String Evaluators
A string evaluator is a component within LangChain designed to assess the performance of a language model by comparing its generated outputs (predictions) to a reference string or an input. This comparison is a crucial step in the evaluation of language models, providing a measure of the accuracy or quality of the generated text.
In practice, string evaluators are typically used to evaluate a predicted string against a given input, such as a question or a prompt. Often, a reference label or context string is provided to define what a correct or ideal response would look like. These evaluators can be customized to tailor the evaluation process to fit your application's specific requirements.
To create a custom string evaluator, inherit from the StringEvaluator class and implement the _evaluate_strings method. If you require asynchronous support, also implement the _aevaluate_strings method.
Here's a summary of the key attributes and methods associated with a string evaluator:
evaluation_name: Specifies the name of the evaluation.requires_input: Boolean attribute that indicates whether the evaluator requires an input string. If True, the evaluator will raise an error when the input isn't provided. If False, a warning will be logged if an input is provided, indicating that it will not be considered in the evaluation.requires_reference: Boolean attribute specifying whether the evaluator requires a reference label. If True, the evaluator will raise an error when the reference isn't provided. If False, a warning will be logged if a reference is provided, indicating that it will not be considered in the evaluation.
String evaluators also implement the following methods:
aevaluate_strings: Asynchronously evaluates the output of the Chain or Language Model, with support for optional input and label.evaluate_strings: Synchronously evaluates the output of the Chain or Language Model, with support for optional input and label.
The following sections provide detailed information on available string evaluator implementations as well as how to create a custom string evaluator.
📄️ Criteria Evaluation
In scenarios where you wish to assess a model's output using a specific rubric or criteria set, the criteria evaluator proves to be a handy tool. It allows you to verify if an LLM or Chain's output complies with a defined set of criteria.
📄️ Custom String Evaluator
You can make your own custom string evaluators by inheriting from the StringEvaluator class and implementing the evaluatestrings (and aevaluatestrings for async support) methods.
📄️ Embedding Distance
To measure semantic similarity (or dissimilarity) between a prediction and a reference label string, you could use a vector vector distance metric the two embedded representations using the embeddingdistance evaluator.[1]
📄️ String Distance
One of the simplest ways to compare an LLM or chain's string output against a reference label is by using string distance measurements such as Levenshtein or postfix distance. This can be used alongside approximate/fuzzy matching criteria for very basic unit testing.