feat: add baichuan prompt (#985)
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takatost
@@ -130,13 +130,12 @@ class Completion:
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fake_response = agent_execute_result.output
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# get llm prompt
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prompt_messages, stop_words = cls.get_main_llm_prompt(
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prompt_messages, stop_words = model_instance.get_prompt(
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mode=mode,
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model=app_model_config.model_dict,
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pre_prompt=app_model_config.pre_prompt,
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query=query,
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inputs=inputs,
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agent_execute_result=agent_execute_result,
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query=query,
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context=agent_execute_result.output if agent_execute_result else None,
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memory=memory
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)
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@@ -154,113 +153,6 @@ class Completion:
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return response
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@classmethod
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def get_main_llm_prompt(cls, mode: str, model: dict,
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pre_prompt: str, query: str, inputs: dict,
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agent_execute_result: Optional[AgentExecuteResult],
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memory: Optional[ReadOnlyConversationTokenDBBufferSharedMemory]) -> \
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Tuple[List[PromptMessage], Optional[List[str]]]:
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if mode == 'completion':
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prompt_template = JinjaPromptTemplate.from_template(
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template=("""Use the following context as your learned knowledge, inside <context></context> XML tags.
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<context>
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{{context}}
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</context>
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When answer to user:
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- If you don't know, just say that you don't know.
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- If you don't know when you are not sure, ask for clarification.
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Avoid mentioning that you obtained the information from the context.
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And answer according to the language of the user's question.
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""" if agent_execute_result else "")
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+ (pre_prompt + "\n" if pre_prompt else "")
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+ "{{query}}\n"
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)
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if agent_execute_result:
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inputs['context'] = agent_execute_result.output
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prompt_inputs = {k: inputs[k] for k in prompt_template.input_variables if k in inputs}
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prompt_content = prompt_template.format(
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query=query,
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**prompt_inputs
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)
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return [PromptMessage(content=prompt_content)], None
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else:
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messages: List[BaseMessage] = []
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human_inputs = {
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"query": query
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}
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human_message_prompt = ""
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if pre_prompt:
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pre_prompt_inputs = {k: inputs[k] for k in
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JinjaPromptTemplate.from_template(template=pre_prompt).input_variables
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if k in inputs}
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if pre_prompt_inputs:
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human_inputs.update(pre_prompt_inputs)
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if agent_execute_result:
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human_inputs['context'] = agent_execute_result.output
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human_message_prompt += """Use the following context as your learned knowledge, inside <context></context> XML tags.
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<context>
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{{context}}
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</context>
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When answer to user:
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- If you don't know, just say that you don't know.
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- If you don't know when you are not sure, ask for clarification.
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Avoid mentioning that you obtained the information from the context.
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And answer according to the language of the user's question.
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"""
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if pre_prompt:
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human_message_prompt += pre_prompt
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query_prompt = "\n\nHuman: {{query}}\n\nAssistant: "
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if memory:
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# append chat histories
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tmp_human_message = PromptBuilder.to_human_message(
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prompt_content=human_message_prompt + query_prompt,
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inputs=human_inputs
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)
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if memory.model_instance.model_rules.max_tokens.max:
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curr_message_tokens = memory.model_instance.get_num_tokens(to_prompt_messages([tmp_human_message]))
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max_tokens = model.get("completion_params").get('max_tokens')
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rest_tokens = memory.model_instance.model_rules.max_tokens.max - max_tokens - curr_message_tokens
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rest_tokens = max(rest_tokens, 0)
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else:
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rest_tokens = 2000
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histories = cls.get_history_messages_from_memory(memory, rest_tokens)
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human_message_prompt += "\n\n" if human_message_prompt else ""
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human_message_prompt += "Here is the chat histories between human and assistant, " \
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"inside <histories></histories> XML tags.\n\n<histories>\n"
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human_message_prompt += histories + "\n</histories>"
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human_message_prompt += query_prompt
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# construct main prompt
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human_message = PromptBuilder.to_human_message(
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prompt_content=human_message_prompt,
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inputs=human_inputs
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)
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messages.append(human_message)
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for message in messages:
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message.content = re.sub(r'<\|.*?\|>', '', message.content)
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return to_prompt_messages(messages), ['\nHuman:', '</histories>']
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@classmethod
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def get_history_messages_from_memory(cls, memory: ReadOnlyConversationTokenDBBufferSharedMemory,
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max_token_limit: int) -> str:
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@@ -307,13 +199,12 @@ And answer according to the language of the user's question.
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max_tokens = 0
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# get prompt without memory and context
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prompt_messages, _ = cls.get_main_llm_prompt(
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prompt_messages, _ = model_instance.get_prompt(
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mode=mode,
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model=app_model_config.model_dict,
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pre_prompt=app_model_config.pre_prompt,
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query=query,
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inputs=inputs,
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agent_execute_result=None,
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query=query,
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context=None,
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memory=None
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)
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@@ -358,13 +249,12 @@ 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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old_prompt_messages, _ = cls.get_main_llm_prompt(
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mode="completion",
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model=app_model_config.model_dict,
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old_prompt_messages, _ = final_model_instance.get_prompt(
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mode='completion',
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pre_prompt=pre_prompt,
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query=message.query,
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inputs=message.inputs,
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agent_execute_result=None,
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query=message.query,
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context=None,
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memory=None
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)
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