Hybrid Search

Hybrid search combines vector similarity (semantic meaning) with keyword matching (exact terms) to get the best of both approaches. It’s the recommended search type for most production use cases.

from agno.knowledge.knowledge import Knowledge
from agno.vectordb.pgvector import PgVector, SearchType

knowledge = Knowledge(
    vector_db=PgVector(
        table_name="docs",
        db_url=db_url,
        search_type=SearchType.hybrid,
    ),
)

How It Works

Hybrid search runs two searches in parallel:

Vector search finds semantically similar content (meaning-based)
Keyword search finds exact term matches (text-based)
Fusion combines results using Reciprocal Rank Fusion (RRF)

The RRF algorithm merges rankings with the formula: RRF(d) = Σ 1/(k + rank) This ensures documents that rank well in both searches appear at the top, while documents that only match one method still surface.

Not all vector databases support hybrid search or RRF.

When to Use Hybrid Search

Scenario	Why Hybrid Helps
User queries vary in phrasing	Vector catches meaning, keywords catch exact terms
Technical content with specific terms	Keywords match error codes, product names exactly
Mixed content types	Balances conceptual and precise matching
Production systems	Best overall accuracy for diverse queries

Use vector-only if your queries are always conceptual with no specific terms. Use keyword-only if you need exact matching (e.g., search by ID or code).

Configuration

Basic Setup

from agno.vectordb.pgvector import PgVector, SearchType

vector_db = PgVector(
    table_name="docs",
    db_url=db_url,
    search_type=SearchType.hybrid,
)

With Reranking

Add a reranker to improve result ordering after fusion:

from agno.knowledge.reranker.cohere import CohereReranker

vector_db = PgVector(
    table_name="docs",
    db_url=db_url,
    search_type=SearchType.hybrid,
    reranker=CohereReranker(),
)

RRF Constant

The k constant in RRF controls how much weight lower-ranked results receive. Higher values (e.g., 60) smooth out rankings; lower values make top results more dominant.

from agno.vectordb.chroma import ChromaDb, SearchType

vector_db = ChromaDb(
    collection="docs",
    path="tmp/chromadb",
    search_type=SearchType.hybrid,
    hybrid_rrf_k=60,  # Default is 60
)

Example

hybrid_search.py

from agno.knowledge.knowledge import Knowledge
from agno.vectordb.pgvector import PgVector, SearchType

db_url = "postgresql+psycopg://ai:ai@localhost:5532/ai"

knowledge = Knowledge(
    vector_db=PgVector(
        table_name="recipes",
        db_url=db_url,
        search_type=SearchType.hybrid,
    ),
)

# Load content
knowledge.insert(
    url="https://agno-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf",
)

# Search combines semantic similarity + keyword matching
results = knowledge.search("chicken coconut soup", max_results=5)
for doc in results:
    print(doc.content[:200])

Supported Vector Databases

Hybrid search is available in:

Check individual vector database docs for specific hybrid search capabilities.

Get Started

Basics

Advanced

Production

Providers

Other

Additional Resources

Hybrid Search

How It Works

When to Use Hybrid Search

Configuration

Basic Setup

With Reranking

RRF Constant

Example

Supported Vector Databases

​How It Works

​When to Use Hybrid Search

​Configuration

​Basic Setup

​With Reranking

​RRF Constant

​Example

​Supported Vector Databases

How It Works

When to Use Hybrid Search

Configuration

Basic Setup

With Reranking

RRF Constant

Example

Supported Vector Databases