from collections import deque from datetime import timedelta from typing import Dict, Any, Union, List, Optional, Deque 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: Dict[ str, List[Dict[str, Union[str, Dict[str, Any]]]] ] = {"messages": []} 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 self.tool_results: List[Dict[str, Any]] = [] @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.add_message("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: workflow.logger.info("Chat ended.") return f"{self.conversation_history}" # 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 confirmed_tool_data = self.tool_data.copy() confirmed_tool_data["next"] = "user_confirmed_tool_run" self.add_message("user_confirmed_tool_run", confirmed_tool_data) # 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), ) dynamic_result["tool"] = current_tool self.add_message( "tool_result", {"tool": current_tool, "result": 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, the list of tools in sequence and the conversation history to figure out next steps, if any. " "DON'T ask any clarifying questions that are outside of the tools and args specified. " ) # 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() if prompt.startswith("###"): pass else: self.add_message("user", prompt) # Pass entire conversation + Tools to LLM context_instructions = generate_genai_prompt( tools_data, self.conversation_history, self.tool_data ) # tools_list = ", ".join([t.name for t in tools_data.tools]) prompt_input = ToolPromptInput( prompt=prompt, context_instructions=context_instructions, ) tool_data = await workflow.execute_activity( 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 # 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: # tmp arg check args = self.tool_data.get("args") # check each argument for null values missing_args = [] for key, value in args.items(): if value is None: next_step = "question" missing_args.append(key) if missing_args: # self.add_message("response_confirm_missing_args", tool_data) # Enqueue a follow-up prompt for the LLM self.prompt_queue.append( f"### INSTRUCTIONS set next='question', combine this response response='{tool_data.get('response')}' and following missing arguments for tool {current_tool}: {missing_args}. " "Only provide a valid JSON response without any comments or metadata." ) workflow.logger.info( f"Missing arguments for tool: {current_tool}: {' '.join(missing_args)}" ) # Loop around again continue 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.") self.add_message("agent", tool_data) return str(self.conversation_history) self.add_message("agent", tool_data) # Possibly continue-as-new after many turns # todo ensure this doesn't lose critical context if ( len(self.conversation_history["messages"]) >= 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, ) -> Dict[str, List[Dict[str, Union[str, Dict[str, Any]]]]]: # Return the whole conversation as a dict 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( str(msg["response"]) for msg in self.conversation_history["messages"] ) # 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) def add_message(self, actor: str, response: Union[str, Dict[str, Any]]) -> None: # Append a message object to the "messages" list self.conversation_history["messages"].append( {"actor": actor, "response": response} )