Why Vector Search
Keyword search fails when users phrase queries differently than stored context. Vector search finds semantically similar content regardless of exact wording—essential for effective context retrieval.
Components Overview
- Embedding Model: Converts text to vectors
- Vector Store: Stores and searches vectors efficiently
- Query Pipeline: Orchestrates retrieval
Step 1: Choose Your Embedding Model
Options range from OpenAI's text-embedding-ada-002 to open-source alternatives like sentence-transformers. Consider: embedding dimension, domain relevance, cost, and latency requirements.
Step 2: Set Up Vector Storage
For PostgreSQL, add the pgvector extension. For dedicated solutions, consider Pinecone, Weaviate, or Qdrant. Each offers different tradeoffs in performance, features, and operational complexity.
-- PostgreSQL with pgvector
CREATE EXTENSION vector;
ALTER TABLE contexts
ADD COLUMN embedding vector(1536);Step 3: Build the Embedding Pipeline
When context is created or updated, generate and store embeddings. Implement batch processing for efficiency. Handle embedding model failures gracefully.
Step 4: Implement Search
Query by embedding similarity. Combine with metadata filters for hybrid search. Tune similarity thresholds and result counts based on evaluation.
Performance Tips
Index vectors with appropriate algorithm (IVF, HNSW) based on dataset size. Pre-filter by metadata before vector search. Cache frequent queries.
