AI21Embeddings

class langchain_ai21.embeddings.AI21Embeddings

Bases: Embeddings, AI21Base

AI21 embedding model.

To use, you should have the ‘AI21_API_KEY’ environment variable set or pass as a named parameter to the constructor.

Example

from langchain_ai21 import AI21Embeddings

embeddings = AI21Embeddings()
query_result = embeddings.embed_query("Hello embeddings world!")

Create a new model by parsing and validating input data from keyword arguments.

Raises ValidationError if the input data cannot be parsed to form a valid model.

param api_host: str | None = None

param api_key: SecretStr | None = None

Constraints:

• type = string

• writeOnly = True

• format = password

param batch_size: int = 128

Maximum number of texts to embed in each batch

param num_retries: int | None = None

param timeout_sec: float | None = None

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], *, batch_size: int | None = None, **kwargs: Any) → List[List[float]]

Embed search docs.

Parameters:

• texts (List[str])

• batch_size (int | None)

• kwargs (Any)

Return type:

List[List[float]]

embed_query(text: str, *, batch_size: int | None = None, **kwargs: Any) → List[float]

Embed query text.

Parameters:

• text (str)

• batch_size (int | None)

• kwargs (Any)

Return type:

List[float]