langchain_community.embeddings.deepinfra
.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 if the input data cannot be parsed to form a valid model.
- param batch_size: int = 1024¶
Batch size for embedding requests.
- param deepinfra_api_token: Optional[str] = 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: Optional[dict] = 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]]