#   Copyright (C) 2023-2024 Credit Mutuel Arkea
#
#   Licensed under the Apache License, Version 2.0 (the "License");
#   you may not use this file except in compliance with the License.
#   You may obtain a copy of the License at
#
#   http://www.apache.org/licenses/LICENSE-2.0
#
#   Unless required by applicable law or agreed to in writing, software
#   distributed under the License is distributed on an "AS IS" BASIS,
#   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
#   See the License for the specific language governing permissions and
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#
"""Model for creating AzureOpenAIEMFactory"""
from typing import List

from langchain.embeddings.base import Embeddings
from langchain_openai import AzureOpenAIEmbeddings, OpenAIEmbeddings

from gen_ai_orchestrator.configurations.environment.settings import (
    application_settings,
)
from gen_ai_orchestrator.errors.handlers.openai.openai_exception_handler import (
    openai_exception_handler,
)
from gen_ai_orchestrator.models.em.azureopenai.azure_openai_em_setting import (
    AzureOpenAIEMSetting,
)
from gen_ai_orchestrator.services.langchain.factories.em.em_factory import (
    LangChainEMFactory,
)
from gen_ai_orchestrator.services.security.security_service import (
    fetch_secret_key_value,
)


class AzureOpenAIEMFactory(LangChainEMFactory):
    """A class for LangChain Azure OpenAI Embedding Factory"""

    setting: AzureOpenAIEMSetting

    def get_embedding_model(self) -> Embeddings:
        return AzureOpenAIEmbeddings(
            openai_api_key=fetch_secret_key_value(self.setting.api_key),
            openai_api_version=self.setting.api_version,
            azure_endpoint=str(self.setting.api_base),
            azure_deployment=self.setting.deployment_name,
            # the model is not Nullable, it has a default value
            model=self.setting.model or OpenAIEmbeddings.__fields__["model"].default,
            timeout=application_settings.em_provider_timeout,
        )

    @openai_exception_handler(provider='AzureOpenAIService')
    async def embed_query(self, text: str) -> List[float]:
        return await super().embed_query(text)
