mirror of
https://github.com/aljazceru/dev-gpt.git
synced 2025-12-19 06:34:21 +01:00
refactor: cleanup
This commit is contained in:
Florian Hönicke
@@ -1,18 +1,15 @@
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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.chains.user_confirmation_feedback_loop import user_feedback_loop
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from dev_gpt.options.generate.condition import is_false, is_true
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from dev_gpt.options.generate.chains.get_user_input_if_needed import get_user_input_if_needed
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from dev_gpt.options.generate.parser import identity_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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def refine_specification(self, microservice_description):
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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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@@ -34,152 +31,29 @@ Description of the microservice:
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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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def refine(self, microservice_description):
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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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context = {
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'microservice_description': microservice_description
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}
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self.auto_refine_description(context)
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user_feedback_loop(context, microservice_description)
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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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**context
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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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'Request schema': context['request_schema'],
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'Response schema': context['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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@@ -192,30 +66,151 @@ Description of the microservice:
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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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def auto_refine_description(self, context):
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context['microservice_description'] = ask_gpt(
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better_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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questions=questions
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**context
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)
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context['request_schema'] = ask_gpt(
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generate_request_schema_prompt,
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identity_parser,
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**context
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)
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context['response_schema'] = ask_gpt(
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generate_output_schema_prompt,
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identity_parser,
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**context
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)
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context['microservice_description'] = ask_gpt(
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summarize_description_and_schemas_prompt, identity_parser,
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**context
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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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# 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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#
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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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#
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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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#
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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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# )
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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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#
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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 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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# return self.refine_user_feedback(microservice_description)
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# def refine_user_feedback(self, microservice_description):
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@@ -247,7 +242,7 @@ Example for the description: "return the average temperature of the 5 days weath
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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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Note: If you are not sure about the details, then 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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@@ -255,7 +250,7 @@ request json schema:
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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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Note: If you are not sure about the details, then 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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@@ -282,132 +277,127 @@ Note: You must not mention any details about algorithms or the technical impleme
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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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# 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.
|
||||
# Note: You must ignore facts that are not specified.
|
||||
# Note: You must ignore facts that are not relevant.
|
||||
# Note: You must ignore facts that are unknown.
|
||||
# Note: You must ignore facts that are unclear.'''
|
||||
|
||||
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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# construct_sub_task_tree_prompt = client_description + '''
|
||||
# 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.
|
||||
# Example:
|
||||
# Input: "Input: list of integers, Output: Audio file of short story where each number is mentioned exactly once."
|
||||
# Output:
|
||||
# {{
|
||||
# "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.",
|
||||
# "python_fn_signature": "def generate_integer_story_audio(numbers: List[int]) -> str:",
|
||||
# "sub_fns": [
|
||||
# {{
|
||||
# "description": "Generate sentence from integer.",
|
||||
# "python_fn_signature": "def generate_sentence_from_integer(number: int) -> int:",
|
||||
# "sub_fns": []
|
||||
# }},
|
||||
# {{
|
||||
# "description": "Convert the story into an audio file.",
|
||||
# "python_fn_signature": "def convert_story_to_audio(story: str) -> bytes:",
|
||||
# "sub_fns": []
|
||||
# }}
|
||||
# ]
|
||||
# }}
|
||||
#
|
||||
# Note: you must only output the json string - nothing else.
|
||||
# Note: you must pretty print the json string.'''
|
||||
|
||||
sub_task_tree_reflections_prompt = client_description + '''
|
||||
Sub task tree:
|
||||
```
|
||||
{sub_task_tree}
|
||||
```
|
||||
Write down 3 arguments why the sub task tree might not perfectly represents the information mentioned in the microservice description. (5 words per argument)'''
|
||||
# sub_task_tree_reflections_prompt = client_description + '''
|
||||
# Sub task tree:
|
||||
# ```
|
||||
# {sub_task_tree}
|
||||
# ```
|
||||
# Write down 3 arguments why the sub task tree might not perfectly represents the information mentioned in the microservice description. (5 words per argument)'''
|
||||
#
|
||||
# sub_task_tree_solutions_prompt = client_description + '''
|
||||
# Sub task tree:
|
||||
# ```
|
||||
# {sub_task_tree}
|
||||
# ```
|
||||
# Reflections:
|
||||
# ```
|
||||
# {reflections}
|
||||
# ```
|
||||
# For each constructive criticism, write a solution (5 words) that address the criticism.'''
|
||||
#
|
||||
# sub_task_tree_update_prompt = client_description + '''
|
||||
# Sub task tree:
|
||||
# ```
|
||||
# {sub_task_tree}
|
||||
# ```
|
||||
# Solutions:
|
||||
# ```
|
||||
# {solutions}
|
||||
# ```
|
||||
# Update the sub task tree by applying the solutions. (pretty print the json string)'''
|
||||
#
|
||||
# ask_questions_prompt = client_description + '''
|
||||
# Request json schema:
|
||||
# ```
|
||||
# {request_schema}
|
||||
# ```
|
||||
# Response json schema:
|
||||
# ```
|
||||
# {response_schema}
|
||||
# ```
|
||||
# Ask the user up to 5 unique detailed questions (5 words) about the microservice description that are not yet answered.
|
||||
# '''
|
||||
|
||||
sub_task_tree_solutions_prompt = client_description + '''
|
||||
Sub task tree:
|
||||
```
|
||||
{sub_task_tree}
|
||||
```
|
||||
Reflections:
|
||||
```
|
||||
{reflections}
|
||||
```
|
||||
For each constructive criticism, write a solution (5 words) that address the criticism.'''
|
||||
# answer_questions_prompt = client_description + '''
|
||||
# Request json schema:
|
||||
# ```
|
||||
# {request_schema}
|
||||
# ```
|
||||
# Response json schema:
|
||||
# ```
|
||||
# {response_schema}
|
||||
# ```
|
||||
# Questions:
|
||||
# ```
|
||||
# {questions}
|
||||
# ```
|
||||
# Answer all questions where you can think of a plausible answer.
|
||||
# Note: You must not answer questions with something like "...is not specified", "I don't know" or "Unknown".
|
||||
# '''
|
||||
|
||||
sub_task_tree_update_prompt = client_description + '''
|
||||
Sub task tree:
|
||||
```
|
||||
{sub_task_tree}
|
||||
```
|
||||
Solutions:
|
||||
```
|
||||
{solutions}
|
||||
```
|
||||
Update the sub task tree by applying the solutions. (pretty print the json string)'''
|
||||
# is_task_requiring_information_template = '''\
|
||||
# {description_title}
|
||||
# ```
|
||||
# {description_text}
|
||||
# ```
|
||||
# Does the implementation of the {description_title} require information about "{potentially_required_information}"?
|
||||
# Note: You must either answer "yes" or "no".'''
|
||||
|
||||
ask_questions_prompt = client_description + '''
|
||||
Request json schema:
|
||||
```
|
||||
{request_schema}
|
||||
```
|
||||
Response json schema:
|
||||
```
|
||||
{response_schema}
|
||||
```
|
||||
Ask the user up to 5 unique detailed questions (5 words) about the microservice description that are not yet answered.
|
||||
'''
|
||||
# generate_question_for_required_information_template = '''\
|
||||
# {description_title}
|
||||
# ```
|
||||
# {description_text}
|
||||
# ```
|
||||
# Generate a question that asks for the information "{potentially_required_information}" regarding "{description_title}".
|
||||
# Note: you must only output the question - nothing else.'''
|
||||
|
||||
answer_questions_prompt = client_description + '''
|
||||
Request json schema:
|
||||
```
|
||||
{request_schema}
|
||||
```
|
||||
Response json schema:
|
||||
```
|
||||
{response_schema}
|
||||
```
|
||||
Questions:
|
||||
```
|
||||
{questions}
|
||||
```
|
||||
Answer all questions where you can think of a plausible answer.
|
||||
Note: You must not answer questions with something like "...is not specified", "I don't know" or "Unknown".
|
||||
'''
|
||||
|
||||
is_task_requiring_information_template = '''\
|
||||
{description_title}
|
||||
```
|
||||
{description_text}
|
||||
```
|
||||
Does the implementation of the {description_title} require information about "{potentially_required_information}"?
|
||||
Note: You must either answer "yes" or "no".'''
|
||||
|
||||
generate_question_for_required_information_template = '''\
|
||||
{description_title}
|
||||
```
|
||||
{description_text}
|
||||
```
|
||||
Generate a question that asks for the information "{potentially_required_information}" regarding "{description_title}".
|
||||
Note: you must only output the question - nothing else.'''
|
||||
|
||||
get_nlp_fns_prompt = client_description + '''
|
||||
Respond with all code parts that could be accomplished by GPT 3.
|
||||
Example for "Take a video and/or a pdf as input, extract the subtitles from the video and the text from the pdf, \
|
||||
summarize the extracted text and translate it to German":
|
||||
```
|
||||
[
|
||||
"summarize the text",
|
||||
"translate the text to German"
|
||||
]
|
||||
```
|
||||
Note: only list code parts that could be expressed as a function that takes a string as input and returns a string as output.
|
||||
Note: the output must be parsable by the python function json.loads.'''
|
||||
# get_nlp_fns_prompt = client_description + '''
|
||||
# Respond with all code parts that could be accomplished by GPT 3.
|
||||
# Example for "Take a video and/or a pdf as input, extract the subtitles from the video and the text from the pdf, \
|
||||
# summarize the extracted text and translate it to German":
|
||||
# ```
|
||||
# [
|
||||
# "summarize the text",
|
||||
# "translate the text to German"
|
||||
# ]
|
||||
# ```
|
||||
# Note: only list code parts that could be expressed as a function that takes a string as input and returns a string as output.
|
||||
# Note: the output must be parsable by the python function json.loads.'''
|
||||
|
||||
generate_test_description_prompt = client_description + '''
|
||||
Request json schema:
|
||||
|
||||
Reference in New Issue
Block a user