> ## Documentation Index
> Fetch the complete documentation index at: https://docs-v1.agno.com/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. Jina provides high-quality embeddings with support for different embedding types and late chunking for improved processing of long documents. Get your API key from [here](https://cloud.jina.ai/).

## Setup

Set your `JINA_API_KEY` environment variable.

```bash theme={null}
export JINA_API_KEY="xxx"
```

## Run PgVector

```bash theme={null}
docker run - d \
    - e POSTGRES_DB = ai \
    - e POSTGRES_USER = ai \
    - e POSTGRES_PASSWORD = ai \
    - e PGDATA = /var/lib/postgresql/data/pgdata \
    - v pgvolume: / var/lib/postgresql/data \
    - p 5532: 5432 \
    - -name pgvector \
    agnohq/pgvector: 16
```

## Usage

```python cookbook/embedders/jina_embedder.py theme={null}
from agno.agent import AgentKnowledge
from agno.vectordb.pgvector import PgVector
from agno.embedder.jina import JinaEmbedder

# Basic usage - automatically loads from JINA_API_KEY environment variable
embeddings = JinaEmbedder().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)}")

# Custom configuration with late chunking for long documents
custom_embedder = JinaEmbedder(
    dimensions=1024,
    late_chunking=True,  # Improved processing for long documents
    timeout=30.0,  # Request timeout in seconds
)

# Get embedding with usage information
embedding, usage = custom_embedder.get_embedding_and_usage(
    "Advanced text processing with Jina embeddings and late chunking."
)
print(f"Embedding dimensions: {len(embedding)}")
if usage:
    print(f"Usage info: {usage}")

# Use an embedder in a knowledge base
knowledge_base = AgentKnowledge(
    vector_db=PgVector(
        db_url="postgresql+psycopg://ai:ai@localhost:5532/ai",
        table_name="jina_embeddings",
        embedder=JinaEmbedder(
            late_chunking=True,  # Better handling of long documents
            timeout=30.0,  # Configure request timeout
        ),
    ),
    num_documents=2,
)
```

## Params

| Parameter        | Type                                 | Default                               | Description                                                                               |
| ---------------- | ------------------------------------ | ------------------------------------- | ----------------------------------------------------------------------------------------- |
| `id`             | `str`                                | `"jina-embeddings-v3"`                | The model ID used for generating embeddings.                                              |
| `dimensions`     | `int`                                | `1024`                                | The dimensionality of the embeddings generated by the model.                              |
| `embedding_type` | `Literal['float', 'base64', 'int8']` | `"float"`                             | The format in which the embeddings are encoded. Options are "float", "base64", or "int8". |
| `late_chunking`  | `bool`                               | `False`                               | Whether to use late chunking for improved processing of long documents.                   |
| `user`           | `Optional[str]`                      | -                                     | The user associated with the API request.                                                 |
| `api_key`        | `Optional[str]`                      | -                                     | The API key used for authenticating requests.                                             |
| `base_url`       | `str`                                | `"https://api.jina.ai/v1/embeddings"` | The base URL for the API endpoint.                                                        |
| `headers`        | `Optional[Dict[str, str]]`           | -                                     | Additional headers to include in the API request.                                         |
| `request_params` | `Optional[Dict[str, Any]]`           | -                                     | Additional parameters to include in the API request.                                      |
| `timeout`        | `Optional[float]`                    | -                                     | Request timeout in seconds.                                                               |

## Developer Resources

* View [Cookbook](https://github.com/agno-agi/agno/blob/main/cookbook/agent_concepts/knowledge/embedders/jina_embedder.py)