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https://github.com/aljazceru/dev-gpt.git
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🪓 feat: sub task refinement
This commit is contained in:
Florian Hönicke
@@ -18,6 +18,7 @@ from dev_gpt.constants import FILE_AND_TAG_PAIRS, NUM_IMPLEMENTATION_STRATEGIES,
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BLACKLISTED_PACKAGES, EXECUTOR_FILE_NAME, TEST_EXECUTOR_FILE_NAME, TEST_EXECUTOR_FILE_TAG, \
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REQUIREMENTS_FILE_NAME, REQUIREMENTS_FILE_TAG, DOCKER_FILE_NAME, IMPLEMENTATION_FILE_NAME, \
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IMPLEMENTATION_FILE_TAG, LANGUAGE_PACKAGES, UNNECESSARY_PACKAGES, DOCKER_BASE_IMAGE_VERSION
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from dev_gpt.options.generate.pm import PM
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from dev_gpt.options.generate.templates_system import system_task_iteration, system_task_introduction, system_test_iteration
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from dev_gpt.options.generate.templates_user import template_generate_microservice_name, \
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template_generate_possible_packages, \
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@@ -467,20 +468,8 @@ dev-gpt deploy --path {self.microservice_root_path}
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if not original_task:
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self.microservice_specification.task = self.get_user_input(pm, 'What should your microservice do?')
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self.refine_requirements(
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pm,
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[
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SystemMessage(content=system_task_introduction + system_task_iteration),
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],
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'task',
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'',
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template_pm_task_iteration,
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micro_service_initial_description=f'''Microservice description:
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```
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{self.microservice_specification.task}
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```
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''',
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)
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self.microservice_specification.task = PM(self.gpt_session).refine(self.microservice_specification.task)
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self.refine_requirements(
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pm,
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[
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245
dev_gpt/options/generate/pm.py
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245
dev_gpt/options/generate/pm.py
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@@ -0,0 +1,245 @@
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import json
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import os
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import re
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from dev_gpt.apis import gpt
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class PM:
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def __init__(self, gpt_session):
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self.gpt_session = gpt_session
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def refine(self, microservice_description):
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microservice_description = self.refine_description(microservice_description)
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sub_task_tree = self.construct_sub_task_tree(microservice_description)
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def construct_sub_task_tree(self, microservice_description):
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"""
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takes a microservice description an recursively constructs a tree of sub-tasks that need to be done to implement the microservice
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Example1:
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Input: "I want to implement a microservice that takes a list of numbers and returns the sum of the numbers"
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Output:
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[
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{
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"task": "I want to implement a microservice that takes a list of numbers and returns the sum of the numbers",
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"request_json_schema": {
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"type": "array",
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"items": {
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"type": "number"
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}
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},
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"response_json_schema": {
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"type": "number"
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},
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"sub_tasks": [
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{
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"task": "Calculate the sum of the numbers",
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"python_fn_signature": "def calculate_sum(numbers: List[float]) -> float:",
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"python_fn_docstring": "Calculates the sum of the numbers\n\nArgs:\n numbers: a list of numbers\n\nReturns:\n the sum of the numbers",",
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"sub_tasks": []
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},
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]
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}
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]
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Example2: "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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[
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{
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"task": "Extract company domains from the list of emails",
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"sub_tasks": []
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},
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{
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"task": "Retrieve company logos for the extracted domains",
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"sub_tasks": [
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{
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"task": "Find logo URL for each company domain",
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"sub_tasks": []
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},
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{
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"task": "Download company logos from the URLs",
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"sub_tasks": []
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}
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]
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},
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{
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"task": "Create a collage of company logos",
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"sub_tasks": [
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{
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"task": "Determine collage layout and dimensions",
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"sub_tasks": []
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},
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{
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"task": "Position and resize logos in the collage",
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"sub_tasks": []
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},
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{
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"task": "Combine logos into a single image",
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"sub_tasks": []
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}
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]
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},
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{
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"task": "Return the collage of company logos",
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"sub_tasks": []
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}
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]
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"""
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sub_task_tree = self.ask_gpt(construct_sub_task_tree_prompt, json_parser, microservice_description=microservice_description)
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return sub_task_tree
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def refine_description(self, microservice_description):
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request_schema = self.ask_gpt(generate_request_schema_prompt, identity_parser,
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microservice_description=microservice_description)
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response_schema = self.ask_gpt(generate_output_schema_prompt, identity_parser,
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microservice_description=microservice_description, request_schema=request_schema)
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microservice_description = self.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, response_schema=response_schema)
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while (user_feedback := self.get_user_feedback(microservice_description)):
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microservice_description = self.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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return microservice_description
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def get_user_feedback(self, microservice_description):
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while True:
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user_feedback = input(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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# Prompting
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def ask_gpt(self, prompt_template, parser, **kwargs):
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prompt = prompt_template.format(**kwargs)
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conversation = self.gpt_session.get_conversation(
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[],
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print_stream=os.environ['VERBOSE'].lower() == 'true',
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print_costs=False
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)
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agent_response_raw = conversation.chat(prompt, role='user')
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agent_response = parser(agent_response_raw)
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return agent_response
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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 self.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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def identity_parser(x):
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return x
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def boolean_parser(x):
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return 'yes' in x.lower()
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def json_parser(x):
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if '```' in x:
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pattern = r'```(.+)```'
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x = re.findall(pattern, x, re.DOTALL)[-1]
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return json.loads(x)
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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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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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Update the 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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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 sub-tasks that need to be done to implement the microservice
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Example1:
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Input: "I want to implement a microservice that takes a list of numbers and returns the sum of the numbers"
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Output:
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[
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{{
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"task": "I want to implement a microservice that takes a list of numbers and returns the sum of the numbers",
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"request_json_schema": {{
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"type": "array",
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"items": {{
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"type": "number"
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}}
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}},
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"response_json_schema": {{
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"type": "number"
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}},
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"sub_tasks": [
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{{
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"task": "Calculate the sum of the numbers",
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"python_fn_signature": "def calculate_sum(numbers: List[float]) -> float:",
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"python_fn_docstring": "Calculates the sum of the numbers\\n\\nArgs:\\n numbers: a list of numbers\\n\\nReturns:\\n the sum of the numbers",
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"sub_tasks": []
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}}
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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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if __name__ == '__main__':
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gpt_session = gpt.GPTSession(None, '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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PM(gpt_session).refine(initial_description)
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# PM(gpt_session).construct_sub_task_tree(initial_description)#.refine(initial_description)
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