Vector DBs power semantic search, RAG, and AI memory. Optimize for billion-scale embedding search.

Implementation

```python import faiss import numpy as np

Index for billion vectors

d = 768 # Embedding dimension index = faiss.IndexHNSWFlat(d, 32) # HNSW with 32 neighbors

Add vectors

embeddings = np.random.random((1_000_000, 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