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
Open In Colab
📄️ Custom String Evaluator
Open In Colab
📄️ Embedding Distance
Open In Colab
📄️ Exact Match
Open In Colab
📄️ JSON Evaluators
Evaluating extraction and function calling applications often comes down to validation that the LLM's string output can be parsed correctly and how it compares to a reference object. The following JSON validators provide functionality to check your model's output consistently.
📄️ Regex Match
Open In Colab
📄️ Scoring Evaluator
The Scoring Evaluator instructs a language model to assess your model's predictions on a specified scale (default is 1-10) based on your custom criteria or rubric. This feature provides a nuanced evaluation instead of a simplistic binary score, aiding in evaluating models against tailored rubrics and comparing model performance on specific tasks.
📄️ String Distance
Open In Colab