Agent skill

faiss

Facebook's library for efficient similarity search and clustering of dense vectors. Supports billions of vectors, GPU acceleration, and various index types (Flat, IVF, HNSW). Use for fast k-NN search, large-scale vector retrieval, or when you need pure similarity search without metadata. Best for high-performance applications.

View SKILL.md on GitHub Repository
Stars 56,643
Forks 7,481

Install this agent skill to your Project

npx add-skill https://github.com/NousResearch/hermes-agent/tree/main/optional-skills/mlops/faiss

Metadata

Additional technical details for this skill

hermes 
{
    "tags": [
        "RAG",
        "FAISS",
        "Similarity Search",
        "Vector Search",
        "Facebook AI",
        "GPU Acceleration",
        "Billion-Scale",
        "K-NN",
        "HNSW",
        "High Performance",
        "Large Scale"
    ]
}

SKILL.md

FAISS - Efficient Similarity Search

Facebook AI's library for billion-scale vector similarity search.

When to use FAISS

Use FAISS when:

  • Need fast similarity search on large vector datasets (millions/billions)
  • GPU acceleration required
  • Pure vector similarity (no metadata filtering needed)
  • High throughput, low latency critical
  • Offline/batch processing of embeddings

Metrics:

  • 31,700+ GitHub stars
  • Meta/Facebook AI Research
  • Handles billions of vectors
  • C++ with Python bindings

Use alternatives instead:

  • Chroma/Pinecone: Need metadata filtering
  • Weaviate: Need full database features
  • Annoy: Simpler, fewer features

Quick start

Installation

bash
# CPU only
pip install faiss-cpu

# GPU support
pip install faiss-gpu

Basic usage

python
import faiss
import numpy as np

# Create sample data (1000 vectors, 128 dimensions)
d = 128
nb = 1000
vectors = np.random.random((nb, d)).astype('float32')

# Create index
index = faiss.IndexFlatL2(d)  # L2 distance
index.add(vectors)             # Add vectors

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

print(f"Nearest neighbors: {indices}")
print(f"Distances: {distances}")

Index types

1. Flat (exact search)

python
# L2 (Euclidean) distance
index = faiss.IndexFlatL2(d)

# Inner product (cosine similarity if normalized)
index = faiss.IndexFlatIP(d)

# Slowest, most accurate

2. IVF (inverted file) - Fast approximate

python
# Create quantizer
quantizer = faiss.IndexFlatL2(d)

# IVF index with 100 clusters
nlist = 100
index = faiss.IndexIVFFlat(quantizer, d, nlist)

# Train on data
index.train(vectors)

# Add vectors
index.add(vectors)

# Search (nprobe = clusters to search)
index.nprobe = 10
distances, indices = index.search(query, k)

3. HNSW (Hierarchical NSW) - Best quality/speed

python
# HNSW index
M = 32  # Number of connections per layer
index = faiss.IndexHNSWFlat(d, M)

# No training needed
index.add(vectors)

# Search
distances, indices = index.search(query, k)

4. Product Quantization - Memory efficient

python
# PQ reduces memory by 16-32×
m = 8   # Number of subquantizers
nbits = 8
index = faiss.IndexPQ(d, m, nbits)

# Train and add
index.train(vectors)
index.add(vectors)

Save and load

python
# Save index
faiss.write_index(index, "large.index")

# Load index
index = faiss.read_index("large.index")

# Continue using
distances, indices = index.search(query, k)

GPU acceleration

python
# Single GPU
res = faiss.StandardGpuResources()
index_cpu = faiss.IndexFlatL2(d)
index_gpu = faiss.index_cpu_to_gpu(res, 0, index_cpu)  # GPU 0

# Multi-GPU
index_gpu = faiss.index_cpu_to_all_gpus(index_cpu)

# 10-100× faster than CPU

LangChain integration

python
from langchain_community.vectorstores import FAISS
from langchain_openai import OpenAIEmbeddings

# Create FAISS vector store
vectorstore = FAISS.from_documents(docs, OpenAIEmbeddings())

# Save
vectorstore.save_local("faiss_index")

# Load
vectorstore = FAISS.load_local(
    "faiss_index",
    OpenAIEmbeddings(),
    allow_dangerous_deserialization=True
)

# Search
results = vectorstore.similarity_search("query", k=5)

LlamaIndex integration

python
from llama_index.vector_stores.faiss import FaissVectorStore
import faiss

# Create FAISS index
d = 1536
faiss_index = faiss.IndexFlatL2(d)

vector_store = FaissVectorStore(faiss_index=faiss_index)

Best practices

  1. Choose right index type - Flat for <10K, IVF for 10K-1M, HNSW for quality
  2. Normalize for cosine - Use IndexFlatIP with normalized vectors
  3. Use GPU for large datasets - 10-100× faster
  4. Save trained indices - Training is expensive
  5. Tune nprobe/ef_search - Balance speed/accuracy
  6. Monitor memory - PQ for large datasets
  7. Batch queries - Better GPU utilization

Performance

Index Type Build Time Search Time Memory Accuracy
Flat Fast Slow High 100%
IVF Medium Fast Medium 95-99%
HNSW Slow Fastest High 99%
PQ Medium Fast Low 90-95%

Resources

Recommended Agent Skills

Expand your agent's capabilities with these related and highly-rated skills.

NousResearch/hermes-agent

agentmail

Give the agent its own dedicated email inbox via AgentMail. Send, receive, and manage email autonomously using agent-owned email addresses (e.g. hermes-agent@agentmail.to).

56,643 7,481
Explore
NousResearch/hermes-agent

base

Query Base (Ethereum L2) blockchain data with USD pricing — wallet balances, token info, transaction details, gas analysis, contract inspection, whale detection, and live network stats. Uses Base RPC + CoinGecko. No API key required.

56,643 7,481
Explore
NousResearch/hermes-agent

solana

Query Solana blockchain data with USD pricing — wallet balances, token portfolios with values, transaction details, NFTs, whale detection, and live network stats. Uses Solana RPC + CoinGecko. No API key required.

56,643 7,481
Explore
NousResearch/hermes-agent

one-three-one-rule

Structured decision-making framework for technical proposals and trade-off analysis. When the user faces a choice between multiple approaches (architecture decisions, tool selection, refactoring strategies, migration paths), this skill produces a 1-3-1 format: one clear problem statement, three distinct options with pros/cons, and one concrete recommendation with definition of done and implementation plan. Use when the user asks for a "1-3-1", says "give me options", or needs help choosing between competing approaches.

56,643 7,481
Explore
NousResearch/hermes-agent

fastmcp

Build, test, inspect, install, and deploy MCP servers with FastMCP in Python. Use when creating a new MCP server, wrapping an API or database as MCP tools, exposing resources or prompts, or preparing a FastMCP server for Claude Code, Cursor, or HTTP deployment.

56,643 7,481
Explore
NousResearch/hermes-agent

qdrant-vector-search

High-performance vector similarity search engine for RAG and semantic search. Use when building production RAG systems requiring fast nearest neighbor search, hybrid search with filtering, or scalable vector storage with Rust-powered performance.

56,643 7,481
Explore

Didn't find tool you were looking for?

Be as detailed as possible for better results