Validation part 1 proposed edits #125
Conversation
WalkthroughThe changes introduce dynamic validation using Pydantic and Instructor libraries in Python. They enhance the validation process, demonstrate the use of field and model validators, and provide examples of handling errors and re-asking. The changes also include a patching mechanism for OpenAI models and Pydantic objects, and a custom llm_validator for validating citations. Changes
TipsChat with CodeRabbit Bot (
|
![coderabbitai[bot]](https://avatars.githubusercontent.com/in/347564?s=60&v=4){kind=small}
There was a problem hiding this comment.
Review Status
Actionable comments generated: 0
Configuration used: CodeRabbit UI
Files selected for processing (1)
- docs/blog/posts/validation-part1.md (13} hunks)
Additional comments: 13
docs/blog/posts/validation-part1.md (13)
28-37: The new hunk introduces the
Instructorlibrary and its patching mechanism for theChatCompletionclass. It also imports theBaseModelfrompydanticand defines aUserDetailclass. The changes are consistent with the old hunk and there are no significant issues. However, it's important to ensure that theInstructorlibrary is installed and updated to the latest version to avoid any compatibility issues.50-55: The new hunk introduces a
max_retriesparameter to handle invalid responses. This is a good practice as it prevents the program from getting stuck in an infinite loop if the response is consistently invalid. Theassertstatements are used to verify the output of the function call. Ensure that themax_retriesparameter is set to a reasonable value to avoid excessive retries.74-79: The new hunk introduces the concept of using the
field_validatordecorator in Pydantic for validation. This is a standard practice in Pydantic and there are no significant issues with this change.90-90: The new hunk introduces a
field_validatorfor themessagefield. The validator checks if any word in the message is in the blacklist (rob,steal). If a blacklisted word is found, it raises aValueError. This is a good practice for input validation and there are no significant issues with this change.102-104: The new hunk explains the logic of the
field_validatorintroduced in the previous hunk. It's a good practice to explain the logic of the code for better understanding. There are no significant issues with this change.157-157: The new hunk introduces the
llm_validatorclass fromInstructor. This class uses a statement to verify the value, which is a good practice for dynamic validation. Ensure that thellm_validatorclass is available in theInstructorlibrary.187-187: The new hunk introduces the concept of building your own
llm_validatorwithInstructor. This is a good practice for creating custom validators. There are no significant issues with this change.206-206: The new hunk imports the
instructorlibrary. This is necessary for using thellm_validatorclass. Ensure that theinstructorlibrary is installed and updated to the latest version to avoid any compatibility issues.230-235: The new hunk introduces a
response_modelparameter for theChatCompletionAPI call. This parameter is set to theValidationPydantic class defined earlier. If the response is not valid, aValueErroris raised. This is a good practice for handling invalid responses. Ensure that theValidationclass is defined correctly and theis_validattribute is available in the response object.252-252: The new hunk introduces the concept of validating the chain of thought reasoning using
PydanticandInstructor. This is a good practice for validating the reasoning of AI models. There are no significant issues with this change.283-283: The new hunk imports the
BaseModelandmodel_validatorfrompydantic. These are necessary for defining theAIResponseclass and its validators. Ensure that these are available in thepydanticlibrary.316-331: The new hunk introduces the concept of validating citations from the original text using
PydanticandInstructor. It also introduces themodel_validatefunction inPydanticfor passing additional context to the validators. This is a good practice for validating the context of AI responses. Ensure that themodel_validatefunction is available in thepydanticlibrary.436-436: The new hunk provides a link to the
Instructorlibrary on GitHub. This is a good practice for directing readers to the source code. Ensure that the link is correct and accessible.
There was a problem hiding this comment.
Review Status
Actionable comments generated: 0
Configuration used: CodeRabbit UI
Files selected for processing (1)
- docs/blog/posts/validation-part1.md (14} hunks)
Additional comments: 14
docs/blog/posts/validation-part1.md (14)
28-28: The new hunk provides a more detailed explanation of the
Instructorlibrary and its benefits. It emphasizes the library's ability to handle complex logic, parse and validate responses, and automatically retry invalid responses. This change enhances the clarity and understanding of theInstructorlibrary.37-37: The new hunk adds a comment to the
instructor.patch()line, explaining its purpose. This comment enhances the readability and understanding of the code.50-55: The new hunk introduces a
max_retriesparameter to manage invalid responses and adds assertions to verify the response. This change improves the robustness of the code by handling invalid responses and verifying the output.74-79: The new hunk does not introduce any significant changes. It maintains the explanation of Pydantic's validation approaches.
90-90: The new hunk adds a comment to the line where the sentence is split into individual words. This comment enhances the readability and understanding of the code.
102-104: The new hunk adds a detailed explanation of how the validation process works. This change enhances the clarity and understanding of the validation process.
157-157: The new hunk does not introduce any significant changes. It maintains the explanation of the
llm_validatorclass fromInstructor.187-187: The new hunk does not introduce any significant changes. It maintains the explanation of creating a custom
llm_validator.206-206: The new hunk does not introduce any significant changes. It maintains the explanation of using
Instructorto generate the validation.230-235: The new hunk adds a comment to the line where the
response_modelparameter is passed to theChatCompletionAPI call. This comment enhances the readability and understanding of the code.252-252: The new hunk does not introduce any significant changes. It maintains the explanation of validating the chain of thought using
PydanticandInstructor.283-283: The new hunk does not introduce any significant changes. It maintains the explanation of creating a
BaseModelclass for AI responses.316-324: The new hunk introduces a new section on validating citations from original text. It explains how to use
PydanticandInstructorto perform this validation and how to pass additional context to validation functions. This change enhances the scope and understanding of the validation process.436-436: The new hunk does not introduce any significant changes. It maintains the conclusion of the blog post and the link to the
InstructorGitHub repository.
There was a problem hiding this comment.
Review Status
Actionable comments generated: 10
Configuration used: CodeRabbit UI
Files selected for processing (1)
- docs/blog/posts/validation-part1.md (18} hunks)
Additional comments: 4
docs/blog/posts/validation-part1.md (4)
17-17: The function
validation_function(value)is mentioned but not defined or used in the code. Please ensure that this function is defined and used correctly in the actual code.157-157: The
llm_validatorclass is mentioned but not used in the code. Please ensure that this class is used correctly in the actual code.187-187: The
llm_validatorclass is mentioned but not used in the code. Please ensure that this class is used correctly in the actual code.393-393: The
max_retriesparameter is mentioned but not used in the code. Please ensure that this parameter is used correctly in the actual code.
Co-authored-by: Jason Liu <[email protected]>
Summary by CodeRabbit
llm_validatorfor more flexible validation using language models.ChatCompletionclass for easier integration with OpenAI models and Pydantic objects.max_retriesparameter to handle invalid responses more effectively.