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Fix/human in answer (#174)
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@ -1,4 +1,4 @@
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from typing import Optional, List, Union
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from typing import Optional, List, Union, Tuple
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from langchain.callbacks import CallbackManager
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from langchain.chat_models.base import BaseChatModel
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@ -97,7 +97,7 @@ class Completion:
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)
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# get llm prompt
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prompt = cls.get_main_llm_prompt(
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prompt, stop_words = cls.get_main_llm_prompt(
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mode=mode,
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llm=final_llm,
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pre_prompt=app_model_config.pre_prompt,
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@ -115,7 +115,7 @@ class Completion:
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mode=mode
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)
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response = final_llm.generate([prompt])
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response = final_llm.generate([prompt], stop_words)
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return response
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@ -123,7 +123,7 @@ class Completion:
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def get_main_llm_prompt(cls, mode: str, llm: BaseLanguageModel, pre_prompt: str, query: str, inputs: dict,
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chain_output: Optional[str],
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memory: Optional[ReadOnlyConversationTokenDBBufferSharedMemory]) -> \
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Union[str | List[BaseMessage]]:
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Tuple[Union[str | List[BaseMessage]], Optional[List[str]]]:
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# disable template string in query
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query_params = OutLinePromptTemplate.from_template(template=query).input_variables
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if query_params:
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@ -165,9 +165,9 @@ And answer according to the language of the user's question.
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if isinstance(llm, BaseChatModel):
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# use chat llm as completion model
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return [HumanMessage(content=prompt_content)]
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return [HumanMessage(content=prompt_content)], None
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else:
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return prompt_content
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return prompt_content, None
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else:
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messages: List[BaseMessage] = []
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@ -236,7 +236,7 @@ And answer according to the language of the user's question.
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messages.append(human_message)
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return messages
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return messages, ['\nHuman:']
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@classmethod
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def get_llm_callback_manager(cls, llm: Union[StreamableOpenAI, StreamableChatOpenAI],
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@ -323,7 +323,7 @@ And answer according to the language of the user's question.
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)
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# get llm prompt
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original_prompt = cls.get_main_llm_prompt(
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original_prompt, _ = cls.get_main_llm_prompt(
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mode="completion",
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llm=llm,
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pre_prompt=pre_prompt,
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