similarity-search-patterns

# Similarity Search Patterns

Patterns for implementing efficient similarity search in production systems.

## When to Use This Skill

- Building semantic search systems
- Implementing RAG retrieval
- Creating recommendation engines
- Optimizing search latency
- Scaling to millions of vectors
- Combining semantic and keyword search

## Core Concepts

### 1. Distance Metrics

| Metric | Formula | Best For |
|--------|---------|----------|
| **Cosine** | 1 - (A·B)/(‖A‖‖B‖) | Normalized embeddings |
| **Euclidean (L2)** | √Σ(a-b)² | Raw embeddings |
| **Dot Product** | A·B | Magnitude matters |
| **Manhattan (L1)** | Σ|a-b| | Sparse vectors |

### 2. Index Types

```
┌─────────────────────────────────────────────────┐
│                 Index Types                      │
├─────────────┬───────────────┬───────────────────┤
│    Flat     │     HNSW      │    IVF+PQ         │
│ (Exact)     │ (Graph-based) │ (Quantized)       │
├─────────────┼───────────────┼───────────────────┤
│ O(n) search │ O(log n)      │ O(√n)             │
│ 100% recall │ ~95-99%       │ ~90-95%           │
│ Small data  │ Medium-Large  │ Very Large        │
└─────────────┴───────────────┴───────────────────┘
```

## Templates

### Template 1: Pinecone Implementation

```python
from pinecone import Pinecone, ServerlessSpec
from typing import List, Dict, Optional
import hashlib

class PineconeVectorStore:
    def __init__(
        self,
        api_key: str,
        index_name: str,
        dimension: int = 1536,
        metric: str = "cosine"
    ):
        self.pc = Pinecone(api_key=api_key)

        # Create index if not exists
        if index_name not in self.pc.list_indexes().names():
            self.pc.create_index(
                name=index_name,
                dimension=dimension,
                metric=metric,
                spec=ServerlessSpec(cloud="aws", region="us-east-1")
            )

        self.index = self.pc.Index(index_name)

    def upsert(
        self,
        vectors: Li