mirror of
https://github.com/aljazceru/dev-gpt.git
synced 2025-12-22 16:14:20 +01:00
feat: uncomment
This commit is contained in:
Florian Hönicke
146
micro_chain.py
146
micro_chain.py
@@ -18,85 +18,83 @@ def wrap_content_in_code_block(executor_content, file_name, tag):
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def main(
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executor_description,
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input_modality,
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# input_doc_field,
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output_modality,
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# output_doc_field,
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test_scenario,
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do_validation=True
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):
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# input_doc_field = 'text' if input_modality == 'text' else 'blob'
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# output_doc_field = 'text' if output_modality == 'text' else 'blob'
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# # random integer at the end of the executor name to avoid name clashes
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# executor_name = f'MicroChainExecutor{random.randint(0, 1000_000)}'
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# recreate_folder('executor')
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# EXECUTOR_FOLDER_v1 = 'executor/v1'
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# recreate_folder(EXECUTOR_FOLDER_v1)
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# recreate_folder('flow')
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#
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# print_colored('', '############# Executor #############', 'red')
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# user_query = (
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# general_guidelines()
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# + executor_file_task(executor_name, executor_description, input_modality, input_doc_field,
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# output_modality, output_doc_field)
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# + chain_of_thought_creation()
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# )
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# conversation = gpt.Conversation()
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# conversation.query(user_query)
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# executor_content_raw = conversation.query(chain_of_thought_optimization('python', 'executor.py'))
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# executor_content = extract_content_from_result(executor_content_raw, 'executor.py')
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# persist_file(executor_content, 'executor.py')
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#
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# print_colored('', '############# Test Executor #############', 'red')
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# user_query = (
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# general_guidelines()
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# + wrap_content_in_code_block(executor_content, 'executor.py', 'python')
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# + test_executor_file_task(executor_name, test_scenario)
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# )
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# conversation = gpt.Conversation()
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# conversation.query(user_query)
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# test_executor_content_raw = conversation.query(
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# chain_of_thought_optimization('python', 'test_executor.py')
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# + "Don't add any additional tests. "
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# )
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# test_executor_content = extract_content_from_result(test_executor_content_raw, 'test_executor.py')
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# persist_file(test_executor_content, 'test_executor.py')
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#
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# print_colored('', '############# Requirements #############', 'red')
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# user_query = (
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# general_guidelines()
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# + wrap_content_in_code_block(executor_content, 'executor.py', 'python')
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# + wrap_content_in_code_block(test_executor_content, 'test_executor.py', 'python')
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# + requirements_file_task()
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# )
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# conversation = gpt.Conversation()
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# conversation.query(user_query)
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# requirements_content_raw = conversation.query(chain_of_thought_optimization('', 'requirements.txt') + "Keep the same version of jina ")
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#
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# requirements_content = extract_content_from_result(requirements_content_raw, 'requirements.txt')
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# persist_file(requirements_content, 'requirements.txt')
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#
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# print_colored('', '############# Dockerfile #############', 'red')
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# user_query = (
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# general_guidelines()
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# + wrap_content_in_code_block(executor_content, 'executor.py', 'python')
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# + wrap_content_in_code_block(test_executor_content, 'test_executor.py', 'python')
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# + wrap_content_in_code_block(requirements_content, 'requirements.txt', '')
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# + docker_file_task()
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# )
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# conversation = gpt.Conversation()
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# conversation.query(user_query)
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# dockerfile_content_raw = conversation.query(chain_of_thought_optimization('dockerfile', 'Dockerfile'))
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# dockerfile_content = extract_content_from_result(dockerfile_content_raw, 'Dockerfile')
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# persist_file(dockerfile_content, 'Dockerfile')
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#
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# write_config_yml(executor_name, EXECUTOR_FOLDER_v1)
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input_doc_field = 'text' if input_modality == 'text' else 'blob'
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output_doc_field = 'text' if output_modality == 'text' else 'blob'
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# random integer at the end of the executor name to avoid name clashes
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executor_name = f'MicroChainExecutor{random.randint(0, 1000_000)}'
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recreate_folder('executor')
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EXECUTOR_FOLDER_v1 = 'executor/v1'
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recreate_folder(EXECUTOR_FOLDER_v1)
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recreate_folder('flow')
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print_colored('', '############# Executor #############', 'red')
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user_query = (
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general_guidelines()
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+ executor_file_task(executor_name, executor_description, input_modality, input_doc_field,
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output_modality, output_doc_field)
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+ chain_of_thought_creation()
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)
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conversation = gpt.Conversation()
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conversation.query(user_query)
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executor_content_raw = conversation.query(chain_of_thought_optimization('python', 'executor.py'))
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executor_content = extract_content_from_result(executor_content_raw, 'executor.py')
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persist_file(executor_content, 'executor.py')
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print_colored('', '############# Test Executor #############', 'red')
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user_query = (
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general_guidelines()
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+ wrap_content_in_code_block(executor_content, 'executor.py', 'python')
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+ test_executor_file_task(executor_name, test_scenario)
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)
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conversation = gpt.Conversation()
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conversation.query(user_query)
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test_executor_content_raw = conversation.query(
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chain_of_thought_optimization('python', 'test_executor.py')
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+ "Don't add any additional tests. "
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)
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test_executor_content = extract_content_from_result(test_executor_content_raw, 'test_executor.py')
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persist_file(test_executor_content, 'test_executor.py')
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print_colored('', '############# Requirements #############', 'red')
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user_query = (
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general_guidelines()
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+ wrap_content_in_code_block(executor_content, 'executor.py', 'python')
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+ wrap_content_in_code_block(test_executor_content, 'test_executor.py', 'python')
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+ requirements_file_task()
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)
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conversation = gpt.Conversation()
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conversation.query(user_query)
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requirements_content_raw = conversation.query(chain_of_thought_optimization('', 'requirements.txt') + "Keep the same version of jina ")
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requirements_content = extract_content_from_result(requirements_content_raw, 'requirements.txt')
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persist_file(requirements_content, 'requirements.txt')
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print_colored('', '############# Dockerfile #############', 'red')
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user_query = (
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general_guidelines()
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+ wrap_content_in_code_block(executor_content, 'executor.py', 'python')
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+ wrap_content_in_code_block(test_executor_content, 'test_executor.py', 'python')
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+ wrap_content_in_code_block(requirements_content, 'requirements.txt', '')
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+ docker_file_task()
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)
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conversation = gpt.Conversation()
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conversation.query(user_query)
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dockerfile_content_raw = conversation.query(chain_of_thought_optimization('dockerfile', 'Dockerfile'))
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dockerfile_content = extract_content_from_result(dockerfile_content_raw, 'Dockerfile')
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persist_file(dockerfile_content, 'Dockerfile')
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write_config_yml(executor_name, EXECUTOR_FOLDER_v1)
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for i in range(1, 20):
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conversation = gpt.Conversation()
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error = build_docker(f'executor_level2/v{i}')
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error = build_docker(f'executor/v{i}')
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if error:
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recreate_folder(f'executor_level2/v{i + 1}')
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file_name_to_content = get_all_executor_files_with_content(f'executor_level2/v{i}')
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recreate_folder(f'executor/v{i + 1}')
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file_name_to_content = get_all_executor_files_with_content(f'executor/v{i}')
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all_files_string = files_to_string(file_name_to_content)
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user_query = (
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'Here are all the files I use:\n'
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@@ -120,12 +118,12 @@ def main(
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file_name_to_content[file_name] = updated_file
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for file_name, content in file_name_to_content.items():
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persist_file(content, f'executor_level2/v{i + 1}/{file_name}')
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persist_file(content, f'executor/v{i + 1}/{file_name}')
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else:
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break
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error = jina_cloud.push_executor('executor_level2')
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jina_cloud.push_executor('executor')
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host = jina_cloud.deploy_flow(executor_name, do_validation, 'flow')
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# create playgorund and client.py
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@@ -149,7 +147,7 @@ if __name__ == '__main__':
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"and outputs a 2D image projection of that object where the full object is shown. ",
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input_modality='3d',
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output_modality='image',
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test_scenario='Test that 3d object from https://raw.githubusercontent.com/makehumancommunity/communityassets-wip/master/clothes/leotard_fs/leotard_fs.obj '
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test_scenario='Test that 3d object from https://raw.githubusercontent.com/polygonjs/polygonjs-assets/master/models/wolf.obj '
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'is put in and out comes a 2d rendering of it',
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do_validation=False
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)
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