PremAIEmbeddings

class langchain_community.embeddings.premai.PremAIEmbeddings

Bases: BaseModel, Embeddings

Prem’s Embedding APIs

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 client: Any = None

param max_retries: int = 1

Max number of retries for tenacity

param model: str [Required]

The Embedding model to choose from

param premai_api_key: SecretStr | None = None

Prem AI API Key. Get it here: https://app.premai.io/api_keys/

Constraints:

• type = string

• writeOnly = True

• format = password

param project_id: int [Required]

The project ID in which the experiments or deployments are carried out. You can find all your projects here: https://app.premai.io/projects/

param show_progress_bar: bool = False

Whether to show a tqdm progress bar. Must have tqdm installed.

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 search docs.

Parameters:

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

Returns:

List of embeddings.

Return type:

List[List[float]]

embed_query(text: str) → List[float]

Embed query text

Parameters:

text (str) –

Return type:

List[float]

Examples using PremAIEmbeddings

• PremAI