Skip to main content
The VoyageAIEmbedder class is used to embed text data into vectors using the Voyage AI API. Get your key from here.

​
Usage

cookbook/embedders/voyageai_embedder.py
from agno.agent import AgentKnowledge
from agno.vectordb.pgvector import PgVector
from agno.embedder.voyageai import VoyageAIEmbedder

# Embed sentence in database
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)}")

# Use an embedder in a knowledge base
knowledge_base = AgentKnowledge(
    vector_db=PgVector(
        db_url="postgresql+psycopg://ai:ai@localhost:5532/ai",
        table_name="voyageai_embeddings",
        embedder=VoyageAIEmbedder(),
    ),
    num_documents=2,
)

​
Params

ParameterTypeDefaultDescription
modelstr"voyage-2"The name of the model used for generating embeddings.
dimensionsint1024The dimensionality of the embeddings generated by the model.
request_paramsOptional[Dict[str, Any]]-Additional parameters to include in the API request. Optional.
api_keystr-The API key used for authenticating requests.
base_urlstr"https://api.voyageai.com/v1/embeddings"The base URL for the API endpoint.
max_retriesOptional[int]-The maximum number of retries for API requests. Optional.
timeoutOptional[float]-The timeout duration for API requests. Optional.
client_paramsOptional[Dict[str, Any]]-Additional parameters for configuring the API client. Optional.
voyage_clientOptional[Client]-An instance of the Client to use for making API requests. Optional.

​
Developer Resources