CloudflareWorkersAIEmbeddings#

class langchain_community.embeddings.cloudflare_workersai.CloudflareWorkersAIEmbeddings[source]#

Bases: BaseModel, Embeddings

Cloudflare Workers AI embedding model.

To use, you need to provide an API token and account ID to access Cloudflare Workers AI.

Example

from langchain_community.embeddings import CloudflareWorkersAIEmbeddings

account_id = "my_account_id"
api_token = "my_secret_api_token"
model_name =  "@cf/baai/bge-small-en-v1.5"

cf = CloudflareWorkersAIEmbeddings(
    account_id=account_id,
    api_token=api_token,
    model_name=model_name
)

Initialize the Cloudflare Workers AI client.

param account_id: str [Required]#

param api_base_url: str = 'https://api.cloudflare.com/client/v4/accounts'#

param api_token: str [Required]#

param batch_size: int = 50#

param headers: Dict[str, str] = {'Authorization': 'Bearer '}#

param model_name: str = '@cf/baai/bge-base-en-v1.5'#

param strip_new_lines: bool = True#

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]][source]#

Compute doc embeddings using Cloudflare Workers AI.

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][source]#

Compute query embeddings using Cloudflare Workers AI.

Parameters:: text (str) – The text to embed.
Returns:: Embeddings for the text.
Return type:: List[float]

Examples using CloudflareWorkersAIEmbeddings