TokenEncoder

[mrT23]

Type

enhancement

---

Description

• Introduced a TokenEncoder singleton class to ensure thread-safe token encoding across the application.
• Implemented a locking mechanism in TokenEncoder to prevent multiple instances creation in a multithreaded environment.
• Updated TokenHandler and utility functions to use the new TokenEncoder.get_token_encoder() method, ensuring consistent use of the token encoder throughout the application.

---

Changes walkthrough

Relevant files

Enhancement

token_handler.py Implement Singleton Pattern for Token Encoding                      pr_agent/algo/token_handler.py Introduced TokenEncoder singleton class for thread-safe token encoding. Implemented thread safety using a lock for creating a single instance of the encoder. Updated TokenHandler to use TokenEncoder.get_token_encoder() method. +18/-5    utils.py Update Utils to Use TokenEncoder Singleton                              pr_agent/algo/utils.py Replaced usage of get_token_encoder with TokenEncoder.get_token_encoder() for consistency. +2/-2

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[mrT23]

/review auto_approve

[codiumai-pr-agent-pro]

Auto-approved PR

[codiumai-pr-agent-pro]

PR Description updated to latest commit (9c36732)

• Copy walkthrough table to "Files Changed" Tab

[codiumai-pr-agent-pro]

PR Review

⏱️ Estimated effort to review [1-5]	2, because the changes are focused on implementing a singleton pattern for the TokenEncoder and updating references in the application. The logic is straightforward and well-contained within a single class and its usage.
🏅 Score	85
🧪 Relevant tests	No
🔍 Possible issues	Thread Safety: The implementation checks for the instance existence outside the lock, which is good for performance but requires careful consideration to ensure thread safety is maintained in all scenarios.
	Model Change Handling: The logic to handle changes in the model configuration is present, but it might be worth considering if there are any edge cases where this could lead to unexpected behavior, especially in a multithreaded environment.
🔒 Security concerns	No
🔀 Multiple PR themes	No

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[codiumai-pr-agent-pro]

PR Code Suggestions

Category	Suggestions
Maintainability	Add exception handling around the encoder instantiation. Consider using a more specific exception handling around the encoder instantiation to gracefully handle potential errors during the encoder creation process. This can help in debugging and maintaining the code, especially when dealing with external libraries or services that might fail or change behavior over time. pr_agent/algo/token_handler.py [19-20] -cls._encoder_instance = encoding_for_model(cls._model) if "gpt" in cls._model else get_encoding( - "cl100k_base") +try: + cls._encoder_instance = encoding_for_model(cls._model) if "gpt" in cls._model else get_encoding( + "cl100k_base") +except Exception as e: + logging.error(f"Failed to instantiate encoder: {e}") + cls._encoder_instance = None
	Extract encoder determination logic into a separate method. To improve code readability and maintainability, consider extracting the logic for determining the encoder based on the model string into a separate method. This makes the get_token_encoder method more concise and separates concerns more clearly. pr_agent/algo/token_handler.py [19-20] -cls._encoder_instance = encoding_for_model(cls._model) if "gpt" in cls._model else get_encoding( - "cl100k_base") +cls._encoder_instance = cls._determine_encoder(cls._model) +@classmethod +def _determine_encoder(cls, model): + return encoding_for_model(model) if "gpt" in model else get_encoding("cl100k_base") +
Bug	Move model assignment inside the conditional block to ensure atomic updates. To avoid potential race conditions and ensure that the _encoder_instance is correctly updated before being used, consider moving the assignment of cls._model = model inside the conditional block that checks if the encoder needs to be updated. This ensures that both _model and _encoder_instance are updated atomically within the locked section. pr_agent/algo/token_handler.py [18-20] -cls._model = model -cls._encoder_instance = encoding_for_model(cls._model) if "gpt" in cls._model else get_encoding( - "cl100k_base") +if cls._encoder_instance is None or model != cls._model: + cls._model = model + cls._encoder_instance = encoding_for_model(cls._model) if "gpt" in cls._model else get_encoding( + "cl100k_base")
Best practice	Make class attributes protected for easier extensibility. To ensure that the TokenEncoder class can be easily extended or modified in the future, consider making _encoder_instance, _model, and _lock protected (using a single underscore) instead of private (using a double underscore). This allows subclasses to access and modify these attributes if necessary. pr_agent/algo/token_handler.py [8-10] -_encoder_instance = None -_model = None -_lock = Lock() # Create a lock object +encoder_instance = None +model = None +lock = Lock() # Create a lock object

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[mrT23]

/help

[codiumai-pr-agent-pro]

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[codiumai-pr-agent-pro]

Changes walkthrough

Enhancement token_handler.py (+18/-5) Implement Singleton Pattern for Token Encoding                      pr_agent/algo/token_handler.py Introduced TokenEncoder singleton class for thread-safe token encoding. Implemented thread safety using a lock for creating a single instance of the encoder. Updated TokenHandler to use TokenEncoder.get_token_encoder() method. utils.py (+2/-2) Update Utils to Use TokenEncoder Singleton                              pr_agent/algo/utils.py Replaced usage of get_token_encoder with TokenEncoder.get_token_encoder() for consistency.

[codiumai-pr-agent-pro]

Changelog updates:

2024-04-03

Added

• Introduced a TokenEncoder singleton class for thread-safe token encoding.

Changed

• Implemented a locking mechanism in TokenEncoder to ensure a single instance in a multithreaded environment.
• Updated TokenHandler and utility functions to utilize TokenEncoder.get_token_encoder() for consistent token encoding.

to commit the new content to the CHANGELOG.md file, please type:
'/update_changelog --pr_update_changelog.push_changelog_changes=true'

[codiumai-pr-agent-pro]

PR Documentation

Here is a list of the files that were modified in the PR, with docstring for each altered code component:

token_handler.py                                                                                                                                TokenEncoder (class) [+15/-0]                                                                                                                              TokenEncoder Component signature: class TokenEncoder: Docstring: """ A class responsible for encoding tokens based on the selected model. Attributes: - _encoder_instance: An instance of the token encoder. - _model: The selected model for token encoding. - _lock: A threading lock object for ensuring thread safety. Methods: - get_token_encoder: A class method to retrieve the token encoder instance. """ get_token_encoder Component signature: def get_token_encoder(cls): Docstring: """ Retrieves the token encoder instance based on the selected model. Args: - cls: The class itself. Returns: The token encoder instance based on the selected model. """ __init__ (method) [+2/-2]                                                                                                                                      Component signature: def __init__(self, pr=None, vars: dict = {}, system="", user=""): Docstring: """ Initializes the TokenHandler object. Args: - pr: The pull request object. - vars: A dictionary of variables. - system: The system string. - user: The user string. """

utils.py                                                                                                                                                clip_tokens (function) [+2/-2]                                                                                                                            Component signature: def clip_tokens(text: str, max_tokens: int, add_three_dots=True) -> str: Docstring: """ Clip the number of tokens in a string to a maximum number of tokens. Args: text (str): The string to clip. max_tokens (int): The maximum number of tokens allowed in the string. add_three_dots (bool, optional): A boolean indicating whether to add three dots at the end of the clipped string. Returns: str: The clipped string. """

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PR-Agent: Missing component name in test command: /test <component_name>.
Running automatically on the largest changed component.

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Generated tests for 'TokenEncoder'

TokenEncoder (class) ^[+15/-0]



Component signature:

class TokenEncoder:


Tests for code changes in TokenEncoder class:

[happy path] get_token_encoder should return the same instance for subsequent calls with the same model configuration test_code: import pytest from pr_agent.algo.token_handler import TokenEncoder from pr_agent.config_loader import get_settings def test_get_token_encoder_singleton_behavior(): # Given the same model configuration original_model = get_settings().config.model # When `get_token_encoder` is called multiple times encoder_first_call = TokenEncoder.get_token_encoder() encoder_second_call = TokenEncoder.get_token_encoder() # Then the same instance should be returned assert encoder_first_call is encoder_second_call, "TokenEncoder should return the same instance for the same model configuration"

[edge case] get_token_encoder should return a different instance when the model configuration changes test_code: import pytest from unittest.mock import patch from pr_agent.algo.token_handler import TokenEncoder @patch('pr_agent.config_loader.get_settings') def test_get_token_encoder_returns_different_instance_on_model_change(mock_get_settings): # Given a mocked model configuration that changes after the first call mock_get_settings().config.model = "gpt-2" encoder_first_call = TokenEncoder.get_token_encoder() mock_get_settings().config.model = "gpt-3" # When `get_token_encoder` is called after the model configuration changes encoder_second_call = TokenEncoder.get_token_encoder() # Then a different instance should be returned assert encoder_first_call is not encoder_second_call, "TokenEncoder should return a different instance when the model configuration changes"

[edge case] get_token_encoder should handle concurrent calls safely and return the correct instance test_code: import pytest from concurrent.futures import ThreadPoolExecutor from pr_agent.algo.token_handler import TokenEncoder def test_get_token_encoder_thread_safety(): # Given concurrent calls to `get_token_encoder` with ThreadPoolExecutor(max_workers=4) as executor: futures = [executor.submit(TokenEncoder.get_token_encoder) for _ in range(4)] instances = [future.result() for future in futures] # When the results are collected # Then all instances should be the same, indicating thread safety assert all(instance is instances[0] for instance in instances), "TokenEncoder should handle concurrent calls safely"

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PR-Agent: Missing component name in improve_component command: /improve_component <component_name>.
Running automatically on the largest changed component.

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The test tool generate tests for a selected component, based on the PR code changes.
It can be invoked manually by commenting on any PR:

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where 'component_name' is the name of a specific component in the PR. To get a list of the components that changed in the PR, use the analyze tool.
Language that are currently supported: Python, Java, C++, JavaScript, TypeScript.

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• num_tests: number of tests to generate. Default is 3.
• testing_framework: the testing framework to use. If not set, for Python it will use pytest, for Java it will use JUnit, for C++ it will use Catch2, and for JavaScript and TypeScript it will use jest.
• avoid_mocks: if set to true, the tool will try to avoid using mocks in the generated tests. Note that even if this option is set to true, the tool might still use mocks if it cannot generate a test without them. Default is true.
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Generated code suggestions for 'TokenEncoder'

TokenEncoder (class) ^[+15/-0]



Component signature:

class TokenEncoder:


Suggestions and improvements for code changes in TokenEncoder class:

Category	Suggestions
Enhancement	Add exception handling around encoder instantiation. Consider using a more specific exception handling around the encoder instantiation to gracefully handle potential errors during the encoding model initialization. pr_agent/algo/token_handler.py -cls._encoder_instance = encoding_for_model(cls._model) if "gpt" in cls._model else get_encoding("cl100k_base") +try: + cls._encoder_instance = encoding_for_model(cls._model) if "gpt" in cls._model else get_encoding("cl100k_base") +except Exception as e: + logging.error(f"Failed to initialize encoder: {e}") + raise
Maintainability	Refactor condition check to a separate method. Refactor the condition checking for encoder instance or model change to a separate method to improve readability and maintainability. pr_agent/algo/token_handler.py -if cls._encoder_instance is None or model != cls._model: +if cls._should_initialize_encoder(model):
Best practice	Use descriptive variable names. Use a more descriptive variable name than model for the model configuration to enhance code readability. pr_agent/algo/token_handler.py -model = get_settings().config.model +model_config = get_settings().config.model
Performance	Cache expensive operation results. Consider caching the result of get_settings().config.model if it's an expensive operation, to avoid repeated calls in a multi-threaded environment. pr_agent/algo/token_handler.py -model = get_settings().config.model +if not hasattr(cls, '_cached_model_config'): + cls._cached_model_config = get_settings().config.model +model = cls._cached_model_config

[codiumai-pr-agent-pro]

PR Analysis

• This screen contains a list of code components that were changed in this PR.
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file	Changed components
token_handler.py	Test Docs Improve Similar   TokenEncoder (class)   +15/-0   Test Docs Improve Similar   __init__ (method of TokenHandler)   +2/-2
utils.py	Test Docs Improve Similar   clip_tokens (function)   +2/-2

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[codiumai-pr-agent-pro]

Generated tests for 'clip_tokens'

clip_tokens (function) ^[+2/-2]



Component signature:

def clip_tokens(text: str, max_tokens: int, add_three_dots=True) -> str:


Tests for code changes in clip_tokens function:

[happy path] clip_tokens should correctly use TokenEncoder.get_token_encoder() to encode text test_code: import pytest from pr_agent.algo.utils import clip_tokens from pr_agent.algo.token_handler import TokenEncoder def mock_encode(text): return text.split() def test_clip_tokens_uses_token_encoder_correctly(monkeypatch): # Given a text and a mocked TokenEncoder text = "This is a test text" max_tokens = 3 monkeypatch.setattr(TokenEncoder, 'get_token_encoder', lambda: type('MockEncoder', (object,), {'encode': mock_encode})()) # When clip_tokens is called result = clip_tokens(text, max_tokens) # Then the result should be clipped correctly assert result == "This is a...(truncated)", "The text was not clipped correctly using TokenEncoder."

[happy path] clip_tokens should not add "...(truncated)" if add_three_dots is False test_code: import pytest from pr_agent.algo.utils import clip_tokens from pr_agent.algo.token_handler import TokenEncoder def mock_encode(text): return text.split() def test_clip_tokens_without_three_dots(monkeypatch): # Given a text and a mocked TokenEncoder text = "This is a test text" max_tokens = 3 monkeypatch.setattr(TokenEncoder, 'get_token_encoder', lambda: type('MockEncoder', (object,), {'encode': mock_encode})()) # When clip_tokens is called with add_three_dots set to False result = clip_tokens(text, max_tokens, add_three_dots=False) # Then the result should be clipped correctly without "...(truncated)" assert result == "This is a", "The text was not clipped correctly without adding three dots."

[edge case] clip_tokens should return the original text if encoding or clipping fails test_code: import pytest from pr_agent.algo.utils import clip_tokens from pr_agent.algo.token_handler import TokenEncoder def test_clip_tokens_returns_text_on_failure(monkeypatch): # Given a text and a failing TokenEncoder text = "This is a test text" max_tokens = 3 monkeypatch.setattr(TokenEncoder, 'get_token_encoder', lambda: type('FailingEncoder', (object,), {'encode': lambda x: 1/0})()) # When clip_tokens is called result = clip_tokens(text, max_tokens) # Then the original text should be returned assert result == text, "The original text was not returned on failure."

---

✨ Test tool usage guide:

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The test tool generate tests for a selected component, based on the PR code changes.
It can be invoked manually by commenting on any PR:

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where 'component_name' is the name of a specific component in the PR. To get a list of the components that changed in the PR, use the analyze tool.
Language that are currently supported: Python, Java, C++, JavaScript, TypeScript.

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• num_tests: number of tests to generate. Default is 3.
• testing_framework: the testing framework to use. If not set, for Python it will use pytest, for Java it will use JUnit, for C++ it will use Catch2, and for JavaScript and TypeScript it will use jest.
• avoid_mocks: if set to true, the tool will try to avoid using mocks in the generated tests. Note that even if this option is set to true, the tool might still use mocks if it cannot generate a test without them. Default is true.
• extra_instructions: Optional extra instructions to the tool. For example: "use the following mock injection scheme: ...".
• file: in case there are several components with the same name, you can specify the relevant file.
• class_name: in case there are several components with the same name in the same file, you can specify the relevant class name.

See more information about the test tool in the docs.

[codiumai-pr-agent-pro]

Generated docstring for 'clip_tokens'

clip_tokens (function) ^[+2/-2]

Component signature:



def clip_tokens(text: str, max_tokens: int, add_three_dots=True) -> str:

Docstring:


"""
Clip the number of tokens in a string to a maximum number of tokens.

Args:
    text (str): The string to clip.
    max_tokens (int): The maximum number of tokens allowed in the string.
    add_three_dots (bool, optional): A boolean indicating whether to add three dots at the end of the clipped

Returns:
    str: The clipped string.
"""

[codiumai-pr-agent-pro]

Generated code suggestions for 'clip_tokens'

clip_tokens (function) ^[+2/-2]



Component signature:

def clip_tokens(text: str, max_tokens: int, add_three_dots=True) -> str:


Suggestions and improvements for code changes in clip_tokens function:

Category	Suggestions
Best practice	Use specific exceptions for error handling. Replace the generic Exception catch with more specific exceptions that could occur during token encoding and logging, to avoid catching and potentially ignoring unexpected errors. pr_agent/algo/utils.py -except Exception as e: +except (TokenEncodingException, LoggingException) as e:
Enhancement	Improve user feedback on text truncation. Consider adding a more descriptive message when appending "...(truncated)" to make it clear to the end-user why the text was truncated. pr_agent/algo/utils.py -clipped_text += "...(truncated)" +clipped_text += "...(text truncated to fit the maximum token limit)"
Performance	Simplify and optimize character count calculation. Optimize the calculation of num_output_chars by directly multiplying max_tokens with the average token length instead of calculating chars_per_token first. pr_agent/algo/utils.py -chars_per_token = num_chars / num_input_tokens -num_output_chars = int(chars_per_token * max_tokens) +num_output_chars = int((num_chars / num_input_tokens) * max_tokens)
Maintainability	Rename or adjust the functionality of the add_three_dots parameter for clarity. Ensure that the add_three_dots parameter is more accurately named to reflect its purpose, as it currently suggests it adds only three dots, not the string "...(truncated)". pr_agent/algo/utils.py if add_three_dots: - clipped_text += "...(truncated)" + clipped_text += "...(text truncated)"

[codiumai-pr-agent-pro]

🔍 Finding similar code for 'clip_tokens'

clip_tokens (function) ^[+2/-2]

Component signature:

def clip_tokens(text: str, max_tokens: int, add_three_dots=True) -> str:

🏷 Extracted Keywords	clipping , tokens , truncate , encoding , logging
🌍 Search Context	Global Organization
🔍 Similar Code	Repository: magenta/magenta Filename: base_model.py
	Repository: woojinchung/lms Filename: base.py
	Repository: borisdayma/dalle-mini Filename: train.py
	Repository: piskvorky/gensim Filename: word2vec.py
	Repository: modelscope/modelscope Filename: configuration.py
📂 Relevant Repositories	Repository: textattack Description: A framework for generating adversarial examples for text, which includes functionality for manipulating and truncating text inputs, similar to the clip_tokens function.
	Repository: transformers Description: Provides state-of-the-art general-purpose architectures for Natural Language Processing (NLP) including tokenization, which may involve similar clipping or truncating strategies for handling text inputs.

[mrT23]

/ask is there an alternatie for lock ?

[codiumai-pr-agent-pro]

Yes, there are alternatives to using a Lock for synchronization in Python, especially when dealing with shared resources across multiple threads. Here are a few alternatives:

• RLock (Reentrant Lock): Similar to Lock, but allows a thread to acquire it multiple times. This is useful in situations where the same thread needs to re-acquire the lock without causing a deadlock.

• Semaphore: A more advanced locking mechanism that allows a certain number of threads to access a resource at the same time. It's useful when you need to limit access to a resource but allow more than one thread to access it concurrently.

• Event: An event is a simpler synchronization primitive that can be used to signal between threads. One thread signals an event, and other threads wait for it. It's not a lock but can be used to control access or the flow of execution between threads.

• Condition: A condition variable allows one or more threads to wait until they are notified by another thread. It's more flexible than a simple lock as it allows threads to wait for some condition to become true.

• Barrier: A barrier is used to make threads wait until a certain number of threads have reached a point of execution. This is useful in scenarios where you want to synchronize a specific part of the workflow across multiple threads.

Each of these synchronization primitives has its own use cases and choosing the right one depends on the specific requirements of your application. For instance, if you need a simple mutual exclusion, a Lock might be sufficient. However, for more complex synchronization requirements, such as allowing multiple threads to access a resource but with a limit on the number of concurrent accesses, a Semaphore might be more appropriate.

[mrT23]

/review auto_approve

[codiumai-pr-agent-pro]

Auto-approved PR

[mrT23]

/analyze

[codiumai-pr-agent-pro]

PR Analysis

• This screen contains a list of code components that were changed in this PR.
• You can initiate specific actions for each component, by checking the relevant boxes.
• After you check a box, the action will be performed automatically by PR-Agent.
• Results will appear as a comment on the PR, typically after 30-60 seconds.

file	Changed components
token_handler.py	Test Docs Improve Similar   TokenEncoder (class)   +15/-0   Test Docs Improve Similar   __init__ (method of TokenHandler)   +2/-2
utils.py	Test Docs Improve Similar   clip_tokens (function)   +2/-2

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✨ Usage guide:

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Using static code analysis capabilities, the analyze tool scans the PR code changes and find the code components (methods, functions, classes) that changed in the PR.
The tool can be triggered automatically every time a new PR is opened, or can be invoked manually by commenting on any PR:

/analyze

Language that are currently supported: Python, Java, C++, JavaScript, TypeScript.
See more information about the tool in the docs.

[mrT23]

/custom_suggestions

[codiumai-pr-agent-pro]

PR Custom Suggestions

	Suggestions
Performance	Improve thread safety and performance by using a double-checked locking pattern in get_token_encoder method. Consider using a double-checked locking pattern to ensure thread safety and performance. In the current implementation, the lock is acquired every time get_token_encoder is called, even if _encoder_instance is already initialized. This can be avoided by adding an additional check for _encoder_instance before acquiring the lock. pr_agent/algo/token_handler.py [13-21] @classmethod def get_token_encoder(cls): model = get_settings().config.model if cls._encoder_instance is None or model != cls._model: # Check without acquiring the lock for performance - with cls._lock: # Lock acquisition to ensure thread safety - if cls._encoder_instance is None or model != cls._model: - cls._model = model - cls._encoder_instance = encoding_for_model(cls._model) if "gpt" in cls._model else get_encoding( - "cl100k_base") + if cls._encoder_instance is None or model != cls._model: # Double-checked locking + with cls._lock: # Lock acquisition to ensure thread safety + if cls._encoder_instance is None or model != cls._model: + cls._model = model + cls._encoder_instance = encoding_for_model(cls._model) if "gpt" in cls._model else get_encoding( + "cl100k_base") return cls._encoder_instance
Readability	Add docstrings to the TokenEncoder class and its get_token_encoder method. Consider adding a docstring to the TokenEncoder class and its get_token_encoder method to explain their purpose and usage. pr_agent/algo/token_handler.py [7-21] class TokenEncoder: + """ + Singleton class for token encoding. Ensures thread safety and performance. + """ _encoder_instance = None _model = None _lock = Lock() # Create a lock object + @classmethod + def get_token_encoder(cls): + """ + Returns the token encoder instance. If it doesn't exist or the model has changed, + it creates a new one. + """ + model = get_settings().config.model + if cls._encoder_instance is None or model != cls._model: # Check without acquiring the lock for performance + with cls._lock: # Lock acquisition to ensure thread safety + if cls._encoder_instance is None or model != cls._model: + cls._model = model + cls._encoder_instance = encoding_for_model(cls._model) if "gpt" in cls._model else get_encoding( + "cl100k_base") + return cls._encoder_instance +

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✨ Usage guide:

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Overview:
The custom_suggestions tool scans the PR code changes, and automatically generates custom suggestions for improving the PR code.

Unlike the improve tool, which offers more general suggestion according to a pre-configured set of rules, the custom_suggestions tool will only propose suggestion that follow specific guidelines defined by the prompt in: pr_custom_suggestions.prompt configuration.

The tool can be triggered automatically every time a new PR is opened, or can be invoked manually by commenting on a PR.
When commenting, use the following template:

/custom_suggestions --pr_custom_suggestions.prompt="The suggestions should focus only on the following:\n-...\n-...\n-..."

With a https://pr-agent-docs.codium.ai/usage-guide/configuration_options/, use the following template:

[pr_custom_suggestions]
prompt="""\
The suggestions should focus only on the following:
-...
-...
-...
"""

Using a configuration file is recommended, since it allows to use multi-line instructions.

Don't forget - with this tool, you are the prompter. Be specific, clear, and concise in the instructions. Specify relevant aspects that you want the model to focus on. You might benefit from several trial-and-error iterations, until you get the correct prompt for your use case.

Example usage Here is an example for a possible prompt: [pr_custom_suggestions] prompt="""\ The suggestions should focus only on the following: - look for edge cases when implementing a new function - make sure every variable has a meaningful name - make sure the code is efficient """ The instructions above are just an example. We want to emphasize that the prompt should be specific and clear, and be tailored to the needs of your project.

More PR-Agent commands To invoke the PR-Agent, add a comment using one of the following commands: /review: Request a review of your Pull Request. /describe: Update the PR title and description based on the contents of the PR. /improve [--extended]: Suggest code improvements. Extended mode provides a higher quality feedback. /ask <QUESTION>: Ask a question about the PR. /update_changelog: Update the changelog based on the PR's contents. /add_docs 💎: Generate docstring for new components introduced in the PR. /generate_labels 💎: Generate labels for the PR based on the PR's contents. /analyze 💎: Automatically analyzes the PR, and presents changes walkthrough for each component. See the tools guide for more details. To list the possible configuration parameters, add a /config comment.

See the custom suggestions usage page for a more comprehensive guide on using this tool.