> ## Documentation Index
> Fetch the complete documentation index at: https://phidatainc-studio-tools-doc.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# Jina Embedder

The `JinaEmbedder` class is used to embed text data into vectors using the Jina AI API. You can get started with Jina AI [here](https://jina.ai/embeddings/).

Get your [API key](https://jina.ai/embeddings/).

## Usage

```python jina_embedder.py theme={null}
from agno.knowledge.knowledge import Knowledge
from agno.vectordb.pgvector import PgVector
from agno.knowledge.embedder.jina import JinaEmbedder

# Add embedding to database
embeddings = JinaEmbedder(id="jina-embeddings-v3").get_embedding("The quick brown fox jumps over the lazy dog.")
# Print the embeddings and their dimensions
print(f"Embeddings: {embeddings[:5]}")
print(f"Dimensions: {len(embeddings)}")

# Use an embedder in a knowledge base
knowledge = Knowledge(
    vector_db=PgVector(
        db_url="postgresql+psycopg://ai:ai@localhost:5532/ai",
        table_name="jina_embeddings",
        embedder=JinaEmbedder(id="jina-embeddings-v3"),
    ),
    max_results=2,
)
```

## Advanced Usage

```python theme={null}
# Configure embedder with custom settings
embedder = JinaEmbedder(
    id="jina-embeddings-v3",
    dimensions=1024,
    embedding_type="float",
    late_chunking=True,
    batch_size=50,
    timeout=30.0
)

# Use async methods for better performance
import asyncio

async def embed_texts():
    embedder = JinaEmbedder()
    texts = ["First text", "Second text", "Third text"]
    
    # Get embeddings in batches
    embeddings, usage = await embedder.async_get_embeddings_batch_and_usage(texts)
    print(f"Generated {len(embeddings)} embeddings")
    print(f"Usage info: {usage[0]}")

# Run async example
asyncio.run(embed_texts())
```

## Params

| Parameter        | Type                                 | Default                               | Description                                                       |
| ---------------- | ------------------------------------ | ------------------------------------- | ----------------------------------------------------------------- |
| `id`             | `str`                                | `"jina-embeddings-v3"`                | The model ID to use for generating embeddings.                    |
| `dimensions`     | `int`                                | `1024`                                | The number of dimensions for the embedding vectors.               |
| `embedding_type` | `Literal["float", "base64", "int8"]` | `"float"`                             | The format type of the returned embeddings.                       |
| `late_chunking`  | `bool`                               | `False`                               | Whether to enable late chunking optimization.                     |
| `user`           | `Optional[str]`                      | `None`                                | User identifier for tracking purposes. Optional.                  |
| `api_key`        | `Optional[str]`                      | `JINA_API_KEY` env var                | The Jina AI API key. Can be set via environment variable.         |
| `base_url`       | `str`                                | `"https://api.jina.ai/v1/embeddings"` | The base URL for the Jina API.                                    |
| `headers`        | `Optional[Dict[str, str]]`           | `None`                                | Additional headers to include in API requests. Optional.          |
| `request_params` | `Optional[Dict[str, Any]]`           | `None`                                | Additional parameters to include in the API request. Optional.    |
| `timeout`        | `Optional[float]`                    | `None`                                | Timeout in seconds for API requests. Optional.                    |
| `enable_batch`   | `bool`                               | `False`                               | Enable batch processing to reduce API calls and avoid rate limits |
| `batch_size`     | `int`                                | `100`                                 | Number of texts to process in each API call for batch operations. |

## Features

* **Async Support**: Full async/await support for better performance in concurrent applications
* **Batch Processing**: Efficient batch processing of multiple texts with configurable batch size
* **Late Chunking**: Support for Jina's late chunking optimization technique
* **Flexible Output**: Multiple embedding formats (float, base64, int8)
* **Usage Tracking**: Get detailed usage information for API calls
* **Error Handling**: Robust error handling with fallback mechanisms

## Developer Resources

* View [Cookbook](https://github.com/agno-agi/agno/tree/main/cookbook/08_knowledge/embedders/jina_embedder.py)
* [Jina AI Documentation](https://jina.ai/embeddings/)