Vector Database Optimization for Semantic Search
Vector DBs power semantic search, RAG, and AI memory. Optimize for billion-scale embedding search.
Implementation
\\\`python
Index for billion vectors
d = 768 # Embedding dimension index = faiss.IndexHNSWFlat(d, 32) # HNSW with 32 neighbors
Add vectors
embeddings = np.random.random((1000000, d)).astype('float32') index.add(embeddings)
Search
query = np.random.random((1, d)).astype('float32') D, I = index.search(query, k=10) # Top 10 nearest neighbors \\\` Performance: Sub-millisecond search in billion-vector DB Tools: Pinecone, Milvus, Weaviate