HuggingFace Embedder

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

Usage

huggingface_embedder.py

from agno.knowledge.knowledge import Knowledge
from agno.vectordb.pgvector import PgVector
from agno.knowledge.embedder.huggingface import HuggingfaceCustomEmbedder

# Embed sentence in database
embeddings = HuggingfaceCustomEmbedder().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="huggingface_embeddings",
        embedder=HuggingfaceCustomEmbedder(),
    ),
    max_results=2,
)

Params

Parameter	Type	Default	Description
dimensions	int	-	The dimensionality of the generated embeddings
model	str	all-MiniLM-L6-v2	The name of the HuggingFace model to use
api_key	str	-	The API key used for authenticating requests
client_params	Optional[Dict[str, Any]]	-	Optional dictionary of parameters for the HuggingFace client
huggingface_client	Any	-	Optional pre-configured HuggingFace client instance

Developer Resources

• View Cookbook