Spaces:
Runtime error
Runtime error
| import time | |
| from typing import Any, List | |
| from swarms.models.prompts.agent_prompt_generator import get_prompt | |
| class TokenUtils: | |
| def count_tokens(text: str) -> int: | |
| return len(text.split()) | |
| class PromptConstructor: | |
| def __init__(self, ai_name: str, ai_role: str, tools): | |
| self.ai_name = ai_name | |
| self.ai_role = ai_role | |
| self.tools = tools | |
| def construct_full_prompt(self, goals: List[str]) -> str: | |
| prompt_start = ( | |
| """Your decisions must always be made independently | |
| without seeking user assistance.\n | |
| Play to your strengths as an LLM and pursue simple | |
| strategies with no legal complications.\n | |
| If you have completed all your tasks, make sure to | |
| use the "finish" command.""" | |
| ) | |
| # Construct full prompt | |
| full_prompt = ( | |
| f"You are {self.ai_name}, {self.ai_role}\n{prompt_start}\n\nGOALS:\n\n" | |
| ) | |
| for i, goal in enumerate(goals): | |
| full_prompt += f"{i+1}. {goal}\n" | |
| full_prompt += f"\n\n{get_prompt(self.tools)}" | |
| return full_prompt | |
| class Message: | |
| content: str | |
| def count_tokens(self) -> int: | |
| return TokenUtils.count_tokens(self.content) | |
| def format_content(self) -> str: | |
| return self.content | |
| class SystemMessage(Message): | |
| pass | |
| class HumanMessage(Message): | |
| pass | |
| class MessageFormatter: | |
| send_token_limit: int = 4196 | |
| def format_messages(self, **kwargs: Any) -> List[Message]: | |
| prompt_constructor = PromptConstructor(ai_name=kwargs["ai_name"], | |
| ai_role=kwargs["ai_role"], | |
| tools=kwargs["tools"]) | |
| base_prompt = SystemMessage(content=prompt_constructor.construct_full_prompt(kwargs["goals"])) | |
| time_prompt = SystemMessage( | |
| content=f"The current time and date is {time.strftime('%c')}" | |
| ) | |
| used_tokens = base_prompt.count_tokens() + time_prompt.count_tokens() | |
| memory: VectorStoreRetriever = kwargs["memory"] | |
| previous_messages = kwargs["messages"] | |
| relevant_docs = memory.get_relevant_documents(str(previous_messages[-10:])) | |
| relevant_memory = [d.page_content for d in relevant_docs] | |
| relevant_memory_tokens = sum( | |
| [TokenUtils.count_tokens(doc) for doc in relevant_memory] | |
| ) | |
| while used_tokens + relevant_memory_tokens > 2500: | |
| relevant_memory = relevant_memory[:-1] | |
| relevant_memory_tokens = sum( | |
| [TokenUtils.count_tokens(doc) for doc in relevant_memory] | |
| ) | |
| content_format = ( | |
| f"This reminds you of these events " | |
| f"from your past:\n{relevant_memory}\n\n" | |
| ) | |
| memory_message = SystemMessage(content=content_format) | |
| used_tokens += memory_message.count_tokens() | |
| historical_messages: List[Message] = [] | |
| for message in previous_messages[-10:][::-1]: | |
| message_tokens = message.count_tokens() | |
| if used_tokens + message_tokens > self.send_token_limit - 1000: | |
| break | |
| historical_messages = [message] + historical_messages | |
| used_tokens += message_tokens | |
| input_message = HumanMessage(content=kwargs["user_input"]) | |
| messages: List[Message] = [base_prompt, time_prompt, memory_message] | |
| messages += historical_messages | |
| messages.append(input_message) | |
| return messages | |