> ## 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.

# Vector Search

> Find content by semantic meaning using vector similarity.

Vector search finds content by meaning rather than exact word matches. When you search for "How do I reset my password?", it finds documents about "changing credentials" even if those exact words don't appear.

```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.vector,
    ),
)
```

## How It Works

1. **Query embedding**: Your search query is converted to a vector (list of numbers capturing meaning)
2. **Similarity matching**: The system finds stored vectors closest to the query vector
3. **Ranking**: Results are ordered by cosine similarity (how close the meanings are)

The embedding model determines how well semantic relationships are captured. General-purpose models like OpenAI's `text-embedding-3-small` work well for most content.

## When to Use Vector Search

| Scenario                        | Why Vector Search Works                          |
| ------------------------------- | ------------------------------------------------ |
| Conceptual questions            | Matches meaning, not just words                  |
| Users phrase things differently | Finds relevant content regardless of terminology |
| Natural language queries        | Understands intent behind questions              |
| Content with varied vocabulary  | Connects synonyms and related concepts           |

Use **hybrid search** if you also need exact term matching (product names, error codes).
Use **keyword search** if you need precise text matching only.

## Configuration

### Basic Setup

```python theme={null}
from agno.knowledge.embedder.openai import OpenAIEmbedder

vector_db = PgVector(
    table_name="docs",
    db_url=db_url,
    search_type=SearchType.vector,
    embedder=OpenAIEmbedder(id="text-embedding-3-small"),
)
```

### With Reranking

Add a reranker to improve result ordering:

```python theme={null}
from agno.knowledge.reranker.cohere import CohereReranker

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

## Example

```python vector_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.vector,
        embedder=OpenAIEmbedder(id="text-embedding-3-small"),
    ),
)

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

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

## Next Steps

<CardGroup cols={2}>
  <Card title="Hybrid Search" icon="magnifying-glass" href="/knowledge/concepts/search-and-retrieval/hybrid-search">
    Combine vector search with keyword matching
  </Card>

  <Card title="Embedders" icon="code" href="/knowledge/concepts/embedder/overview">
    Choose the right embedding model
  </Card>
</CardGroup>