Code
from agno.knowledge.embedder.voyageai import VoyageAIEmbedder
from agno.knowledge.knowledge import Knowledge
from agno.vectordb.pgvector import PgVector
embeddings = VoyageAIEmbedder().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(
db_url="postgresql+psycopg://ai:ai@localhost:5532/ai",
table_name="voyageai_embeddings",
embedder=VoyageAIEmbedder(),
),
max_results=2,
)
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
Install dependencies
uv pip install -U sqlalchemy psycopg pgvector voyageai agno
3
Run PgVector
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
4
Run Agent
python cookbook/08_knowledge/embedders/voyageai_embedder.py
python cookbook/08_knowledge/embedders/voyageai_embedder.py