> ## Documentation Index
> Fetch the complete documentation index at: https://docs.agno.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Hybrid Search

> Combine vector and lexical search signals with a supported vector database.

Hybrid search combines vector similarity with a database-specific lexical search signal.

```python 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="docs",
        db_url=db_url,
        search_type=SearchType.hybrid,
    ),
)
```

## How It Works

The implementation depends on the vector database. A hybrid search generally:

1. Computes a vector similarity signal.
2. Computes a lexical signal from the query and document text.
3. Combines or fuses the signals into one ranking.

PgVector combines normalized vector similarity and PostgreSQL full-text ranking in one query. Set `vector_score_weight` between `0` and `1` to control their relative contribution. The default is `0.5`.

Chroma runs vector search and a lexical candidate search, then merges their rankings with Reciprocal Rank Fusion (RRF). Its lexical path filters on the first query token and scores term overlap.

<Note>
  Each vector database maps `SearchType.hybrid` to its own query and ranking algorithm. Check the selected integration before tuning retrieval.
</Note>

## When to Use Hybrid Search

| Query Pattern                                       | Search Type to Test |
| --------------------------------------------------- | ------------------- |
| Conceptual questions with varied phrasing           | Vector              |
| IDs, codes, or terms that must occur in the text    | Keyword             |
| Queries that mix concepts with specific terminology | Hybrid              |

Evaluate the available search types against representative queries and expected documents. Ranking behavior also depends on the embedder, content, chunking strategy, and database configuration.

## Configuration

### Basic Setup

```python theme={null}
from agno.vectordb.pgvector import PgVector, SearchType

vector_db = PgVector(
    table_name="docs",
    db_url="postgresql+psycopg://ai:ai@localhost:5532/ai",
    search_type=SearchType.hybrid,
    vector_score_weight=0.7,
)
```

### With Reranking

Apply a reranker to the fused candidates:

```python theme={null}
from agno.knowledge.reranker.cohere import CohereReranker
from agno.vectordb.pgvector import PgVector, SearchType

vector_db = PgVector(
    table_name="docs",
    db_url="postgresql+psycopg://ai:ai@localhost:5532/ai",
    search_type=SearchType.hybrid,
    reranker=CohereReranker(),
)
```

### Chroma RRF Constant

For Chroma, `hybrid_rrf_k` controls how strongly rank position affects the fused score. Higher values reduce the difference between adjacent ranks. The default is `60`.

```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

Install the dependencies used on this page and start a PostgreSQL instance with pgvector enabled:

```bash theme={null}
uv pip install -U agno chromadb cohere openai pgvector psycopg pypdf sqlalchemy
```

```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,
    ),
)

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

results = knowledge.search("chicken coconut soup", max_results=5)
for doc in results:
    print(doc.content[:200])
```

## Supported Vector Databases

| Vector Database                                        | Hybrid Search Notes                                            |
| ------------------------------------------------------ | -------------------------------------------------------------- |
| [PgVector](/knowledge/vector-stores/pgvector/overview) | Weighted PostgreSQL full-text and vector scores                |
| [Chroma](/knowledge/vector-stores/chroma/overview)     | Term-overlap lexical ranking and vector ranking fused with RRF |
| [LanceDB](/knowledge/vector-stores/lancedb/overview)   | Supports `SearchType.hybrid`                                   |
| [Weaviate](/knowledge/vector-stores/weaviate/overview) | Uses Weaviate hybrid queries                                   |
| [Milvus](/knowledge/vector-stores/milvus/overview)     | Uses dense and sparse vectors                                  |
| [Pinecone](/knowledge/vector-stores/pinecone/overview) | Requires `use_hybrid_search=True`                              |
| [Qdrant](/knowledge/vector-stores/qdrant/overview)     | Uses dense and sparse named vectors                            |
| [MongoDB](/knowledge/vector-stores/mongodb/overview)   | Supports `SearchType.hybrid`                                   |
| [Redis](/knowledge/vector-stores/redis/overview)       | Supports vector, keyword, and hybrid search                    |

## Developer Resources

* [Search and retrieval](/knowledge/concepts/search-and-retrieval/overview)
* [Vector database integrations](/knowledge/vector-stores/index)