The SentenceTransformerEmbedder class is used to embed text data into vectors using the SentenceTransformers library.

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Usage

cookbook/embedders/sentence_transformer_embedder.py
from agno.agent import AgentKnowledge
from agno.vectordb.pgvector import PgVector
from agno.embedder.sentence_transformer import SentenceTransformerEmbedder

# Embed sentence in database
embeddings = SentenceTransformerEmbedder().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="sentence_transformer_embeddings",
        embedder=SentenceTransformerEmbedder(),
    ),
    num_documents=2,
)

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Params

ParameterTypeDefaultDescription
dimensionsint-The dimensionality of the generated embeddings
modelstrall-mpnet-base-v2The name of the SentenceTransformers model to use
sentence_transformer_clientOptional[Client]-Optional pre-configured SentenceTransformers client instance

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Developer Resources

FastEmbedTogether