hybrid-search-implementation

name	hybrid-search-implementation
description	Combine vector and keyword search for improved retrieval. Use when implementing RAG systems, building search engines, or when neither approach alone provides sufficient recall.

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

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

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, scores):
            candidate["rerank_score"] = float(score)

        # Sort by rerank score and return top results
        reranked = sorted(candidates, key=lambda x: x["rerank_score"], reverse=True)
        return reranked[:limit]

Template 3: Elasticsearch Hybrid Search

from elasticsearch import Elasticsearch
from typing import List, Dict, Optional

class ElasticsearchHybridSearch:
    """Hybrid search with Elasticsearch and dense vectors."""

    def __init__(
        self,
        es_client: Elasticsearch,
        index_name: str = "documents"
    ):
        self.es = es_client
        self.index_name = index_name

    def create_index(self, vector_dims: int = 1536):
        """Create index with dense vector and text fields."""
        mapping = {
            "mappings": {
                "properties": {
                    "content": {
                        "type": "text",
                        "analyzer": "english"
                    },
                    "embedding": {
                        "type": "dense_vector",
                        "dims": vector_dims,
                        "index": True,
                        "similarity": "cosine"
                    },
                    "metadata": {
                        "type": "object",
                        "enabled": True
                    }
                }
            }
        }
        self.es.indices.create(index=self.index_name, body=mapping, ignore=400)

    def hybrid_search(
        self,
        query: str,
        query_embedding: List[float],
        limit: int = 10,
        boost_vector: float = 1.0,
        boost_text: float = 1.0,
        filter: Optional[Dict] = None
    ) -> List[Dict]:
        """
        Hybrid search using Elasticsearch's built-in capabilities.
        """
        # Build the hybrid query
        search_body = {
            "size": limit,
            "query": {
                "bool": {
                    "should": [
                        # Vector search (kNN)
                        {
                            "script_score": {
                                "query": {"match_all": {}},
                                "script": {
                                    "source": f"cosineSimilarity(params.query_vector, 'embedding') * {boost_vector} + 1.0",
                                    "params": {"query_vector": query_embedding}
                                }
                            }
                        },
                        # Text search (BM25)
                        {
                            "match": {
                                "content": {
                                    "query": query,
                                    "boost": boost_text
                                }
                            }
                        }
                    ],
                    "minimum_should_match": 1
                }
            }
        }

        # Add filter if provided
        if filter:
            search_body["query"]["bool"]["filter"] = filter

        response = self.es.search(index=self.index_name, body=search_body)

        return [
            {
                "id": hit["_id"],
                "content": hit["_source"]["content"],
                "metadata": hit["_source"].get("metadata", {}),
                "score": hit["_score"]
            }
            for hit in response["hits"]["hits"]
        ]

    def hybrid_search_rrf(
        self,
        query: str,
        query_embedding: List[float],
        limit: int = 10,
        window_size: int = 100
    ) -> List[Dict]:
        """
        Hybrid search using Elasticsearch 8.x RRF.
        """
        search_body = {
            "size": limit,
            "sub_searches": [
                {
                    "query": {
                        "match": {
                            "content": query
                        }
                    }
                },
                {
                    "query": {
                        "knn": {
                            "field": "embedding",
                            "query_vector": query_embedding,
                            "k": window_size,
                            "num_candidates": window_size * 2
                        }
                    }
                }
            ],
            "rank": {
                "rrf": {
                    "window_size": window_size,
                    "rank_constant": 60
                }
            }
        }

        response = self.es.search(index=self.index_name, body=search_body)

        return [
            {
                "id": hit["_id"],
                "content": hit["_source"]["content"],
                "score": hit["_score"]
            }
            for hit in response["hits"]["hits"]
        ]

Template 4: Custom Hybrid RAG Pipeline

from typing import List, Dict, Optional, Callable
from dataclasses import dataclass

@dataclass
class SearchResult:
    id: str
    content: str
    score: float
    source: str  # "vector", "keyword", "hybrid"
    metadata: Dict = None


class HybridRAGPipeline:
    """Complete hybrid search pipeline for RAG."""

    def __init__(
        self,
        vector_store,
        keyword_store,
        embedder,
        reranker=None,
        fusion_method: str = "rrf",
        vector_weight: float = 0.5
    ):
        self.vector_store = vector_store
        self.keyword_store = keyword_store
        self.embedder = embedder
        self.reranker = reranker
        self.fusion_method = fusion_method
        self.vector_weight = vector_weight

    async def search(
        self,
        query: str,
        top_k: int = 10,
        filter: Optional[Dict] = None,
        use_rerank: bool = True
    ) -> List[SearchResult]:
        """Execute hybrid search pipeline."""

        # Step 1: Get query embedding
        query_embedding = self.embedder.embed(query)

        # Step 2: Execute parallel searches
        vector_results, keyword_results = await asyncio.gather(
            self._vector_search(query_embedding, top_k * 3, filter),
            self._keyword_search(query, top_k * 3, filter)
        )

        # Step 3: Fuse results
        if self.fusion_method == "rrf":
            fused = self._rrf_fusion(vector_results, keyword_results)
        else:
            fused = self._linear_fusion(vector_results, keyword_results)

        # Step 4: Rerank if enabled
        if use_rerank and self.reranker:
            fused = await self._rerank(query, fused[:top_k * 2])

        return fused[:top_k]

    async def _vector_search(
        self,
        embedding: List[float],
        limit: int,
        filter: Dict
    ) -> List[SearchResult]:
        results = await self.vector_store.search(embedding, limit, filter)
        return [
            SearchResult(
                id=r["id"],
                content=r["content"],
                score=r["score"],
                source="vector",
                metadata=r.get("metadata")
            )
            for r in results
        ]

    async def _keyword_search(
        self,
        query: str,
        limit: int,
        filter: Dict
    ) -> List[SearchResult]:
        results = await self.keyword_store.search(query, limit, filter)
        return [
            SearchResult(
                id=r["id"],
                content=r["content"],
                score=r["score"],
                source="keyword",
                metadata=r.get("metadata")
            )
            for r in results
        ]

    def _rrf_fusion(
        self,
        vector_results: List[SearchResult],
        keyword_results: List[SearchResult]
    ) -> List[SearchResult]:
        """Fuse with RRF."""
        k = 60
        scores = {}
        content_map = {}

        for rank, result in enumerate(vector_results):
            scores[result.id] = scores.get(result.id, 0) + 1 / (k + rank + 1)
            content_map[result.id] = result

        for rank, result in enumerate(keyword_results):
            scores[result.id] = scores.get(result.id, 0) + 1 / (k + rank + 1)
            if result.id not in content_map:
                content_map[result.id] = result

        sorted_ids = sorted(scores.keys(), key=lambda x: scores[x], reverse=True)

        return [
            SearchResult(
                id=doc_id,
                content=content_map[doc_id].content,
                score=scores[doc_id],
                source="hybrid",
                metadata=content_map[doc_id].metadata
            )
            for doc_id in sorted_ids
        ]

    async def _rerank(
        self,
        query: str,
        results: List[SearchResult]
    ) -> List[SearchResult]:
        """Rerank with cross-encoder."""
        if not results:
            return results

        pairs = [(query, r.content) for r in results]
        scores = self.reranker.predict(pairs)

        for result, score in zip(results, scores):
            result.score = float(score)

        return sorted(results, key=lambda x: x.score, reverse=True)

Best Practices

Do's

• Tune weights empirically - Test on your data
• Use RRF for simplicity - Works well without tuning
• Add reranking - Significant quality improvement
• Log both scores - Helps with debugging
• A/B test - Measure real user impact

Don'ts

• Don't assume one size fits all - Different queries need different weights
• Don't skip keyword search - Handles exact matches better
• Don't over-fetch - Balance recall vs latency
• Don't ignore edge cases - Empty results, single word queries