hybrid-search-implementation

upstream

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

ID: hybrid-search-implementation

Volver

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 ÔåÆ Ôö¼ÔöÇÔû║ Vector Search ÔöÇÔöÇÔû║ Candidates ÔöÇÔöÉ
        Ôöé                                  Ôöé
        ÔööÔöÇÔû║ Keyword Search ÔöÇÔû║ Candidates ÔöÇÔö┤ÔöÇÔû║ Fusion ÔöÇÔû║ 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, 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

`python


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

`python


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

Resources

[RRF Paper](https://plg.uwaterloo.ca/~gvcormac/cormacksigir09-rrf.pdf)
[Vespa Hybrid Search](https://blog.vespa.ai/improving-text-ranking-with-few-shot-prompting/)
[Cohere Rerank](https://docs.cohere.com/docs/reranking)

Informacion

Estado	Activo
Origen	upstream
Usos	3
Ultimo uso	23/01 16:57
Actualizado	09/01/2026 02:14

Archivo origen

Z:\Repositorios\core\upstream\plugins\llm-application-dev\skills\hybrid-search-implementation\SKILL.md