Source code for langchain_community.embeddings.embaas

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

import requests
from langchain_core.embeddings import Embeddings
from langchain_core.pydantic_v1 import BaseModel, Extra, SecretStr, root_validator
from langchain_core.utils import convert_to_secret_str, get_from_dict_or_env
from requests.adapters import HTTPAdapter, Retry
from typing_extensions import NotRequired, TypedDict

# Currently supported maximum batch size for embedding requests

[docs]class EmbaasEmbeddingsPayload(TypedDict): """Payload for the Embaas embeddings API.""" model: str texts: List[str] instruction: NotRequired[str]
[docs]class EmbaasEmbeddings(BaseModel, Embeddings): """Embaas's embedding service. To use, you should have the environment variable ``EMBAAS_API_KEY`` set with your API key, or pass it as a named parameter to the constructor. Example: .. code-block:: python # initialize with default model and instruction from langchain_community.embeddings import EmbaasEmbeddings emb = EmbaasEmbeddings() # initialize with custom model and instruction from langchain_community.embeddings import EmbaasEmbeddings emb_model = "instructor-large" emb_inst = "Represent the Wikipedia document for retrieval" emb = EmbaasEmbeddings( model=emb_model, instruction=emb_inst ) """ model: str = "e5-large-v2" """The model used for embeddings.""" instruction: Optional[str] = None """Instruction used for domain-specific embeddings.""" api_url: str = EMBAAS_API_URL """The URL for the embaas embeddings API.""" embaas_api_key: Optional[SecretStr] = None """max number of retries for requests""" max_retries: Optional[int] = 3 """request timeout in seconds""" timeout: Optional[int] = 30 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.""" embaas_api_key = convert_to_secret_str( get_from_dict_or_env(values, "embaas_api_key", "EMBAAS_API_KEY") ) values["embaas_api_key"] = embaas_api_key return values @property def _identifying_params(self) -> Mapping[str, Any]: """Get the identifying params.""" return {"model": self.model, "instruction": self.instruction} def _generate_payload(self, texts: List[str]) -> EmbaasEmbeddingsPayload: """Generates payload for the API request.""" payload = EmbaasEmbeddingsPayload(texts=texts, model=self.model) if self.instruction: payload["instruction"] = self.instruction return payload def _handle_request(self, payload: EmbaasEmbeddingsPayload) -> List[List[float]]: """Sends a request to the Embaas API and handles the response.""" headers = { "Authorization": f"Bearer {self.embaas_api_key.get_secret_value()}", # type: ignore[union-attr] "Content-Type": "application/json", } session = requests.Session() retries = Retry( total=self.max_retries, backoff_factor=0.5, allowed_methods=["POST"], raise_on_status=True, ) session.mount("http://", HTTPAdapter(max_retries=retries)) session.mount("https://", HTTPAdapter(max_retries=retries)) response = self.api_url, headers=headers, json=payload, timeout=self.timeout, ) parsed_response = response.json() embeddings = [item["embedding"] for item in parsed_response["data"]] return embeddings def _generate_embeddings(self, texts: List[str]) -> List[List[float]]: """Generate embeddings using the Embaas API.""" payload = self._generate_payload(texts) try: return self._handle_request(payload) except requests.exceptions.RequestException as e: if e.response is None or not e.response.text: raise ValueError(f"Error raised by embaas embeddings API: {e}") parsed_response = e.response.json() if "message" in parsed_response: raise ValueError( "Validation Error raised by embaas embeddings API:" f"{parsed_response['message']}" ) raise
[docs] def embed_documents(self, texts: List[str]) -> List[List[float]]: """Get embeddings for a list of texts. Args: texts: The list of texts to get embeddings for. Returns: List of embeddings, one for each text. """ batches = [ texts[i : i + MAX_BATCH_SIZE] for i in range(0, len(texts), MAX_BATCH_SIZE) ] embeddings = [self._generate_embeddings(batch) for batch in batches] # flatten the list of lists into a single list return [embedding for batch in embeddings for embedding in batch]
[docs] def embed_query(self, text: str) -> List[float]: """Get embeddings for a single text. Args: text: The text to get embeddings for. Returns: List of embeddings. """ return self.embed_documents([text])[0]