mirror of
https://github.com/aljazceru/dev-gpt.git
synced 2025-12-19 06:34:21 +01:00
🪓 feat: sub task refinement
This commit is contained in:
Florian Hönicke
437
dev_gpt/options/generate/pm/pm.py
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437
dev_gpt/options/generate/pm/pm.py
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import json
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import re
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from typing import Generator
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from dev_gpt.apis import gpt
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from dev_gpt.apis.gpt import ask_gpt
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from dev_gpt.options.generate.chains.condition import is_false, is_true
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from dev_gpt.options.generate.chains.get_user_input_if_neede import get_user_input_if_needed
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from dev_gpt.options.generate.parser import identity_parser, boolean_parser, json_parser
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from dev_gpt.options.generate.pm.task_tree_schema import TaskTree
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from dev_gpt.options.generate.ui import get_random_employee
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class PM:
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def refine_specification(self, microservice_description) -> TaskTree:
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pm = get_random_employee('pm')
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print(f'{pm.emoji}👋 Hi, I\'m {pm.name}, a PM at Jina AI. Gathering the requirements for our engineers.')
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original_task = microservice_description
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if not original_task:
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microservice_description = self.get_user_input(pm, 'What should your microservice do?')
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microservice_description, test_description = self.refine(microservice_description)
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print(f'''
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{pm.emoji} 👍 Great, I will handover the following requirements to our engineers:
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Description of the microservice:
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{microservice_description}
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''')
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return microservice_description, test_description
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@staticmethod
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def get_user_input(employee, prompt_to_user):
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val = input(f'{employee.emoji}❓ {prompt_to_user}\nyou: ')
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print()
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while not val:
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val = input('you: ')
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return val
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def refine(self, microservice_description) -> TaskTree:
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microservice_description, test_description = self.refine_description(microservice_description)
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return microservice_description, test_description
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# sub_task_tree = self.construct_sub_task_tree(microservice_description)
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# return sub_task_tree
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def get_nlp_fns(self, microservice_description):
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return ask_gpt(
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get_nlp_fns_prompt,
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json_parser,
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microservice_description=microservice_description
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)
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def construct_sub_task_tree(self, microservice_description):
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"""
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takes a microservice description and recursively constructs a tree of sub-tasks that need to be done to implement the microservice
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"""
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#
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# nlp_fns = self.get_nlp_fns(
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# microservice_description
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# )
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sub_task_tree_dict = ask_gpt(
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construct_sub_task_tree_prompt, json_parser,
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microservice_description=microservice_description,
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# nlp_fns=nlp_fns
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)
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reflections = ask_gpt(
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sub_task_tree_reflections_prompt, identity_parser,
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microservice_description=microservice_description,
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# nlp_fns=nlp_fns,
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sub_task_tree=sub_task_tree_dict,
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)
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solutions = ask_gpt(
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sub_task_tree_solutions_prompt, identity_parser,
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# nlp_fns=nlp_fns,
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microservice_description=microservice_description, sub_task_tree=sub_task_tree_dict,
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reflections=reflections,
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)
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sub_task_tree_updated = ask_gpt(
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sub_task_tree_update_prompt,
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json_parser,
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microservice_description=microservice_description,
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# nlp_fns=nlp_fns,
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sub_task_tree=sub_task_tree_dict, solutions=solutions
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)
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# for task_dict in self.iterate_over_sub_tasks(sub_task_tree_updated):
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# task_dict.update(self.get_additional_task_info(task_dict['task']))
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sub_task_tree = TaskTree.parse_obj(sub_task_tree_updated)
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return sub_task_tree
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def get_additional_task_info(self, sub_task_description):
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additional_info_dict = self.get_additional_infos(
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description=sub_task_description,
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parameter={
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'display_name': 'Task description',
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'text': sub_task_description,
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},
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potentially_required_information_list=[
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{
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'field_name': 'api_key',
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'display_name': 'valid API key',
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}, {
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'field_name': 'database_access',
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'display_name': 'database access',
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}, {
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'field_name': 'documentation',
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'display_name': 'documentation',
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}, {
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'field_name': 'example_api_call',
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'display_name': 'curl command or sample code for api call',
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},
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],
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)
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return additional_info_dict
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def get_additional_infos(self, description, parameter, potentially_required_information_list):
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additional_info_dict = {}
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for potentially_required_information in potentially_required_information_list:
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is_task_requiring_information = ask_gpt(
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is_task_requiring_information_template,
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boolean_parser,
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description=description,
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description_title=parameter['display_name'],
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description_text=parameter['text'],
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potentially_required_information=potentially_required_information
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)
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if is_task_requiring_information:
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generated_question = ask_gpt(
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generate_question_for_required_information_template,
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identity_parser,
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description=description,
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description_title=parameter['display_name'],
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description_text=parameter['text'],
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potentially_required_information=potentially_required_information
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)
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user_answer = input(generated_question)
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additional_info_dict[potentially_required_information] = user_answer
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return additional_info_dict
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def iterate_over_sub_tasks(self, sub_task_tree_updated):
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sub_tasks = sub_task_tree_updated['sub_tasks'] if 'sub_tasks' in sub_task_tree_updated else []
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for sub_task in sub_tasks:
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yield sub_task
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yield from self.iterate_over_sub_tasks(sub_task)
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def iterate_over_sub_tasks_pydantic(self, sub_task_tree: TaskTree) -> Generator[TaskTree, None, None]:
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sub_tasks = sub_task_tree.sub_fns
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for sub_task in sub_tasks:
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yield sub_task
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yield from self.iterate_over_sub_tasks_pydantic(sub_task)
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def refine_description(self, microservice_description):
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microservice_description = ask_gpt(better_description_prompt, identity_parser,
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microservice_description=microservice_description)
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request_schema = ask_gpt(generate_request_schema_prompt, identity_parser,
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microservice_description=microservice_description)
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response_schema = ask_gpt(generate_output_schema_prompt, identity_parser,
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microservice_description=microservice_description, request_schema=request_schema)
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# additional_specifications = self.add_additional_specifications(microservice_description, request_schema,
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# response_schema)
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microservice_description = ask_gpt(summarize_description_and_schemas_prompt, identity_parser,
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microservice_description=microservice_description,
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request_schema=request_schema,
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response_schema=response_schema,
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# additional_specifications=additional_specifications
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)
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while (user_feedback := self.get_user_feedback(microservice_description)):
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microservice_description = ask_gpt(add_feedback_prompt, identity_parser,
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microservice_description=microservice_description,
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user_feedback=user_feedback)
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test_description = ask_gpt(
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generate_test_description_prompt,
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identity_parser,
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microservice_description=microservice_description,
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request_schema=request_schema,
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response_schema=response_schema
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)
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example_file_url = get_user_input_if_needed(
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context={
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'Microservice description': microservice_description,
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'Request schema': request_schema,
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'Response schema': response_schema,
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},
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conditions=[
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is_true('Does request schema contain an example file url?'),
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is_false('Is input url specified in the description?'),
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],
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question_gen_prompt_part="Generate a question that asks for an example file url.",
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)
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if example_file_url:
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test_description += f'\nInput Example: {example_file_url}'
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return microservice_description, test_description
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def add_additional_specifications(self, microservice_description, request_schema, response_schema):
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questions = ask_gpt(
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ask_questions_prompt, identity_parser,
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microservice_description=microservice_description,
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request_schema=request_schema, response_schema=response_schema)
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additional_specifications = ask_gpt(
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answer_questions_prompt,
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identity_parser,
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microservice_description=microservice_description,
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request_schema=request_schema,
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response_schema=response_schema,
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questions=questions
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)
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return additional_specifications
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def get_user_feedback(self, microservice_description):
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while True:
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user_feedback = input(
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f'I suggest that we implement the following microservice:\n{microservice_description}\nDo you agree? [y/n]')
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if user_feedback.lower() in ['y', 'yes', 'yeah', 'yep', 'yup', 'sure', 'ok', 'okay']:
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print('Great! I will hand this over to the developers!')
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return None
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elif user_feedback.lower() in ['n', 'no', 'nope', 'nah', 'nay', 'not']:
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return input('What do you want to change?')
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# return self.refine_user_feedback(microservice_description)
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# def refine_user_feedback(self, microservice_description):
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# while True:
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# user_feedback = input('What do you want to change?')
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# if ask_gpt(is_feedback_valuable_prompt, boolean_parser, user_feedback=user_feedback,
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# microservice_description=microservice_description):
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# return user_feedback
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# else:
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# print('Sorry, I can not handle this feedback. Please formulate it more precisely.')
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client_description = '''\
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Microservice description:
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```
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{microservice_description}
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```'''
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better_description_prompt = client_description + '''
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Update the description of the Microservice to make it more precise without adding or removing information.
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Note: the output must be a list of tasks the Microservice has to perform.
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Example for the description: "return the average temperature of the 5 days weather forecast for a given location."
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1. get the 5 days weather forcast from the https://openweathermap.org/ API
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2. extract the temperature from the response
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3. calculate the average temperature'''
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# better_description_prompt = client_description + '''
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# Update the description of the Microservice to make it more precise without adding or removing information.'''
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generate_request_schema_prompt = client_description + '''
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Generate the lean request json schema of the Microservice.
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Note: If you are not sure about the details, the come up with the minimal number of parameters possible.'''
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generate_output_schema_prompt = client_description + '''
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request json schema:
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```
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{request_schema}
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```
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Generate the lean response json schema for the Microservice.
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Note: If you are not sure about the details, the come up with the minimal number of parameters possible.'''
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# If we want to activate this back, then it first needs to work. Currently, it outputs "no" for too many cases.
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# is_feedback_valuable_prompt = client_description + '''
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# User feedback:
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# ```
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# {user_feedback}
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# ```
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# Can this feedback be used to update the microservice description?
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# Note: You must either answer "yes" or "no".
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# Note: If the user does not want to provide feedback, then you must answer "no".'''
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summarize_description_and_schemas_prompt = client_description + '''
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Request json schema:
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```
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{request_schema}
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```
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Response json schema:
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```
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{response_schema}
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```
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Write an updated microservice description by incorporating information about the request and response parameters in a concise way without losing any information.
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Note: You must not mention any details about algorithms or the technical implementation.
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Note: You must not mention that there is a request and response JSON schema
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Note: You must not use any formatting like triple backticks.'''
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add_feedback_prompt = client_description + '''
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User feedback:
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```
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{user_feedback}
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```
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Update the microservice description by incorporating the user feedback in a concise way without losing any information.'''
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summarize_description_prompt = client_description + '''
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Make the description more concise without losing any information.
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Note: You must not mention any details about algorithms or the technical implementation.
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Note: You must ignore facts that are not specified.
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Note: You must ignore facts that are not relevant.
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Note: You must ignore facts that are unknown.
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Note: You must ignore facts that are unclear.'''
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construct_sub_task_tree_prompt = client_description + '''
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Recursively constructs a tree of functions that need to be implemented for the endpoint_function that retrieves a json string and returns a json string.
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Example:
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Input: "Input: list of integers, Output: Audio file of short story where each number is mentioned exactly once."
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Output:
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{{
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"description": "Create an audio file containing a short story in which each integer from the provided list is seamlessly incorporated, ensuring that every integer is mentioned exactly once.",
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"python_fn_signature": "def generate_integer_story_audio(numbers: List[int]) -> str:",
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"sub_fns": [
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{{
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"description": "Generate sentence from integer.",
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"python_fn_signature": "def generate_sentence_from_integer(number: int) -> int:",
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"sub_fns": []
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}},
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{{
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"description": "Convert the story into an audio file.",
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"python_fn_signature": "def convert_story_to_audio(story: str) -> bytes:",
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"sub_fns": []
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}}
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]
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}}
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Note: you must only output the json string - nothing else.
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Note: you must pretty print the json string.'''
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sub_task_tree_reflections_prompt = client_description + '''
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Sub task tree:
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```
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{sub_task_tree}
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```
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Write down 3 arguments why the sub task tree might not perfectly represents the information mentioned in the microservice description. (5 words per argument)'''
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sub_task_tree_solutions_prompt = client_description + '''
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Sub task tree:
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```
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{sub_task_tree}
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```
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Reflections:
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```
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{reflections}
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```
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For each constructive criticism, write a solution (5 words) that address the criticism.'''
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sub_task_tree_update_prompt = client_description + '''
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Sub task tree:
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```
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{sub_task_tree}
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```
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Solutions:
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```
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{solutions}
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```
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Update the sub task tree by applying the solutions. (pretty print the json string)'''
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ask_questions_prompt = client_description + '''
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Request json schema:
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```
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{request_schema}
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```
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Response json schema:
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```
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{response_schema}
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```
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Ask the user up to 5 unique detailed questions (5 words) about the microservice description that are not yet answered.
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'''
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answer_questions_prompt = client_description + '''
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Request json schema:
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```
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{request_schema}
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```
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Response json schema:
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```
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{response_schema}
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```
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Questions:
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```
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{questions}
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```
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Answer all questions where you can think of a plausible answer.
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Note: You must not answer questions with something like "...is not specified", "I don't know" or "Unknown".
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'''
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is_task_requiring_information_template = '''\
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{description_title}
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```
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{description_text}
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```
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Does the implementation of the {description_title} require information about "{potentially_required_information}"?
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Note: You must either answer "yes" or "no".'''
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generate_question_for_required_information_template = '''\
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{description_title}
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```
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{description_text}
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```
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Generate a question that asks for the information "{potentially_required_information}" regarding "{description_title}".
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Note: you must only output the question - nothing else.'''
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get_nlp_fns_prompt = client_description + '''
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Respond with all code parts that could be accomplished by GPT 3.
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Example for "Take a video and/or a pdf as input, extract the subtitles from the video and the text from the pdf, \
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summarize the extracted text and translate it to German":
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```
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[
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"summarize the text",
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"translate the text to German"
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]
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```
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Note: only list code parts that could be expressed as a function that takes a string as input and returns a string as output.
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Note: the output must be parsable by the python function json.loads.'''
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generate_test_description_prompt = client_description + '''
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Request json schema:
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```
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{request_schema}
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```
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Response json schema:
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```
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{response_schema}
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```
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Generate the description of the test scenario for the microservice.
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Note: you must only output the test description - nothing else.
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Note: you must not use any formatting like triple backticks.
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Note: the test must insert data in defined in the request schema and validate that the type of the response is matching with the response schema.
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'''
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if __name__ == '__main__':
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gpt_session = gpt.GPTSession('GPT-3.5-turbo')
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first_question = 'Please specify your microservice.'
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initial_description = 'mission generator'
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# initial_description = 'convert png to svg'
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# initial_description = "Input is a list of emails. For all the companies from the emails belonging to, it gets the company's logo. All logos are arranged in a collage and returned."
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# initial_description = "Given an image, write a joke on it that is relevant to the image."
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# initial_description = "This microservice receives an image as input and generates a joke based on its content and context. The input must be a binary string of the image. The output is an image with the generated joke overlaid on it."
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initial_description = 'Build me a serch system for lottiefiles animations'
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PM().refine(initial_description)
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# PM(gpt_session).construct_sub_task_tree(initial_description)#.refine(initial_description)
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Block a user