Agno uses the OpenAIEmbedder as the default embeder for the vector database. The OpenAIEmbedder class is used to embed text data into vectors using the OpenAI API. Get your key from here.

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Usage

cookbook/embedders/openai_embedder.py
from agno.agent import AgentKnowledge
from agno.vectordb.pgvector import PgVector
from agno.embedder.openai import OpenAIEmbedder

# Embed sentence in database
embeddings = OpenAIEmbedder().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="openai_embeddings",
        embedder=OpenAIEmbedder(),
    ),
    num_documents=2,
)

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Params

ParameterTypeDefaultDescription
modelstr"text-embedding-ada-002"The name of the model used for generating embeddings.
dimensionsint1536The dimensionality of the embeddings generated by the model.
encoding_formatLiteral['float', 'base64']"float"The format in which the embeddings are encoded. Options are “float” or “base64”.
userstr-The user associated with the API request.
api_keystr-The API key used for authenticating requests.
organizationstr-The organization associated with the API request.
base_urlstr-The base URL for the API endpoint.
request_paramsOptional[Dict[str, Any]]-Additional parameters to include in the API request.
client_paramsOptional[Dict[str, Any]]-Additional parameters for configuring the API client.
openai_clientOptional[OpenAIClient]-An instance of the OpenAIClient to use for making API requests.

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Developer Resources

IntroductionGemini