Embedding¶

Kura converts text into numerical vectors (embeddings) for clustering and similarity analysis. Embeddings are automatically generated during clustering, but you can configure which model to use.

Embedding Models¶

Choose between cloud and local models:

from kura.embedding import OpenAIEmbeddingModel, SentenceTransformerEmbeddingModel

# Cloud embeddings (high quality, requires API key)
openai_model = OpenAIEmbeddingModel(
    model_name="text-embedding-3-large",
    model_batch_size=100,
    n_concurrent_jobs=10
)

# Local embeddings (private, runs on your machine)
local_model = SentenceTransformerEmbeddingModel(
    model_name="all-MiniLM-L6-v2",
    model_batch_size=32,
    device="cuda"  # Use "cpu" if no GPU
)

Using with Clustering¶

Embeddings are used automatically in clustering:

from kura import generate_base_clusters_from_conversation_summaries

# Custom embedding model
embedding_model = OpenAIEmbeddingModel(model_name="text-embedding-3-large")

clusters = await generate_base_clusters_from_conversation_summaries(
    summaries,
    embedding_model=embedding_model
)

The default uses OpenAIEmbeddingModel() if no model is specified.