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CodeWriterAskUserFlow.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 CodeWriterAskUserFlow(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["human_feedback"] = human_input
+        response["code"] = "no code was written"
+
+        return response

CodeWriterAskUserFlow.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"

CodeWriterCtrlFlow.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 CodeWriterCtrlFlow(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 code writer, the tester, or to finish",
+              "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)

CodeWriterCtrlFlow.yaml

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+name: "CodeWriterControllerFlow"
+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:
+  - "code"
+  - "feedback"
+
+#######################################################
+# Output keys
+#######################################################
+
+output_interface:
+  - 'command'
+  - 'command_args'
+
+backend:
+  api_infos: ???
+  model_name:
+    openai: gpt-4
+    azure: azure/gpt-4
+
+commands:
+  write_code:
+    description: "Write code to finish the goal with user interaction"
+    input_args: ["goal"]
+  finish:
+    description: "Signal that the objective has been satisfied, and returns the answer to the user."
+    input_args: []
+  ask_user:
+    description: "Ask user a question for confirmation or assistance"
+    input_args: ["question"]
+  test:
+    description: "test the code generated from write_code"
+    input_args: []
+
+system_message_prompt_template:
+  _target_: langchain.PromptTemplate
+  template: |2-
+          You are in charge of a department of writing code to solve a certain goal. You work with a coder, who does all the coding job; and a code tester, who does all the testing job.
+    
+          Your **ONLY** task is to take the user's goal for you, to decide whether to call the coder to write or re-write the code, to call the tester to test the code, or to finish the current task.
+    
+          When you need to call the code writer, call the `write_code` command with the goal specified.
+          When you need to call the code tester, call the `test` command to test the code written.
+          When the code is written and the user is satisfied, call the `finish` command to terminate the current process.
+          Whenever you are in doubt, or need to confirm something to the user, call `ask_user` with the question.
+          
+          The coder will only write one function per goal, make sure you are not asking the coder to write more than one function.
+    
+          You **must not** write code yourself. You only decide whether to call the coder with specified goals or to finish.
+    
+          Your workflow:
+          1. Upon user request, call the `write_code` with the goal given.
+          2. The coder will write code, which is a function. The user will examine the code, and provide feedback.
+          3. Depending on the feedback of the user:
+            3.1. The user provides feedback on how to change the code, **call the coder with user's specific requirements again, to ask the coder to refine the code**. Go back to step 2.
+            3.2. The user does not provide details about refining the code, for example, just stating the fact that the user has updated the code, **this means the user is satisfied with the code written, call the `finish` command.**
+          4. If the user is satisfied with the code, call `test` to test the code
+          5. Depending on the result of the test:
+            5.1 Test passes, terminate the current process with the `finish command`
+            5.2 Test fails, **call the coder with details of the test results to instruct the coder to refine the code**, go back to step 2.
+          
+          If you have completed all your tasks, make sure to use the "finish" command.
+    
+          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 code writer, the tester, 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 code written by the coder, it might have been updated by the user, depending on the user's feedback:
+    {{code}}
+    Here is the feedback, depending on the last command you called, it either came from the user or the tester:
+    {{feedback}}
+  input_variables:
+    - "code"
+    - "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

CodeWriterFlow.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 CodeWriterFlow(ContentWriterFlow):
+    def _on_reach_max_round(self):
+        self._state_update_dict({
+            "code": "The maximum amount of rounds was reached before the model generated the code.",
+            "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":
+            # ~~~ delete the temp code file ~~~
+            keys_to_fetch_from_state = ["temp_code_file_location", "code"]
+            fetched_state = self._fetch_state_attributes_by_keys(keys=keys_to_fetch_from_state)
+            temp_code_file_location = fetched_state["temp_code_file_location"]
+            code_content = fetched_state["code"]
+            if os.path.exists(temp_code_file_location):
+                os.remove(temp_code_file_location)
+            # ~~~ return the code content ~~~
+            return {
+                "EARLY_EXIT": True,
+                "code": code_content,
+                "status": "finished"
+            }
+        elif command == "test":
+            # ~~~ fetch code string from flow state ~~~
+            keys_to_fetch_from_state = ["code"]
+            fetched_state = self._fetch_state_attributes_by_keys(keys=keys_to_fetch_from_state)
+
+            # ~~~ add code content to the command args (branch input data) ~~~
+            code_content = fetched_state["code"]
+            output_payload["command_args"]["code"] = code_content
+
+            return output_payload
+        else:
+            return output_payload

CodeWriterFlow.yaml

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+name: "CodeWriter"
+description: "Generates code with interactions with the user"
+
+_target_: Tachi67.CodeWriterFlowModule.CodeWriterFlow.instantiate_from_default_config
+
+output_interface:
+  - "code"
+  - "status"
+
+### Subflows specification
+subflows_config:
+  Controller:
+    _target_: Tachi67.CodeWriterFlowModule.CodeWriterCtrlFlow.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_code:
+        _target_: Tachi67.InteractiveCodeGenFlowModule.InteractiveCodeGenFlow.instantiate_from_default_config
+        subflows_config:
+          MemoryReading:
+            _target_: Tachi67.MemoryReadingFlowModule.MemoryReadingAtomicFlow.instantiate_from_default_config
+
+          CodeGenerator:
+            _target_: Tachi67.CodeGeneratorFlowModule.CodeGeneratorAtomicFlow.instantiate_from_default_config
+            backend:
+              api_infos: ???
+              model_name:
+                openai: gpt-4
+                azure: azure/gpt-4
+
+          CodeFileEditor:
+            _target_: Tachi67.CodeFileEditFlowModule.CodeFileEditAtomicFlow.instantiate_from_default_config
+
+          ParseFeedback:
+            _target_: Tachi67.ParseFeedbackFlowModule.ParseFeedbackAtomicFlow.instantiate_from_default_config
+
+      ask_user:
+        _target_: Tachi67.CodeWriterFlowModule.CodeWriterAskUserFlow.instantiate_from_default_config
+
+      test:
+        _target_: Tachi67.TestCodeFlowModule.TestCodeFlow.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_: CodeWriterFlow.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/InteractiveCodeGenFlowModule", "revision": "main"},
+    {"url": "Tachi67/TestCodeFlowModule", "revision": "main"},
+    {"url": "aiflows/ChatFlowModule", "revision": "a749ad10ed39776ba6721c37d0dc22af49ca0f17"},
+    {"url": "aiflows/HumanStandardInputFlowModule", "revision": "5683a922372c5fa90be9f6447d6662d8d80341fc"}
+]
+from flows import flow_verse
+
+flow_verse.sync_dependencies(dependencies)
+
+from .CodeWriterFlow import CodeWriterFlow
+from .CodeWriterCtrlFlow import CodeWriterCtrlFlow
+from .CodeWriterAskUserFlow import CodeWriterAskUserFlow