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NebiusEmbedder is used to embed text data into vectors using the Nebius API. Nebius uses the OpenAI API specification, so the NebiusEmbedder class is similar to the OpenAIEmbedder class, incorporating adjustments to ensure compatibility with the Nebius platform.
Get your key from here
from agno.agent import AgentKnowledge
from agno.embedder.nebius import NebiusEmbedder
from agno.vectordb.pgvector import PgVector
embeddings = NebiusEmbedder().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_base = AgentKnowledge(
vector_db=PgVector(
db_url="postgresql+psycopg://ai:ai@localhost:5532/ai",
table_name="nebius_embeddings",
embedder=NebiusEmbedder(),
),
num_documents=2,
)
| Parameter | Type | Default | Description |
|---|---|---|---|
id | str | "BAAI/bge-en-icl" | The name of the model used for generating embeddings. |
dimensions | int | 1024 | The dimensionality of the embeddings generated by the model. |
api_key | str | The API key used for authenticating requests. | |
base_url | str | "https://api.studio.nebius.com/v1/" | The base URL for the API endpoint. |
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