mirror of
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FEAT: NEW WORKFLOW ENGINE (#3160)
Co-authored-by: Joel <iamjoel007@gmail.com> Co-authored-by: Yeuoly <admin@srmxy.cn> Co-authored-by: JzoNg <jzongcode@gmail.com> Co-authored-by: StyleZhang <jasonapring2015@outlook.com> Co-authored-by: jyong <jyong@dify.ai> Co-authored-by: nite-knite <nkCoding@gmail.com> Co-authored-by: jyong <718720800@qq.com>
This commit is contained in:
takatost
0
api/core/prompt/__init__.py
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0
api/core/prompt/__init__.py
Normal file
235
api/core/prompt/advanced_prompt_transform.py
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235
api/core/prompt/advanced_prompt_transform.py
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from typing import Optional, Union
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from core.app.entities.app_invoke_entities import ModelConfigWithCredentialsEntity
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from core.file.file_obj import FileVar
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from core.memory.token_buffer_memory import TokenBufferMemory
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from core.model_runtime.entities.message_entities import (
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AssistantPromptMessage,
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PromptMessage,
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PromptMessageRole,
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SystemPromptMessage,
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TextPromptMessageContent,
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UserPromptMessage,
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)
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from core.prompt.entities.advanced_prompt_entities import ChatModelMessage, CompletionModelPromptTemplate, MemoryConfig
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from core.prompt.prompt_transform import PromptTransform
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from core.prompt.simple_prompt_transform import ModelMode
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from core.prompt.utils.prompt_template_parser import PromptTemplateParser
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class AdvancedPromptTransform(PromptTransform):
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"""
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Advanced Prompt Transform for Workflow LLM Node.
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"""
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def __init__(self, with_variable_tmpl: bool = False) -> None:
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self.with_variable_tmpl = with_variable_tmpl
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def get_prompt(self, prompt_template: Union[list[ChatModelMessage], CompletionModelPromptTemplate],
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inputs: dict,
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query: str,
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files: list[FileVar],
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context: Optional[str],
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memory_config: Optional[MemoryConfig],
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memory: Optional[TokenBufferMemory],
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model_config: ModelConfigWithCredentialsEntity) -> list[PromptMessage]:
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prompt_messages = []
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model_mode = ModelMode.value_of(model_config.mode)
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if model_mode == ModelMode.COMPLETION:
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prompt_messages = self._get_completion_model_prompt_messages(
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prompt_template=prompt_template,
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inputs=inputs,
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query=query,
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files=files,
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context=context,
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memory_config=memory_config,
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memory=memory,
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model_config=model_config
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)
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elif model_mode == ModelMode.CHAT:
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prompt_messages = self._get_chat_model_prompt_messages(
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prompt_template=prompt_template,
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inputs=inputs,
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query=query,
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files=files,
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context=context,
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memory_config=memory_config,
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memory=memory,
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model_config=model_config
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)
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return prompt_messages
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def _get_completion_model_prompt_messages(self,
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prompt_template: CompletionModelPromptTemplate,
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inputs: dict,
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query: Optional[str],
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files: list[FileVar],
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context: Optional[str],
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memory_config: Optional[MemoryConfig],
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memory: Optional[TokenBufferMemory],
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model_config: ModelConfigWithCredentialsEntity) -> list[PromptMessage]:
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"""
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Get completion model prompt messages.
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"""
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raw_prompt = prompt_template.text
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prompt_messages = []
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prompt_template = PromptTemplateParser(template=raw_prompt, with_variable_tmpl=self.with_variable_tmpl)
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prompt_inputs = {k: inputs[k] for k in prompt_template.variable_keys if k in inputs}
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prompt_inputs = self._set_context_variable(context, prompt_template, prompt_inputs)
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if memory and memory_config:
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role_prefix = memory_config.role_prefix
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prompt_inputs = self._set_histories_variable(
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memory=memory,
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memory_config=memory_config,
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raw_prompt=raw_prompt,
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role_prefix=role_prefix,
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prompt_template=prompt_template,
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prompt_inputs=prompt_inputs,
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model_config=model_config
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)
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if query:
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prompt_inputs = self._set_query_variable(query, prompt_template, prompt_inputs)
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prompt = prompt_template.format(
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prompt_inputs
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)
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if files:
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prompt_message_contents = [TextPromptMessageContent(data=prompt)]
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for file in files:
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prompt_message_contents.append(file.prompt_message_content)
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prompt_messages.append(UserPromptMessage(content=prompt_message_contents))
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else:
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prompt_messages.append(UserPromptMessage(content=prompt))
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return prompt_messages
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def _get_chat_model_prompt_messages(self,
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prompt_template: list[ChatModelMessage],
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inputs: dict,
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query: Optional[str],
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files: list[FileVar],
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context: Optional[str],
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memory_config: Optional[MemoryConfig],
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memory: Optional[TokenBufferMemory],
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model_config: ModelConfigWithCredentialsEntity) -> list[PromptMessage]:
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"""
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Get chat model prompt messages.
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"""
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raw_prompt_list = prompt_template
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prompt_messages = []
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for prompt_item in raw_prompt_list:
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raw_prompt = prompt_item.text
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prompt_template = PromptTemplateParser(template=raw_prompt, with_variable_tmpl=self.with_variable_tmpl)
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prompt_inputs = {k: inputs[k] for k in prompt_template.variable_keys if k in inputs}
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prompt_inputs = self._set_context_variable(context, prompt_template, prompt_inputs)
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prompt = prompt_template.format(
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prompt_inputs
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)
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if prompt_item.role == PromptMessageRole.USER:
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prompt_messages.append(UserPromptMessage(content=prompt))
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elif prompt_item.role == PromptMessageRole.SYSTEM and prompt:
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prompt_messages.append(SystemPromptMessage(content=prompt))
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elif prompt_item.role == PromptMessageRole.ASSISTANT:
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prompt_messages.append(AssistantPromptMessage(content=prompt))
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if memory and memory_config:
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prompt_messages = self._append_chat_histories(memory, memory_config, prompt_messages, model_config)
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if files:
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prompt_message_contents = [TextPromptMessageContent(data=query)]
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for file in files:
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prompt_message_contents.append(file.prompt_message_content)
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prompt_messages.append(UserPromptMessage(content=prompt_message_contents))
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else:
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prompt_messages.append(UserPromptMessage(content=query))
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elif files:
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if not query:
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# get last message
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last_message = prompt_messages[-1] if prompt_messages else None
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if last_message and last_message.role == PromptMessageRole.USER:
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# get last user message content and add files
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prompt_message_contents = [TextPromptMessageContent(data=last_message.content)]
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for file in files:
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prompt_message_contents.append(file.prompt_message_content)
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last_message.content = prompt_message_contents
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else:
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prompt_message_contents = [TextPromptMessageContent(data='')] # not for query
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for file in files:
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prompt_message_contents.append(file.prompt_message_content)
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prompt_messages.append(UserPromptMessage(content=prompt_message_contents))
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else:
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prompt_message_contents = [TextPromptMessageContent(data=query)]
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for file in files:
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prompt_message_contents.append(file.prompt_message_content)
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prompt_messages.append(UserPromptMessage(content=prompt_message_contents))
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elif query:
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prompt_messages.append(UserPromptMessage(content=query))
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return prompt_messages
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def _set_context_variable(self, context: str, prompt_template: PromptTemplateParser, prompt_inputs: dict) -> dict:
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if '#context#' in prompt_template.variable_keys:
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if context:
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prompt_inputs['#context#'] = context
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else:
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prompt_inputs['#context#'] = ''
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return prompt_inputs
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def _set_query_variable(self, query: str, prompt_template: PromptTemplateParser, prompt_inputs: dict) -> dict:
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if '#query#' in prompt_template.variable_keys:
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if query:
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prompt_inputs['#query#'] = query
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else:
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prompt_inputs['#query#'] = ''
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return prompt_inputs
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def _set_histories_variable(self, memory: TokenBufferMemory,
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memory_config: MemoryConfig,
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raw_prompt: str,
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role_prefix: MemoryConfig.RolePrefix,
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prompt_template: PromptTemplateParser,
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prompt_inputs: dict,
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model_config: ModelConfigWithCredentialsEntity) -> dict:
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if '#histories#' in prompt_template.variable_keys:
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if memory:
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inputs = {'#histories#': '', **prompt_inputs}
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prompt_template = PromptTemplateParser(template=raw_prompt, with_variable_tmpl=self.with_variable_tmpl)
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prompt_inputs = {k: inputs[k] for k in prompt_template.variable_keys if k in inputs}
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tmp_human_message = UserPromptMessage(
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content=prompt_template.format(prompt_inputs)
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)
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rest_tokens = self._calculate_rest_token([tmp_human_message], model_config)
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histories = self._get_history_messages_from_memory(
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memory=memory,
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memory_config=memory_config,
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max_token_limit=rest_tokens,
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human_prefix=role_prefix.user,
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ai_prefix=role_prefix.assistant
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)
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prompt_inputs['#histories#'] = histories
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else:
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prompt_inputs['#histories#'] = ''
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return prompt_inputs
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0
api/core/prompt/entities/__init__.py
Normal file
0
api/core/prompt/entities/__init__.py
Normal file
42
api/core/prompt/entities/advanced_prompt_entities.py
Normal file
42
api/core/prompt/entities/advanced_prompt_entities.py
Normal file
@@ -0,0 +1,42 @@
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from typing import Optional
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from pydantic import BaseModel
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from core.model_runtime.entities.message_entities import PromptMessageRole
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class ChatModelMessage(BaseModel):
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"""
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Chat Message.
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"""
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text: str
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role: PromptMessageRole
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class CompletionModelPromptTemplate(BaseModel):
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"""
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Completion Model Prompt Template.
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"""
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text: str
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class MemoryConfig(BaseModel):
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"""
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Memory Config.
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"""
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class RolePrefix(BaseModel):
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"""
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Role Prefix.
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"""
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user: str
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assistant: str
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class WindowConfig(BaseModel):
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"""
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Window Config.
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"""
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enabled: bool
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size: Optional[int] = None
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role_prefix: Optional[RolePrefix] = None
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window: WindowConfig
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@@ -1,13 +0,0 @@
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{
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"human_prefix": "Human",
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"assistant_prefix": "Assistant",
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"context_prompt": "Use the following context as your learned knowledge, inside <context></context> XML tags.\n\n<context>\n{{context}}\n</context>\n\nWhen answer to user:\n- If you don't know, just say that you don't know.\n- If you don't know when you are not sure, ask for clarification.\nAvoid mentioning that you obtained the information from the context.\nAnd answer according to the language of the user's question.\n\n",
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"histories_prompt": "Here is the chat histories between human and assistant, inside <histories></histories> XML tags.\n\n<histories>\n{{histories}}\n</histories>\n\n",
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"system_prompt_orders": [
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"context_prompt",
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"pre_prompt",
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"histories_prompt"
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],
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"query_prompt": "\n\nHuman: {{query}}\n\nAssistant: ",
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"stops": ["\nHuman:", "</histories>"]
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}
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@@ -1,9 +0,0 @@
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{
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"context_prompt": "Use the following context as your learned knowledge, inside <context></context> XML tags.\n\n<context>\n{{context}}\n</context>\n\nWhen answer to user:\n- If you don't know, just say that you don't know.\n- If you don't know when you are not sure, ask for clarification.\nAvoid mentioning that you obtained the information from the context.\nAnd answer according to the language of the user's question.\n\n",
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"system_prompt_orders": [
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"context_prompt",
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"pre_prompt"
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],
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"query_prompt": "{{query}}",
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"stops": null
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}
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@@ -1,33 +0,0 @@
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from typing import Any
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from langchain.schema import BaseOutputParser, OutputParserException
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from core.prompt.prompts import RULE_CONFIG_GENERATE_TEMPLATE
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from libs.json_in_md_parser import parse_and_check_json_markdown
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class RuleConfigGeneratorOutputParser(BaseOutputParser):
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def get_format_instructions(self) -> str:
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return RULE_CONFIG_GENERATE_TEMPLATE
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def parse(self, text: str) -> Any:
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try:
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expected_keys = ["prompt", "variables", "opening_statement"]
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parsed = parse_and_check_json_markdown(text, expected_keys)
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if not isinstance(parsed["prompt"], str):
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raise ValueError("Expected 'prompt' to be a string.")
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if not isinstance(parsed["variables"], list):
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raise ValueError(
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"Expected 'variables' to be a list."
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)
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if not isinstance(parsed["opening_statement"], str):
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raise ValueError(
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"Expected 'opening_statement' to be a str."
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)
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return parsed
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except Exception as e:
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raise OutputParserException(
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f"Parsing text\n{text}\n of rule config generator raised following error:\n{e}"
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)
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@@ -1,23 +0,0 @@
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import json
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import re
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from typing import Any
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from langchain.schema import BaseOutputParser
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from core.prompt.prompts import SUGGESTED_QUESTIONS_AFTER_ANSWER_INSTRUCTION_PROMPT
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class SuggestedQuestionsAfterAnswerOutputParser(BaseOutputParser):
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def get_format_instructions(self) -> str:
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return SUGGESTED_QUESTIONS_AFTER_ANSWER_INSTRUCTION_PROMPT
|
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|
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def parse(self, text: str) -> Any:
|
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action_match = re.search(r"\[.*?\]", text.strip(), re.DOTALL)
|
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if action_match is not None:
|
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json_obj = json.loads(action_match.group(0).strip())
|
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else:
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json_obj= []
|
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print(f"Could not parse LLM output: {text}")
|
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|
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return json_obj
|
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@@ -1,10 +0,0 @@
|
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from core.prompt.prompt_template import PromptTemplateParser
|
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|
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|
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class PromptBuilder:
|
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@classmethod
|
||||
def parse_prompt(cls, prompt: str, inputs: dict) -> str:
|
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prompt_template = PromptTemplateParser(prompt)
|
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prompt_inputs = {k: inputs[k] for k in prompt_template.variable_keys if k in inputs}
|
||||
prompt = prompt_template.format(prompt_inputs)
|
||||
return prompt
|
||||
0
api/core/prompt/prompt_templates/__init__.py
Normal file
0
api/core/prompt/prompt_templates/__init__.py
Normal file
@@ -1,13 +1,13 @@
|
||||
{
|
||||
"human_prefix": "用户",
|
||||
"assistant_prefix": "助手",
|
||||
"context_prompt": "用户在与一个客观的助手对话。助手会尊重找到的材料，给出全面专业的解释，但不会过度演绎。同时回答中不会暴露引用的材料：\n\n```\n{{context}}\n```\n\n",
|
||||
"histories_prompt": "用户和助手的历史对话内容如下：\n```\n{{histories}}\n```\n\n",
|
||||
"context_prompt": "用户在与一个客观的助手对话。助手会尊重找到的材料，给出全面专业的解释，但不会过度演绎。同时回答中不会暴露引用的材料：\n\n```\n{{#context#}}\n```\n\n",
|
||||
"histories_prompt": "用户和助手的历史对话内容如下：\n```\n{{#histories#}}\n```\n\n",
|
||||
"system_prompt_orders": [
|
||||
"context_prompt",
|
||||
"pre_prompt",
|
||||
"histories_prompt"
|
||||
],
|
||||
"query_prompt": "\n\n用户：{{query}}",
|
||||
"query_prompt": "\n\n用户：{{#query#}}",
|
||||
"stops": ["用户："]
|
||||
}
|
||||
@@ -1,9 +1,9 @@
|
||||
{
|
||||
"context_prompt": "用户在与一个客观的助手对话。助手会尊重找到的材料，给出全面专业的解释，但不会过度演绎。同时回答中不会暴露引用的材料：\n\n```\n{{context}}\n```\n",
|
||||
"context_prompt": "用户在与一个客观的助手对话。助手会尊重找到的材料，给出全面专业的解释，但不会过度演绎。同时回答中不会暴露引用的材料：\n\n```\n{{#context#}}\n```\n",
|
||||
"system_prompt_orders": [
|
||||
"context_prompt",
|
||||
"pre_prompt"
|
||||
],
|
||||
"query_prompt": "{{query}}",
|
||||
"query_prompt": "{{#query#}}",
|
||||
"stops": null
|
||||
}
|
||||
13
api/core/prompt/prompt_templates/common_chat.json
Normal file
13
api/core/prompt/prompt_templates/common_chat.json
Normal file
@@ -0,0 +1,13 @@
|
||||
{
|
||||
"human_prefix": "Human",
|
||||
"assistant_prefix": "Assistant",
|
||||
"context_prompt": "Use the following context as your learned knowledge, inside <context></context> XML tags.\n\n<context>\n{{#context#}}\n</context>\n\nWhen answer to user:\n- If you don't know, just say that you don't know.\n- If you don't know when you are not sure, ask for clarification.\nAvoid mentioning that you obtained the information from the context.\nAnd answer according to the language of the user's question.\n\n",
|
||||
"histories_prompt": "Here is the chat histories between human and assistant, inside <histories></histories> XML tags.\n\n<histories>\n{{#histories#}}\n</histories>\n\n",
|
||||
"system_prompt_orders": [
|
||||
"context_prompt",
|
||||
"pre_prompt",
|
||||
"histories_prompt"
|
||||
],
|
||||
"query_prompt": "\n\nHuman: {{#query#}}\n\nAssistant: ",
|
||||
"stops": ["\nHuman:", "</histories>"]
|
||||
}
|
||||
9
api/core/prompt/prompt_templates/common_completion.json
Normal file
9
api/core/prompt/prompt_templates/common_completion.json
Normal file
@@ -0,0 +1,9 @@
|
||||
{
|
||||
"context_prompt": "Use the following context as your learned knowledge, inside <context></context> XML tags.\n\n<context>\n{{#context#}}\n</context>\n\nWhen answer to user:\n- If you don't know, just say that you don't know.\n- If you don't know when you are not sure, ask for clarification.\nAvoid mentioning that you obtained the information from the context.\nAnd answer according to the language of the user's question.\n\n",
|
||||
"system_prompt_orders": [
|
||||
"context_prompt",
|
||||
"pre_prompt"
|
||||
],
|
||||
"query_prompt": "{{#query#}}",
|
||||
"stops": null
|
||||
}
|
||||
@@ -1,419 +1,26 @@
|
||||
import enum
|
||||
import json
|
||||
import os
|
||||
import re
|
||||
from typing import Optional, cast
|
||||
|
||||
from core.entities.application_entities import (
|
||||
AdvancedCompletionPromptTemplateEntity,
|
||||
ModelConfigEntity,
|
||||
PromptTemplateEntity,
|
||||
)
|
||||
from core.file.file_obj import FileObj
|
||||
from core.app.entities.app_invoke_entities import ModelConfigWithCredentialsEntity
|
||||
from core.memory.token_buffer_memory import TokenBufferMemory
|
||||
from core.model_runtime.entities.message_entities import (
|
||||
AssistantPromptMessage,
|
||||
PromptMessage,
|
||||
PromptMessageRole,
|
||||
SystemPromptMessage,
|
||||
TextPromptMessageContent,
|
||||
UserPromptMessage,
|
||||
)
|
||||
from core.model_runtime.entities.message_entities import PromptMessage
|
||||
from core.model_runtime.entities.model_entities import ModelPropertyKey
|
||||
from core.model_runtime.model_providers.__base.large_language_model import LargeLanguageModel
|
||||
from core.prompt.prompt_builder import PromptBuilder
|
||||
from core.prompt.prompt_template import PromptTemplateParser
|
||||
|
||||
|
||||
class AppMode(enum.Enum):
|
||||
COMPLETION = 'completion'
|
||||
CHAT = 'chat'
|
||||
|
||||
@classmethod
|
||||
def value_of(cls, value: str) -> 'AppMode':
|
||||
"""
|
||||
Get value of given mode.
|
||||
|
||||
:param value: mode value
|
||||
:return: mode
|
||||
"""
|
||||
for mode in cls:
|
||||
if mode.value == value:
|
||||
return mode
|
||||
raise ValueError(f'invalid mode value {value}')
|
||||
|
||||
|
||||
class ModelMode(enum.Enum):
|
||||
COMPLETION = 'completion'
|
||||
CHAT = 'chat'
|
||||
|
||||
@classmethod
|
||||
def value_of(cls, value: str) -> 'ModelMode':
|
||||
"""
|
||||
Get value of given mode.
|
||||
|
||||
:param value: mode value
|
||||
:return: mode
|
||||
"""
|
||||
for mode in cls:
|
||||
if mode.value == value:
|
||||
return mode
|
||||
raise ValueError(f'invalid mode value {value}')
|
||||
from core.prompt.entities.advanced_prompt_entities import MemoryConfig
|
||||
|
||||
|
||||
class PromptTransform:
|
||||
def get_prompt(self,
|
||||
app_mode: str,
|
||||
prompt_template_entity: PromptTemplateEntity,
|
||||
inputs: dict,
|
||||
query: str,
|
||||
files: list[FileObj],
|
||||
context: Optional[str],
|
||||
memory: Optional[TokenBufferMemory],
|
||||
model_config: ModelConfigEntity) -> \
|
||||
tuple[list[PromptMessage], Optional[list[str]]]:
|
||||
app_mode = AppMode.value_of(app_mode)
|
||||
model_mode = ModelMode.value_of(model_config.mode)
|
||||
|
||||
prompt_rules = self._read_prompt_rules_from_file(self._prompt_file_name(
|
||||
app_mode=app_mode,
|
||||
provider=model_config.provider,
|
||||
model=model_config.model
|
||||
))
|
||||
|
||||
if app_mode == AppMode.CHAT and model_mode == ModelMode.CHAT:
|
||||
stops = None
|
||||
|
||||
prompt_messages = self._get_simple_chat_app_chat_model_prompt_messages(
|
||||
prompt_rules=prompt_rules,
|
||||
pre_prompt=prompt_template_entity.simple_prompt_template,
|
||||
inputs=inputs,
|
||||
query=query,
|
||||
files=files,
|
||||
context=context,
|
||||
memory=memory,
|
||||
model_config=model_config
|
||||
)
|
||||
else:
|
||||
stops = prompt_rules.get('stops')
|
||||
if stops is not None and len(stops) == 0:
|
||||
stops = None
|
||||
|
||||
prompt_messages = self._get_simple_others_prompt_messages(
|
||||
prompt_rules=prompt_rules,
|
||||
pre_prompt=prompt_template_entity.simple_prompt_template,
|
||||
inputs=inputs,
|
||||
query=query,
|
||||
files=files,
|
||||
context=context,
|
||||
memory=memory,
|
||||
model_config=model_config
|
||||
)
|
||||
return prompt_messages, stops
|
||||
|
||||
def get_advanced_prompt(self, app_mode: str,
|
||||
prompt_template_entity: PromptTemplateEntity,
|
||||
inputs: dict,
|
||||
query: str,
|
||||
files: list[FileObj],
|
||||
context: Optional[str],
|
||||
memory: Optional[TokenBufferMemory],
|
||||
model_config: ModelConfigEntity) -> list[PromptMessage]:
|
||||
app_mode = AppMode.value_of(app_mode)
|
||||
model_mode = ModelMode.value_of(model_config.mode)
|
||||
|
||||
prompt_messages = []
|
||||
|
||||
if app_mode == AppMode.CHAT:
|
||||
if model_mode == ModelMode.COMPLETION:
|
||||
prompt_messages = self._get_chat_app_completion_model_prompt_messages(
|
||||
prompt_template_entity=prompt_template_entity,
|
||||
inputs=inputs,
|
||||
query=query,
|
||||
files=files,
|
||||
context=context,
|
||||
memory=memory,
|
||||
model_config=model_config
|
||||
)
|
||||
elif model_mode == ModelMode.CHAT:
|
||||
prompt_messages = self._get_chat_app_chat_model_prompt_messages(
|
||||
prompt_template_entity=prompt_template_entity,
|
||||
inputs=inputs,
|
||||
query=query,
|
||||
files=files,
|
||||
context=context,
|
||||
memory=memory,
|
||||
model_config=model_config
|
||||
)
|
||||
elif app_mode == AppMode.COMPLETION:
|
||||
if model_mode == ModelMode.CHAT:
|
||||
prompt_messages = self._get_completion_app_chat_model_prompt_messages(
|
||||
prompt_template_entity=prompt_template_entity,
|
||||
inputs=inputs,
|
||||
files=files,
|
||||
context=context,
|
||||
)
|
||||
elif model_mode == ModelMode.COMPLETION:
|
||||
prompt_messages = self._get_completion_app_completion_model_prompt_messages(
|
||||
prompt_template_entity=prompt_template_entity,
|
||||
inputs=inputs,
|
||||
context=context,
|
||||
)
|
||||
|
||||
return prompt_messages
|
||||
|
||||
def _get_history_messages_from_memory(self, memory: TokenBufferMemory,
|
||||
max_token_limit: int,
|
||||
human_prefix: Optional[str] = None,
|
||||
ai_prefix: Optional[str] = None) -> str:
|
||||
"""Get memory messages."""
|
||||
kwargs = {
|
||||
"max_token_limit": max_token_limit
|
||||
}
|
||||
|
||||
if human_prefix:
|
||||
kwargs['human_prefix'] = human_prefix
|
||||
|
||||
if ai_prefix:
|
||||
kwargs['ai_prefix'] = ai_prefix
|
||||
|
||||
return memory.get_history_prompt_text(
|
||||
**kwargs
|
||||
)
|
||||
|
||||
def _get_history_messages_list_from_memory(self, memory: TokenBufferMemory,
|
||||
max_token_limit: int) -> list[PromptMessage]:
|
||||
"""Get memory messages."""
|
||||
return memory.get_history_prompt_messages(
|
||||
max_token_limit=max_token_limit
|
||||
)
|
||||
|
||||
def _prompt_file_name(self, app_mode: AppMode, provider: str, model: str) -> str:
|
||||
# baichuan
|
||||
if provider == 'baichuan':
|
||||
return self._prompt_file_name_for_baichuan(app_mode)
|
||||
|
||||
baichuan_supported_providers = ["huggingface_hub", "openllm", "xinference"]
|
||||
if provider in baichuan_supported_providers and 'baichuan' in model.lower():
|
||||
return self._prompt_file_name_for_baichuan(app_mode)
|
||||
|
||||
# common
|
||||
if app_mode == AppMode.COMPLETION:
|
||||
return 'common_completion'
|
||||
else:
|
||||
return 'common_chat'
|
||||
|
||||
def _prompt_file_name_for_baichuan(self, app_mode: AppMode) -> str:
|
||||
if app_mode == AppMode.COMPLETION:
|
||||
return 'baichuan_completion'
|
||||
else:
|
||||
return 'baichuan_chat'
|
||||
|
||||
def _read_prompt_rules_from_file(self, prompt_name: str) -> dict:
|
||||
# Get the absolute path of the subdirectory
|
||||
prompt_path = os.path.join(
|
||||
os.path.dirname(os.path.realpath(__file__)),
|
||||
'generate_prompts')
|
||||
|
||||
json_file_path = os.path.join(prompt_path, f'{prompt_name}.json')
|
||||
# Open the JSON file and read its content
|
||||
with open(json_file_path, encoding='utf-8') as json_file:
|
||||
return json.load(json_file)
|
||||
|
||||
def _get_simple_chat_app_chat_model_prompt_messages(self, prompt_rules: dict,
|
||||
pre_prompt: str,
|
||||
inputs: dict,
|
||||
query: str,
|
||||
context: Optional[str],
|
||||
files: list[FileObj],
|
||||
memory: Optional[TokenBufferMemory],
|
||||
model_config: ModelConfigEntity) -> list[PromptMessage]:
|
||||
prompt_messages = []
|
||||
|
||||
context_prompt_content = ''
|
||||
if context and 'context_prompt' in prompt_rules:
|
||||
prompt_template = PromptTemplateParser(template=prompt_rules['context_prompt'])
|
||||
context_prompt_content = prompt_template.format(
|
||||
{'context': context}
|
||||
)
|
||||
|
||||
pre_prompt_content = ''
|
||||
if pre_prompt:
|
||||
prompt_template = PromptTemplateParser(template=pre_prompt)
|
||||
prompt_inputs = {k: inputs[k] for k in prompt_template.variable_keys if k in inputs}
|
||||
pre_prompt_content = prompt_template.format(
|
||||
prompt_inputs
|
||||
)
|
||||
|
||||
prompt = ''
|
||||
for order in prompt_rules['system_prompt_orders']:
|
||||
if order == 'context_prompt':
|
||||
prompt += context_prompt_content
|
||||
elif order == 'pre_prompt':
|
||||
prompt += pre_prompt_content
|
||||
|
||||
prompt = re.sub(r'<\|.*?\|>', '', prompt)
|
||||
|
||||
if prompt:
|
||||
prompt_messages.append(SystemPromptMessage(content=prompt))
|
||||
|
||||
self._append_chat_histories(
|
||||
memory=memory,
|
||||
prompt_messages=prompt_messages,
|
||||
model_config=model_config
|
||||
)
|
||||
|
||||
if files:
|
||||
prompt_message_contents = [TextPromptMessageContent(data=query)]
|
||||
for file in files:
|
||||
prompt_message_contents.append(file.prompt_message_content)
|
||||
|
||||
prompt_messages.append(UserPromptMessage(content=prompt_message_contents))
|
||||
else:
|
||||
prompt_messages.append(UserPromptMessage(content=query))
|
||||
|
||||
return prompt_messages
|
||||
|
||||
def _get_simple_others_prompt_messages(self, prompt_rules: dict,
|
||||
pre_prompt: str,
|
||||
inputs: dict,
|
||||
query: str,
|
||||
context: Optional[str],
|
||||
memory: Optional[TokenBufferMemory],
|
||||
files: list[FileObj],
|
||||
model_config: ModelConfigEntity) -> list[PromptMessage]:
|
||||
context_prompt_content = ''
|
||||
if context and 'context_prompt' in prompt_rules:
|
||||
prompt_template = PromptTemplateParser(template=prompt_rules['context_prompt'])
|
||||
context_prompt_content = prompt_template.format(
|
||||
{'context': context}
|
||||
)
|
||||
|
||||
pre_prompt_content = ''
|
||||
if pre_prompt:
|
||||
prompt_template = PromptTemplateParser(template=pre_prompt)
|
||||
prompt_inputs = {k: inputs[k] for k in prompt_template.variable_keys if k in inputs}
|
||||
pre_prompt_content = prompt_template.format(
|
||||
prompt_inputs
|
||||
)
|
||||
|
||||
prompt = ''
|
||||
for order in prompt_rules['system_prompt_orders']:
|
||||
if order == 'context_prompt':
|
||||
prompt += context_prompt_content
|
||||
elif order == 'pre_prompt':
|
||||
prompt += pre_prompt_content
|
||||
|
||||
query_prompt = prompt_rules['query_prompt'] if 'query_prompt' in prompt_rules else '{{query}}'
|
||||
|
||||
if memory and 'histories_prompt' in prompt_rules:
|
||||
# append chat histories
|
||||
tmp_human_message = UserPromptMessage(
|
||||
content=PromptBuilder.parse_prompt(
|
||||
prompt=prompt + query_prompt,
|
||||
inputs={
|
||||
'query': query
|
||||
}
|
||||
)
|
||||
)
|
||||
|
||||
rest_tokens = self._calculate_rest_token([tmp_human_message], model_config)
|
||||
|
||||
histories = self._get_history_messages_from_memory(
|
||||
memory=memory,
|
||||
max_token_limit=rest_tokens,
|
||||
ai_prefix=prompt_rules['human_prefix'] if 'human_prefix' in prompt_rules else 'Human',
|
||||
human_prefix=prompt_rules['assistant_prefix'] if 'assistant_prefix' in prompt_rules else 'Assistant'
|
||||
)
|
||||
prompt_template = PromptTemplateParser(template=prompt_rules['histories_prompt'])
|
||||
histories_prompt_content = prompt_template.format({'histories': histories})
|
||||
|
||||
prompt = ''
|
||||
for order in prompt_rules['system_prompt_orders']:
|
||||
if order == 'context_prompt':
|
||||
prompt += context_prompt_content
|
||||
elif order == 'pre_prompt':
|
||||
prompt += (pre_prompt_content + '\n') if pre_prompt_content else ''
|
||||
elif order == 'histories_prompt':
|
||||
prompt += histories_prompt_content
|
||||
|
||||
prompt_template = PromptTemplateParser(template=query_prompt)
|
||||
query_prompt_content = prompt_template.format({'query': query})
|
||||
|
||||
prompt += query_prompt_content
|
||||
|
||||
prompt = re.sub(r'<\|.*?\|>', '', prompt)
|
||||
|
||||
model_mode = ModelMode.value_of(model_config.mode)
|
||||
|
||||
if model_mode == ModelMode.CHAT and files:
|
||||
prompt_message_contents = [TextPromptMessageContent(data=prompt)]
|
||||
for file in files:
|
||||
prompt_message_contents.append(file.prompt_message_content)
|
||||
|
||||
prompt_message = UserPromptMessage(content=prompt_message_contents)
|
||||
else:
|
||||
if files:
|
||||
prompt_message_contents = [TextPromptMessageContent(data=prompt)]
|
||||
for file in files:
|
||||
prompt_message_contents.append(file.prompt_message_content)
|
||||
|
||||
prompt_message = UserPromptMessage(content=prompt_message_contents)
|
||||
else:
|
||||
prompt_message = UserPromptMessage(content=prompt)
|
||||
|
||||
return [prompt_message]
|
||||
|
||||
def _set_context_variable(self, context: str, prompt_template: PromptTemplateParser, prompt_inputs: dict) -> None:
|
||||
if '#context#' in prompt_template.variable_keys:
|
||||
if context:
|
||||
prompt_inputs['#context#'] = context
|
||||
else:
|
||||
prompt_inputs['#context#'] = ''
|
||||
|
||||
def _set_query_variable(self, query: str, prompt_template: PromptTemplateParser, prompt_inputs: dict) -> None:
|
||||
if '#query#' in prompt_template.variable_keys:
|
||||
if query:
|
||||
prompt_inputs['#query#'] = query
|
||||
else:
|
||||
prompt_inputs['#query#'] = ''
|
||||
|
||||
def _set_histories_variable(self, memory: TokenBufferMemory,
|
||||
raw_prompt: str,
|
||||
role_prefix: AdvancedCompletionPromptTemplateEntity.RolePrefixEntity,
|
||||
prompt_template: PromptTemplateParser,
|
||||
prompt_inputs: dict,
|
||||
model_config: ModelConfigEntity) -> None:
|
||||
if '#histories#' in prompt_template.variable_keys:
|
||||
if memory:
|
||||
tmp_human_message = UserPromptMessage(
|
||||
content=PromptBuilder.parse_prompt(
|
||||
prompt=raw_prompt,
|
||||
inputs={'#histories#': '', **prompt_inputs}
|
||||
)
|
||||
)
|
||||
|
||||
rest_tokens = self._calculate_rest_token([tmp_human_message], model_config)
|
||||
|
||||
histories = self._get_history_messages_from_memory(
|
||||
memory=memory,
|
||||
max_token_limit=rest_tokens,
|
||||
human_prefix=role_prefix.user,
|
||||
ai_prefix=role_prefix.assistant
|
||||
)
|
||||
prompt_inputs['#histories#'] = histories
|
||||
else:
|
||||
prompt_inputs['#histories#'] = ''
|
||||
|
||||
def _append_chat_histories(self, memory: TokenBufferMemory,
|
||||
memory_config: MemoryConfig,
|
||||
prompt_messages: list[PromptMessage],
|
||||
model_config: ModelConfigEntity) -> None:
|
||||
if memory:
|
||||
rest_tokens = self._calculate_rest_token(prompt_messages, model_config)
|
||||
histories = self._get_history_messages_list_from_memory(memory, rest_tokens)
|
||||
prompt_messages.extend(histories)
|
||||
model_config: ModelConfigWithCredentialsEntity) -> list[PromptMessage]:
|
||||
rest_tokens = self._calculate_rest_token(prompt_messages, model_config)
|
||||
histories = self._get_history_messages_list_from_memory(memory, memory_config, rest_tokens)
|
||||
prompt_messages.extend(histories)
|
||||
|
||||
def _calculate_rest_token(self, prompt_messages: list[PromptMessage], model_config: ModelConfigEntity) -> int:
|
||||
return prompt_messages
|
||||
|
||||
def _calculate_rest_token(self, prompt_messages: list[PromptMessage],
|
||||
model_config: ModelConfigWithCredentialsEntity) -> int:
|
||||
rest_tokens = 2000
|
||||
|
||||
model_context_tokens = model_config.model_schema.model_properties.get(ModelPropertyKey.CONTEXT_SIZE)
|
||||
@@ -439,152 +46,38 @@ class PromptTransform:
|
||||
|
||||
return rest_tokens
|
||||
|
||||
def _format_prompt(self, prompt_template: PromptTemplateParser, prompt_inputs: dict) -> str:
|
||||
prompt = prompt_template.format(
|
||||
prompt_inputs
|
||||
def _get_history_messages_from_memory(self, memory: TokenBufferMemory,
|
||||
memory_config: MemoryConfig,
|
||||
max_token_limit: int,
|
||||
human_prefix: Optional[str] = None,
|
||||
ai_prefix: Optional[str] = None) -> str:
|
||||
"""Get memory messages."""
|
||||
kwargs = {
|
||||
"max_token_limit": max_token_limit
|
||||
}
|
||||
|
||||
if human_prefix:
|
||||
kwargs['human_prefix'] = human_prefix
|
||||
|
||||
if ai_prefix:
|
||||
kwargs['ai_prefix'] = ai_prefix
|
||||
|
||||
if memory_config.window.enabled and memory_config.window.size is not None and memory_config.window.size > 0:
|
||||
kwargs['message_limit'] = memory_config.window.size
|
||||
|
||||
return memory.get_history_prompt_text(
|
||||
**kwargs
|
||||
)
|
||||
|
||||
prompt = re.sub(r'<\|.*?\|>', '', prompt)
|
||||
return prompt
|
||||
|
||||
def _get_chat_app_completion_model_prompt_messages(self,
|
||||
prompt_template_entity: PromptTemplateEntity,
|
||||
inputs: dict,
|
||||
query: str,
|
||||
files: list[FileObj],
|
||||
context: Optional[str],
|
||||
memory: Optional[TokenBufferMemory],
|
||||
model_config: ModelConfigEntity) -> list[PromptMessage]:
|
||||
|
||||
raw_prompt = prompt_template_entity.advanced_completion_prompt_template.prompt
|
||||
role_prefix = prompt_template_entity.advanced_completion_prompt_template.role_prefix
|
||||
|
||||
prompt_messages = []
|
||||
|
||||
prompt_template = PromptTemplateParser(template=raw_prompt)
|
||||
prompt_inputs = {k: inputs[k] for k in prompt_template.variable_keys if k in inputs}
|
||||
|
||||
self._set_context_variable(context, prompt_template, prompt_inputs)
|
||||
|
||||
self._set_query_variable(query, prompt_template, prompt_inputs)
|
||||
|
||||
self._set_histories_variable(
|
||||
memory=memory,
|
||||
raw_prompt=raw_prompt,
|
||||
role_prefix=role_prefix,
|
||||
prompt_template=prompt_template,
|
||||
prompt_inputs=prompt_inputs,
|
||||
model_config=model_config
|
||||
def _get_history_messages_list_from_memory(self, memory: TokenBufferMemory,
|
||||
memory_config: MemoryConfig,
|
||||
max_token_limit: int) -> list[PromptMessage]:
|
||||
"""Get memory messages."""
|
||||
return memory.get_history_prompt_messages(
|
||||
max_token_limit=max_token_limit,
|
||||
message_limit=memory_config.window.size
|
||||
if (memory_config.window.enabled
|
||||
and memory_config.window.size is not None
|
||||
and memory_config.window.size > 0)
|
||||
else 10
|
||||
)
|
||||
|
||||
prompt = self._format_prompt(prompt_template, prompt_inputs)
|
||||
|
||||
if files:
|
||||
prompt_message_contents = [TextPromptMessageContent(data=prompt)]
|
||||
for file in files:
|
||||
prompt_message_contents.append(file.prompt_message_content)
|
||||
|
||||
prompt_messages.append(UserPromptMessage(content=prompt_message_contents))
|
||||
else:
|
||||
prompt_messages.append(UserPromptMessage(content=prompt))
|
||||
|
||||
return prompt_messages
|
||||
|
||||
def _get_chat_app_chat_model_prompt_messages(self,
|
||||
prompt_template_entity: PromptTemplateEntity,
|
||||
inputs: dict,
|
||||
query: str,
|
||||
files: list[FileObj],
|
||||
context: Optional[str],
|
||||
memory: Optional[TokenBufferMemory],
|
||||
model_config: ModelConfigEntity) -> list[PromptMessage]:
|
||||
raw_prompt_list = prompt_template_entity.advanced_chat_prompt_template.messages
|
||||
|
||||
prompt_messages = []
|
||||
|
||||
for prompt_item in raw_prompt_list:
|
||||
raw_prompt = prompt_item.text
|
||||
|
||||
prompt_template = PromptTemplateParser(template=raw_prompt)
|
||||
prompt_inputs = {k: inputs[k] for k in prompt_template.variable_keys if k in inputs}
|
||||
|
||||
self._set_context_variable(context, prompt_template, prompt_inputs)
|
||||
|
||||
prompt = self._format_prompt(prompt_template, prompt_inputs)
|
||||
|
||||
if prompt_item.role == PromptMessageRole.USER:
|
||||
prompt_messages.append(UserPromptMessage(content=prompt))
|
||||
elif prompt_item.role == PromptMessageRole.SYSTEM and prompt:
|
||||
prompt_messages.append(SystemPromptMessage(content=prompt))
|
||||
elif prompt_item.role == PromptMessageRole.ASSISTANT:
|
||||
prompt_messages.append(AssistantPromptMessage(content=prompt))
|
||||
|
||||
self._append_chat_histories(memory, prompt_messages, model_config)
|
||||
|
||||
if files:
|
||||
prompt_message_contents = [TextPromptMessageContent(data=query)]
|
||||
for file in files:
|
||||
prompt_message_contents.append(file.prompt_message_content)
|
||||
|
||||
prompt_messages.append(UserPromptMessage(content=prompt_message_contents))
|
||||
else:
|
||||
prompt_messages.append(UserPromptMessage(content=query))
|
||||
|
||||
return prompt_messages
|
||||
|
||||
def _get_completion_app_completion_model_prompt_messages(self,
|
||||
prompt_template_entity: PromptTemplateEntity,
|
||||
inputs: dict,
|
||||
context: Optional[str]) -> list[PromptMessage]:
|
||||
raw_prompt = prompt_template_entity.advanced_completion_prompt_template.prompt
|
||||
|
||||
prompt_messages = []
|
||||
|
||||
prompt_template = PromptTemplateParser(template=raw_prompt)
|
||||
prompt_inputs = {k: inputs[k] for k in prompt_template.variable_keys if k in inputs}
|
||||
|
||||
self._set_context_variable(context, prompt_template, prompt_inputs)
|
||||
|
||||
prompt = self._format_prompt(prompt_template, prompt_inputs)
|
||||
|
||||
prompt_messages.append(UserPromptMessage(content=prompt))
|
||||
|
||||
return prompt_messages
|
||||
|
||||
def _get_completion_app_chat_model_prompt_messages(self,
|
||||
prompt_template_entity: PromptTemplateEntity,
|
||||
inputs: dict,
|
||||
files: list[FileObj],
|
||||
context: Optional[str]) -> list[PromptMessage]:
|
||||
raw_prompt_list = prompt_template_entity.advanced_chat_prompt_template.messages
|
||||
|
||||
prompt_messages = []
|
||||
|
||||
for prompt_item in raw_prompt_list:
|
||||
raw_prompt = prompt_item.text
|
||||
|
||||
prompt_template = PromptTemplateParser(template=raw_prompt)
|
||||
prompt_inputs = {k: inputs[k] for k in prompt_template.variable_keys if k in inputs}
|
||||
|
||||
self._set_context_variable(context, prompt_template, prompt_inputs)
|
||||
|
||||
prompt = self._format_prompt(prompt_template, prompt_inputs)
|
||||
|
||||
if prompt_item.role == PromptMessageRole.USER:
|
||||
prompt_messages.append(UserPromptMessage(content=prompt))
|
||||
elif prompt_item.role == PromptMessageRole.SYSTEM and prompt:
|
||||
prompt_messages.append(SystemPromptMessage(content=prompt))
|
||||
elif prompt_item.role == PromptMessageRole.ASSISTANT:
|
||||
prompt_messages.append(AssistantPromptMessage(content=prompt))
|
||||
|
||||
for prompt_message in prompt_messages[::-1]:
|
||||
if prompt_message.role == PromptMessageRole.USER:
|
||||
if files:
|
||||
prompt_message_contents = [TextPromptMessageContent(data=prompt_message.content)]
|
||||
for file in files:
|
||||
prompt_message_contents.append(file.prompt_message_content)
|
||||
|
||||
prompt_message.content = prompt_message_contents
|
||||
break
|
||||
|
||||
return prompt_messages
|
||||
|
||||
@@ -1,144 +0,0 @@
|
||||
# Written by YORKI MINAKO🤡, Edited by Xiaoyi
|
||||
CONVERSATION_TITLE_PROMPT = """You need to decompose the user's input into "subject" and "intention" in order to accurately figure out what the user's input language actually is.
|
||||
Notice: the language type user use could be diverse, which can be English, Chinese, Español, Arabic, Japanese, French, and etc.
|
||||
MAKE SURE your output is the SAME language as the user's input!
|
||||
Your output is restricted only to: (Input language) Intention + Subject(short as possible)
|
||||
Your output MUST be a valid JSON.
|
||||
|
||||
Tip: When the user's question is directed at you (the language model), you can add an emoji to make it more fun.
|
||||
|
||||
|
||||
example 1:
|
||||
User Input: hi, yesterday i had some burgers.
|
||||
{
|
||||
"Language Type": "The user's input is pure English",
|
||||
"Your Reasoning": "The language of my output must be pure English.",
|
||||
"Your Output": "sharing yesterday's food"
|
||||
}
|
||||
|
||||
example 2:
|
||||
User Input: hello
|
||||
{
|
||||
"Language Type": "The user's input is written in pure English",
|
||||
"Your Reasoning": "The language of my output must be pure English.",
|
||||
"Your Output": "Greeting myself☺️"
|
||||
}
|
||||
|
||||
|
||||
example 3:
|
||||
User Input: why mmap file: oom
|
||||
{
|
||||
"Language Type": "The user's input is written in pure English",
|
||||
"Your Reasoning": "The language of my output must be pure English.",
|
||||
"Your Output": "Asking about the reason for mmap file: oom"
|
||||
}
|
||||
|
||||
|
||||
example 4:
|
||||
User Input: www.convinceme.yesterday-you-ate-seafood.tv讲了什么？
|
||||
{
|
||||
"Language Type": "The user's input English-Chinese mixed",
|
||||
"Your Reasoning": "The English-part is an URL, the main intention is still written in Chinese, so the language of my output must be using Chinese.",
|
||||
"Your Output": "询问网站www.convinceme.yesterday-you-ate-seafood.tv"
|
||||
}
|
||||
|
||||
example 5:
|
||||
User Input: why小红的年龄is老than小明？
|
||||
{
|
||||
"Language Type": "The user's input is English-Chinese mixed",
|
||||
"Your Reasoning": "The English parts are subjective particles, the main intention is written in Chinese, besides, Chinese occupies a greater \"actual meaning\" than English, so the language of my output must be using Chinese.",
|
||||
"Your Output": "询问小红和小明的年龄"
|
||||
}
|
||||
|
||||
example 6:
|
||||
User Input: yo, 你今天咋样？
|
||||
{
|
||||
"Language Type": "The user's input is English-Chinese mixed",
|
||||
"Your Reasoning": "The English-part is a subjective particle, the main intention is written in Chinese, so the language of my output must be using Chinese.",
|
||||
"Your Output": "查询今日我的状态☺️"
|
||||
}
|
||||
|
||||
User Input:
|
||||
"""
|
||||
|
||||
SUGGESTED_QUESTIONS_AFTER_ANSWER_INSTRUCTION_PROMPT = (
|
||||
"Please help me predict the three most likely questions that human would ask, "
|
||||
"and keeping each question under 20 characters.\n"
|
||||
"The output must be an array in JSON format following the specified schema:\n"
|
||||
"[\"question1\",\"question2\",\"question3\"]\n"
|
||||
)
|
||||
|
||||
GENERATOR_QA_PROMPT = (
|
||||
'<Task> The user will send a long text. Generate a Question and Answer pairs only using the knowledge in the long text. Please think step by step.'
|
||||
'Step 1: Understand and summarize the main content of this text.\n'
|
||||
'Step 2: What key information or concepts are mentioned in this text?\n'
|
||||
'Step 3: Decompose or combine multiple pieces of information and concepts.\n'
|
||||
'Step 4: Generate questions and answers based on these key information and concepts.\n'
|
||||
'<Constraints> The questions should be clear and detailed, and the answers should be detailed and complete. '
|
||||
'You must answer in {language}, in a style that is clear and detailed in {language}. No language other than {language} should be used. \n'
|
||||
'<Format> Use the following format: Q1:\nA1:\nQ2:\nA2:...\n'
|
||||
'<QA Pairs>'
|
||||
)
|
||||
|
||||
RULE_CONFIG_GENERATE_TEMPLATE = """Given MY INTENDED AUDIENCES and HOPING TO SOLVE using a language model, please select \
|
||||
the model prompt that best suits the input.
|
||||
You will be provided with the prompt, variables, and an opening statement.
|
||||
Only the content enclosed in double curly braces, such as {{variable}}, in the prompt can be considered as a variable; \
|
||||
otherwise, it cannot exist as a variable in the variables.
|
||||
If you believe revising the original input will result in a better response from the language model, you may \
|
||||
suggest revisions.
|
||||
|
||||
<<PRINCIPLES OF GOOD PROMPT>>
|
||||
Integrate the intended audience in the prompt e.g. the audience is an expert in the field.
|
||||
Break down complex tasks into a sequence of simpler prompts in an interactive conversation.
|
||||
Implement example-driven prompting (Use few-shot prompting).
|
||||
When formatting your prompt start with Instruction followed by either Example if relevant. \
|
||||
Subsequently present your content. Use one or more line breaks to separate instructions examples questions context and input data.
|
||||
Incorporate the following phrases: “Your task is” and “You MUST”.
|
||||
Incorporate the following phrases: “You will be penalized”.
|
||||
Use leading words like writing “think step by step”.
|
||||
Add to your prompt the following phrase “Ensure that your answer is unbiased and does not rely on stereotypes”.
|
||||
Assign a role to the large language models.
|
||||
Use Delimiters.
|
||||
To write an essay /text /paragraph /article or any type of text that should be detailed: “Write a detailed [essay/text/paragraph] for me on [topic] in detail by adding all the information necessary”.
|
||||
Clearly state the requirements that the model must follow in order to produce content in the form of the keywords regulations hint or instructions
|
||||
|
||||
<< FORMATTING >>
|
||||
Return a markdown code snippet with a JSON object formatted to look like, \
|
||||
no any other string out of markdown code snippet:
|
||||
```json
|
||||
{{{{
|
||||
"prompt": string \\ generated prompt
|
||||
"variables": list of string \\ variables
|
||||
"opening_statement": string \\ an opening statement to guide users on how to ask questions with generated prompt \
|
||||
and fill in variables, with a welcome sentence, and keep TLDR.
|
||||
}}}}
|
||||
```
|
||||
|
||||
<< EXAMPLES >>
|
||||
[EXAMPLE A]
|
||||
```json
|
||||
{
|
||||
"prompt": "I need your help to translate the following {{Input_language}}paper paragraph into {{Target_language}}, in a style similar to a popular science magazine in {{Target_language}}. #### Rules Ensure accurate conveyance of the original text's facts and context during translation. Maintain the original paragraph format and retain technical terms and company abbreviations ",
|
||||
"variables": ["Input_language", "Target_language"],
|
||||
"opening_statement": " Hi. I am your translation assistant. I can help you with any translation and ensure accurate conveyance of information. "
|
||||
}
|
||||
```
|
||||
|
||||
[EXAMPLE B]
|
||||
```json
|
||||
{
|
||||
"prompt": "Your task is to review the provided meeting notes and create a concise summary that captures the essential information, focusing on key takeaways and action items assigned to specific individuals or departments during the meeting. Use clear and professional language, and organize the summary in a logical manner using appropriate formatting such as headings, subheadings, and bullet points. Ensure that the summary is easy to understand and provides a comprehensive but succinct overview of the meeting's content, with a particular focus on clearly indicating who is responsible for each action item.",
|
||||
"variables": ["meeting_notes"],
|
||||
"opening_statement": "Hi! I'm your meeting notes summarizer AI. I can help you with any meeting notes and ensure accurate conveyance of information."
|
||||
}
|
||||
```
|
||||
|
||||
<< MY INTENDED AUDIENCES >>
|
||||
{{audiences}}
|
||||
|
||||
<< HOPING TO SOLVE >>
|
||||
{{hoping_to_solve}}
|
||||
|
||||
<< OUTPUT >>
|
||||
"""
|
||||
319
api/core/prompt/simple_prompt_transform.py
Normal file
319
api/core/prompt/simple_prompt_transform.py
Normal file
@@ -0,0 +1,319 @@
|
||||
import enum
|
||||
import json
|
||||
import os
|
||||
from typing import Optional
|
||||
|
||||
from core.app.app_config.entities import PromptTemplateEntity
|
||||
from core.app.entities.app_invoke_entities import ModelConfigWithCredentialsEntity
|
||||
from core.file.file_obj import FileVar
|
||||
from core.memory.token_buffer_memory import TokenBufferMemory
|
||||
from core.model_runtime.entities.message_entities import (
|
||||
PromptMessage,
|
||||
SystemPromptMessage,
|
||||
TextPromptMessageContent,
|
||||
UserPromptMessage,
|
||||
)
|
||||
from core.prompt.entities.advanced_prompt_entities import MemoryConfig
|
||||
from core.prompt.prompt_transform import PromptTransform
|
||||
from core.prompt.utils.prompt_template_parser import PromptTemplateParser
|
||||
from models.model import AppMode
|
||||
|
||||
|
||||
class ModelMode(enum.Enum):
|
||||
COMPLETION = 'completion'
|
||||
CHAT = 'chat'
|
||||
|
||||
@classmethod
|
||||
def value_of(cls, value: str) -> 'ModelMode':
|
||||
"""
|
||||
Get value of given mode.
|
||||
|
||||
:param value: mode value
|
||||
:return: mode
|
||||
"""
|
||||
for mode in cls:
|
||||
if mode.value == value:
|
||||
return mode
|
||||
raise ValueError(f'invalid mode value {value}')
|
||||
|
||||
|
||||
prompt_file_contents = {}
|
||||
|
||||
|
||||
class SimplePromptTransform(PromptTransform):
|
||||
"""
|
||||
Simple Prompt Transform for Chatbot App Basic Mode.
|
||||
"""
|
||||
|
||||
def get_prompt(self,
|
||||
app_mode: AppMode,
|
||||
prompt_template_entity: PromptTemplateEntity,
|
||||
inputs: dict,
|
||||
query: str,
|
||||
files: list[FileVar],
|
||||
context: Optional[str],
|
||||
memory: Optional[TokenBufferMemory],
|
||||
model_config: ModelConfigWithCredentialsEntity) -> \
|
||||
tuple[list[PromptMessage], Optional[list[str]]]:
|
||||
model_mode = ModelMode.value_of(model_config.mode)
|
||||
if model_mode == ModelMode.CHAT:
|
||||
prompt_messages, stops = self._get_chat_model_prompt_messages(
|
||||
app_mode=app_mode,
|
||||
pre_prompt=prompt_template_entity.simple_prompt_template,
|
||||
inputs=inputs,
|
||||
query=query,
|
||||
files=files,
|
||||
context=context,
|
||||
memory=memory,
|
||||
model_config=model_config
|
||||
)
|
||||
else:
|
||||
prompt_messages, stops = self._get_completion_model_prompt_messages(
|
||||
app_mode=app_mode,
|
||||
pre_prompt=prompt_template_entity.simple_prompt_template,
|
||||
inputs=inputs,
|
||||
query=query,
|
||||
files=files,
|
||||
context=context,
|
||||
memory=memory,
|
||||
model_config=model_config
|
||||
)
|
||||
|
||||
return prompt_messages, stops
|
||||
|
||||
def get_prompt_str_and_rules(self, app_mode: AppMode,
|
||||
model_config: ModelConfigWithCredentialsEntity,
|
||||
pre_prompt: str,
|
||||
inputs: dict,
|
||||
query: Optional[str] = None,
|
||||
context: Optional[str] = None,
|
||||
histories: Optional[str] = None,
|
||||
) -> tuple[str, dict]:
|
||||
# get prompt template
|
||||
prompt_template_config = self.get_prompt_template(
|
||||
app_mode=app_mode,
|
||||
provider=model_config.provider,
|
||||
model=model_config.model,
|
||||
pre_prompt=pre_prompt,
|
||||
has_context=context is not None,
|
||||
query_in_prompt=query is not None,
|
||||
with_memory_prompt=histories is not None
|
||||
)
|
||||
|
||||
variables = {k: inputs[k] for k in prompt_template_config['custom_variable_keys'] if k in inputs}
|
||||
|
||||
for v in prompt_template_config['special_variable_keys']:
|
||||
# support #context#, #query# and #histories#
|
||||
if v == '#context#':
|
||||
variables['#context#'] = context if context else ''
|
||||
elif v == '#query#':
|
||||
variables['#query#'] = query if query else ''
|
||||
elif v == '#histories#':
|
||||
variables['#histories#'] = histories if histories else ''
|
||||
|
||||
prompt_template = prompt_template_config['prompt_template']
|
||||
prompt = prompt_template.format(variables)
|
||||
|
||||
return prompt, prompt_template_config['prompt_rules']
|
||||
|
||||
def get_prompt_template(self, app_mode: AppMode,
|
||||
provider: str,
|
||||
model: str,
|
||||
pre_prompt: str,
|
||||
has_context: bool,
|
||||
query_in_prompt: bool,
|
||||
with_memory_prompt: bool = False) -> dict:
|
||||
prompt_rules = self._get_prompt_rule(
|
||||
app_mode=app_mode,
|
||||
provider=provider,
|
||||
model=model
|
||||
)
|
||||
|
||||
custom_variable_keys = []
|
||||
special_variable_keys = []
|
||||
|
||||
prompt = ''
|
||||
for order in prompt_rules['system_prompt_orders']:
|
||||
if order == 'context_prompt' and has_context:
|
||||
prompt += prompt_rules['context_prompt']
|
||||
special_variable_keys.append('#context#')
|
||||
elif order == 'pre_prompt' and pre_prompt:
|
||||
prompt += pre_prompt + '\n'
|
||||
pre_prompt_template = PromptTemplateParser(template=pre_prompt)
|
||||
custom_variable_keys = pre_prompt_template.variable_keys
|
||||
elif order == 'histories_prompt' and with_memory_prompt:
|
||||
prompt += prompt_rules['histories_prompt']
|
||||
special_variable_keys.append('#histories#')
|
||||
|
||||
if query_in_prompt:
|
||||
prompt += prompt_rules['query_prompt'] if 'query_prompt' in prompt_rules else '{{#query#}}'
|
||||
special_variable_keys.append('#query#')
|
||||
|
||||
return {
|
||||
"prompt_template": PromptTemplateParser(template=prompt),
|
||||
"custom_variable_keys": custom_variable_keys,
|
||||
"special_variable_keys": special_variable_keys,
|
||||
"prompt_rules": prompt_rules
|
||||
}
|
||||
|
||||
def _get_chat_model_prompt_messages(self, app_mode: AppMode,
|
||||
pre_prompt: str,
|
||||
inputs: dict,
|
||||
query: str,
|
||||
context: Optional[str],
|
||||
files: list[FileVar],
|
||||
memory: Optional[TokenBufferMemory],
|
||||
model_config: ModelConfigWithCredentialsEntity) \
|
||||
-> tuple[list[PromptMessage], Optional[list[str]]]:
|
||||
prompt_messages = []
|
||||
|
||||
# get prompt
|
||||
prompt, _ = self.get_prompt_str_and_rules(
|
||||
app_mode=app_mode,
|
||||
model_config=model_config,
|
||||
pre_prompt=pre_prompt,
|
||||
inputs=inputs,
|
||||
query=None,
|
||||
context=context
|
||||
)
|
||||
|
||||
if prompt and query:
|
||||
prompt_messages.append(SystemPromptMessage(content=prompt))
|
||||
|
||||
if memory:
|
||||
prompt_messages = self._append_chat_histories(
|
||||
memory=memory,
|
||||
memory_config=MemoryConfig(
|
||||
window=MemoryConfig.WindowConfig(
|
||||
enabled=False,
|
||||
)
|
||||
),
|
||||
prompt_messages=prompt_messages,
|
||||
model_config=model_config
|
||||
)
|
||||
|
||||
if query:
|
||||
prompt_messages.append(self.get_last_user_message(query, files))
|
||||
else:
|
||||
prompt_messages.append(self.get_last_user_message(prompt, files))
|
||||
|
||||
return prompt_messages, None
|
||||
|
||||
def _get_completion_model_prompt_messages(self, app_mode: AppMode,
|
||||
pre_prompt: str,
|
||||
inputs: dict,
|
||||
query: str,
|
||||
context: Optional[str],
|
||||
files: list[FileVar],
|
||||
memory: Optional[TokenBufferMemory],
|
||||
model_config: ModelConfigWithCredentialsEntity) \
|
||||
-> tuple[list[PromptMessage], Optional[list[str]]]:
|
||||
# get prompt
|
||||
prompt, prompt_rules = self.get_prompt_str_and_rules(
|
||||
app_mode=app_mode,
|
||||
model_config=model_config,
|
||||
pre_prompt=pre_prompt,
|
||||
inputs=inputs,
|
||||
query=query,
|
||||
context=context
|
||||
)
|
||||
|
||||
if memory:
|
||||
tmp_human_message = UserPromptMessage(
|
||||
content=prompt
|
||||
)
|
||||
|
||||
rest_tokens = self._calculate_rest_token([tmp_human_message], model_config)
|
||||
histories = self._get_history_messages_from_memory(
|
||||
memory=memory,
|
||||
memory_config=MemoryConfig(
|
||||
window=MemoryConfig.WindowConfig(
|
||||
enabled=False,
|
||||
)
|
||||
),
|
||||
max_token_limit=rest_tokens,
|
||||
ai_prefix=prompt_rules['human_prefix'] if 'human_prefix' in prompt_rules else 'Human',
|
||||
human_prefix=prompt_rules['assistant_prefix'] if 'assistant_prefix' in prompt_rules else 'Assistant'
|
||||
)
|
||||
|
||||
# get prompt
|
||||
prompt, prompt_rules = self.get_prompt_str_and_rules(
|
||||
app_mode=app_mode,
|
||||
model_config=model_config,
|
||||
pre_prompt=pre_prompt,
|
||||
inputs=inputs,
|
||||
query=query,
|
||||
context=context,
|
||||
histories=histories
|
||||
)
|
||||
|
||||
stops = prompt_rules.get('stops')
|
||||
if stops is not None and len(stops) == 0:
|
||||
stops = None
|
||||
|
||||
return [self.get_last_user_message(prompt, files)], stops
|
||||
|
||||
def get_last_user_message(self, prompt: str, files: list[FileVar]) -> UserPromptMessage:
|
||||
if files:
|
||||
prompt_message_contents = [TextPromptMessageContent(data=prompt)]
|
||||
for file in files:
|
||||
prompt_message_contents.append(file.prompt_message_content)
|
||||
|
||||
prompt_message = UserPromptMessage(content=prompt_message_contents)
|
||||
else:
|
||||
prompt_message = UserPromptMessage(content=prompt)
|
||||
|
||||
return prompt_message
|
||||
|
||||
def _get_prompt_rule(self, app_mode: AppMode, provider: str, model: str) -> dict:
|
||||
"""
|
||||
Get simple prompt rule.
|
||||
:param app_mode: app mode
|
||||
:param provider: model provider
|
||||
:param model: model name
|
||||
:return:
|
||||
"""
|
||||
prompt_file_name = self._prompt_file_name(
|
||||
app_mode=app_mode,
|
||||
provider=provider,
|
||||
model=model
|
||||
)
|
||||
|
||||
# Check if the prompt file is already loaded
|
||||
if prompt_file_name in prompt_file_contents:
|
||||
return prompt_file_contents[prompt_file_name]
|
||||
|
||||
# Get the absolute path of the subdirectory
|
||||
prompt_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), 'prompt_templates')
|
||||
json_file_path = os.path.join(prompt_path, f'{prompt_file_name}.json')
|
||||
|
||||
# Open the JSON file and read its content
|
||||
with open(json_file_path, encoding='utf-8') as json_file:
|
||||
content = json.load(json_file)
|
||||
|
||||
# Store the content of the prompt file
|
||||
prompt_file_contents[prompt_file_name] = content
|
||||
|
||||
return content
|
||||
|
||||
def _prompt_file_name(self, app_mode: AppMode, provider: str, model: str) -> str:
|
||||
# baichuan
|
||||
is_baichuan = False
|
||||
if provider == 'baichuan':
|
||||
is_baichuan = True
|
||||
else:
|
||||
baichuan_supported_providers = ["huggingface_hub", "openllm", "xinference"]
|
||||
if provider in baichuan_supported_providers and 'baichuan' in model.lower():
|
||||
is_baichuan = True
|
||||
|
||||
if is_baichuan:
|
||||
if app_mode == AppMode.COMPLETION:
|
||||
return 'baichuan_completion'
|
||||
else:
|
||||
return 'baichuan_chat'
|
||||
|
||||
# common
|
||||
if app_mode == AppMode.COMPLETION:
|
||||
return 'common_completion'
|
||||
else:
|
||||
return 'common_chat'
|
||||
0
api/core/prompt/utils/__init__.py
Normal file
0
api/core/prompt/utils/__init__.py
Normal file
85
api/core/prompt/utils/prompt_message_util.py
Normal file
85
api/core/prompt/utils/prompt_message_util.py
Normal file
@@ -0,0 +1,85 @@
|
||||
from typing import cast
|
||||
|
||||
from core.model_runtime.entities.message_entities import (
|
||||
ImagePromptMessageContent,
|
||||
PromptMessage,
|
||||
PromptMessageContentType,
|
||||
PromptMessageRole,
|
||||
TextPromptMessageContent,
|
||||
)
|
||||
from core.prompt.simple_prompt_transform import ModelMode
|
||||
|
||||
|
||||
class PromptMessageUtil:
|
||||
@staticmethod
|
||||
def prompt_messages_to_prompt_for_saving(model_mode: str, prompt_messages: list[PromptMessage]) -> list[dict]:
|
||||
"""
|
||||
Prompt messages to prompt for saving.
|
||||
:param model_mode: model mode
|
||||
:param prompt_messages: prompt messages
|
||||
:return:
|
||||
"""
|
||||
prompts = []
|
||||
if model_mode == ModelMode.CHAT.value:
|
||||
for prompt_message in prompt_messages:
|
||||
if prompt_message.role == PromptMessageRole.USER:
|
||||
role = 'user'
|
||||
elif prompt_message.role == PromptMessageRole.ASSISTANT:
|
||||
role = 'assistant'
|
||||
elif prompt_message.role == PromptMessageRole.SYSTEM:
|
||||
role = 'system'
|
||||
else:
|
||||
continue
|
||||
|
||||
text = ''
|
||||
files = []
|
||||
if isinstance(prompt_message.content, list):
|
||||
for content in prompt_message.content:
|
||||
if content.type == PromptMessageContentType.TEXT:
|
||||
content = cast(TextPromptMessageContent, content)
|
||||
text += content.data
|
||||
else:
|
||||
content = cast(ImagePromptMessageContent, content)
|
||||
files.append({
|
||||
"type": 'image',
|
||||
"data": content.data[:10] + '...[TRUNCATED]...' + content.data[-10:],
|
||||
"detail": content.detail.value
|
||||
})
|
||||
else:
|
||||
text = prompt_message.content
|
||||
|
||||
prompts.append({
|
||||
"role": role,
|
||||
"text": text,
|
||||
"files": files
|
||||
})
|
||||
else:
|
||||
prompt_message = prompt_messages[0]
|
||||
text = ''
|
||||
files = []
|
||||
if isinstance(prompt_message.content, list):
|
||||
for content in prompt_message.content:
|
||||
if content.type == PromptMessageContentType.TEXT:
|
||||
content = cast(TextPromptMessageContent, content)
|
||||
text += content.data
|
||||
else:
|
||||
content = cast(ImagePromptMessageContent, content)
|
||||
files.append({
|
||||
"type": 'image',
|
||||
"data": content.data[:10] + '...[TRUNCATED]...' + content.data[-10:],
|
||||
"detail": content.detail.value
|
||||
})
|
||||
else:
|
||||
text = prompt_message.content
|
||||
|
||||
params = {
|
||||
"role": 'user',
|
||||
"text": text,
|
||||
}
|
||||
|
||||
if files:
|
||||
params['files'] = files
|
||||
|
||||
prompts.append(params)
|
||||
|
||||
return prompts
|
||||
@@ -1,6 +1,9 @@
|
||||
import re
|
||||
|
||||
REGEX = re.compile(r"\{\{([a-zA-Z_][a-zA-Z0-9_]{0,29}|#histories#|#query#|#context#)\}\}")
|
||||
WITH_VARIABLE_TMPL_REGEX = re.compile(
|
||||
r"\{\{([a-zA-Z_][a-zA-Z0-9_]{0,29}|#[a-zA-Z0-9_]{1,50}\.[a-zA-Z0-9_\.]{1,100}#|#histories#|#query#|#context#)\}\}"
|
||||
)
|
||||
|
||||
|
||||
class PromptTemplateParser:
|
||||
@@ -15,13 +18,15 @@ class PromptTemplateParser:
|
||||
`{{#histories#}}` `{{#query#}}` `{{#context#}}`. No other `{{##}}` template variables are allowed.
|
||||
"""
|
||||
|
||||
def __init__(self, template: str):
|
||||
def __init__(self, template: str, with_variable_tmpl: bool = False):
|
||||
self.template = template
|
||||
self.with_variable_tmpl = with_variable_tmpl
|
||||
self.regex = WITH_VARIABLE_TMPL_REGEX if with_variable_tmpl else REGEX
|
||||
self.variable_keys = self.extract()
|
||||
|
||||
def extract(self) -> list:
|
||||
# Regular expression to match the template rules
|
||||
return re.findall(REGEX, self.template)
|
||||
return re.findall(self.regex, self.template)
|
||||
|
||||
def format(self, inputs: dict, remove_template_variables: bool = True) -> str:
|
||||
def replacer(match):
|
||||
@@ -29,11 +34,12 @@ class PromptTemplateParser:
|
||||
value = inputs.get(key, match.group(0)) # return original matched string if key not found
|
||||
|
||||
if remove_template_variables:
|
||||
return PromptTemplateParser.remove_template_variables(value)
|
||||
return PromptTemplateParser.remove_template_variables(value, self.with_variable_tmpl)
|
||||
return value
|
||||
|
||||
return re.sub(REGEX, replacer, self.template)
|
||||
prompt = re.sub(self.regex, replacer, self.template)
|
||||
return re.sub(r'<\|.*?\|>', '', prompt)
|
||||
|
||||
@classmethod
|
||||
def remove_template_variables(cls, text: str):
|
||||
return re.sub(REGEX, r'{\1}', text)
|
||||
def remove_template_variables(cls, text: str, with_variable_tmpl: bool = False):
|
||||
return re.sub(WITH_VARIABLE_TMPL_REGEX if with_variable_tmpl else REGEX, r'{\1}', text)
|
||||
Reference in New Issue
Block a user