Source code for langchain_community.embeddings.deepinfra

from typing import Any, Dict, List, Mapping, Optional

import requests
from langchain_core.embeddings import Embeddings
from langchain_core.pydantic_v1 import BaseModel, Extra, root_validator
from langchain_core.utils import get_from_dict_or_env

DEFAULT_MODEL_ID = "sentence-transformers/clip-ViT-B-32"
MAX_BATCH_SIZE = 1024


[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_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" ) """ 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 """Other model keyword args""" 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'.""" batch_size: int = MAX_BATCH_SIZE """Batch size for embedding requests.""" class Config: """Configuration for this pydantic object.""" extra = Extra.forbid @root_validator() def validate_environment(cls, values: Dict) -> Dict: """Validate that api key and python package exists in environment.""" deepinfra_api_token = get_from_dict_or_env( values, "deepinfra_api_token", "DEEPINFRA_API_TOKEN" ) values["deepinfra_api_token"] = deepinfra_api_token return values @property def _identifying_params(self) -> Mapping[str, Any]: """Get the identifying parameters.""" return {"model_id": self.model_id} def _embed(self, input: List[str]) -> List[List[float]]: _model_kwargs = self.model_kwargs or {} # HTTP headers for authorization headers = { "Authorization": f"bearer {self.deepinfra_api_token}", "Content-Type": "application/json", } # send request try: res = requests.post( f"https://api.deepinfra.com/v1/inference/{self.model_id}", headers=headers, json={"inputs": input, "normalize": self.normalize, **_model_kwargs}, ) except requests.exceptions.RequestException as e: raise ValueError(f"Error raised by inference endpoint: {e}") if res.status_code != 200: raise ValueError( "Error raised by inference API HTTP code: %s, %s" % (res.status_code, res.text) ) try: t = res.json() embeddings = t["embeddings"] except requests.exceptions.JSONDecodeError as e: raise ValueError( f"Error raised by inference API: {e}.\nResponse: {res.text}" ) return embeddings
[docs] def embed_documents(self, texts: List[str]) -> List[List[float]]: """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. Args: texts: The list of texts to embed. Returns: List of embeddings, one for each text. """ embeddings = [] instruction_pairs = [f"{self.embed_instruction}{text}" for text in texts] chunks = [ instruction_pairs[i : i + self.batch_size] for i in range(0, len(instruction_pairs), self.batch_size) ] for chunk in chunks: embeddings += self._embed(chunk) return embeddings
[docs] def embed_query(self, text: str) -> List[float]: """Embed a query using a Deep Infra deployed embedding model. Args: text: The text to embed. Returns: Embeddings for the text. """ instruction_pair = f"{self.query_instruction}{text}" embedding = self._embed([instruction_pair])[0] return embedding