| from typing import Dict, Any |
|
|
| from flow_modules.Tachi67.AbstractBossFlowModule import CtrlExMemFlow |
| from aiflows.base_flows import CircularFlow |
|
|
|
|
| class CtrlExMem_JarvisFlow(CtrlExMemFlow): |
| """This class inherits from the CtrlExMemFlow class from AbstractBossFlowModule. |
| See: https://huggingface.co/Tachi67/AbstractBossFlowModule/blob/main/CtrlExMemFlow.py |
| |
| Take notice that: |
| 1. In the controller, we only keep the previous 3 messages for memory management, that will be: |
| a. The assistant message (controller's last command) |
| b. Manually updated new system prompt (new logs, new plans, etc.) |
| c. The user message (result, feedback) |
| 2. Each time one executor from the branch is executed, the logs is updated, this means: |
| a. The logs file of Jarvis is updated. |
| b. After MemoryReading at the end of each run of the loop, the logs in the flow_state is updated. |
| c. The next time the controller is called, the updated logs is injected into the system prompts. |
| 3. In the prompts of the controller, when the controller realizes one step of the plan is done, |
| we ask the controller to revise what was done and mark the current step as done. This means: |
| a. The plan file is updated. |
| b. The plan in the flow_state is updated. |
| c. The next time the controller is called, the updated plan is injected into the system prompts. |
| |
| This is basically how the memory management works, to allow for more space for llm execution, and make sure the llm |
| does not forget important information. |
| """ |
| def _on_reach_max_round(self): |
| self._state_update_dict({ |
| "result": "the maximum amount of rounds was reached before the Jarvis flow has done the job", |
| "summary": "JarvisFlow: the maximum amount of rounds was reached before the flow has done the job", |
| "status": "unfinished" |
| }) |
|
|
| @CircularFlow.output_msg_payload_processor |
| def detect_finish_or_continue(self, output_payload: Dict[str, Any], src_flow) -> Dict[str, Any]: |
| command = output_payload["command"] |
| if command == "finish": |
| return { |
| "EARLY_EXIT": True, |
| "result": output_payload["command_args"]["summary"], |
| "summary": "Jarvis: " + output_payload["command_args"]["summary"], |
| "status": "finished" |
| } |
| elif command == "manual_finish": |
| |
| return { |
| "EARLY_EXIT": True, |
| "result": "JarvisFlow was terminated explicitly by the user, process is unfinished", |
| "summary": "Jarvis: process terminated by the user explicitly, nothing generated", |
| "status": "unfinished" |
| } |
| elif command == "update_plan": |
| keys_to_fetch_from_state = ["memory_files"] |
| fetched_state = self._fetch_state_attributes_by_keys(keys=keys_to_fetch_from_state) |
| output_payload["command_args"]["memory_files"] = fetched_state["memory_files"] |
| return output_payload |
|
|
| elif command == "re_plan": |
| keys_to_fetch_from_state = ["plan", "memory_files"] |
| fetched_state = self._fetch_state_attributes_by_keys(keys=keys_to_fetch_from_state) |
| output_payload["command_args"]["plan_file_location"] = fetched_state["memory_files"]["plan"] |
| output_payload["command_args"]["plan"] = fetched_state["plan"] |
| return output_payload |
|
|
| else: |
| return output_payload |
