hybrid-search-implementation

What this skill does

# 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 lower ranks)
        weights: Optional weights per result list

    Returns:
        Fused ranking as (doc_id, score) tuples
    """
    if weights is None:
        weights = [1.0] * len(result_lists)

    scores = defaultdict(float)

    for result_list, weight in zip(result_lists, weights):
        for rank, (doc_id, _) in enumerate(result_list):
            # RRF formula: 1 / (k + rank)
            scores[doc_id] += weight * (1.0 / (k + rank + 1))

    # Sort by fused score
    return sorted(scores.items(), key=lambda x: x[1], reverse=True)


def linear_combination(
    vector_results: List[Tuple[str, float]],
    keyword_results: List[Tuple[str, float]],
    alpha: float = 0.5
) -> List[Tuple[str, float]]:
    """
    Combine results with linear interpolation.

    Args:
        vector_results: (doc_id, similarity_score) from vector search
        keyword_results: (doc_id, bm25_score) from keyword search
        alpha: Weight for vector search (1-alpha for keyword)
    """
    # Normalize scores to [0, 1]
    def normalize(results):
        if not results:
            return {}
        scores = [s for _, s in results]
        min_s, max_s = min(scores), max(scores)
        range_s = max_s - min_s if max_s != min_s else 1
        return {doc_id: (score - min_s) / range_s for doc_id, score in results}

    vector_scores = normalize(vector_results)
    keyword_scores = normalize(keyword_results)

    # Combine
    all_docs = set(vector_scores.keys()) | set(keyword_scores.keys())
    combined = {}

    for doc_id in all_docs:
        v_score = vector_scores.get(doc_id, 0)
        k_score = keyword_scores.get(doc_id, 0)
        combined[doc_id] = alpha * v_score + (1 - alpha) * k_score

    return sorted(combined.items(), key=lambda x: x[1], reverse=True)
```

### Template 2: PostgreSQL Hybrid Search

```python
import asyncpg
from typing import List, Dict, Optional
import numpy as np

class PostgresHybridSearch:
    """Hybrid search with pgvector and full-text search."""

    def __init__(self, pool: asyncpg.Pool):
        self.pool = pool

    async def setup_schema(self):
        """Create tables and indexes."""
        async with self.pool.acquire() as conn:
            await conn.execute("""
                CREATE EXTENSION IF NOT EXISTS vector;

                CREATE TABLE IF NOT EXISTS documents (
                    id TEXT PRIMARY KEY,
                    content TEXT NOT NULL,
                    embedding vector(1536),
                    metadata JSONB DEFAULT '{}',
                    ts_content tsvector GENERATED ALWAYS AS (
                        to_tsvector('english', content)
                    ) STORED
                );

                -- Vector index (HNSW)
                CREATE INDEX IF NOT EXISTS documents_embedding_idx
                ON documents USING hnsw (embedding vector_cosine_ops);

                -- Full-text index (GIN)
                CREATE INDEX IF NOT EXISTS documents_fts_idx
                ON documents USING gin (ts_content);
            """)

    async def hybrid_search(
        self,
        query: str,
        query_embedding: List[float],
        limit: int = 10,
        vector_weight: float = 0.5,
        filter_metadata: Optional[Dict] = None
    ) -> List[Dict]:
        """
        Perform hybrid search combining vector and full-text.

        Uses RRF fusion for combining results.
        """
        async with self.pool.acquire() as conn:
            # Build filter clause
            where_clause = "1=1"
            params = [query_embedding, query, limit * 3]

            if filter_metadata:
                for key, value in filter_metadata.items():
                    params.append(value)
                    where_clause += f" AND metadata->>'{key}' = ${len(params)}"

            results = await conn.fetch(f"""
                WITH vector_search AS (
                    SELECT
                        id,
                        content,
                        metadata,
                        ROW_NUMBER() OVER (ORDER BY embedding <=> $1::vector) as vector_rank,
                        1 - (embedding <=> $1::vector) as vector_score
                    FROM documents
                    WHERE {where_clause}
                    ORDER BY embedding <=> $1::vector
                    LIMIT $3
                ),
                keyword_search AS (
                    SELECT
                        id,
                        content,
                        metadata,
                        ROW_NUMBER() OVER (ORDER BY ts_rank(ts_content, websearch_to_tsquery('english', $2)) DESC) as keyword_rank,
                        ts_rank(ts_content, websearch_to_tsquery('english', $2)) as keyword_score
                    FROM documents
                    WHERE ts_content @@ websearch_to_tsquery('english', $2)
                      AND {where_clause}
                    ORDER BY ts_rank(ts_content, websearch_to_tsquery('english', $2)) DESC
                    LIMIT $3
                )
                SELECT
                    COALESCE(v.id, k.id) as id,
                    COALESCE(v.content, k.content) as content,
                    COALESCE(v.metadata, k.metadata) as metadata,
                    v.vector_score,
                    k.keyword_score,
                    -- RRF fusion
                    COALESCE(1.0 / (60 + v.vector_rank), 0) * $4::float +
                    COALESCE(1.0 / (60 + k.keyword_rank), 0) * (1 - $4::float) as rrf_score
                FROM vector_search v
                FULL OUTER JOIN keyword_search k ON v.id = k.id
                ORDER BY rrf_score DESC
                LIMIT $3 / 3
            """, *params, vector_weight)

            return [dict(row) for row in results]

    async def search_with_rerank(
        self,
        query: str,
        query_embedding: List[float],
        limit: int = 10,
        rerank_candidates: int = 50
    ) -> List[Dict]:
        """Hybrid search with cross-encoder reranking."""
        from sentence_transformers import CrossEncoder

        # Get candidates
        candidates = await self.hybrid_search(
            query, query_embedding, limit=rerank_candidates
        )

        if not candidates:
            return []

        # Rerank with cross-encoder
        model = CrossEncoder('cross-encoder/ms-marco-MiniLM-L-6-v2')

        pairs = [(query, c["content"]) for c in candidates]
        scores = model.predict(pairs)

        for candidate, score in zip(candidates,