FAISS & Vector Databases

Why This Matters

Once you have embeddings, you need somewhere to store and search them efficiently. FAISS (Facebook AI Similarity Search) is a library purpose-built for this: finding the most similar vectors in a collection of millions.

The Intuition

Imagine you have a library with 10,000 books, each with GPS coordinates on a concept map (their embeddings). Someone asks a question, and you need to find the 5 nearest books. Checking every single book is slow. FAISS uses clever indexing tricks — like dividing the map into neighborhoods — so you only need to check a few dozen candidates instead of all 10,000.

How FAISS Works

Index Types

Index	Speed	Accuracy	Memory	Best For
`IndexFlatL2`	Slow (exact)	Perfect	High	< 10K vectors
`IndexIVFFlat`	Fast	Very good	Medium	10K–1M vectors
`IndexIVFPQ`	Very fast	Good	Low	> 1M vectors
`IndexHNSW`	Fast	Very good	Medium	Read-heavy workloads

Basic Usage

import faiss
import numpy as np

# Create index for 384-dimensional vectors
index = faiss.IndexFlatL2(384)

# Add vectors
vectors = np.random.rand(1000, 384).astype('float32')
index.add(vectors)

# Search for 5 nearest neighbors
query = np.random.rand(1, 384).astype('float32')
distances, indices = index.search(query, k=5)

FAISS vs Vector Databases

Feature	FAISS	Pinecone/Weaviate/Qdrant
Deployment	Library (in-process)	Managed service
Metadata filtering	Manual	Built-in
Persistence	File-based	Cloud-managed
Cost	Free	Per-query pricing
Best for	Embedded, local, < 1M vectors	Production, managed, > 1M vectors

Amprealize uses FAISS because behavior collections are small enough (< 10K) to fit in-process, and it avoids external service dependencies.