mirror of
https://github.com/hwchase17/langchain
synced 2024-11-08 07:10:35 +00:00
131 lines
4.4 KiB
Python
131 lines
4.4 KiB
Python
from __future__ import annotations
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import logging
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from typing import Any, Callable, Dict, List, Optional, Union
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from langchain_core.embeddings import Embeddings
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from langchain_core.language_models.llms import create_base_retry_decorator
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from langchain_core.pydantic_v1 import BaseModel, SecretStr, root_validator
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from langchain_core.utils import get_from_dict_or_env
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logger = logging.getLogger(__name__)
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class PremAIEmbeddings(BaseModel, Embeddings):
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"""Prem's Embedding APIs"""
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project_id: int
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"""The project ID in which the experiments or deployments are carried out.
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You can find all your projects here: https://app.premai.io/projects/"""
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premai_api_key: Optional[SecretStr] = None
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"""Prem AI API Key. Get it here: https://app.premai.io/api_keys/"""
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model: str
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"""The Embedding model to choose from"""
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show_progress_bar: bool = False
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"""Whether to show a tqdm progress bar. Must have `tqdm` installed."""
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max_retries: int = 1
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"""Max number of retries for tenacity"""
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client: Any
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@root_validator()
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def validate_environments(cls, values: Dict) -> Dict:
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"""Validate that the package is installed and that the API token is valid"""
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try:
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from premai import Prem
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except ImportError as error:
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raise ImportError(
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"Could not import Prem Python package."
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"Please install it with: `pip install premai`"
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) from error
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try:
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premai_api_key = get_from_dict_or_env(
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values, "premai_api_key", "PREMAI_API_KEY"
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)
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values["client"] = Prem(api_key=premai_api_key)
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except Exception as error:
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raise ValueError("Your API Key is incorrect. Please try again.") from error
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return values
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def embed_query(self, text: str) -> List[float]:
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"""Embed query text"""
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embeddings = embed_with_retry(
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self, model=self.model, project_id=self.project_id, input=text
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)
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return embeddings.data[0].embedding
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def embed_documents(self, texts: List[str]) -> List[List[float]]:
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embeddings = embed_with_retry(
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self, model=self.model, project_id=self.project_id, input=texts
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).data
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return [embedding.embedding for embedding in embeddings]
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def create_prem_retry_decorator(
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embedder: PremAIEmbeddings,
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*,
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max_retries: int = 1,
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) -> Callable[[Any], Any]:
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"""Create a retry decorator for PremAIEmbeddings.
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Args:
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embedder (PremAIEmbeddings): The PremAIEmbeddings instance
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max_retries (int): The maximum number of retries
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Returns:
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Callable[[Any], Any]: The retry decorator
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"""
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import premai.models
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errors = [
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premai.models.api_response_validation_error.APIResponseValidationError,
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premai.models.conflict_error.ConflictError,
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premai.models.model_not_found_error.ModelNotFoundError,
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premai.models.permission_denied_error.PermissionDeniedError,
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premai.models.provider_api_connection_error.ProviderAPIConnectionError,
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premai.models.provider_api_status_error.ProviderAPIStatusError,
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premai.models.provider_api_timeout_error.ProviderAPITimeoutError,
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premai.models.provider_internal_server_error.ProviderInternalServerError,
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premai.models.provider_not_found_error.ProviderNotFoundError,
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premai.models.rate_limit_error.RateLimitError,
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premai.models.unprocessable_entity_error.UnprocessableEntityError,
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premai.models.validation_error.ValidationError,
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]
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decorator = create_base_retry_decorator(
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error_types=errors, max_retries=max_retries, run_manager=None
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)
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return decorator
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def embed_with_retry(
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embedder: PremAIEmbeddings,
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model: str,
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project_id: int,
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input: Union[str, List[str]],
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) -> Any:
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"""Using tenacity for retry in embedding calls"""
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retry_decorator = create_prem_retry_decorator(
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embedder, max_retries=embedder.max_retries
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)
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@retry_decorator
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def _embed_with_retry(
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embedder: PremAIEmbeddings,
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project_id: int,
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model: str,
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input: Union[str, List[str]],
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) -> Any:
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embedding_response = embedder.client.embeddings.create(
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project_id=project_id, model=model, input=input
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)
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return embedding_response
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return _embed_with_retry(embedder, project_id=project_id, model=model, input=input)
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