08. FAISS Installation

Chapter 8 of 18 · 20 min

KEY INSIGHT

FAISS provides GPU-accelerated nearest-neighbor search on billions of vectorsâ€”install via conda for CUDA support or pip for CPU-only. FAISS (Facebook AI Similarity Search) is a library for efficient similarity search and clustering of dense vectors. Unlike ChromaDB, FAISS is a library, not a database. You manage indexing and persistence yourself. ### CPU Installation (pip) ```bash pip install faiss-cpu ``` ### GPU Installation (conda recommended) FAISS GPU support requires CUDA and works best with conda: ```bash conda install -c pytorch faiss-gpu cudatoolkit=11.7 ``` The conda approach handles CUDA library dependencies that pip cannot manage. ### Verification ```python import faiss import numpy as np print(f"FAISS version: {faiss.__version__}") # Create a simple index dimension = 128 index = faiss.IndexFlatL2(dimension) # L2 distance index # Generate random test vectors vectors = np.random.random((1000, dimension)).astype('float32') # Add vectors to index index.add(vectors) print(f"Index size: {index.ntotal} vectors") print(f"Is trained: {index.is_trained}") ``` Output: ``` FAISS version: 1.7.4 Index size: 1000 vectors Is trained: True ``` The flat index stores all vectors exactlyâ€”no compression, no approximation. For 1000 vectors, this is fine. For 10 million vectors, you need a different index type.

Local verification checkpoint

Run the smallest example from this chapter in a local workspace and record the package version, runtime, data path, and observed output. If the result depends on model size, vector count, CPU/GPU backend, or available memory, note that constraint beside the exercise so the lesson remains reproducible.

Local verification checkpoint

EXERCISE

Install FAISS (CPU or GPU based on your system). Create an index with 10,000 random 256-dimensional vectors. Verify the count and run a test query.