FAISS Essentials

Master FAISS for Similarity Search

Open-source library for efficient similarity search and clustering of dense vectors—ideal for recommendation, RAG, and semantic retrieval.

Models Deployed

12,430+

Active Developers

58,900+

Key Features

Similarity Search

Find nearest neighbors in high-dimensional vector spaces using efficient indexing.

Indexing Strategies

Supports flat, IVF, HNSW, PQ, and hybrid indexes for speed and memory trade-offs.

GPU Acceleration

Run large-scale searches with CUDA support for massive speedups.

Python & C++ APIs

Use FAISS in Python for prototyping or C++ for production performance.

Clustering & Quantization

Cluster vectors and compress indexes for memory-efficient search.

How It Works

Install FAISS

Use `pip install faiss-cpu` or `faiss-gpu` depending on your hardware.

Prepare Vectors

Generate embeddings using models like OpenAI, Hugging Face, or custom encoders.

Build Index

Choose an index type (e.g., Flat, IVF, HNSW) and train if needed.

Add Vectors

Insert vectors into the index using `add()` or `add_with_ids()`.

Query Neighbors

Use `search()` to find top-k similar vectors for a given query.

Code Example

// FAISS Model Training

import faiss
import numpy as np

# Create sample data
d = 128  # vector dimension
nb = 10000  # database size
np.random.seed(1234)
xb = np.random.random((nb, d)).astype('float32')

# Build index
index = faiss.IndexFlatL2(d)
index.add(xb)

# Query
xq = np.random.random((5, d)).astype('float32')
D, I = index.search(xq, k=5)
print(I)

Use Cases

Semantic Search

Search documents, FAQs, or transcripts using vector similarity.

Recommendation Systems

Suggest items based on user or item embeddings.

Image Retrieval

Find visually similar images using CNN or CLIP embeddings.

RAG Pipelines

Retrieve relevant context for LLMs using FAISS indexes.

Anomaly Detection

Detect outliers by comparing vectors to known distributions.

Integrations & Resources

Explore FAISS’s ecosystem and find the tools, platforms, and docs to accelerate your workflow.

Popular Integrations

Hugging Face Transformers for embeddings
OpenAI and Cohere for semantic vectors
LangChain and LlamaIndex for RAG
FastAPI and Flask for deployment
Streamlit and Gradio for demos
NumPy and PyTorch for preprocessing

Helpful Resources

GitHub Repo Official Docs Tutorials