Vector Databases Explained: FAISS, Chroma, and Milvus
Large Language Models rely on retrieval-augmented memory. Vector databases make it possible. This post explores FAISS, Chroma, and Milvus — how they store embeddings, perform similarity search, and scale RAG pipelines.
1) Why Vector Search?
Traditional databases match exact keys; vector DBs match semantic proximity. Instead of equality, they compute cosine or L2 distances between embedding vectors.
2) Core Components
- Encoder: converts text/image into numerical vectors.
- Index: organizes those vectors for fast lookup.
- Storage: persists vectors + metadata.
3) FAISS (Meta AI)
import faiss, numpy as np
data = np.random.rand(10000,
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