Traditional keyword search relies on exact word matches. If a user searches for "cheap running shoes," keyword search returns results containing those exact words. But a user might also want results for "affordable sneakers" or "budget athletic footwear." Semantic search understands the intent behind the query, not just the literal words.
How Semantic Search Works
Semantic search converts both queries and documents into vector embeddings using a neural network model. These embeddings capture meaning, not just vocabulary. When a user enters a query, the system:
- Converts the query into a vector embedding
- Searches the vector database for the closest document embeddings
- Returns the most semantically similar results
Choosing an Embedding Model
| Model | Dimensions | Best For |
|---|---|---|
| text-embedding-3-small | 1536 | General purpose, cost-effective |
| text-embedding-3-large | 3072 | High-accuracy needs |
| BAAI/bge-large-en-v1.5 | 1024 | Open-source, good latency |
| sentence-transformers/all-MiniLM-L6-v2 | 384 | Lightweight, fast |
For most web applications, text-embedding-3-small from OpenAI offers the best quality-speed-cost ratio. For privacy-sensitive applications, use open-source models like BGE or MiniLM hosted on your infrastructure.
Implementation Architecture
A production semantic search stack typically includes:
- PostgreSQL for storing documents and metadata
- pgvector extension for vector similarity search
- An embedding service (API or self-hosted) to generate vectors
- A re-ranking step for precision improvement
The query flow:
- User types a search query
- Frontend sends query to your search API
- API generates query embedding
- Performs vector similarity search (ANN) on pgvector
- Returns top-K results (typically 20-50)
- Re-rank results using a cross-encoder model
- Return top-10 final results to the user
Hybrid Search for Better Results
Pure semantic search can miss exact matches. A user searching for "Model X123" needs exact string matching, not semantic similarity. Hybrid search combines:
- Vector search for semantic understanding
- BM25 keyword search for exact/rare term matching
- Weighted fusion to merge and rank results
Use a weighting scheme like 0.7 vector + 0.3 BM25, adjustable based on your domain. E-commerce benefits from higher BM25 weight for product codes; content sites benefit from higher vector weight.
Performance Optimization
Indexing: Build HNSW indexes with ef_construction=200 and m=16 for most applications. This balances index build time with search speed.
Caching: Cache frequent query embeddings and their results in Redis. Typically, 20% of queries account for 80% of search volume.
Batch Processing: Generate embeddings in batches for new documents. Most embedding providers support batch endpoints that are 5-10x faster than individual API calls.
Conclusion
AI-powered search dramatically improves user experience by understanding intent. The combination of vector embeddings for semantic understanding and keyword search for precision creates a search experience that feels intelligent. At Rudra IT Solutions, we build custom search pipelines tailored to each client's content type, scale, and latency requirements.