Together Embedder

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.

​Usage

cookbook/embedders/together_embedder.py

from bitca.agent import AgentKnowledge
from bitca.vectordb.pgvector import PgVector
from bitca.embedder.together import TogetherEmbedder

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)}")

# Example usage:
knowledge_base = AgentKnowledge(
    vector_db=PgVector(
        db_url="postgresql+psycopg://ai:ai@localhost:5532/ai",
        table_name="together_embeddings",
        embedder=TogetherEmbedder(),
    ),
    num_documents=2,
)

​Params

Parameter	Type	Default	Description
model	str	"nomic-ai/nomic-embed-text-v1.5"	The name of the model used for generating embeddings.
dimensions	int	768	The dimensionality of the embeddings generated by the model.
api_key	str		The API key used for authenticating requests.
base_url	str	"https://api.Together.ai/inference/v1"	The base URL for the API endpoint.