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

# Cohere Embedder

The `CohereEmbedder` class is used to embed text data into vectors using the Cohere API. You can get started with Cohere from [here](https://docs.cohere.com/reference/about)

Get your key from [here](https://dashboard.cohere.com/api-keys).

## Usage

```python cohere_embedder.py theme={null}
from agno.knowledge.knowledge import Knowledge
from agno.vectordb.pgvector import PgVector
from agno.knowledge.embedder.cohere import CohereEmbedder

# Add embedding to database
embeddings = CohereEmbedder(id="embed-english-v3.0").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="cohere_embeddings",
        embedder=CohereEmbedder(id="embed-english-v3.0"),
    ),
    max_results=2,
)
```

## Params

| Parameter         | Type                       | Default                | Description                                                                                                                    |
| ----------------- | -------------------------- | ---------------------- | ------------------------------------------------------------------------------------------------------------------------------ |
| `model`           | `str`                      | `"embed-english-v3.0"` | The name of the model used for generating embeddings.                                                                          |
| `input_type`      | `str`                      | `search_query`         | The type of input to embed. You can find more details [here](https://docs.cohere.com/docs/embeddings#the-input_type-parameter) |
| `embedding_types` | `Optional[List[str]]`      | -                      | The type of embeddings to generate. Optional.                                                                                  |
| `api_key`         | `str`                      | -                      | The Cohere API key used for authenticating requests.                                                                           |
| `request_params`  | `Optional[Dict[str, Any]]` | -                      | Additional parameters to include in the API request. Optional.                                                                 |
| `client_params`   | `Optional[Dict[str, Any]]` | -                      | Additional parameters for configuring the API client. Optional.                                                                |
| `cohere_client`   | `Optional[CohereClient]`   | -                      | An instance of the CohereClient to use for making 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.                                                              |

## Developer Resources

* View [Cookbook](https://github.com/agno-agi/agno/tree/main/cookbook/08_knowledge/embedders/cohere_embedder.py)