> ## 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.

# AWS Bedrock Embedder

The `AwsBedrockEmbedder` class is used to embed text data into vectors using the AWS Bedrock API. By default, it uses the Cohere Embed Multilingual V3 model for generating embeddings.

# Setup

## Set your AWS credentials

```bash theme={null}
export AWS_ACCESS_KEY_ID = xxx
export AWS_SECRET_ACCESS_KEY = xxx
export AWS_REGION = xxx
```

<Note>
  By default, this embedder uses the `cohere.embed-multilingual-v3` model. You must enable access to this model from the AWS Bedrock model catalog before using this embedder.
</Note>

## 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 aws_bedrock_embedder.py theme={null}
import asyncio
from agno.knowledge.embedder.aws_bedrock import AwsBedrockEmbedder
from agno.knowledge.knowledge import Knowledge
from agno.knowledge.reader.pdf_reader import PDFReader
from agno.vectordb.pgvector import PgVector

embeddings = AwsBedrockEmbedder().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)}")

# Example usage:
knowledge = Knowledge(
    vector_db=PgVector(
        table_name="recipes",
        db_url="postgresql+psycopg://ai:ai@localhost:5532/ai",
        embedder=AwsBedrockEmbedder(),
    ),
)

knowledge.insert(
    url="https://agno-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf",
    reader=PDFReader(
        chunk_size=2048
    ),  # Required because cohere has a fixed size of 2048
)
```

# Params

| Parameter               | Type                       | Default                          | Description                                                                                                                   |
| ----------------------- | -------------------------- | -------------------------------- | ----------------------------------------------------------------------------------------------------------------------------- |
| `id`                    | `str`                      | `"cohere.embed-multilingual-v3"` | The model ID to use. You need to enable this model in your AWS Bedrock model catalog.                                         |
| `dimensions`            | `int`                      | `1024`                           | The dimensionality of the embeddings generated by the model(1024 for Cohere models).                                          |
| `input_type`            | `str`                      | `"search_query"`                 | Prepends special tokens to differentiate types. Options: 'search\_document', 'search\_query', 'classification', 'clustering'. |
| `truncate`              | `Optional[str]`            | `None`                           | How to handle inputs longer than the maximum token length. Options: 'NONE', 'START', 'END'.                                   |
| `embedding_types`       | `Optional[List[str]]`      | `None`                           | Types of embeddings to return . Options: 'float', 'int8', 'uint8', 'binary', 'ubinary'.                                       |
| `aws_region`            | `Optional[str]`            | `None`                           | The AWS region to use. If not provided, falls back to AWS\_REGION env variable.                                               |
| `aws_access_key_id`     | `Optional[str]`            | `None`                           | The AWS access key ID. If not provided, falls back to AWS\_ACCESS\_KEY\_ID env variable.                                      |
| `aws_secret_access_key` | `Optional[str]`            | `None`                           | The AWS secret access key. If not provided, falls back to AWS\_SECRET\_ACCESS\_KEY env variable.                              |
| `session`               | `Optional[Session]`        | `None`                           | A boto3 Session object to use for authentication.                                                                             |
| `request_params`        | `Optional[Dict[str, Any]]` | `None`                           | Additional parameters to pass to the API requests.                                                                            |
| `client_params`         | `Optional[Dict[str, Any]]` | `None`                           | Additional parameters to pass to the boto3 client.                                                                            |
| `client`                | `Optional[AwsClient]`      | `None`                           | An instance of the AWS Bedrock client to use for making API requests.                                                         |

# Developer Resources

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