> ## Documentation Index
> Fetch the complete documentation index at: https://docs.agno.com/llms.txt
> Use this file to discover all available pages before exploring further.

# AWS Bedrock Embedder

## Code

```python cookbook/07_knowledge/09_archive/embedders/aws_bedrock_embedder.py theme={null}
from agno.knowledge.chunking.fixed import FixedSizeChunking
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(input_type="search_document"),
    ),
)

knowledge.insert(
    url="https://agno-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf",
    reader=PDFReader(chunking_strategy=FixedSizeChunking(chunk_size=1500)),
)
```

## Usage

<Steps>
  <Snippet file="create-venv-step.mdx" />

  <Step title="Set your API key">
    ```bash theme={null}
    export AWS_ACCESS_KEY_ID=xxx
    export AWS_SECRET_ACCESS_KEY=xxx
    export AWS_REGION=xxx
    ```
  </Step>

  <Step title="Install dependencies">
    ```bash theme={null}
    uv pip install -U sqlalchemy psycopg pgvector pypdf boto3 agno
    ```
  </Step>

  <Snippet file="run-pgvector-step.mdx" />

  <Step title="Run Agent">
    <CodeGroup>
      ```bash Mac theme={null}
      python cookbook/07_knowledge/09_archive/embedders/aws_bedrock_embedder.py
      ```

      ```bash Windows theme={null}
      python cookbook/07_knowledge/09_archive/embedders/aws_bedrock_embedder.py
      ```
    </CodeGroup>
  </Step>
</Steps>