DynamicPrompt class creation #13
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Clone of the PR langchain-ai/langchain#49 |
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WalkthroughThe recent updates to the Langchain library enhance its prompt management capabilities by introducing the Changes
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| @root_validator() | ||
| def template_is_valid(cls, values: Dict) -> Dict: | ||
| """Check that suffix and input variables are consistent.""" | ||
| input_variables = values["input_variables"] | ||
| suffix = values["suffix"] | ||
| template_format = values["template_format"] |
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The template_is_valid method in the DynamicPrompt class currently only validates the suffix of the template. For a dynamic prompt, it's important to validate the entire template, including the prefix and examples. Please update this method to construct and validate the full template to ensure that all parts of the dynamic prompt are consistent with the input variables.
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| def format(self, **kwargs: Any) -> str: | ||
| """Dynamically format the prompt with the inputs. | ||
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| Args: | ||
| kwargs: Any arguments to be passed to the prompt template. | ||
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| Returns: | ||
| A formatted string. | ||
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| Example: | ||
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| .. code-block:: python | ||
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| prompt.format(variable1="foo") | ||
| """ | ||
| # TODO segment self.examples based on example length & | ||
| # input_variables length here | ||
| example_str = self.example_separator.join(self.examples) | ||
| template = self.prefix + example_str + self.suffix | ||
| return _FORMATTER_MAPPING[self.template_format](template, **kwargs) |
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The format method in the DynamicPrompt class lacks proper error handling. It directly uses the formatted template without catching and handling potential exceptions that could occur during the formatting process. To improve the robustness of the code, consider wrapping the formatting logic inside a try-except block. This will allow you to catch and handle any exceptions, such as missing or invalid input variables, and provide meaningful error messages or raise appropriate exceptions to handle exceptional cases gracefully.
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Actionable comments posted: 1
Outside diff range, codebase verification and nitpick comments (1)
langchain/prompt.py (1)
166-184: Review thetemplate_is_validmethod implementations for consistency.There are two implementations of the
template_is_validmethod inlangchain/prompt.py. One checks the entire template and input variables, while the other only checks the suffix. Ensure that the validation logic is consistent and that the correct version is being used.
- File:
langchain/prompt.py- Lines: Look for both implementations and verify their intended use.
Analysis chain
Expand validation to the entire template in
template_is_valid.The method currently only validates the suffix. Consider expanding the validation to include the prefix and examples to ensure consistency across the entire template, as noted in previous comments.
Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Description: Verify the usage of `template_is_valid` to ensure it is only validating the suffix. # Test: Search for the method definition and usage. Expect: Validation logic should cover entire template. rg --type python -A 5 $'def template_is_valid'Length of output: 78
Script:
#!/bin/bash # Description: Verify the usage of `template_is_valid` to ensure it is only validating the suffix. # Corrected Test: Search for the method definition and usage. Expect: Validation logic should cover entire template. rg --type py -A 5 $'def template_is_valid'Length of output: 920
Tools
Ruff
183-183: Within an
exceptclause, raise exceptions withraise ... from errorraise ... from Noneto distinguish them from errors in exception handling(B904)
Review details
Configuration used: CodeRabbit UI
Review profile: CHILL
Files selected for processing (2)
- langchain/init.py (2 hunks)
- langchain/prompt.py (1 hunks)
Additional context used
Ruff
langchain/prompt.py
183-183: Within an
exceptclause, raise exceptions withraise ... from errorraise ... from Noneto distinguish them from errors in exception handling(B904)
Additional comments not posted (4)
langchain/__init__.py (1)
Line range hint
20-31: LGTM! The import and__all__list update are correct.The addition of
DynamicPromptto the import statement and the__all__list is appropriate and ensures that the class is accessible from the top level of the module.langchain/prompt.py (3)
106-120: ClassDynamicPromptdocumentation is clear and informative.The docstring provides a clear example of how to use the
DynamicPromptclass, which is helpful for users.
122-138: Attributes are well-defined and documented.The attributes of
DynamicPromptare clearly defined with appropriate docstrings, enhancing readability and understanding.
186-202:from_examplesmethod is correctly implemented.The method correctly initializes a
DynamicPromptinstance with the provided examples, prefix, suffix, and other parameters.
| def format(self, **kwargs: Any) -> str: | ||
| """Dynamically format the prompt with the inputs. | ||
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| Args: | ||
| kwargs: Any arguments to be passed to the prompt template. | ||
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| Returns: | ||
| A formatted string. | ||
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| Example: | ||
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| .. code-block:: python | ||
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| prompt.format(variable1="foo") | ||
| """ | ||
| # TODO segment self.examples based on example length & | ||
| # input_variables length here | ||
| example_str = self.example_separator.join(self.examples) | ||
| template = self.prefix + example_str + self.suffix | ||
| return _FORMATTER_MAPPING[self.template_format](template, **kwargs) |
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Address TODO and improve error handling in format.
The format method has a TODO comment indicating incomplete logic. Additionally, consider wrapping the formatting logic in a try-except block to handle potential errors gracefully, as suggested in previous comments.
Would you like assistance in implementing the error handling or completing the TODO logic?
Checking that this structure looks generally ok -- going to sub in logic where the TODO comment is then add a test.
Description by Korbit AI
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What change is being made?
Add a new
DynamicPromptclass to thelangchainmodule for creating dynamic prompts for language models.Why are these changes being made?
This change introduces a flexible and reusable way to generate prompts dynamically based on provided examples, prefixes, and suffixes, enhancing the prompt generation capabilities of the library. This approach allows for more complex and varied prompt structures, which can be beneficial for different use cases and improves the overall functionality of the
langchainmodule.Summary by CodeRabbit
New Features
DynamicPromptclass for enhanced prompt management with large language models.DynamicPromptaccessible at the top level of the package for easier use.Documentation
DynamicPromptin the module's__all__list.