mirror of
https://github.com/temporal-community/temporal-ai-agent.git
synced 2026-03-15 14:08:08 +01:00
basic react API
This commit is contained in:
Steve Androulakis
@@ -1,6 +1,6 @@
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from collections import deque
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from datetime import timedelta
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from typing import Deque, List, Optional, Tuple
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from typing import Dict, Any, Union, List, Optional, Tuple, Deque
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from temporalio.common import RetryPolicy
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from temporalio import workflow
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@@ -16,7 +16,9 @@ with workflow.unsafe.imports_passed_through():
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@workflow.defn
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class ToolWorkflow:
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def __init__(self) -> None:
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self.conversation_history: List[Tuple[str, str]] = []
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self.conversation_history: Dict[
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str, List[Dict[str, Union[str, Dict[str, Any]]]]
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] = {"messages": []}
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self.prompt_queue: Deque[str] = deque()
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self.conversation_summary: Optional[str] = None
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self.chat_ended: bool = False
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@@ -31,9 +33,7 @@ class ToolWorkflow:
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tool_data = None
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if params and params.conversation_summary:
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self.conversation_history.append(
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("conversation_summary", params.conversation_summary)
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)
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self.add_message("conversation_summary", params.conversation_summary)
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self.conversation_summary = params.conversation_summary
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if params and params.prompt_queue:
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@@ -51,7 +51,7 @@ class ToolWorkflow:
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# 1) If chat_ended was signaled, handle end and return
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if self.chat_ended:
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# possibly do a summary if multiple turns
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if len(self.conversation_history) > 1:
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if len(self.conversation_history["messages"]) > 1:
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summary_context, summary_prompt = self.prompt_summary_with_history()
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summary_input = ToolPromptInput(
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prompt=summary_prompt, context_instructions=summary_context
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@@ -73,6 +73,11 @@ class ToolWorkflow:
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self.confirm = False
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waiting_for_confirm = False
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confirmed_tool_data = self.tool_data.copy()
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confirmed_tool_data["next"] = "confirmed"
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self.add_message("userToolConfirm", confirmed_tool_data)
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# Run the tool
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workflow.logger.info(f"Confirmed. Proceeding with tool: {current_tool}")
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dynamic_result = await workflow.execute_activity(
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@@ -80,15 +85,14 @@ class ToolWorkflow:
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self.tool_data["args"],
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schedule_to_close_timeout=timedelta(seconds=20),
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)
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self.conversation_history.append(
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(f"{current_tool}_result", str(dynamic_result))
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)
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dynamic_result["tool"] = current_tool
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self.add_message(f"tool_result", dynamic_result)
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# Enqueue a follow-up prompt for the LLM
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self.prompt_queue.append(
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f"The '{current_tool}' tool completed successfully with {dynamic_result}. "
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f"### The '{current_tool}' tool completed successfully with {dynamic_result}. "
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"INSTRUCTIONS: Use this tool result, and the conversation history to figure out next steps. "
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"If all listed tools have run, then produce a done response."
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"IMPORTANT: If all listed tools have run, you are up to the final step. Mark 'next':'done' and respond with your final confirmation."
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)
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# Loop around again
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continue
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@@ -96,7 +100,11 @@ class ToolWorkflow:
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# 3) If there's a user prompt waiting, process it (unless we're in some other skipping logic).
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if self.prompt_queue:
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prompt = self.prompt_queue.popleft()
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self.conversation_history.append(("user", prompt))
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if prompt.startswith("###"):
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# this is a custom prompt where the tool result is sent to the LLM
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self.add_message("tool_result_to_llm", prompt)
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else:
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self.add_message("user", prompt)
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# Pass entire conversation + Tools to LLM
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context_instructions = generate_genai_prompt(
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@@ -115,7 +123,7 @@ class ToolWorkflow:
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),
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)
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self.tool_data = tool_data
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self.conversation_history.append(("response", str(tool_data)))
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self.add_message("response", tool_data)
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# Check the next step from LLM
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next_step = self.tool_data.get("next")
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@@ -134,7 +142,10 @@ class ToolWorkflow:
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# Possibly continue-as-new after many turns
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# todo ensure this doesn't lose critical context
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if len(self.conversation_history) >= self.max_turns_before_continue:
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if (
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len(self.conversation_history["messages"])
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>= self.max_turns_before_continue
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):
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summary_context, summary_prompt = self.prompt_summary_with_history()
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summary_input = ToolPromptInput(
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prompt=summary_prompt, context_instructions=summary_context
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@@ -175,7 +186,10 @@ class ToolWorkflow:
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self.confirm = True
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@workflow.query
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def get_conversation_history(self) -> List[Tuple[str, str]]:
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def get_conversation_history(
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self,
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) -> Dict[str, List[Dict[str, Union[str, Dict[str, Any]]]]]:
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# Return the whole conversation as a dict
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return self.conversation_history
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@workflow.query
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@@ -187,8 +201,11 @@ class ToolWorkflow:
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return self.tool_data
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# Helper: generate text of the entire conversation so far
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def format_history(self) -> str:
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return " ".join(f"{text}" for _, text in self.conversation_history)
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return " ".join(
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str(msg["response"]) for msg in self.conversation_history["messages"]
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)
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# Return (context_instructions, prompt)
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def prompt_with_history(self, prompt: str) -> tuple[str, str]:
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@@ -210,3 +227,9 @@ class ToolWorkflow:
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'Put the summary in the format { "summary": "<plain text>" }'
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)
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return (context_instructions, actual_prompt)
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def add_message(self, actor: str, response: Union[str, Dict[str, Any]]) -> None:
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# Append a message object to the "messages" list
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self.conversation_history["messages"].append(
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{"actor": actor, "response": response}
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)
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