SKILLoptionalHermes Optional Skills

Faiss

Name: Faiss
Author: Orchestra Research

Fast vector similarity search at billion scale

RAGFAISSSimilarity SearchVector SearchFacebook AIGPU AccelerationBillion-ScaleK-NN

Last registry verification2026-08-18v1.0.0Orchestra Research

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Plain meaning

What does it add to Hermes?

Fast vector similarity search at billion scale

Faiss is a skill related to memory and knowledge. It helps Hermes retrieve past information or stored knowledge instead of starting from zero in every session.

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Who is it for?

Best for users who want a repeatable way of working inside Hermes.

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Original publisher description

Fast vector similarity search at billion scale

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Install command

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hermes skills install faiss

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The full skill definition

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Fast vector similarity search at billion scale.

Skill metadata

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Source	Optional — install with `hermes skills install official/mlops/faiss`
Path	`optional-skills/mlops/faiss`
Version	`1.0.0`
Author	Orchestra Research
License	MIT
Dependencies	`faiss-cpu`, `faiss-gpu`, `numpy`
Platforms	linux, macos
Tags	`RAG`, `FAISS`, `Similarity Search`, `Vector Search`, `Facebook AI`, `GPU Acceleration`, `Billion-Scale`, `K-NN`, `HNSW`, `High Performance`, `Large Scale`

Reference: full SKILL.md

Explains the idea itself. Read it slowly; the later sections build on it.

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

When to use FAISS

Explains the idea itself. Read it slowly; the later sections build on it.

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

Ordered, practical steps. Run one and confirm it worked before moving on.

Installation

Shell5 lines

# CPU only
pip install faiss-cpu

# GPU support
pip install faiss-gpu

Basic usage

Python19 lines




# 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

Explains the idea itself. Read it slowly; the later sections build on it.

1. Flat (exact search)

Python7 lines

# 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

Python16 lines

# 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

Python9 lines

# 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

Python8 lines

# 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

Explains the idea itself. Read it slowly; the later sections build on it.

Python8 lines

# 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

Explains the idea itself. Read it slowly; the later sections build on it.

Python9 lines

# 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

Explains the idea itself. Read it slowly; the later sections build on it.

Python18 lines

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

Explains the idea itself. Read it slowly; the later sections build on it.

Python8 lines

from llama_index.vector_stores.faiss import FaissVectorStore


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

vector_store = FaissVectorStore(faiss_index=faiss_index)

Best practices

Explains the idea itself. Read it slowly; the later sections build on it.

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

Performance

A lookup table. Do not read it all; find the row that applies to you.

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

Explains the idea itself. Read it slowly; the later sections build on it.

GitHub: https://github.com/facebookresearch/faiss ⭐ 31,700+
Wiki: https://github.com/facebookresearch/faiss/wiki
License: MIT