from collections import deque from datetime import timedelta from typing import Deque, List, Optional, Tuple from temporalio.common import RetryPolicy from temporalio import workflow with workflow.unsafe.imports_passed_through(): from activities.tool_activities import ToolActivities, ToolPromptInput from prompts.agent_prompt_generators import ( generate_genai_prompt, ) from models.data_types import CombinedInput, ToolWorkflowParams @workflow.defn class ToolWorkflow: def __init__(self) -> None: self.conversation_history: List[Tuple[str, str]] = [] self.prompt_queue: Deque[str] = deque() self.conversation_summary: Optional[str] = None self.chat_ended: bool = False self.tool_data = None self.max_turns_before_continue: int = 250 self.confirm = False @workflow.run async def run(self, combined_input: CombinedInput) -> str: params = combined_input.tool_params tools_data = combined_input.tools_data tool_data = None if params and params.conversation_summary: self.conversation_history.append( ("conversation_summary", params.conversation_summary) ) self.conversation_summary = params.conversation_summary if params and params.prompt_queue: self.prompt_queue.extend(params.prompt_queue) waiting_for_confirm = False current_tool = None while True: # Wait until *any* signal or user prompt arrives: await workflow.wait_condition( lambda: bool(self.prompt_queue) or self.chat_ended or self.confirm ) # 1) If chat_ended was signaled, handle end and return if self.chat_ended: # possibly do a summary if multiple turns if len(self.conversation_history) > 1: summary_context, summary_prompt = self.prompt_summary_with_history() summary_input = ToolPromptInput( prompt=summary_prompt, context_instructions=summary_context ) self.conversation_summary = await workflow.start_activity_method( ToolActivities.prompt_llm, summary_input, schedule_to_close_timeout=timedelta(seconds=20), ) workflow.logger.info( "Chat ended. Conversation summary:\n" + f"{self.conversation_summary}" ) return f"{self.conversation_summary}" # 2) If we received a confirm signal: if self.confirm and waiting_for_confirm and current_tool: # Clear the confirm flag so we don't repeatedly confirm self.confirm = False waiting_for_confirm = False # Run the tool workflow.logger.info(f"Confirmed. Proceeding with tool: {current_tool}") dynamic_result = await workflow.execute_activity( current_tool, self.tool_data["args"], schedule_to_close_timeout=timedelta(seconds=20), ) self.conversation_history.append( (f"{current_tool}_result", str(dynamic_result)) ) # Enqueue a follow-up prompt for the LLM self.prompt_queue.append( f"The '{current_tool}' tool completed successfully with {dynamic_result}. " "INSTRUCTIONS: Use this tool result, and the conversation history to figure out next steps. " "If all listed tools have run, then produce a done response." ) # Loop around again continue # 3) If there's a user prompt waiting, process it (unless we're in some other skipping logic). if self.prompt_queue: prompt = self.prompt_queue.popleft() self.conversation_history.append(("user", prompt)) # Pass entire conversation + Tools to LLM context_instructions = generate_genai_prompt( tools_data, self.format_history(), self.tool_data ) prompt_input = ToolPromptInput( prompt=prompt, context_instructions=context_instructions, ) tool_data = await workflow.execute_activity_method( ToolActivities.prompt_llm, prompt_input, schedule_to_close_timeout=timedelta(seconds=60), retry_policy=RetryPolicy( maximum_attempts=5, initial_interval=timedelta(seconds=12) ), ) self.tool_data = tool_data self.conversation_history.append(("response", str(tool_data))) # Check the next step from LLM next_step = self.tool_data.get("next") current_tool = self.tool_data.get("tool") if next_step == "confirm" and current_tool: waiting_for_confirm = True self.confirm = False # Clear any stale confirm workflow.logger.info("Waiting for user confirm signal...") # We do NOT do an immediate wait_condition here; # instead, let the loop continue so we can still handle prompts/end_chat signals. elif next_step == "done": workflow.logger.info("All steps completed. Exiting workflow.") return str(self.conversation_history) # Possibly continue-as-new after many turns # todo ensure this doesn't lose critical context if len(self.conversation_history) >= self.max_turns_before_continue: summary_context, summary_prompt = self.prompt_summary_with_history() summary_input = ToolPromptInput( prompt=summary_prompt, context_instructions=summary_context ) self.conversation_summary = await workflow.start_activity_method( ToolActivities.prompt_llm, summary_input, schedule_to_close_timeout=timedelta(seconds=20), ) workflow.logger.info( f"Continuing as new after {self.max_turns_before_continue} turns." ) workflow.continue_as_new( args=[ CombinedInput( tool_params=ToolWorkflowParams( conversation_summary=self.conversation_summary, prompt_queue=self.prompt_queue, ), tools_data=tools_data, ) ] ) @workflow.signal async def user_prompt(self, prompt: str) -> None: if self.chat_ended: workflow.logger.warn(f"Message dropped due to chat closed: {prompt}") return self.prompt_queue.append(prompt) @workflow.signal async def end_chat(self) -> None: self.chat_ended = True @workflow.signal async def confirm(self) -> None: self.confirm = True @workflow.query def get_conversation_history(self) -> List[Tuple[str, str]]: return self.conversation_history @workflow.query def get_summary_from_history(self) -> Optional[dict]: return self.conversation_summary @workflow.query def get_tool_data(self) -> Optional[dict]: return self.tool_data # Helper: generate text of the entire conversation so far def format_history(self) -> str: return " ".join(f"{text}" for _, text in self.conversation_history) # Return (context_instructions, prompt) def prompt_with_history(self, prompt: str) -> tuple[str, str]: history_string = self.format_history() context_instructions = ( f"Here is the conversation history: {history_string} " "Please add a few sentence response in plain text sentences. " "Don't editorialize or add metadata. " "Keep the text a plain explanation based on the history." ) return (context_instructions, prompt) # Return (context_instructions, prompt) for summarizing the conversation def prompt_summary_with_history(self) -> tuple[str, str]: history_string = self.format_history() context_instructions = f"Here is the conversation history between a user and a chatbot: {history_string}" actual_prompt = ( "Please produce a two sentence summary of this conversation. " 'Put the summary in the format { "summary": "" }' ) return (context_instructions, actual_prompt)