Embeddings

Embeddings allow you to convert text into vector representations, which are useful for semantic search, clustering, and similarity comparisons.

Setup

You need an embedding model. Ollama supports models like mxbai-embed-large or nomic-embed-text.

from mojentic.llm.gateways import EmbeddingsGateway

# Initialize gateway
gateway = EmbeddingsGateway(model="mxbai-embed-large")

Generating Embeddings

text = "The quick brown fox jumps over the lazy dog."
vector = gateway.embed(text)

print(vector[:5])
# => [0.123, -0.456, ...]

Batch Processing

You can embed multiple texts at once:

texts = ["Hello", "World"]
vectors = gateway.embed_batch(texts)

Cosine Similarity

Mojentic provides utilities to calculate similarity between vectors:

from mojentic.utils.math import cosine_similarity

similarity = cosine_similarity(vector1, vector2)