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"""
AWS Bedrock Embedder v4
=======================

Demonstrates Cohere v4 embeddings on AWS Bedrock with configurable dimensions.

Requirements:
- AWS credentials (AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY)
- AWS region configured (AWS_REGION)
- boto3 installed: pip install boto3
"""

from agno.knowledge.embedder.aws_bedrock import AwsBedrockEmbedder
from agno.knowledge.knowledge import Knowledge
from agno.vectordb.pgvector import PgVector

# ---------------------------------------------------------------------------
# Setup
# ---------------------------------------------------------------------------
embedder_v4 = AwsBedrockEmbedder(
    id="cohere.embed-v4:0",
    output_dimension=1024,
    input_type="search_query",
)

# ---------------------------------------------------------------------------
# Create Knowledge Base
# ---------------------------------------------------------------------------
knowledge = Knowledge(
    vector_db=PgVector(
        table_name="ml_knowledge",
        db_url="postgresql+psycopg://ai:ai@localhost:5532/ai",
        embedder=AwsBedrockEmbedder(
            id="cohere.embed-v4:0",
            output_dimension=1024,
            input_type="search_document",
        ),
    ),
)


# ---------------------------------------------------------------------------
# Run Agent
# ---------------------------------------------------------------------------
def main() -> None:
    text = "What is machine learning?"
    embeddings = embedder_v4.get_embedding(text)
    print(f"Model: {embedder_v4.id}")
    print(f"Embeddings (first 5): {embeddings[:5]}")
    print(f"Dimensions: {len(embeddings)}")

    print("\n--- Testing different dimensions ---")
    for dim in [256, 512, 1024, 1536]:
        emb = AwsBedrockEmbedder(id="cohere.embed-v4:0", output_dimension=dim)
        result = emb.get_embedding("Test text")
        print(f"Dimension {dim}: Got {len(result)} dimensional vector")

    _ = knowledge


if __name__ == "__main__":
    main()

​
Run the Example

# Clone and setup repo
git clone https://github.com/agno-agi/agno.git
cd agno/cookbook/07_knowledge/embedders

# Create and activate virtual environment
./scripts/demo_setup.sh
source .venvs/demo/bin/activate

# Optiona: Run PgVector (needs docker)
./cookbook/scripts/run_pgvector.sh

python aws_bedrock_embedder_v4.py