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Showing content from https://python.langchain.com/docs/integrations/text_embedding/model2vec/ below:

model2vec | 🦜️🔗 LangChain

Model2Vec is a technique to turn any sentence transformer into a really small static model model2vec can be used to generate embeddings.

pip install -U langchain-community

Ensure that model2vec is installed

from langchain_community.embeddings import Model2vecEmbeddings

embeddings = Model2vecEmbeddings("minishlab/potion-base-8M")

query_text = "This is a test query."
query_result = embeddings.embed_query(query_text)

document_text = "This is a test document."
document_result = embeddings.embed_documents([document_text])

Here's how you would directly make use of model2vec

from model2vec import StaticModel


model = StaticModel.from_pretrained("minishlab/potion-base-8M")


embeddings = model.encode(["It's dangerous to go alone!", "It's a secret to everybody."])


token_embeddings = model.encode_as_sequence(["It's dangerous to go alone!", "It's a secret to everybody."])

For more information check out the model2vec github repo

• Embedding model conceptual guide
• Embedding model how-to guides

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