jadegate 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