PineconeEmbeddings

class langchain_pinecone.embeddings.PineconeEmbeddings

Bases: BaseModel, Embeddings

PineconeEmbeddings embedding model.

Example

from langchain_pinecone import PineconeEmbeddings

model = PineconeEmbeddings(model="multilingual-e5-large")

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 batch_size: int | None = None

Batch size for embedding documents.

param dimension: int | None = None

param document_params: Dict [Optional]

Parameters for embedding document

param model: str [Required]

Model to use for example ‘multilingual-e5-large’.

param pinecone_api_key: SecretStr | None [Optional] (alias 'api_key')

Pinecone API key.

If not provided, will look for the PINECONE_API_KEY environment variable.

Constraints:

• type = string

• writeOnly = True

• format = password

param query_params: Dict [Optional]

Parameters for embedding query.

param show_progress_bar: bool = False

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]

Asynchronously embed query text.

Parameters:

text (str) –

Return type:

List[float]

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

Embed search docs.

Parameters:

texts (List[str]) –

Return type:

List[List[float]]

embed_query(text: str) → List[float]

Embed query text.

Parameters:

text (str) –

Return type:

List[float]