EdenAiEmbeddings#

class langchain_community.embeddings.edenai.EdenAiEmbeddings[source]#

Bases: BaseModel, Embeddings

EdenAI embedding. environment variable EDENAI_API_KEY set with your API key, or pass it as a named parameter.

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

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

param edenai_api_key: SecretStr | None = None#

EdenAI API Token

param model: str | None = None#

model name for above provider (eg: ‘gpt-3.5-turbo-instruct’ for openai) available models are shown on https://docs.edenai.co/ under ‘available providers’

param provider: str = 'openai'#

embedding provider to use (eg: openai,google etc.)

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]#

Embed a list of documents using EdenAI.

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]#

Embed a query using EdenAI.

Parameters:

text (str) – The text to embed.

Returns:

Embeddings for the text.

Return type:

List[float]

static get_user_agent() → str[source]#
Return type:

str

classmethod validate_environment(values: Dict) → Dict[source]#

Validate that api key exists in environment.

Parameters:

values (Dict)

Return type:

Dict

Examples using EdenAiEmbeddings