MosaicMLInstructorEmbeddings#

class langchain_community.embeddings.mosaicml.MosaicMLInstructorEmbeddings[source]#

Bases: BaseModel, Embeddings

MosaicML embedding service.

To use, you should have the environment variable MOSAICML_API_TOKEN set with your API token, or pass it as a named parameter to the constructor.

Example

from langchain_community.llms import MosaicMLInstructorEmbeddings
endpoint_url = (
    "https://models.hosted-on.mosaicml.hosting/instructor-large/v1/predict"
)
mosaic_llm = MosaicMLInstructorEmbeddings(
    endpoint_url=endpoint_url,
    mosaicml_api_token="my-api-key"
)

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 embed_instruction: str = 'Represent the document for retrieval: '#: Instruction used to embed documents.

param endpoint_url: str = 'https://models.hosted-on.mosaicml.hosting/instructor-xl/v1/predict'#: Endpoint URL to use.

param mosaicml_api_token: str | None = None#

param query_instruction: str = 'Represent the question for retrieving supporting documents: '#: Instruction used to embed the query.

param retry_sleep: float = 1.0#: How long to try sleeping for if a rate limit is encountered

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 documents using a MosaicML deployed instructor embedding model.

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 a MosaicML deployed instructor embedding model.

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

Examples using MosaicMLInstructorEmbeddings

MosaicML