01. FAISS Fundamentals

📖 Overview

FAISS is a library for efficient similarity search over dense vectors.

Python Example

import faiss
import numpy as np

dimension = 384

index = faiss.IndexFlatL2(dimension)

vectors = np.random.random(
    (1000, dimension)
).astype("float32")

index.add(vectors)

query = np.random.random(
    (1, dimension)
).astype("float32")

distances, ids = index.search(query, 5)

print(ids)
print(distances)

Architecture

flowchart TD
    A["Documents"] --> B["Chunking"]
    B --> C["Embedding Model"]
    C --> D["Dense Vectors"]
    D --> E["FAISS Index"]
    E --> F["Similarity Search"]

Key Takeaways

• FAISS provides vector similarity search.
• Flat indexes provide exact search.
• HNSW and IVF provide approximate search.
• IndexFlatIP can support cosine similarity with normalized vectors.
• FAISS should normally be treated as a retrieval infrastructure component rather than a complete enterprise database.

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