Ecommerce Vector Database & RAG

Ecommerce search often fails when customers use language that does not exactly match product titles, categories, or attributes. A customer may describe a need, use informal terminology, or ask a question rather than search for a specific product name. Traditional keyword search can miss these opportunities.

This project addressed that challenge by building an ecommerce vector database and retrieval-augmented generation system. Product catalogue data, descriptions, FAQs, buying guides, reviews, policy content, and support documentation were prepared for embedding and retrieval. The aim was to create a search and AI foundation that understood meaning, not just keywords.

The vector database was structured with ecommerce-specific metadata so retrieval could account for product type, category, brand, price range, availability, customer segment, and other relevant filters. This allowed semantic search to remain commercially useful and operationally controlled. For example, a query could retrieve products that matched customer intent while still respecting stock, category, or merchandising constraints.

The RAG layer used retrieved product and content records to generate grounded answers for customer-facing and internal workflows. This supported use cases such as product search assistance, buying guidance, support responses, product comparison, and content generation.

The final system improved semantic matching and provided a flexible retrieval layer that could support multiple ecommerce AI features. It helped bridge the gap between how customers describe what they want and how product data is stored.