DeepInfraEmbeddings#

class langchain_community.embeddings.deepinfra.DeepInfraEmbeddings[source]#

Bases: BaseModel, Embeddings

Deep Infra’s embedding inference service.

To use, you should have the environment variable DEEPINFRA_API_TOKEN set with your API token, or pass it as a named parameter to the constructor. There are multiple embeddings models available, see https://deepinfra.com/models?type=embeddings.

Example

from langchain_community.embeddings import DeepInfraEmbeddings
deepinfra_emb = DeepInfraEmbeddings(
    model_id="sentence-transformers/clip-ViT-B-32",
    deepinfra_api_token="my-api-key"
)
r1 = deepinfra_emb.embed_documents(
    [
        "Alpha is the first letter of Greek alphabet",
        "Beta is the second letter of Greek alphabet",
    ]
)
r2 = deepinfra_emb.embed_query(
    "What is the second letter of Greek alphabet"
)

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 batch_size: int = 1024#: Batch size for embedding requests.

param deepinfra_api_token: str | None = None#: API token for Deep Infra. If not provided, the token is fetched from the environment variable ‘DEEPINFRA_API_TOKEN’.

param embed_instruction: str = 'passage: '#: Instruction used to embed documents.

param model_id: str = 'sentence-transformers/clip-ViT-B-32'#: Embeddings model to use.

param model_kwargs: dict | None = None#: Other model keyword args

param normalize: bool = False#: whether to normalize the computed embeddings

param query_instruction: str = 'query: '#: Instruction used to embed the query.

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 Deep Infra deployed embedding model. For larger batches, the input list of texts is chunked into smaller batches to avoid exceeding the maximum request size.

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 Deep Infra deployed embedding model.

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

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

Validate that api key and python package exists in environment.

Parameters:: values (Dict)
Return type:: Dict

Examples using DeepInfraEmbeddings

DeepInfra