> ## Documentation Index
> Fetch the complete documentation index at: https://agno-v2-update-deprecated-models.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# Hybrid Search

> Combine vector similarity with keyword matching for better retrieval accuracy.

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.

```python theme={null}
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:

1. **Vector search** finds semantically similar content (meaning-based)
2. **Keyword search** finds exact term matches (text-based)
3. **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.

<Note>
  Not all vector databases support hybrid search or RRF.
</Note>

## 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

```python theme={null}
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:

```python theme={null}
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.

```python theme={null}
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

```python hybrid_search.py theme={null}
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:

* [PgVector](/knowledge/vector-stores/pgvector/overview)
* [ChromaDB](/knowledge/vector-stores/chroma/overview)
* [LanceDB](/knowledge/vector-stores/lancedb/overview)
* [Weaviate](/knowledge/vector-stores/weaviate/overview)
* [Milvus](/knowledge/vector-stores/milvus/overview)
* [Pinecone](/knowledge/vector-stores/pinecone/overview)

Check individual vector database docs for specific hybrid search capabilities.