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Chonkie provides a variety of embeddings handlers to handle different embedding models in a consistent manner. Embeddings handlers are used in conjunction with chunkers to embed chunks of text. Only few chunkers require embeddings, see the Chunkers Overview for more information.

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Installation

Embeddings handlers require additional dependencies. See the Installation Guide for more information.
By default, Chonkie semantic installation includes Model2VecEmbeddings, which is the current default embeddings handler

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Available Embeddings

AutoEmbeddings

Automatically select the best embeddings handler for your use case.

CohereEmbeddings

Embed text using Cohere embeddings (requires cohere).

SentenceTransformerEmbeddings

Embed text using SentenceTransformer embeddings (requires sentence-transformers).

OpenAIEmbeddings

Embed text using OpenAI embeddings (requires openai).

Model2VecEmbeddings

Embed text using Model2Vec embeddings (requires model2vec).

GeminiEmbeddings

Embed text using Google Gemini embeddings (requires google-genai).

JinaEmbeddings

Embed text using JinaAI embeddings (requires jina).

AzureOpenAIEmbeddings

Embed text using Azure OpenAI embeddings (requires openai, azure-identity).

VoyageAIEmbeddings

Embed text using VoyageAI embeddings (requires voyageai).

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Common Interface

All embeddings handlers share a consistent interface: 
# Single text embedding
emb = embeddings.embed(text)

# Batch processing
emb = embeddings.embed_batch(texts)

# Direct calling
emb = embeddings(text)  # or embeddings([text1, text2])
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