hybrid-search-implementationlisted

# Hybrid Search Implementation Patterns for combining vector similarity and keyword-based search. ## When to Use This Skill - Building RAG systems with improved recall - Combining semantic understanding with exact matching - Handling queries with specific terms (names, codes) - Improving search for domain-specific vocabulary - When pure vector search misses keyword matches ## Core Concepts ### 1. Hybrid Search Architecture ``` Query → ┬─► Vector Search ──► Candidates ─┐ │ │ └─► Keyword Search ─► Candidates ─┴─► Fusion ─► Results ``` ### 2. Fusion Methods | Method | Description | Best For | | ----------------- | ------------------------ | --------------- | | **RRF** | Reciprocal Rank Fusion | General purpose | | **Linear** | Weighted sum of scores | Tunable balance | | **Cross-encoder** | Rerank with neural model | Highest quality | | **Cascade** | Filter then rerank | Efficiency | ## Templates ### Template 1: Reciprocal Rank Fusion ```python from typing import List, Dict, Tuple from collections import defaultdict def reciprocal_rank_fusion( result_lists: List[List[Tuple[str, float]]], k: int = 60, weights: List[float] = None ) -> List[Tuple[str, float]]: """ Combine multiple ranked lists using RRF. Args: result_lists: List of (doc_id, score) tuples per search method k: RRF constant (higher = more weight to l