Model2vecEmbeddings

class langchain_community.embeddings.model2vec.Model2vecEmbeddings(model: str)

Model2Vec embedding models.

Install model2vec first, run ‘pip install -U model2vec’. The github repository for model2vec is : MinishLab/model2vec

Example

from langchain_community.embeddings import Model2vecEmbeddings

embedding = Model2vecEmbeddings("minishlab/potion-base-8M")
embedding.embed_documents([
    "It's dangerous to go alone!",
    "It's a secret to everybody.",
])
embedding.embed_query(
    "Take this with you."
)

Initialize embeddings.

Parameters:

model (str) – Model name.

Methods

__init__(model)	Initialize embeddings.
aembed_documents(texts)	Asynchronous Embed search docs.
aembed_query(text)	Asynchronous Embed query text.
embed_documents(texts)	Embed documents using the model2vec embeddings model.
embed_query(text)	Embed a query using the model2vec embeddings model.

__init__(model: str)

Initialize embeddings.

Parameters:

model (str) – Model name.

async aembed_documents(texts: list[str]) → list[list[float]]

Asynchronous Embed search docs.

Parameters:

texts (list[str]) – List of text to embed.

Returns:

List of embeddings.

Return type:

list[list[float]]

async aembed_query(text: str) → list[float]

Asynchronous Embed query text.

Parameters:

text (str) – Text to embed.

Returns:

Embedding.

Return type:

list[float]

embed_documents(texts: List[str]) → List[List[float]]

Embed documents using the model2vec embeddings model.

Parameters:

texts (List[str]) – The list of texts to embed.

Returns:

List of embeddings, one for each text.

Return type:

List[List[float]]

embed_query(text: str) → List[float]

Embed a query using the model2vec embeddings model.

Parameters:

text (str) – The text to embed.

Returns:

Embeddings for the text.

Return type:

List[float]