41 lines
1.4 KiB
Python
41 lines
1.4 KiB
Python
from typing import Optional
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from langchain.chat_models import ChatOpenAI
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from langchain.schema import HumanMessage, SystemMessage
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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 OpenAILlm(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) -> str:
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response = OpenAILlm._get_answer(prompt, self.config)
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return response
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def _get_answer(prompt: str, config: BaseLlmConfig) -> str:
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messages = []
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if config.system_prompt:
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messages.append(SystemMessage(content=config.system_prompt))
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messages.append(HumanMessage(content=prompt))
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kwargs = {
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"model": 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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"model_kwargs": {},
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}
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if config.top_p:
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kwargs["model_kwargs"]["top_p"] = config.top_p
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if config.stream:
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from langchain.callbacks.streaming_stdout import \
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StreamingStdOutCallbackHandler
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chat = ChatOpenAI(**kwargs, streaming=config.stream, callbacks=[StreamingStdOutCallbackHandler()])
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else:
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chat = ChatOpenAI(**kwargs)
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return chat(messages).content
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