129242534d
Co-authored-by: cachho <admin@ch-webdev.com> Co-authored-by: Taranjeet Singh <reachtotj@gmail.com>
43 lines
1.5 KiB
Python
43 lines
1.5 KiB
Python
from typing import Optional
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import openai
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from embedchain.config import BaseLlmConfig
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from embedchain.helper_classes.json_serializable import register_deserializable
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from embedchain.llm.base_llm 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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# NOTE: This class does not use langchain. One reason is that `top_p` is not supported.
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def get_llm_model_answer(self, prompt):
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messages = []
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if self.config.system_prompt:
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messages.append({"role": "system", "content": self.config.system_prompt})
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messages.append({"role": "user", "content": prompt})
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response = openai.ChatCompletion.create(
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model=self.config.model or "gpt-3.5-turbo-0613",
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messages=messages,
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temperature=self.config.temperature,
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max_tokens=self.config.max_tokens,
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top_p=self.config.top_p,
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stream=self.config.stream,
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)
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if self.config.stream:
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return self._stream_llm_model_response(response)
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else:
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return response["choices"][0]["message"]["content"]
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def _stream_llm_model_response(self, response):
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"""
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This is a generator for streaming response from the OpenAI completions API
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"""
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for line in response:
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chunk = line["choices"][0].get("delta", {}).get("content", "")
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yield chunk
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