39 lines
1.3 KiB
Python
39 lines
1.3 KiB
Python
import logging
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from typing import Optional
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from embedchain.config import BaseLlmConfig
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from embedchain.helper.json_serializable import register_deserializable
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from embedchain.llm.base import BaseLlm
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@register_deserializable
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class AzureOpenAILlm(BaseLlm):
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def __init__(self, config: Optional[BaseLlmConfig] = None):
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super().__init__(config=config)
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def get_llm_model_answer(self, prompt):
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return AzureOpenAILlm._get_answer(prompt=prompt, config=self.config)
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@staticmethod
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def _get_answer(prompt: str, config: BaseLlmConfig) -> str:
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from langchain.chat_models import AzureChatOpenAI
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if not config.deployment_name:
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raise ValueError("Deployment name must be provided for Azure OpenAI")
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chat = AzureChatOpenAI(
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deployment_name=config.deployment_name,
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openai_api_version="2023-05-15",
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model_name=config.model or "gpt-3.5-turbo",
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temperature=config.temperature,
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max_tokens=config.max_tokens,
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streaming=config.stream,
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)
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if config.top_p and config.top_p != 1:
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logging.warning("Config option `top_p` is not supported by this model.")
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messages = BaseLlm._get_messages(prompt, system_prompt=config.system_prompt)
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return chat(messages).content
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