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

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Usage

cookbook/embedders/huggingface_embedder.py
from agno.agent import AgentKnowledge
from agno.vectordb.pgvector import PgVector
from agno.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_base = AgentKnowledge(
    vector_db=PgVector(
        db_url="postgresql+psycopg://ai:ai@localhost:5532/ai",
        table_name="huggingface_embeddings",
        embedder=HuggingfaceCustomEmbedder(),
    ),
    num_documents=2,
)

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Params

ParameterTypeDefaultDescription
dimensionsint-The dimensionality of the generated embeddings
modelstrall-MiniLM-L6-v2The name of the HuggingFace model to use
api_keystr-The API key used for authenticating requests
client_paramsOptional[Dict[str, Any]]-Optional dictionary of parameters for the HuggingFace client
huggingface_clientAny-Optional pre-configured HuggingFace client instance

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Developer Resources