faiss

Original source / Raw SKILL.md

---
name: faiss
description: 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.
version: 1.0.0
author: Orchestra Research
license: MIT
tags: [RAG, FAISS, Similarity Search, Vector Search, Facebook AI, GPU Acceleration, Billion-Scale, K-NN, HNSW, High Performance, Large Scale]
dependencies: [faiss-cpu, faiss-gpu, numpy]
---

# 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

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




---

## Skill Companion Files

> Additional files collected from the skill directory layout.

### references/index_types.md

```markdown
# FAISS Index Types Guide

Complete guide to choosing and using FAISS index types.

## Index selection guide

| Dataset Size | Index Type | Training | Accuracy | Speed |
|--------------|------------|----------|----------|-------|
| < 10K | Flat | No | 100% | Slow |
| 10K-1M | IVF | Yes | 95-99% | Fast |
| 1M-10M | HNSW | No | 99% | Fastest |
| > 10M | IVF+PQ | Yes | 90-95% | Fast, low memory |

## Flat indices (exact search)

### IndexFlatL2 - L2 (Euclidean) distance

```python
import faiss
import numpy as np

d = 128  # Dimension
index = faiss.IndexFlatL2(d)

# Add vectors
vectors = np.random.random((1000, d)).astype('float32')
index.add(vectors)

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

**Use when:**
- Dataset < 10,000 vectors
- Need 100% accuracy
- Serving as baseline

### IndexFlatIP - Inner product (cosine similarity)

```python
# For cosine similarity, normalize vectors first
import faiss

d = 128
index = faiss.IndexFlatIP(d)

# Normalize vectors (required for cosine similarity)
faiss.normalize_L2(vectors)
index.add(vectors)

# Search
faiss.normalize_L2(query)
distances, indices = index.search(query, k)
```

**Use when:**
- Need cosine similarity
- Recommendation systems
- Text embeddings

## IVF indices (inverted file)

### IndexIVFFlat - Cluster-based search

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

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

# Train on data (required!)
index.train(vectors)

# Add vectors
index.add(vectors)

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

**Parameters:**
- `nlist`: Number of clusters (√N to 4√N recommended)
- `nprobe`: Clusters to search (1-nlist, higher = more accurate)

**Use when:**
- Dataset 10K-1M vectors
- Need fast approximate search
- Can afford training time

### Tuning nprobe

```python
# Test different nprobe values
for nprobe in [1, 5, 10, 20, 50]:
    index.nprobe = nprobe
    distances, indices = index.search(query, k)
    # Measure recall/speed trade-off
```

**Guidelines:**
- `nprobe=1`: Fastest, ~50% recall
- `nprobe=10`: Good balance, ~95% recall
- `nprobe=nlist`: Exact search (same as Flat)

## HNSW indices (graph-based)

### IndexHNSWFlat - Hierarchical NSW

```python
# HNSW index
M = 32  # Number of connections per layer (16-64)
index = faiss.IndexHNSWFlat(d, M)

# Optional: Set ef_construction (build time parameter)
index.hnsw.efConstruction = 40  # Higher = better quality, slower build

# Add vectors (no training needed!)
index.add(vectors)

# Search
index.hnsw.efSearch = 16  # Search time parameter
distances, indices = index.search(query, k)
```

**Parameters:**
- `M`: Connections per layer (16-64, default 32)
- `efConstruction`: Build quality (40-200, higher = better)
- `efSearch`: Search quality (16-512, higher = more accurate)

**Use when:**
- Need best quality approximate search
- Can afford higher memory (more connections)
- Dataset 1M-10M vectors

## PQ indices (product quantization)

### IndexPQ - Memory-efficient

```python
# PQ reduces memory by 16-32×
m = 8   # Number of subquantizers (divides d)
nbits = 8  # Bits per subquantizer

index = faiss.IndexPQ(d, m, nbits)

# Train (required!)
index.train(vectors)

# Add vectors
index.add(vectors)

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

**Parameters:**
- `m`: Subquantizers (d must be divisible by m)
- `nbits`: Bits per code (8 or 16)

**Memory savings:**
- Original: d × 4 bytes (float32)
- PQ: m bytes
- Compression ratio: 4d/m

**Use when:**
- Limited memory
- Large datasets (> 10M vectors)
- Can accept ~90-95% accuracy

### IndexIVFPQ - IVF + PQ combined

```python
# Best for very large datasets
nlist = 4096
m = 8
nbits = 8

quantizer = faiss.IndexFlatL2(d)
index = faiss.IndexIVFPQ(quantizer, d, nlist, m, nbits)

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

# Search
index.nprobe = 32
distances, indices = index.search(query, k)
```

**Use when:**
- Dataset > 10M vectors
- Need fast search + low memory
- Can accept 90-95% accuracy

## GPU indices

### Single GPU

```python
import faiss

# Create CPU index
index_cpu = faiss.IndexFlatL2(d)

# Move to GPU
res = faiss.StandardGpuResources()  # GPU resources
index_gpu = faiss.index_cpu_to_gpu(res, 0, index_cpu)  # GPU 0

# Use normally
index_gpu.add(vectors)
distances, indices = index_gpu.search(query, k)
```

### Multi-GPU

```python
# Use all available GPUs
index_gpu = faiss.index_cpu_to_all_gpus(index_cpu)

# Or specific GPUs
gpus = [0, 1, 2, 3]  # Use GPUs 0-3
index_gpu = faiss.index_cpu_to_gpus_list(index_cpu, gpus)
```

**Speedup:**
- Single GPU: 10-50× faster than CPU
- Multi-GPU: Near-linear scaling

## Index factory

```python
# Easy index creation with string descriptors
index = faiss.index_factory(d, "IVF100,Flat")
index = faiss.index_factory(d, "HNSW32")
index = faiss.index_factory(d, "IVF4096,PQ8")

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

**Common descriptors:**
- `"Flat"`: Exact search
- `"IVF100,Flat"`: IVF with 100 clusters
- `"HNSW32"`: HNSW with M=32
- `"IVF4096,PQ8"`: IVF + PQ compression

## Performance comparison

### Search speed (1M vectors, k=10)

| Index | Build Time | Search Time | Memory | Recall |
|-------|------------|-------------|--------|--------|
| Flat | 0s | 50ms | 512 MB | 100% |
| IVF100 | 5s | 2ms | 512 MB | 95% |
| HNSW32 | 60s | 1ms | 1GB | 99% |
| IVF4096+PQ8 | 30s | 3ms | 32 MB | 90% |

*CPU (16 cores), 128-dim vectors*

## Best practices

1. **Start with Flat** - Baseline for comparison
2. **Use IVF for medium datasets** - Good balance
3. **Use HNSW for best quality** - If memory allows
4. **Add PQ for memory savings** - Large datasets
5. **GPU for > 100K vectors** - 10-50× speedup
6. **Tune nprobe/efSearch** - Trade-off speed/accuracy
7. **Train on representative data** - Better clustering
8. **Save trained indices** - Avoid retraining

## Resources

- **Wiki**: https://github.com/facebookresearch/faiss/wiki
- **Paper**: https://arxiv.org/abs/1702.08734

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