353 lines
16 KiB
Python
353 lines
16 KiB
Python
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import random
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from textwrap import dedent
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import copy
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from loguru import logger
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from langchain.agents.tools import Tool
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from coagent.connector.schema import Memory, Message
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from coagent.connector.utils import extract_section, parse_section
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class PromptManager:
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def __init__(self, role_prompt="", prompt_config=None, monitored_agents=[], monitored_fields=[]):
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self.role_prompt = role_prompt
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self.monitored_agents = monitored_agents
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self.monitored_fields = monitored_fields
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self.field_handlers = {}
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self.context_handlers = {}
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self.field_order = [] # 用于普通字段的顺序
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self.context_order = [] # 单独维护上下文字段的顺序
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self.field_descriptions = {}
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self.omit_if_empty_flags = {}
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self.context_title = "### Context Data\n\n"
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self.prompt_config = prompt_config
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if self.prompt_config:
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self.register_fields_from_config()
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def register_field(self, field_name, function=None, title=None, description=None, is_context=True, omit_if_empty=True):
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"""
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注册一个新的字段及其处理函数。
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Args:
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field_name (str): 字段名称。
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function (callable): 处理字段数据的函数。
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title (str, optional): 字段的自定义标题（可选）。
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description (str, optional): 字段的描述（可选，可以是几句话）。
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is_context (bool, optional): 指示该字段是否为上下文字段。
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omit_if_empty (bool, optional): 如果数据为空，是否省略该字段。
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"""
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if not function:
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function = self.handle_custom_data
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# Register the handler function based on context flag
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if is_context:
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self.context_handlers[field_name] = function
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else:
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self.field_handlers[field_name] = function
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# Store the custom title if provided and adjust the title prefix based on context
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title_prefix = "####" if is_context else "###"
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if title is not None:
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self.field_descriptions[field_name] = f"{title_prefix} {title}\n\n"
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elif description is not None:
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# If title is not provided but description is, use description as title
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self.field_descriptions[field_name] = f"{title_prefix} {field_name.replace('_', ' ').title()}\n\n{description}\n\n"
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else:
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# If neither title nor description is provided, use the field name as title
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self.field_descriptions[field_name] = f"{title_prefix} {field_name.replace('_', ' ').title()}\n\n"
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# Store the omit_if_empty flag for this field
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self.omit_if_empty_flags[field_name] = omit_if_empty
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if is_context and field_name != 'context_placeholder':
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self.context_handlers[field_name] = function
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self.context_order.append(field_name)
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else:
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self.field_handlers[field_name] = function
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self.field_order.append(field_name)
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def generate_full_prompt(self, **kwargs):
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full_prompt = []
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context_prompts = [] # 用于收集上下文内容
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is_pre_print = kwargs.get("is_pre_print", False) # 用于强制打印所有prompt 字段信息，不管有没有空
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# 先处理上下文字段
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for field_name in self.context_order:
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handler = self.context_handlers[field_name]
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processed_prompt = handler(**kwargs)
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# Check if the field should be omitted when empty
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if self.omit_if_empty_flags.get(field_name, False) and not processed_prompt and not is_pre_print:
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continue # Skip this field
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title_or_description = self.field_descriptions.get(field_name, f"#### {field_name.replace('_', ' ').title()}\n\n")
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context_prompts.append(title_or_description + processed_prompt + '\n\n')
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# 处理普通字段，同时查找 context_placeholder 的位置
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for field_name in self.field_order:
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if field_name == 'context_placeholder':
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# 在 context_placeholder 的位置插入上下文数据
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full_prompt.append(self.context_title) # 添加上下文部分的大标题
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full_prompt.extend(context_prompts) # 添加收集的上下文内容
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else:
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handler = self.field_handlers[field_name]
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processed_prompt = handler(**kwargs)
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# Check if the field should be omitted when empty
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if self.omit_if_empty_flags.get(field_name, False) and not processed_prompt and not is_pre_print:
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continue # Skip this field
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title_or_description = self.field_descriptions.get(field_name, f"### {field_name.replace('_', ' ').title()}\n\n")
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full_prompt.append(title_or_description + processed_prompt + '\n\n')
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# 返回完整的提示，移除尾部的空行
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return ''.join(full_prompt).rstrip('\n')
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def pre_print(self, **kwargs):
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kwargs.update({"is_pre_print": True})
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prompt = self.generate_full_prompt(**kwargs)
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input_keys = parse_section(self.role_prompt, 'Response Output Format')
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llm_predict = "\n".join([f"**{k}:**" for k in input_keys])
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return prompt + "\n\n" + "#"*19 + "\n<<<<LLM PREDICT>>>>\n" + "#"*19 + f"\n\n{llm_predict}\n"
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def handle_custom_data(self, **kwargs):
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return ""
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def handle_tool_data(self, **kwargs):
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if 'previous_agent_message' not in kwargs:
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return ""
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previous_agent_message = kwargs.get('previous_agent_message')
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tools: list[Tool] = previous_agent_message.tools
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if not tools:
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return ""
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tool_strings = []
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for tool in tools:
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args_str = f'args: {str(tool.args)}' if tool.args_schema else ""
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tool_strings.append(f"{tool.name}: {tool.description}, {args_str}")
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formatted_tools = "\n".join(tool_strings)
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tool_names = ", ".join([tool.name for tool in tools])
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tool_prompt = dedent(f"""
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Below is a list of tools that are available for your use:
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{formatted_tools}
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valid "tool_name" value is:
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{tool_names}
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""")
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return tool_prompt
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def handle_agent_data(self, **kwargs):
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if 'agents' not in kwargs:
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return ""
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agents = kwargs.get('agents')
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random.shuffle(agents)
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agent_names = ", ".join([f'{agent.role.role_name}' for agent in agents])
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agent_descs = []
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for agent in agents:
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role_desc = agent.role.role_prompt.split("####")[1]
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while "\n\n" in role_desc:
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role_desc = role_desc.replace("\n\n", "\n")
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role_desc = role_desc.replace("\n", ",")
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agent_descs.append(f'"role name: {agent.role.role_name}\nrole description: {role_desc}"')
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agents = "\n".join(agent_descs)
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agent_prompt = f'''
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Please ensure your selection is one of the listed roles. Available roles for selection:
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{agents}
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Please ensure select the Role from agent names, such as {agent_names}'''
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return dedent(agent_prompt)
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def handle_doc_info(self, **kwargs) -> str:
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if 'previous_agent_message' not in kwargs:
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return ""
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previous_agent_message: Message = kwargs.get('previous_agent_message')
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db_docs = previous_agent_message.db_docs
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search_docs = previous_agent_message.search_docs
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code_cocs = previous_agent_message.code_docs
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doc_infos = "\n".join([doc.get_snippet() for doc in db_docs] + [doc.get_snippet() for doc in search_docs] +
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[doc.get_code() for doc in code_cocs])
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return doc_infos
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def handle_session_records(self, **kwargs) -> str:
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memory_pool: Memory = kwargs.get('memory_pool', Memory(messages=[]))
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memory_pool = self.select_memory_by_agent_name(memory_pool)
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memory_pool = self.select_memory_by_parsed_key(memory_pool)
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return memory_pool.to_str_messages(content_key="parsed_output_list", with_tag=True)
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def handle_current_plan(self, **kwargs) -> str:
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if 'previous_agent_message' not in kwargs:
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return ""
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previous_agent_message = kwargs['previous_agent_message']
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return previous_agent_message.parsed_output.get("CURRENT_STEP", "")
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def handle_agent_profile(self, **kwargs) -> str:
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return extract_section(self.role_prompt, 'Agent Profile')
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def handle_output_format(self, **kwargs) -> str:
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return extract_section(self.role_prompt, 'Response Output Format')
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def handle_response(self, **kwargs) -> str:
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if 'react_memory' not in kwargs:
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return ""
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react_memory = kwargs.get('react_memory', Memory(messages=[]))
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if react_memory is None:
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return ""
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return "\n".join(["\n".join([f"**{k}:**\n{v}" for k,v in _dict.items()]) for _dict in react_memory.get_parserd_output()])
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def handle_task_records(self, **kwargs) -> str:
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if 'task_memory' not in kwargs:
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return ""
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task_memory: Memory = kwargs.get('task_memory', Memory(messages=[]))
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if task_memory is None:
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return ""
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return "\n".join(["\n".join([f"**{k}:**\n{v}" for k,v in _dict.items() if k not in ["CURRENT_STEP"]]) for _dict in task_memory.get_parserd_output()])
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def handle_previous_message(self, message: Message) -> str:
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pass
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def handle_message_by_role_name(self, message: Message) -> str:
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pass
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def handle_message_by_role_type(self, message: Message) -> str:
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pass
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def handle_current_agent_react_message(self, message: Message) -> str:
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pass
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def extract_codedoc_info_for_prompt(self, message: Message) -> str:
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code_docs = message.code_docs
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doc_infos = "\n".join([doc.get_code() for doc in code_docs])
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return doc_infos
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def select_memory_by_parsed_key(self, memory: Memory) -> Memory:
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return Memory(
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messages=[self.select_message_by_parsed_key(message) for message in memory.messages
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if self.select_message_by_parsed_key(message) is not None]
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)
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def select_memory_by_agent_name(self, memory: Memory) -> Memory:
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return Memory(
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messages=[self.select_message_by_agent_name(message) for message in memory.messages
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if self.select_message_by_agent_name(message) is not None]
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)
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def select_message_by_agent_name(self, message: Message) -> Message:
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# assume we focus all agents
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if self.monitored_agents == []:
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return message
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return None if message is None or message.role_name not in self.monitored_agents else self.select_message_by_parsed_key(message)
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def select_message_by_parsed_key(self, message: Message) -> Message:
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# assume we focus all key contents
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if message is None:
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return message
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if self.monitored_fields == []:
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return message
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message_c = copy.deepcopy(message)
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message_c.parsed_output = {k: v for k,v in message_c.parsed_output.items() if k in self.monitored_fields}
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message_c.parsed_output_list = [{k: v for k,v in parsed_output.items() if k in self.monitored_fields} for parsed_output in message_c.parsed_output_list]
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return message_c
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def get_memory(self, content_key="role_content"):
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return self.memory.to_tuple_messages(content_key="step_content")
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def get_memory_str(self, content_key="role_content"):
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return "\n".join([": ".join(i) for i in self.memory.to_tuple_messages(content_key="step_content")])
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def register_fields_from_config(self):
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for prompt_field in self.prompt_config:
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function_name = prompt_field.function_name
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# 检查function_name是否是self的一个方法
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if function_name and hasattr(self, function_name):
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function = getattr(self, function_name)
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else:
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function = self.handle_custom_data
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self.register_field(prompt_field.field_name,
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function=function,
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title=prompt_field.title,
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description=prompt_field.description,
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is_context=prompt_field.is_context,
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omit_if_empty=prompt_field.omit_if_empty)
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def register_standard_fields(self):
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self.register_field('agent_profile', function=self.handle_agent_profile, is_context=False)
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self.register_field('tool_information', function=self.handle_tool_data, is_context=False)
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self.register_field('context_placeholder', is_context=True) # 用于标记上下文数据部分的位置
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self.register_field('reference_documents', function=self.handle_doc_info, is_context=True)
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self.register_field('session_records', function=self.handle_session_records, is_context=True)
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self.register_field('output_format', function=self.handle_output_format, title='Response Output Format', is_context=False)
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self.register_field('response', function=self.handle_response, is_context=False, omit_if_empty=False)
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def register_executor_fields(self):
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self.register_field('agent_profile', function=self.handle_agent_profile, is_context=False)
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self.register_field('tool_information', function=self.handle_tool_data, is_context=False)
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self.register_field('context_placeholder', is_context=True) # 用于标记上下文数据部分的位置
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self.register_field('reference_documents', function=self.handle_doc_info, is_context=True)
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self.register_field('session_records', function=self.handle_session_records, is_context=True)
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self.register_field('current_plan', function=self.handle_current_plan, is_context=True)
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self.register_field('output_format', function=self.handle_output_format, title='Response Output Format', is_context=False)
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self.register_field('response', function=self.handle_response, is_context=False, omit_if_empty=False)
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def register_fields_from_dict(self, fields_dict):
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# 使用字典注册字段的函数
|
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|
for field_name, field_config in fields_dict.items():
|
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|
|
function_name = field_config.get('function', None)
|
|||
|
|
title = field_config.get('title', None)
|
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|
|
description = field_config.get('description', None)
|
|||
|
|
is_context = field_config.get('is_context', True)
|
|||
|
|
omit_if_empty = field_config.get('omit_if_empty', True)
|
|||
|
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|
|||
|
|
# 检查function_name是否是self的一个方法
|
|||
|
|
if function_name and hasattr(self, function_name):
|
|||
|
|
function = getattr(self, function_name)
|
|||
|
|
else:
|
|||
|
|
function = self.handle_custom_data
|
|||
|
|
|
|||
|
|
# 调用已存在的register_field方法注册字段
|
|||
|
|
self.register_field(field_name, function=function, title=title, description=description, is_context=is_context, omit_if_empty=omit_if_empty)
|
|||
|
|
|
|||
|
|
|
|||
|
|
|
|||
|
|
def main():
|
|||
|
|
manager = PromptManager()
|
|||
|
|
manager.register_standard_fields()
|
|||
|
|
|
|||
|
|
manager.register_field('agents_work_progress', title=f"Agents' Work Progress", is_context=True)
|
|||
|
|
|
|||
|
|
# 创建数据字典
|
|||
|
|
data_dict = {
|
|||
|
|
"agent_profile": "这是代理配置文件...",
|
|||
|
|
# "tool_list": "这是工具列表...",
|
|||
|
|
"reference_documents": "这是参考文档...",
|
|||
|
|
"session_records": "这是会话记录...",
|
|||
|
|
"agents_work_progress": "这是代理工作进展...",
|
|||
|
|
"output_format": "这是预期的输出格式...",
|
|||
|
|
# "response": "这是生成或继续回应的指令...",
|
|||
|
|
"response": "",
|
|||
|
|
"test": 'xxxxx'
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
# 组合完整的提示
|
|||
|
|
full_prompt = manager.generate_full_prompt(data_dict)
|
|||
|
|
print(full_prompt)
|
|||
|
|
|
|||
|
|
if __name__ == "__main__":
|
|||
|
|
main()
|