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The TogetherEmbedder can be used to embed text data into vectors using the Together API. Together uses the OpenAI API specification, so the TogetherEmbedder class is similar to the OpenAIEmbedder class, incorporating adjustments to ensure compatibility with the Together platform. Get your key from here.

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Usage

together_embedder.py
from agno.knowledge.knowledge import Knowledge
from agno.vectordb.pgvector import PgVector
from agno.knowledge.embedder.together import TogetherEmbedder

# Embed sentence in database
embeddings = TogetherEmbedder().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="together_embeddings",
        embedder=TogetherEmbedder(),
    ),
    max_results=2,
)

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Params

ParameterTypeDefaultDescription
modelstr"nomic-ai/nomic-embed-text-v1.5"The name of the model used for generating embeddings.
dimensionsint768The dimensionality of the embeddings generated by the model.
api_keystrThe API key used for authenticating requests.
base_urlstr"https://api.Together.ai/inference/v1"The base URL for the API endpoint.
enable_batchboolFalseEnable batch processing to reduce API calls and avoid rate limits
batch_sizeint100Number of texts to process in each API call for batch operations.

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Developer Resources

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