60 lines
2.3 KiB
Python
60 lines
2.3 KiB
Python
import json
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import os
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from typing import Any, Dict, Optional
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from langchain.chat_models import ChatOpenAI
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from langchain.schema import AIMessage, HumanMessage, SystemMessage
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from embedchain.config import BaseLlmConfig
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from embedchain.helpers.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, functions: Optional[Dict[str, Any]] = None):
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self.functions = functions
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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 = self._get_answer(prompt, self.config)
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return response
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def _get_answer(self, 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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api_key = config.api_key or os.environ["OPENAI_API_KEY"]
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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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callbacks = config.callbacks if config.callbacks else [StreamingStdOutCallbackHandler()]
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chat = ChatOpenAI(**kwargs, streaming=config.stream, callbacks=callbacks, api_key=api_key)
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else:
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chat = ChatOpenAI(**kwargs, api_key=api_key)
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if self.functions is not None:
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from langchain.chains.openai_functions import \
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create_openai_fn_runnable
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from langchain.prompts import ChatPromptTemplate
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structured_prompt = ChatPromptTemplate.from_messages(messages)
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runnable = create_openai_fn_runnable(functions=self.functions, prompt=structured_prompt, llm=chat)
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fn_res = runnable.invoke(
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{
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"input": prompt,
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}
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)
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messages.append(AIMessage(content=json.dumps(fn_res)))
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return chat(messages).content
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