Gemini Embedder

from agno.agent import AgentKnowledge
from agno.vectordb.pgvector import PgVector
from agno.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_base = AgentKnowledge(
    vector_db=PgVector(
        db_url="postgresql+psycopg://ai:ai@localhost:5532/ai",
        table_name="gemini_embeddings",
        embedder=GeminiEmbedder(),
    ),
    num_documents=2,
)

from agno.agent import AgentKnowledge
from agno.vectordb.pgvector import PgVector
from agno.embedder.google import GeminiEmbedder

embedder = GeminiEmbedder(
    vertexai=True,
    project_id="your-gcloud-project-id",  # Optional
    location="us-central1",  # Optional
)

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