To implement faceted search with vector embeddings, you need to combine vector-based similarity searches with traditional filtering based on structured metadata. Start by storing both vector embeddings (for semantic search) and structured metadata (for facets) for each item in your dataset. Use a database or search engine that supports hybrid queries, such as Elasticsearch, PostgreSQL with pgvector, or a dedicated vector database like Pinecone. When a user submits a query, first convert their search term into a vector embedding using a model like Sentence-BERT or OpenAI’s embeddings. Then, perform a nearest-neighbor vector search while applying facet filters (e.g., category, price range) to the metadata. Finally, return results ranked by relevance and aggregate facet counts from the filtered subset.
For example, imagine an e-commerce app where users search for products. Each product has a description converted to a vector and metadata like “category,” “price,” and “brand.” A query for “waterproof hiking boots” would generate a vector embedding, and the system would find similar product vectors. Simultaneously, the user might filter by “price < $100” and “brand: XYZ.” The database retrieves items that match both the vector similarity and the metadata filters. Facet counts (e.g., how many remaining items fall into “size 10” or “color: black”) are calculated from the filtered results. Tools like Elasticsearch handle this efficiently by combining k-nearest-neighbor (kNN) search with aggregations for facet counts, while PostgreSQL with pgvector might require custom SQL joins between vector results and metadata tables.
Key challenges include balancing performance and accuracy. Pre-filtering by facets before vector search can limit results but might exclude relevant items. Post-filtering (applying facets after vector search) ensures better recall but may require re-ranking. For large datasets, approximate nearest neighbor (ANN) indexes like FAISS or HNSW speed up vector searches but need integration with metadata filtering. Code-wise, a simplified workflow might look like:
- Generate embeddings for all items using a model like
all-MiniLM-L6-v2. - Store vectors and metadata in a hybrid database.
- For a query, embed the search term, run a filtered vector search, and compute facets.
For instance, in Elasticsearch, you’d use a kNN query with a filter clause and aggregations for facets. In Python, with libraries like sentence-transformers and pgvector, you might filter metadata with SQL and compute facets manually. Optimize by caching frequent facet values or precomputing common filters to reduce latency.