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 = 1...