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Code

cookbook/07_knowledge/09_archive/embedders/aws_bedrock_embedder.py
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

1

Set up your virtual environment

uv venv --python 3.12
source .venv/bin/activate

uv venv --python 3.12
.venv\Scripts\activate
2

Set your API key

export AWS_ACCESS_KEY_ID=xxx
export AWS_SECRET_ACCESS_KEY=xxx
export AWS_REGION=xxx
3

Install dependencies

uv pip install -U sqlalchemy psycopg pgvector pypdf boto3 agno
4

Run PgVector

docker run -d \
  -e POSTGRES_DB=ai \
  -e POSTGRES_USER=ai \
  -e POSTGRES_PASSWORD=ai \
  -e PGDATA=/var/lib/postgresql \
  -v pgvolume:/var/lib/postgresql \
  -p 5532:5432 \
  --name pgvector \
  agnohq/pgvector:18
5

Run Agent

python cookbook/07_knowledge/09_archive/embedders/aws_bedrock_embedder.py

python cookbook/07_knowledge/09_archive/embedders/aws_bedrock_embedder.py