home / skills / nilecui / skillsbase / hybrid-search-implementation
This skill implements hybrid search by combining vector and keyword results to boost recall in RAG systems and search engines.
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---
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
```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)
This skill shows practical patterns and templates for combining vector similarity and keyword-based search to improve retrieval. It targets RAG systems, search engines, and applications where semantic and exact-match signals both matter. The focus is on fusion strategies, database and search-engine examples, and a pluggable pipeline for reranking.
The skill inspects results from separate vector and keyword searches, then fuses them using methods like Reciprocal Rank Fusion (RRF), linear score interpolation, or cross-encoder reranking. It includes concrete implementations for PostgreSQL (pgvector + full-text), Elasticsearch (dense_vector + BM25), and in-process fusion functions. The pipeline returns merged candidate lists, optionally reranks with a neural model, and supports filtering and weighting controls.
Which fusion method is simplest to start with?
Reciprocal Rank Fusion (RRF) is simple, robust, and requires minimal tuning—good default for most workloads.
When should I use a cross-encoder reranker?
Use a cross-encoder when you need the highest re-ranking quality and can afford CPU/GPU costs on a limited candidate set (e.g., 50 candidates).
How do I pick vector vs keyword weights?
Start with 0.5/0.5, then tune on a labeled validation set to maximize metrics like recall@k or MAP. Consider higher keyword weight when exact tokens matter.