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PlanWriterAskUserFlow.py

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+from flow_modules.aiflows.HumanStandardInputFlowModule import HumanStandardInputFlow
+
+from typing import Dict, Any
+
+from flows.messages import UpdateMessage_Generic
+
+from flows.utils import logging
+
+log = logging.get_logger(f"flows.{__name__}")
+
+
+class PlanWriterAskUserFlow(HumanStandardInputFlow):
+    def run(self,
+            input_data: Dict[str, Any]) -> Dict[str, Any]:
+
+        query_message = self._get_message(self.query_message_prompt_template, input_data)
+        state_update_message = UpdateMessage_Generic(
+            created_by=self.flow_config['name'],
+            updated_flow=self.flow_config["name"],
+            data={"query_message": query_message},
+        )
+        self._log_message(state_update_message)
+
+        log.info(query_message)
+        human_input = self._read_input()
+
+        response = {}
+        response["feedback"] = human_input
+        response["plan"] = "no plan was written"
+
+        return response

PlanWriterAskUserFlow.yaml

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+request_multi_line_input_flag: False
+end_of_input_string: EOI
+
+query_message_prompt_template:
+  template: |2-
+    {{question}}
+  input_variables:
+    - "question"

PlanWriterCtrlFlow.py

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+import json
+from copy import deepcopy
+from typing import Any, Dict, List
+
+from flow_modules.aiflows.ChatFlowModule import ChatAtomicFlow
+
+from dataclasses import dataclass
+
+
+@dataclass
+class Command:
+    name: str
+    description: str
+    input_args: List[str]
+
+class PlanWriterCtrlFlow(ChatAtomicFlow):
+    def __init__(
+            self,
+            commands: List[Command],
+            **kwargs):
+        super().__init__(**kwargs)
+        self.system_message_prompt_template = self.system_message_prompt_template.partial(
+            commands=self._build_commands_manual(commands),
+        )
+        self.hint_for_model = """
+        Make sure your response is in the following format:
+              Response Format:
+              {
+              "command": "call plan writer, or to finish with a summary",
+              "command_args": {
+                  "arg name": "value"
+                  }
+              }
+        """
+
+    @staticmethod
+    def _build_commands_manual(commands: List[Command]) -> str:
+        ret = ""
+        for i, command in enumerate(commands):
+            command_input_json_schema = json.dumps(
+                {input_arg: f"YOUR_{input_arg.upper()}" for input_arg in command.input_args})
+            ret += f"{i + 1}. {command.name}: {command.description} Input arguments (given in the JSON schema): {command_input_json_schema}\n"
+        return ret
+
+    @classmethod
+    def instantiate_from_config(cls, config):
+        flow_config = deepcopy(config)
+
+        kwargs = {"flow_config": flow_config}
+
+        # ~~~ Set up prompts ~~~
+        kwargs.update(cls._set_up_prompts(flow_config))
+
+        # ~~~Set up backend ~~~
+        kwargs.update(cls._set_up_backend(flow_config))
+
+        # ~~~ Set up commands ~~~
+        commands = flow_config["commands"]
+        commands = [
+            Command(name, command_conf["description"], command_conf["input_args"]) for name, command_conf in
+            commands.items()
+        ]
+        kwargs.update({"commands": commands})
+
+        # ~~~ Instantiate flow ~~~
+        return cls(**kwargs)
+
+    def _update_prompts_and_input(self, input_data: Dict[str, Any]):
+        if 'goal' in input_data:
+            input_data['goal'] += self.hint_for_model
+        if 'feedback' in input_data:
+            input_data['feedback'] += self.hint_for_model
+
+    def run(self, input_data: Dict[str, Any]) -> Dict[str, Any]:
+        self._update_prompts_and_input(input_data)
+        api_output = super().run(input_data)["api_output"].strip()
+        try:
+            response = json.loads(api_output)
+            return response
+        except json.decoder.JSONDecodeError:
+            new_input_data = input_data.copy()
+            new_input_data['feedback'] = "The previous respond cannot be parsed with json.loads. Make sure your next response is in JSON format."
+            new_api_output = super().run(new_input_data)["api_output"].strip()
+            return json.loads(new_api_output)

PlanWriterCtrlFlow.yaml

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+name: "PlanWriterControllerFlow"
+description: "Proposes the next action to take towards achieving the goal, and prepares the input for the branching flow"
+enable_cache: True
+
+#######################################################
+# Input keys
+#######################################################
+
+input_interface_non_initialized: # initial input keys
+  - "goal"
+
+input_interface_initialized:
+  - "plan"
+  - "feedback"
+
+#######################################################
+# Output keys
+#######################################################
+
+output_interface:
+  - 'command'
+  - 'command_args'
+
+backend:
+  api_infos: ???
+  model_name:
+    openai: gpt-4
+    azure: azure/gpt-4
+
+commands:
+  write_plan:
+    description: "Write plan to finish the goal with user interaction"
+    input_args: ["goal"]
+  finish:
+    description: "Signal that the objective has been satisfied, return the summary of what was done"
+    input_args: ["summary"]
+  manual_finish:
+    description: "The user demands to quit and terminate the current process"
+    input_args: []
+  ask_user:
+    description: "Ask user a question for confirmation or assistance"
+    input_args: ["question"]
+
+system_message_prompt_template:
+  _target_: langchain.PromptTemplate
+  template: |2-
+          You are in charge of a department of writing plans to solve a certain goal. You work with a planner, who does all the planning job.
+    
+          Your **ONLY** task is to take the user's goal for you, to decide whether to call the planner to write or re-write the plan, or to finish the current task.
+    
+          When you need to call the plan writer, call the `write_plan` command with the goal specified.
+          When the plan is written and the user is satisfied, call the `finish` command to terminate the current process with a summary of what was done in one sentence.
+          Whenever you are in doubt, or need to confirm something to the user, call `ask_user` with the question.
+          
+          You **must not** write plans yourself. You only decide whether to call the planner with specified goals or to finish.
+    
+          Your workflow:
+          0. Whenever the user demands to quit or terminate the current process, call `manual_finish` command.
+          1. Upon user request, call the `write_plan` with the goal given.
+          2. The planner will write the plan. The user will examine the plan, and provide feedback.
+          3. Depending on the feedback of the user:
+            3.1. The user provides feedback on how to change the plan, **call the planner with user's specific requirements again, to ask the planner to refine the plan**. Go back to step 2.
+            3.2. The user does not provide details about refining the plan, for example, just stating the fact that the user has updated the plan, **this means the user is satisfied with the plan written, call the `finish` command.**
+            3.3. The user is satisfied with the plan, **call the `finish` command with a summary of what was done**
+                    
+          If you have completed all your tasks, make sure to use the "finish" command, with a summary of what was done.
+    
+          Constraints:
+          1. Exclusively use the commands listed in double quotes e.g. "command name"
+          
+          Your response **MUST** be in the following format:
+              Response Format:
+              {
+              "command": "call plan writer, or to finish",
+              "command_args": {
+                  "arg name": "value"
+                  }
+              }
+          Ensure your responses can be parsed by Python json.loads
+          
+  
+          Available Functions:
+          {{commands}}
+  input_variables: ["commands"]
+  template_format: jinja2
+
+human_message_prompt_template:
+  _target_: flows.prompt_template.JinjaPrompt
+  template: |2-
+    Here is the plan written by the planner, it might have been updated by the user, depending on the user's feedback:
+    {{plan}}
+    Here is the feedback from the user:
+    {{feedback}}
+  input_variables:
+    - "plan"
+    - "feedback"
+  template_format: jinja2
+
+init_human_message_prompt_template:
+  _target_: flows.prompt_template.JinjaPrompt
+  template: |2-
+    Here is the goal you need to achieve:
+    {{goal}}
+  input_variables:
+    - "goal"
+  template_format: jinja2

PlanWriterFlow.py

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+from typing import Dict, Any
+import os
+
+from flow_modules.Tachi67.ContentWriterFlowModule import ContentWriterFlow
+from flows.base_flows import CircularFlow
+
+
+class PlanWriterFlow(ContentWriterFlow):
+    def _on_reach_max_round(self):
+        self._state_update_dict({
+            "plan": "The maximum amount of rounds was reached before the model generated the plan.",
+            "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":
+            # ~~~ fetch temp file location, plan content, memory file (of upper level flow e.g. ExtLib) from flow state
+            keys_to_fetch_from_state = ["temp_plan_file_location", "plan", "plan_file_location"]
+            fetched_state = self._fetch_state_attributes_by_keys(keys=keys_to_fetch_from_state)
+            temp_plan_file_location = fetched_state["temp_plan_file_location"]
+            plan_content = fetched_state["plan"]
+            plan_file_location = fetched_state["plan_file_location"]
+
+            # ~~~ delete the temp plan file ~~~
+            if os.path.exists(temp_plan_file_location):
+                os.remove(temp_plan_file_location)
+
+            # ~~~ write plan content to plan file ~~~
+            with open(plan_file_location, 'w') as file:
+                file.write(plan_content)
+
+            # ~~~ return the plan content ~~~
+            return {
+                "EARLY_EXIT": True,
+                "plan": plan_content,
+                "summary": "PlanWriter: " + output_payload["command_args"]["summary"],
+                "status": "finished"
+            }
+        elif command == "manual_finish":
+            # ~~~ delete the temp plan file ~~~
+            keys_to_fetch_from_state = ["temp_plan_file_location"]
+            fetched_state = self._fetch_state_attributes_by_keys(keys=keys_to_fetch_from_state)
+            temp_plan_file_location = fetched_state["temp_plan_file_location"]
+            if os.path.exists(temp_plan_file_location):
+                os.remove(temp_plan_file_location)
+            # ~~~ return the manual quit status ~~~
+            return {
+                "EARLY_EXIT": True,
+                "plan": "no plan was generated",
+                "summary": "PlanWriter: PlanWriter was terminated explicitly by the user, process is unfinished",
+                "status": "unfinished"
+            }
+        else:
+            return output_payload

PlanWriterFlow.yaml

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+name: "PlanWriter"
+description: "Generates plan with interactions with the user"
+
+_target_: Tachi67.PlanWriterFlowModule.PlanWriterFlow.instantiate_from_default_config
+
+input_interface:
+  - "goal"
+  - "plan_file_location"
+
+output_interface:
+  - "plan"
+  - "status"
+  - "summary"
+
+### Subflows specification
+subflows_config:
+  Controller:
+    _target_: Tachi67.PlanWriterFlowModule.PlanWriterCtrlFlow.instantiate_from_default_config
+    backend:
+      api_infos: ???
+      model_name:
+        openai: gpt-4
+        azure: azure/gpt-4
+
+  Executor:
+    _target_: flows.base_flows.BranchingFlow.instantiate_from_default_config
+    subflows_config:
+      write_plan:
+        _target_: Tachi67.InteractivePlanGenFlowModule.InteractivePlanGenFlow.instantiate_from_default_config
+        subflows_config:
+          MemoryReading:
+            _target_: Tachi67.MemoryReadingFlowModule.MemoryReadingAtomicFlow.instantiate_from_default_config
+
+          PlanGenerator:
+            _target_: Tachi67.PlanGeneratorFlowModule.PlanGeneratorAtomicFlow.instantiate_from_default_config
+            backend:
+              api_infos: ???
+              model_name:
+                openai: gpt-4
+                azure: azure/gpt-4
+
+          PlanFileEditor:
+            _target_: Tachi67.PlanFileEditFlowModule.PlanFileEditAtomicFlow.instantiate_from_default_config
+
+          ParseFeedback:
+            _target_: Tachi67.ParseFeedbackFlowModule.ParseFeedbackAtomicFlow.instantiate_from_default_config
+            input_interface:
+              - "temp_plan_file_location"
+              - "temp_plan_file_written_timestamp"
+
+      ask_user:
+        _target_: Tachi67.PlanWriterFlowModule.PlanWriterAskUserFlow.instantiate_from_default_config
+
+
+early_exit_key: "EARLY_EXIT"
+
+topology:
+  - goal: "Select the next action and prepare the input for the executor."
+    input_interface:
+      _target_: flows.interfaces.KeyInterface
+      additional_transformations:
+        - _target_: flows.data_transformations.KeyMatchInput
+    flow: Controller
+    output_interface:
+      _target_: PlanWriterFlow.detect_finish_or_continue
+    reset: false
+
+  - goal: "Execute the action specified by the Controller."
+    input_interface:
+      _target_: flows.interfaces.KeyInterface
+      keys_to_rename:
+        command: branch
+        command_args: branch_input_data
+      keys_to_select: ["branch", "branch_input_data"]
+    flow: Executor
+    output_interface:
+      _target_: flows.interfaces.KeyInterface
+      keys_to_rename:
+        branch_output_data: observation
+      keys_to_unpack: ["observation"]
+    reset: false

__init__.py

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+dependencies = [
+    {"url": "Tachi67/ContentWriterFlowModule", "revision": "main"},
+    {"url": "Tachi67/InteractivePlanGenFlowModule", "revision": "main"},
+    {"url": "aiflows/ChatFlowModule", "revision": "a749ad10ed39776ba6721c37d0dc22af49ca0f17"},
+    {"url": "aiflows/HumanStandardInputFlowModule", "revision": "5683a922372c5fa90be9f6447d6662d8d80341fc"}
+]
+from flows import flow_verse
+
+flow_verse.sync_dependencies(dependencies)
+
+from .PlanWriterFlow import PlanWriterFlow
+from .PlanWriterCtrlFlow import PlanWriterCtrlFlow
+from .PlanWriterAskUserFlow import PlanWriterAskUserFlow

run_planwriter.py

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+import os
+
+import hydra
+
+from flows.backends.api_info import ApiInfo
+from flows.messages import InputMessage
+from flows.utils.general_helpers import read_yaml_file
+
+from flows import logging
+from flows.flow_cache import CACHING_PARAMETERS, clear_cache
+
+CACHING_PARAMETERS.do_caching = False  # Set to True in order to disable caching
+# clear_cache() # Uncomment this line to clear the cache
+
+logging.set_verbosity_debug()
+logging.auto_set_dir()
+
+dependencies = [
+    {"url": "Tachi67/ContentWriterFlowModule", "revision": "main"},
+    {"url": "Tachi67/InteractivePlanGenFlowModule", "revision": "main"},
+    {"url": "Tachi67/PlanWriterFlowModule", "revision": "main"},
+    {"url": "aiflows/ChatFlowModule", "revision": "a749ad10ed39776ba6721c37d0dc22af49ca0f17"},
+]
+
+from flows import flow_verse
+
+flow_verse.sync_dependencies(dependencies)
+
+if __name__ == "__main__":
+    # ~~~ make sure to set the openai api key in the envs ~~~
+    key = os.getenv("OPENAI_API_KEY")
+    api_information = [ApiInfo(backend_used="openai", api_key=os.getenv("OPENAI_API_KEY"))]
+    path_to_output_file = None
+
+    # ~~~ setting api information into config ~~~
+    current_dir = os.getcwd()
+    cfg_path = os.path.join(current_dir, "PlanWriterFlow.yaml")
+    cfg = read_yaml_file(cfg_path)
+    cfg["subflows_config"]["Controller"]["backend"]["api_infos"] = api_information
+    cfg["subflows_config"]["Executor"]["subflows_config"]["write_plan"]["subflows_config"]["PlanGenerator"]["backend"]["api_infos"] = api_information
+
+    # ~~~ setting memory file of planner (empty dict)~~~
+    cfg["subflows_config"]["Executor"]["subflows_config"]["write_code"]["memory_files"] = {}
+
+    # ~~~ instantiating the flow and input data ~~~
+    PlanWriterFlow = hydra.utils.instantiate(cfg, _recursive_=False, _convert_="partial")
+
+    # ~~~ creating the plan file location (of the upper level flow e.g. ExtLib)
+    plan_file_location = os.path.join(current_dir, "ExtLib_plan.txt")
+
+    input_data = {
+        "goal": "create a function that adds two numbers and returns the result",
+        "plan_file_location": plan_file_location
+    }
+    input_message = InputMessage.build(
+        data_dict=input_data,
+        src_flow="Launcher",
+        dst_flow=PlanWriterFlow.name
+    )
+
+    # ~~~ calling the flow ~~~
+    output_message = PlanWriterFlow(input_message)