors. Store these vectors alongside product IDs in the vector database (e.g., Pinecone, Milvus, or Qdrant) to maintain a link between the vector and the original product data in your catalog.
Next, integrate the vector database into your backend using its client library or API. For instance, if using Pinecone, you’d install its Python SDK and initialize a client with your API key. When a product is added or updated in your catalog, generate its embedding and upsert it into the vector database using a method like index.upsert(vectors=[(id, embedding, metadata)]). Ensure your backend handles synchronization—if your primary product database uses PostgreSQL, you might add a trigger to generate embeddings after inserts or updates. For querying, create an endpoint in your backend that accepts a search input (e.g., a text query), generates its embedding using the same model, and performs a nearest-neighbor search in the vector database. Return matching product IDs and fetch full details from your primary database to avoid duplicating data.
Optimize for performance and scalability. Partition your vector database indexes by product category or region if your catalog is large, which speeds up queries. Use caching (e.g., Redis) for frequent queries, such as “similar to product X” results. If your application requires real-time updates, ensure your embedding generation and indexing steps are fast enough—consider batch processing for less critical updates. For example, a fashion retailer might run nightly batch jobs to update embeddings for pricing changes but process image uploads in real time. Finally, add monitoring to track query latency and error rates, and secure API keys and database connections with environment variables and encryption in transit.