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 phi.agent import AgentKnowledge
from phi.vectordb.pgvector import PgVector
from phi.embedder.sentence_transformer import SentenceTransformerEmbedder

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)}")

# Example usage:
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