Gemini Embedder

The GeminiEmbedder class is used to embed text data into vectors using the Gemini API. You can get one from here.

Usage

gemini_embedder.py

from agno.knowledge.knowledge import Knowledge
from agno.vectordb.pgvector import PgVector
from agno.knowledge.embedder.google import GeminiEmbedder

# Embed sentence in database
embeddings = GeminiEmbedder().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 = Knowledge(
    vector_db=PgVector(
        db_url="postgresql+psycopg://ai:ai@localhost:5532/ai",
        table_name="gemini_embeddings",
        embedder=GeminiEmbedder(),
    ),
    max_results=2,
)

Params

Parameter	Type	Default	Description
dimensions	int	768	The dimensionality of the generated embeddings
model	str	models/text-embedding-004	The name of the Gemini model to use
task_type	str	-	The type of task for which embeddings are being generated
title	str	-	Optional title for the embedding task
api_key	str	-	The API key used for authenticating requests.
request_params	Optional[Dict[str, Any]]	-	Optional dictionary of parameters for the embedding request
client_params	Optional[Dict[str, Any]]	-	Optional dictionary of parameters for the Gemini client
gemini_client	Optional[Client]	-	Optional pre-configured Gemini client instance
enable_batch	bool	False	Enable batch processing to reduce API calls and avoid rate limits
batch_size	int	100	Number of texts to process in each API call for batch operations.

Developer Resources

• View Cookbook