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https://github.com/aljazceru/dev-gpt.git
synced 2025-12-19 14:44:20 +01:00
🔎 feat: search fix web search
This commit is contained in:
Florian Hönicke
@@ -23,7 +23,7 @@ from dev_gpt.options.generate.templates_user import template_generate_microservi
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template_generate_possible_packages, \
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template_implement_solution_code_issue, \
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template_solve_pip_dependency_issue, template_is_dependency_issue, template_generate_playground, \
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template_generate_function, template_generate_test, template_generate_requirements, \
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template_generate_function_constructor, template_generate_test, template_generate_requirements, \
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template_chain_of_thought, template_summarize_error, \
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template_solve_apt_get_dependency_issue, \
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template_suggest_solutions_code_issue, template_was_error_seen_before, \
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@@ -197,9 +197,11 @@ metas:
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with open(os.path.join(os.path.dirname(__file__), 'static_files', 'microservice', 'apis.py'), 'r', encoding='utf-8') as f:
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persist_file(f.read(), os.path.join(self.cur_microservice_path, 'apis.py'))
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is_using_gpt_3_5_turbo = 'gpt-3-5-turbo' in packages
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is_using_google_custom_search = 'google-custom-search' in packages
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microservice_content = self.generate_and_persist_file(
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section_title='Microservice',
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template=template_generate_function,
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template=template_generate_function_constructor(is_using_gpt_3_5_turbo, is_using_google_custom_search),
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microservice_description=self.microservice_specification.task,
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test_description=self.microservice_specification.test,
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packages=packages,
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@@ -5,7 +5,8 @@ from dev_gpt.options.generate.chains.question_answering import is_question_true
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from dev_gpt.options.generate.chains.translation import translation
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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.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.parser import identity_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.prompt_factory import make_prompt_friendly
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from dev_gpt.options.generate.ui import get_random_employee
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@@ -35,9 +36,9 @@ Description of the microservice:
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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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# sub_task_tree = construct_sub_task_tree(microservice_description)
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# return sub_task_tree
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return microservice_description, test_description
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def refine_description(self, microservice_description):
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context = {'microservice_description': microservice_description}
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@@ -128,44 +129,44 @@ Example:
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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 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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@@ -281,71 +282,71 @@ Example:
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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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#
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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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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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# 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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#
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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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#
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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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#
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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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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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@@ -1,22 +1,22 @@
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# from typing import Dict, List, Union, Optional
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# from pydantic import BaseModel, Field
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#
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# class JSONSchema(BaseModel):
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# type: str
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# format: Union[str, None] = None
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# items: Union['JSONSchema', None] = None
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# properties: Dict[str, 'JSONSchema'] = Field(default_factory=dict)
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# additionalProperties: Union[bool, 'JSONSchema'] = True
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# required: List[str] = Field(default_factory=list)
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#
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# class Config:
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# arbitrary_types_allowed = True
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#
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# class TaskTree(BaseModel):
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# description: Optional[str]
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# python_fn_signature: str
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# sub_fns: List['TaskTree']
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#
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# JSONSchema.update_forward_refs()
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# TaskTree.update_forward_refs()
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from typing import Dict, List, Union, Optional
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from pydantic import BaseModel, Field
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class JSONSchema(BaseModel):
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type: str
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format: Union[str, None] = None
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items: Union['JSONSchema', None] = None
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properties: Dict[str, 'JSONSchema'] = Field(default_factory=dict)
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additionalProperties: Union[bool, 'JSONSchema'] = True
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required: List[str] = Field(default_factory=list)
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class Config:
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arbitrary_types_allowed = True
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class TaskTree(BaseModel):
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description: Optional[str]
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python_fn_signature: str
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sub_fns: List['TaskTree']
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JSONSchema.update_forward_refs()
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TaskTree.update_forward_refs()
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#
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@@ -126,7 +126,8 @@ image_url_list = search_images('<search term>', top_n=10)
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```
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"""
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template_generate_function = PromptTemplate.from_template(
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def template_generate_function_constructor(is_using_gpt_3_5_turbo, is_using_google_custom_search):
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return PromptTemplate.from_template(
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general_guidelines_string + f'''
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Write a python function which receives as \
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@@ -151,10 +152,10 @@ Your approach:
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2. Think about solutions for these challenges.
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3. Decide for one of the solutions.
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4. Write the code for the function. Don't write code for the test.
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{gpt_35_turbo_usage_string}
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{google_custom_search_usage_string}
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{gpt_35_turbo_usage_string if is_using_gpt_3_5_turbo else ''}
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{google_custom_search_usage_string if is_using_google_custom_search else ''}
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{template_code_wrapping_string}'''
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)
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)
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template_generate_test = PromptTemplate.from_template(
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