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PlanWriterAskUserFlow.py

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+from flow_modules.aiflows.HumanStandardInputFlowModule import HumanStandardInputFlow
+
+from typing import Dict, Any
+
+from aiflows.messages import UpdateMessage_Generic
+
+from aiflows.utils import logging
+
+log = logging.get_logger(f"aiflows.{__name__}")
+
+
+class PlanWriterAskUserFlow(HumanStandardInputFlow):
+    """
+    Refer to: https://huggingface.co/Tachi67/ExtendLibraryFlowModule/blob/main/ExtLibAskUserFlow.py
+    """
+    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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+_target_: flow_modules.aiflows.PlanWriterFlowModule.PlanWriterAskUserFlow.instantiate_from_default_config
+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):
+    """Refer to: https://huggingface.co/Tachi67/JarvisFlowModule/blob/main/Controller_JarvisFlow.py
+    """
+    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)
+        
+        # ~~~when conversation is initialized, append the updated system prompts to the chat history ~~~
+        if self._is_conversation_initialized():
+            updated_system_message_content = self._get_message(self.system_message_prompt_template, input_data)
+            self._state_update_add_chat_message(content=updated_system_message_content,
+                                                role=self.flow_config["system_name"])
+        
+        while True:
+            api_output = super().run(input_data)["api_output"].strip()
+            try:
+                response = json.loads(api_output)
+                return response
+            except (json.decoder.JSONDecodeError, json.JSONDecodeError):
+                updated_system_message_content = self._get_message(self.system_message_prompt_template, input_data)
+                self._state_update_add_chat_message(content=updated_system_message_content,
+                                                    role=self.flow_config["system_name"])
+                new_goal = "The previous respond cannot be parsed with json.loads. Next time, do not provide any comments or code blocks. Make sure your next response is purely json parsable."
+                new_input_data = input_data.copy()
+                new_input_data['feedback'] = new_goal
+                input_data = new_input_data

PlanWriterCtrlFlow.yaml

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+_target_: flow_modules.aiflows.PlanWriterFlowModule.PlanWriterCtrlFlow.instantiate_from_default_config
+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:
+  - "goal"
+  - "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.
+    
+          Here is the goal you need to achieve:
+          {{goal}}
+          
+          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", "goal"]
+  template_format: jinja2
+
+human_message_prompt_template:
+  _target_: aiflows.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_: aiflows.prompt_template.JinjaPrompt
+  template: |2-
+    Here is the goal you need to achieve:
+    {{goal}}
+  input_variables:
+    - "goal"
+  template_format: jinja2
+
+previous_messages:
+  last_k: 3

PlanWriterFlow.py

@@ -0,0 +1,90 @@
+from typing import Dict, Any
+import os
+
+from flow_modules.aiflows.ContentWriterFlowModule import ContentWriterFlow
+from aiflows.base_flows import CircularFlow
+
+
+class PlanWriterFlow(ContentWriterFlow):
+    """This flow inherits from ContentWriterFlow.
+    In the subflow of the executor, we specify the InteractivePlanGneFlow (https://huggingface.co/Tachi67/InteractivePlanGenFlowModule)
+
+    *Input Interface*:
+    - `goal`
+
+    *Output Interface*:
+    - `plan`
+    - `result`
+    - `summary`
+    - `status`
+    """
+    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", "memory_files"]
+            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["memory_files"]["plan"]
+
+            # ~~~ 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": "ExtendLibrary/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": "ExtendLibrary/PlanWriter: PlanWriter was terminated explicitly by the user, process is unfinished",
+                "status": "unfinished"
+            }
+        elif command == "write_plan":
+            keys_to_fetch_from_state = ["memory_files"]
+            fetched_state = self._fetch_state_attributes_by_keys(keys=keys_to_fetch_from_state)
+            plan_file_location = fetched_state["memory_files"]["plan"]
+            output_payload["command_args"]["plan_file_location"] = plan_file_location
+            return output_payload
+        else:
+            return output_payload
+
+    def run(self, input_data: Dict[str, Any]) -> Dict[str, Any]:
+        # ~~~ sets the input_data in the flow_state dict ~~~
+        self._state_update_dict(update_data=input_data)
+
+        max_rounds = self.flow_config.get("max_rounds", 1)
+        if max_rounds is None:
+            log.info(f"Running {self.flow_config['name']} without `max_rounds` until the early exit condition is met.")
+
+        self._sequential_run(max_rounds=max_rounds)
+
+        output = self._get_output_from_state()
+
+        self.reset(full_reset=True, recursive=True, src_flow=self)
+
+        return output

PlanWriterFlow.yaml

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+name: "PlanWriter"
+description: "Generates plan with interactions with the user"
+
+_target_: flow_modules.aiflows.PlanWriterFlowModule.PlanWriterFlow.instantiate_from_default_config
+
+input_interface:
+  - "goal"
+  - "memory_files"
+
+output_interface:
+  - "plan"
+  - "status"
+  - "summary"
+
+### Subflows specification
+subflows_config:
+  Controller:
+    _target_: flow_modules.aiflows.PlanWriterFlowModule.PlanWriterCtrlFlow.instantiate_from_default_config
+    backend:
+      api_infos: ???
+      model_name:
+        openai: gpt-4
+        azure: azure/gpt-4
+
+  Executor:
+    _target_: aiflows.base_flows.BranchingFlow.instantiate_from_default_config
+    subflows_config:
+      write_plan:
+        _target_: flow_modules.aiflows.InteractivePlanGenFlowModule.InteractivePlanGenFlow.instantiate_from_default_config
+        subflows_config:
+          PlanGenerator:
+            _target_: flow_modules.aiflows.PlanGeneratorFlowModule.PlanGeneratorAtomicFlow.instantiate_from_default_config
+            backend:
+              api_infos: ???
+              model_name:
+                openai: gpt-4
+                azure: azure/gpt-4
+
+          PlanFileEditor:
+            _target_: flow_modules.aiflows.PlanFileEditFlowModule.PlanFileEditAtomicFlow.instantiate_from_default_config
+
+          ParseFeedback:
+            _target_: flow_modules.aiflows.ParseFeedbackFlowModule.ParseFeedbackAtomicFlow.instantiate_from_default_config
+            input_interface:
+              - "temp_plan_file_location"
+            output_interface:
+              - "plan"
+              - "feedback"
+
+      ask_user:
+        _target_: flow_modules.aiflows.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_: aiflows.interfaces.KeyInterface
+      additional_transformations:
+        - _target_: aiflows.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_: aiflows.interfaces.KeyInterface
+      keys_to_rename:
+        command: branch
+        command_args: branch_input_data
+      keys_to_select: ["branch", "branch_input_data"]
+    flow: Executor
+    output_interface:
+      _target_: aiflows.interfaces.KeyInterface
+      keys_to_rename:
+        branch_output_data.plan: plan
+        branch_output_data.feedback: feedback
+        branch_output_data.temp_plan_file_location: temp_plan_file_location
+      keys_to_delete: ["branch_output_data"]
+    reset: false

README.md

@@ -1,3 +1,119 @@
----
-license: mit
----
+### Structure of PlanWriterFlow
+
+```
+               goal
+                |
+                v
+        +---------------+
+        |  Controller   | --------<<<<-----------+
+        +---------------+                        |
+                |                                |
+                | (command, command args)        |
+                |                                |
+                v                                |
+        +------------------+                     |
+        |   Executor       |  Each branch is an  |
+        | (Tree Structure) |  executor           |
+        +------------------+                     |
+                |                                ^
+                | (summary)                      |
+                |                                |
+                v                                |             
+                |                                |
+                +-> goes back to the Controller>-+
+
+```
+
+Structure of the Executors:
+```
+         +-------------------+
+         |   Branching       |
+         |    Executor       |
+         +-------------------+
+        /                    \
+       /                      \
+      /                        \
+     /                          \
+write_plan                    ask_user
+
+```
+
+About the branches:
+- [ask_user](https://huggingface.co/Tachi67/PlanWriterFlowModule/blob/main/PlanWriterAskUserFlow.py): Ask user for info / confirmation, etc.
+- [write_plan](https://huggingface.co/Tachi67/InteractivePlanGenFlowModule): Generates plan (user edit is allowed) and fetches user feedback.
+
+How it works:
+Controller calls write_plan until user is satisfied in the feedback, finish.
+
+
+# Table of Contents
+
+* [run\_planwriter](#run_planwriter)
+* [PlanWriterAskUserFlow](#PlanWriterAskUserFlow)
+  * [PlanWriterAskUserFlow](#PlanWriterAskUserFlow.PlanWriterAskUserFlow)
+* [PlanWriterFlow](#PlanWriterFlow)
+  * [PlanWriterFlow](#PlanWriterFlow.PlanWriterFlow)
+* [\_\_init\_\_](#__init__)
+* [PlanWriterCtrlFlow](#PlanWriterCtrlFlow)
+  * [PlanWriterCtrlFlow](#PlanWriterCtrlFlow.PlanWriterCtrlFlow)
+
+<a id="run_planwriter"></a>
+
+# run\_planwriter
+
+<a id="PlanWriterAskUserFlow"></a>
+
+# PlanWriterAskUserFlow
+
+<a id="PlanWriterAskUserFlow.PlanWriterAskUserFlow"></a>
+
+## PlanWriterAskUserFlow Objects
+
+```python
+class PlanWriterAskUserFlow(HumanStandardInputFlow)
+```
+
+Refer to: https://huggingface.co/Tachi67/ExtendLibraryFlowModule/blob/main/ExtLibAskUserFlow.py
+
+<a id="PlanWriterFlow"></a>
+
+# PlanWriterFlow
+
+<a id="PlanWriterFlow.PlanWriterFlow"></a>
+
+## PlanWriterFlow Objects
+
+```python
+class PlanWriterFlow(ContentWriterFlow)
+```
+
+This flow inherits from ContentWriterFlow.
+In the subflow of the executor, we specify the InteractivePlanGneFlow (https://huggingface.co/Tachi67/InteractivePlanGenFlowModule)
+
+*Input Interface*:
+- `goal`
+
+*Output Interface*:
+- `plan`
+- `result`
+- `summary`
+- `status`
+
+<a id="__init__"></a>
+
+# \_\_init\_\_
+
+<a id="PlanWriterCtrlFlow"></a>
+
+# PlanWriterCtrlFlow
+
+<a id="PlanWriterCtrlFlow.PlanWriterCtrlFlow"></a>
+
+## PlanWriterCtrlFlow Objects
+
+```python
+class PlanWriterCtrlFlow(ChatAtomicFlow)
+```
+
+Refer to: https://huggingface.co/Tachi67/JarvisFlowModule/blob/main/Controller_JarvisFlow.py
+

__init__.py

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+dependencies = [
+    {"url": "aiflows/ContentWriterFlowModule", "revision": "main"},
+    {"url": "aiflows/InteractivePlanGenFlowModule", "revision": "main"},
+    {"url": "aiflows/ChatFlowModule", "revision": "297c90d08087d9ff3139521f11d1a48d7dc63ed4"},
+    {"url": "aiflows/HumanStandardInputFlowModule", "revision": "4ff043522c89a964ea3a928ce09811c51a2b5b98"}
+]
+from aiflows import flow_verse
+
+flow_verse.sync_dependencies(dependencies)
+
+from .PlanWriterFlow import PlanWriterFlow
+from .PlanWriterCtrlFlow import PlanWriterCtrlFlow
+from .PlanWriterAskUserFlow import PlanWriterAskUserFlow

pip_requirements.txt

@@ -0,0 +1,15 @@
+colorama==0.4.6
+pytest==7.3.1
+pytest-cov==4.1.0
+hydra-core==1.3.2
+hydra-colorlog==1.1.0
+wrapt-timeout-decorator==1.3.12.2
+diskcache==5.6.1
+openai==1.0.0
+huggingface_hub==0.19.4
+jsonlines==3.1.0
+jinja2==3.1.2
+mock==5.0.2
+rich==12.6.0
+litellm==1.0.0
+aiflows

run_planwriter.py

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+import os
+
+import hydra
+
+from aiflows.backends.api_info import ApiInfo
+from aiflows.messages import InputMessage
+from aiflows.utils.general_helpers import read_yaml_file
+
+from aiflows import logging
+from aiflows.flow_cache import CACHING_PARAMETERS, clear_cache
+from aiflows.utils.general_helpers import quick_load
+
+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": "aiflows/ContentWriterFlowModule", "revision": "main"},
+    {"url": "aiflows/InteractivePlanGenFlowModule", "revision": "main"},
+    {"url": "aiflows/PlanWriterFlowModule", "revision": "main"},
+    {"url": "aiflows/ChatFlowModule", "revision": "297c90d08087d9ff3139521f11d1a48d7dc63ed4"},
+]
+
+from aiflows 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
+
+    current_dir = os.getcwd()
+    cfg_path = os.path.join(current_dir, "PlanWriterFlow.yaml")
+    cfg = read_yaml_file(cfg_path)
+    
+    # ~~~ setting api information into config ~~~
+    quick_load(cfg, api_information)
+
+    # ~~~ 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")
+    with open(plan_file_location, 'w') as file:
+        pass
+
+    mem_files = {"plan": plan_file_location}
+
+    input_data = {
+        "goal": "create a function that adds two numbers and returns the result",
+        "memory_files": mem_files
+    }
+    input_message = InputMessage.build(
+        data_dict=input_data,
+        src_flow="Launcher",
+        dst_flow=PlanWriterFlow.name
+    )
+
+    # ~~~ calling the flow ~~~
+    output_message = PlanWriterFlow(input_message)