Source code for langchain.embeddings.deepinfra
from typing import Any, Dict, List, Mapping, Optional
import requests
from langchain.pydantic_v1 import BaseModel, Extra, root_validator
from langchain.schema.embeddings import Embeddings
from langchain.utils import get_from_dict_or_env
DEFAULT_MODEL_ID = "sentence-transformers/clip-ViT-B-32"
[docs]class DeepInfraEmbeddings(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:
        .. code-block:: python
            from langchain.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"
            )
    """
    model_id: str = DEFAULT_MODEL_ID
    """Embeddings model to use."""
    normalize: bool = False
    """whether to normalize the computed embeddings"""
    embed_instruction: str = "passage: "
    """Instruction used to embed documents."""
    query_instruction: str = "query: "
    """Instruction used to embed the query."""
    model_kwargs: Optional[dict] = None