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dev-gpt/src/executor_factory.py
Joschka Braun 72fcae579f fix: pg gets results on text
2023-04-14 12:09:30 +02:00

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import os
import random
import re
from src import gpt, jina_cloud
from src.constants import FILE_AND_TAG_PAIRS
from src.jina_cloud import push_executor, process_error_message
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, not_allowed
from src.utils.io import persist_file
from src.utils.string_tools import print_colored
class ExecutorFactory:
def __init__(self, model='gpt-4'):
self.gpt_session = gpt.GPTSession(model=model)
def extract_content_from_result(self, plain_text, file_name, match_single_block=False):
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:
# Check for a single code block
single_code_block_pattern = r"^```(?:\w+\n)?([\s\S]*?)```"
single_code_block_match = re.findall(single_code_block_pattern, plain_text, re.MULTILINE)
if match_single_block and len(single_code_block_match) == 1:
return single_code_block_match[0].strip()
else:
return ''
def write_config_yml(self, executor_name, dest_folder):
config_content = f'''
jtype: {executor_name}
py_modules:
- executor.py
metas:
name: {executor_name}
'''
with open(os.path.join(dest_folder, 'config.yml'), 'w') as f:
f.write(config_content)
def get_all_executor_files_with_content(self, 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 files_to_string(self, 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.strip()
def wrap_content_in_code_block(self, executor_content, file_name, tag):
return f'**{file_name}**\n```{tag}\n{executor_content}\n```\n\n'
def create_executor(
self,
description,
test,
output_path,
executor_name,
package,
num_approach,
is_chain_of_thought=False,
):
EXECUTOR_FOLDER_v1 = self.get_executor_path(output_path, executor_name, package, num_approach, 1)
os.makedirs(EXECUTOR_FOLDER_v1)
print_colored('', '############# Executor #############', 'red')
user_query = (
general_guidelines()
+ executor_file_task(executor_name, description, test, package)
+ '\n\n' + chain_of_thought_creation()
)
conversation = self.gpt_session.get_conversation()
executor_content_raw = conversation.query(user_query)
if is_chain_of_thought:
executor_content_raw = conversation.query(
f"General rules: " + not_allowed() + chain_of_thought_optimization('python', 'executor.py'))
executor_content = self.extract_content_from_result(executor_content_raw, 'executor.py', match_single_block=True)
persist_file(executor_content, os.path.join(EXECUTOR_FOLDER_v1, 'executor.py'))
print_colored('', '############# Test Executor #############', 'red')
user_query = (
general_guidelines()
+ self.wrap_content_in_code_block(executor_content, 'executor.py', 'python')
+ test_executor_file_task(executor_name, test)
)
conversation = self.gpt_session.get_conversation()
test_executor_content_raw = conversation.query(user_query)
if is_chain_of_thought:
test_executor_content_raw = conversation.query(
f"General rules: " + not_allowed() +
chain_of_thought_optimization('python', 'test_executor.py')
+ "Don't add any additional tests. "
)
test_executor_content = self.extract_content_from_result(
test_executor_content_raw, 'test_executor.py', match_single_block=True
)
persist_file(test_executor_content, os.path.join(EXECUTOR_FOLDER_v1, 'test_executor.py'))
print_colored('', '############# Requirements #############', 'red')
requirements_path = os.path.join(EXECUTOR_FOLDER_v1, 'requirements.txt')
user_query = (
general_guidelines()
+ self.wrap_content_in_code_block(executor_content, 'executor.py', 'python')
+ self.wrap_content_in_code_block(test_executor_content, 'test_executor.py', 'python')
+ requirements_file_task()
)
conversation = self.gpt_session.get_conversation()
requirements_content_raw = conversation.query(user_query)
if is_chain_of_thought:
requirements_content_raw = conversation.query(
chain_of_thought_optimization('', requirements_path) + "Keep the same version of jina ")
requirements_content = self.extract_content_from_result(requirements_content_raw, 'requirements.txt', match_single_block=True)
persist_file(requirements_content, requirements_path)
print_colored('', '############# Dockerfile #############', 'red')
user_query = (
general_guidelines()
+ self.wrap_content_in_code_block(executor_content, 'executor.py', 'python')
+ self.wrap_content_in_code_block(test_executor_content, 'test_executor.py', 'python')
+ self.wrap_content_in_code_block(requirements_content, 'requirements.txt', '')
+ docker_file_task()
)
conversation = self.gpt_session.get_conversation()
dockerfile_content_raw = conversation.query(user_query)
if is_chain_of_thought:
dockerfile_content_raw = conversation.query(
f"General rules: " + not_allowed() + chain_of_thought_optimization('dockerfile', 'Dockerfile'))
dockerfile_content = self.extract_content_from_result(dockerfile_content_raw, 'Dockerfile', match_single_block=True)
persist_file(dockerfile_content, os.path.join(EXECUTOR_FOLDER_v1, 'Dockerfile'))
self.write_config_yml(executor_name, EXECUTOR_FOLDER_v1)
print('First version of the executor created. Start iterating on it to make the tests pass...')
def create_playground(self, executor_name, executor_path, host):
print_colored('', '############# Playground #############', 'red')
file_name_to_content = self.get_all_executor_files_with_content(executor_path)
user_query = (
general_guidelines()
+ self.wrap_content_in_code_block(file_name_to_content['executor.py'], 'executor.py', 'python')
+ self.wrap_content_in_code_block(file_name_to_content['test_executor.py'], 'test_executor.py',
'python')
+ f'''
Create a playground for the executor {executor_name} using streamlit.
The playground must look like it was made by a professional designer.
All the ui elements are well thought out to make them visually appealing and easy to use.
The executor is hosted on {host}.
This is an example how you can connect to the executor assuming the document (d) is already defined:
```
from jina import Client, Document, DocumentArray
client = Client(host='{host}')
response = client.post('/', inputs=DocumentArray([d])) # always use '/'
print(response[0].text) # can also be blob in case of image/audio..., this should be visualized in the streamlit app
```
Note that the response will always be in response[0].text
Please provide the complete file with the exact same syntax to wrap the code.
'''
)
conversation = self.gpt_session.get_conversation([])
conversation.query(user_query)
playground_content_raw = conversation.query(chain_of_thought_optimization('python', 'app.py', 'the playground'))
playground_content = self.extract_content_from_result(playground_content_raw, 'app.py', match_single_block=True)
persist_file(playground_content, os.path.join(executor_path, 'app.py'))
def get_executor_path(self, output_path, executor_name, package, num_approach, version):
package_path = '_'.join(package)
return os.path.join(output_path, executor_name, f'{num_approach}_{package_path}', f'v{version}')
def debug_executor(self, output_path, executor_name, num_approach, packages, description, test):
MAX_DEBUGGING_ITERATIONS = 10
error_before = ''
# conversation = self.gpt_session.get_conversation()
for i in range(1, MAX_DEBUGGING_ITERATIONS):
print('Debugging iteration', i)
print('Trying to build the microservice. Might take a while...')
previous_executor_path = self.get_executor_path(output_path, executor_name, packages, num_approach, i)
next_executor_path = self.get_executor_path(output_path, executor_name, packages, num_approach, i + 1)
log_hubble = push_executor(previous_executor_path)
error = process_error_message(log_hubble)
if error:
os.makedirs(next_executor_path)
file_name_to_content = self.get_all_executor_files_with_content(previous_executor_path)
is_dependency_issue = self.is_dependency_issue(error, file_name_to_content['Dockerfile'])
print(f'Current error is a dependency issue: {is_dependency_issue}')
if is_dependency_issue:
all_files_string = self.files_to_string({
key: val for key, val in file_name_to_content.items() if key in ['requirements.txt', 'Dockerfile']
})
user_query = (
f"Your task is to provide guidance on how to solve an error that occurred during the Docker "
f"build process. The error message is:\n{error}\nTo solve this error, you should first "
f"identify the type of error by examining the stack trace. Once you have identified the "
f"error, you should suggest how to solve it. Your response should include the files that "
f"need to be changed, but not files that don't need to be changed. For files that need to "
f"be changed, please provide the complete file with the exact same syntax to wrap the code.\n\n"
f"You are given the following files:\n\n{all_files_string}"
)
else:
all_files_string = self.files_to_string(file_name_to_content)
user_query = (
f"General rules: " + not_allowed()
+ f'Here is the description of the task the executor must solve:\n{description}'
+ f'\n\nHere is the test scenario the executor must pass:\n{test}'
+ f'Here are all the files I use:\n{all_files_string}'
+ f'\n\nThis error happens during the docker build process:\n{error}\n\n'
+ 'Look at exactly at the stack trace. First, think about what kind of error is this? '
'Then think about possible reasons which might have caused it. Then suggest how to '
'solve it. 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"```"
)
conversation = self.gpt_session.get_conversation()
returned_files_raw = conversation.query(user_query)
for file_name, tag in FILE_AND_TAG_PAIRS:
updated_file = self.extract_content_from_result(returned_files_raw, file_name)
if updated_file and (not is_dependency_issue or file_name in ['requirements.txt', 'Dockerfile']):
file_name_to_content[file_name] = updated_file
for file_name, content in file_name_to_content.items():
persist_file(content, os.path.join(next_executor_path, file_name))
error_before = error_before
else:
break
if i == MAX_DEBUGGING_ITERATIONS - 1:
raise self.MaxDebugTimeReachedException('Could not debug the executor.')
return self.get_executor_path(output_path, executor_name, packages, num_approach, i)
class MaxDebugTimeReachedException(BaseException):
pass
def is_dependency_issue(self, error, docker_file: str):
conversation = self.gpt_session.get_conversation([])
answer = conversation.query(
f'Your task is to assist in identifying the root cause of a Docker build error for a python application. '
f'The error message is as follows::\n\n{error}\n\n'
f'The docker file is as follows:\n\n{docker_file}\n\n'
f'Is this a dependency installation failure? Answer with "yes" or "no".'
)
return 'yes' in answer.lower()
def generate_executor_name(self, description):
conversation = self.gpt_session.get_conversation()
user_query = f'''
Generate a name for the executor matching the description:
"{description}"
The executor name must fulfill the following criteria:
- camel case
- start with a capital letter
- only consists of lower and upper case characters
- end with Executor.
The output is a the raw string wrapped into ``` and starting with **name.txt** like this:
**name.txt**
```
PDFParserExecutor
```
'''
name_raw = conversation.query(user_query)
name = self.extract_content_from_result(name_raw, 'name.txt')
return name
def get_possible_packages(self, description, threads):
print_colored('', '############# What package to use? #############', 'red')
user_query = f'''
Here is the task description of the problme you need to solve:
"{description}"
First, write down all the subtasks you need to solve which require python packages.
For each subtask:
Provide a list of 1 to 3 python packages you could use to solve the subtask. Prefer modern packages.
For each package:
Write down some non-obvious thoughts about the challenges you might face for the task and give multiple approaches on how you handle them.
For example, there might be some packages you must not use because they do not obay the rules:
{not_allowed()}
Discuss the pros and cons for all of these packages.
Create a list of package subsets that you could use to solve the task.
The list is sorted in a way that the most promising subset of packages is at the top.
The maximum length of the list is 5.
The output must be a list of lists wrapped into ``` and starting with **packages.csv** like this:
**packages.csv**
```
package1,package2
package2,package3,...
...
```
'''
conversation = self.gpt_session.get_conversation()
packages_raw = conversation.query(user_query)
packages_csv_string = self.extract_content_from_result(packages_raw, 'packages.csv')
packages = [package.split(',') for package in packages_csv_string.split('\n')]
packages = packages[:threads]
return packages
def create(self, description, num_approaches, output_path, test):
generated_name = self.generate_executor_name(description)
executor_name = f'{generated_name}{random.randint(0, 1000_000)}'
packages_list = self.get_possible_packages(description, num_approaches)
for num_approach, packages in enumerate(packages_list):
try:
self.create_executor(description, test, output_path, executor_name, packages, num_approach)
executor_path = self.debug_executor(output_path, executor_name, num_approach, packages, description, test)
host = jina_cloud.deploy_flow(executor_name, executor_path)
self.create_playground(executor_name, executor_path, host)
except self.MaxDebugTimeReachedException:
print('Could not debug the Executor.')
continue
print(f'''
Executor name: {executor_name}
Executor path: {executor_path}
Host: {host}
Run the following command to start the playground:
streamlit run {os.path.join(executor_path, "app.py")}
'''
)
break
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