AI Sales Consultant for OpenCart (RAG catalog bot)

Description

Regular search looks for exact word matches. A buyer writes "warm jacket for skiing" and does not find "ski jacket". Semantic search based on vector embeddings understands the conceptual similarity between query and products: it finds relevant results even when query words do not match product names.

We implement semantic (vector) search for OpenCart and ocStore based on OpenAI Embeddings or open-source models (sentence-transformers). This technology is particularly effective for large catalogues with complex or technical products.

How It Works

• Each product is converted to a vector representation (embedding) via AI model
• The buyer search query is similarly converted to a vector
• The system finds products with the smallest cosine distance to the query
• Results are ranked by semantic similarity rather than exact match

Advantages Over Elasticsearch

Elasticsearch finds products by keywords. Semantic search finds by meaning. The optimal approach combines both: Elasticsearch for exact matches plus vector search for semantics. Search quality metrics comparison provided before and after implementation.