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feat: error feedback

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This commit is contained in:
Florian Hönicke
2023-03-22 23:34:49 +01:00
parent 1c8272e706
commit f408378c33
7 changed files with 184 additions and 94 deletions
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2
.gitignore vendored
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@@ -1 +1 @@
/executor/
/executor_level2/

44
main.py
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@@ -12,13 +12,16 @@ import re
# from src.utils.string_tools import find_differences
#
#
from src.constants import FILE_AND_TAG_PAIRS
def extract_content_from_result(plain_text, file_name):
pattern = fr"^\*\*{file_name}\*\*\n```(?:\w+\n)?([\s\S]*?)```"
match = re.search(pattern, plain_text, re.MULTILINE)
if match:
return match.group(1).strip()
else:
raise ValueError(f'Could not find {file_name} in result')
return ''
#
#
# def extract_and_write(plain_text, dest_folder):
@@ -41,17 +44,17 @@ metas:
f.write(config_content)
#
#
# def get_all_executor_files_with_content(folder_path):
# file_name_to_content = {}
# for filename in os.listdir(folder_path):
# file_path = os.path.join(folder_path, filename)
#
# if os.path.isfile(file_path):
# with open(file_path, 'r', encoding='utf-8') as file:
# content = file.read()
# file_name_to_content[filename] = content
#
# return file_name_to_content
def get_all_executor_files_with_content(folder_path):
file_name_to_content = {}
for filename in os.listdir(folder_path):
file_path = os.path.join(folder_path, filename)
if os.path.isfile(file_path):
with open(file_path, 'r', encoding='utf-8') as file:
content = file.read()
file_name_to_content[filename] = content
return file_name_to_content
#
#
#
@@ -104,14 +107,15 @@ metas:
# print('DIFFERENCES:', find_differences(all_executor_files_string, all_executor_files_string_improved))
# return all_executor_files_string_improved
#
# def files_to_string(file_name_to_content):
# all_executor_files_string = ''
# for file_name, tag in FILE_AND_TAG_PAIRS:
# all_executor_files_string += f'**{file_name}**\n'
# all_executor_files_string += f'```{tag}\n'
# all_executor_files_string += file_name_to_content[file_name]
# all_executor_files_string += '\n```\n\n'
# return all_executor_files_string
def files_to_string(file_name_to_content):
all_executor_files_string = ''
for file_name, tag in FILE_AND_TAG_PAIRS:
if file_name in file_name_to_content:
all_executor_files_string += f'**{file_name}**\n'
all_executor_files_string += f'```{tag}\n'
all_executor_files_string += file_name_to_content[file_name]
all_executor_files_string += '\n```\n\n'
return all_executor_files_string
#
#
# def main(

185
micro_chain.py
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@@ -1,7 +1,10 @@
import random
from main import extract_content_from_result, write_config_yml
from main import extract_content_from_result, write_config_yml, get_all_executor_files_with_content, files_to_string
from src import gpt, jina_cloud
from src.constants import FILE_AND_TAG_PAIRS
from src.jina_cloud import build_docker
from src.prompt_tasks import general_guidelines, executor_file_task, chain_of_thought_creation, test_executor_file_task, \
chain_of_thought_optimization, requirements_file_task, docker_file_task
from src.utils.io import recreate_folder, persist_file
@@ -21,86 +24,132 @@ def main(
test_scenario,
do_validation=True
):
input_doc_field = 'text' if input_modality == 'text' else 'blob'
output_doc_field = 'text' if output_modality == 'text' else 'blob'
# random integer at the end of the executor name to avoid name clashes
executor_name = f'MicroChainExecutor{random.randint(0, 1000_000)}'
recreate_folder('executor')
recreate_folder('flow')
# input_doc_field = 'text' if input_modality == 'text' else 'blob'
# output_doc_field = 'text' if output_modality == 'text' else 'blob'
# # random integer at the end of the executor name to avoid name clashes
# executor_name = f'MicroChainExecutor{random.randint(0, 1000_000)}'
# recreate_folder('executor')
# EXECUTOR_FOLDER_v1 = 'executor/v1'
# recreate_folder(EXECUTOR_FOLDER_v1)
# recreate_folder('flow')
#
# print_colored('', '############# Executor #############', 'red')
# user_query = (
# general_guidelines()
# + executor_file_task(executor_name, executor_description, input_modality, input_doc_field,
# output_modality, output_doc_field)
# + chain_of_thought_creation()
# )
# conversation = gpt.Conversation()
# conversation.query(user_query)
# executor_content_raw = conversation.query(chain_of_thought_optimization('python', 'executor.py'))
# executor_content = extract_content_from_result(executor_content_raw, 'executor.py')
# persist_file(executor_content, 'executor.py')
#
# print_colored('', '############# Test Executor #############', 'red')
# user_query = (
# general_guidelines()
# + wrap_content_in_code_block(executor_content, 'executor.py', 'python')
# + test_executor_file_task(executor_name, test_scenario)
# )
# conversation = gpt.Conversation()
# conversation.query(user_query)
# test_executor_content_raw = conversation.query(
# chain_of_thought_optimization('python', 'test_executor.py')
# + "Don't add any additional tests. "
# )
# test_executor_content = extract_content_from_result(test_executor_content_raw, 'test_executor.py')
# persist_file(test_executor_content, 'test_executor.py')
#
# print_colored('', '############# Requirements #############', 'red')
# user_query = (
# general_guidelines()
# + wrap_content_in_code_block(executor_content, 'executor.py', 'python')
# + wrap_content_in_code_block(test_executor_content, 'test_executor.py', 'python')
# + requirements_file_task()
# )
# conversation = gpt.Conversation()
# conversation.query(user_query)
# requirements_content_raw = conversation.query(chain_of_thought_optimization('', 'requirements.txt') + "Keep the same version of jina ")
#
# requirements_content = extract_content_from_result(requirements_content_raw, 'requirements.txt')
# persist_file(requirements_content, 'requirements.txt')
#
# print_colored('', '############# Dockerfile #############', 'red')
# user_query = (
# general_guidelines()
# + wrap_content_in_code_block(executor_content, 'executor.py', 'python')
# + wrap_content_in_code_block(test_executor_content, 'test_executor.py', 'python')
# + wrap_content_in_code_block(requirements_content, 'requirements.txt', '')
# + docker_file_task()
# )
# conversation = gpt.Conversation()
# conversation.query(user_query)
# dockerfile_content_raw = conversation.query(chain_of_thought_optimization('dockerfile', 'Dockerfile'))
# dockerfile_content = extract_content_from_result(dockerfile_content_raw, 'Dockerfile')
# persist_file(dockerfile_content, 'Dockerfile')
#
# write_config_yml(executor_name, EXECUTOR_FOLDER_v1)
print_colored('', '############# Executor #############', 'red')
user_query = (
general_guidelines()
+ executor_file_task(executor_name, executor_description, input_modality, input_doc_field,
output_modality, output_doc_field)
+ chain_of_thought_creation()
)
for i in range(1, 20):
conversation = gpt.Conversation()
conversation.query(user_query)
executor_content_raw = conversation.query(chain_of_thought_optimization('python', 'executor.py'))
executor_content = extract_content_from_result(executor_content_raw, 'executor.py')
persist_file(executor_content, 'executor.py')
print_colored('', '############# Test Executor #############', 'red')
error = build_docker(f'executor_level2/v{i}')
if error:
recreate_folder(f'executor_level2/v{i + 1}')
file_name_to_content = get_all_executor_files_with_content(f'executor_level2/v{i}')
all_files_string = files_to_string(file_name_to_content)
user_query = (
general_guidelines()
+ wrap_content_in_code_block(executor_content, 'executor.py', 'python')
+ test_executor_file_task(executor_name, test_scenario)
'Here are all the files I use:\n'
+ all_files_string
+ 'I got the following error:\n'
+ error
+ 'Think quickly about possible reasons. '
'Then output the files that need change. '
"Don't output files that don't need change. "
"If you output a file, then write the complete file. "
"Use the exact same syntax to wrap the code:\n"
f"**...**\n"
f"```...\n"
f"...code...\n"
f"```\n\n"
)
conversation = gpt.Conversation()
conversation.query(user_query)
test_executor_content_raw = conversation.query(
chain_of_thought_optimization('python', 'test_executor.py')
+ "Don't add any additional tests. "
)
test_executor_content = extract_content_from_result(test_executor_content_raw, 'test_executor.py')
persist_file(test_executor_content, 'test_executor.py')
returned_files_raw = conversation.query(user_query)
for file_name, tag in FILE_AND_TAG_PAIRS:
updated_file = extract_content_from_result(returned_files_raw, file_name)
if updated_file:
file_name_to_content[file_name] = updated_file
print_colored('', '############# Requirements #############', 'red')
user_query = (
general_guidelines()
+ wrap_content_in_code_block(executor_content, 'executor.py', 'python')
+ wrap_content_in_code_block(test_executor_content, 'test_executor.py', 'python')
+ requirements_file_task()
)
conversation = gpt.Conversation()
conversation.query(user_query)
requirements_content_raw = conversation.query(chain_of_thought_optimization('', 'requirements.txt'))
for file_name, content in file_name_to_content.items():
persist_file(content, f'executor_level2/v{i + 1}/{file_name}')
else:
break
requirements_content = extract_content_from_result(requirements_content_raw, 'requirements.txt')
persist_file(requirements_content, 'requirements.txt')
print_colored('', '############# Dockerfile #############', 'red')
user_query = (
general_guidelines()
+ wrap_content_in_code_block(executor_content, 'executor.py', 'python')
+ wrap_content_in_code_block(test_executor_content, 'test_executor.py', 'python')
+ wrap_content_in_code_block(requirements_content, 'requirements.txt', '')
+ docker_file_task()
)
conversation = gpt.Conversation()
conversation.query(user_query)
dockerfile_content_raw = conversation.query(chain_of_thought_optimization('dockerfile', 'Dockerfile'))
dockerfile_content = extract_content_from_result(dockerfile_content_raw, 'Dockerfile')
persist_file(dockerfile_content, 'Dockerfile')
write_config_yml(executor_name, 'executor')
jina_cloud.push_executor('executor')
error = jina_cloud.push_executor('executor_level2')
host = jina_cloud.deploy_flow(executor_name, do_validation, 'flow')
# create playgorund and client.py
if __name__ == '__main__':
######## Level 1 task #########
# ######## Level 1 task #########
# main(
# executor_description="OCR detector",
# input_modality='image',
# # input_doc_field='blob',
# output_modality='text',
# # output_doc_field='text',
# test_scenario='Takes https://miro.medium.com/v2/resize:fit:1024/0*4ty0Adbdg4dsVBo3.png as input and returns a string that contains "Hello, world"',
# do_validation=False
# )
######### Level 2 task #########
main(
executor_description="OCR detector",
input_modality='image',
# input_doc_field='blob',
output_modality='text',
# output_doc_field='text',
test_scenario='Takes https://miro.medium.com/v2/resize:fit:1024/0*4ty0Adbdg4dsVBo3.png as input and returns a string that contains "Hello, world"',
executor_description="The executor takes 3D objects in obj format as input "
"and outputs a 2D image projection of that object where the full object is shown. ",
input_modality='3d',
output_modality='image',
test_scenario='Test that 3d object from https://raw.githubusercontent.com/makehumancommunity/communityassets-wip/master/clothes/leotard_fs/leotard_fs.obj '
'is put in and out comes a 2d rendering of it',
do_validation=False
)

2
src/gpt.py
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@@ -30,7 +30,7 @@ def get_response(prompt_list: List[Tuple[str, str]]):
try:
response_generator = openai.ChatCompletion.create(
temperature=0,
max_tokens=4_000,
max_tokens=2_000,
model="gpt-4",
stream=True,
messages=[

35
src/jina_cloud.py
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@@ -1,5 +1,7 @@
import os
from multiprocessing.connection import Client
import subprocess
import re
import hubble
from jcloud.flow import CloudFlow
@@ -79,3 +81,36 @@ def update_client_line_in_file(file_path, host):
file.write(replaced_content)
def build_docker(path):
def process_error_message(error_message):
lines = error_message.split('\n')
relevant_lines = []
pattern = re.compile(r"^#\d+ \[\d+/\d+\]") # Pattern to match lines like "#11 [7/8]"
last_matching_line_index = None
for index, line in enumerate(lines):
if pattern.match(line):
last_matching_line_index = index
if last_matching_line_index is not None:
relevant_lines = lines[last_matching_line_index:]
return '\n'.join(relevant_lines)
# The command to build the Docker image
cmd = f"docker build -t micromagic {path}"
# Run the command and capture the output
process = subprocess.Popen(cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE, shell=True)
stdout, stderr = process.communicate()
# Check if there was an error
if process.returncode != 0:
error_message = stderr.decode("utf-8")
relevant_error_message = process_error_message(error_message)
return relevant_error_message
else:
print("Docker build completed successfully.")
return ''

2
src/prompt_tasks.py
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@@ -36,6 +36,8 @@ It gets a DocumentArray as input where each document has the input modality '{in
It returns a DocumentArray as output where each document has the output modality '{output_modality}' that is stored in document.{output_doc_field}.
Have in mind that d.uri is never a path to a local file. It is always a url.
The executor is not allowed to use the GPU.
The executor is not allowed to access a database.
The executor is not allowed to access a display.
The executor is not allowed to access external apis.
''',
EXECUTOR_FILE_TAG,

2
src/utils/io.py
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@@ -10,7 +10,7 @@ def recreate_folder(folder_path):
os.makedirs(folder_path)
def persist_file(file_content, file_name):
with open(f'executor/{file_name}', 'w') as f:
with open(f'{file_name}', 'w') as f:
f.write(file_content)