Evaluators - LangWatch

LangEvals provides 20 versatile evaluators that are grouped according to their use cases. Each evaluator is a purposeful piece of software that can be explored on this page. For in-depth overview of every evaluator follow to its dedicated page.

General

Evaluator	Description
Azure Jailbreak Detection	This evaluator checks for jailbreak-attempt in the input using Azure’s Content Safety API.
Azure Content Safety	This evaluator detects potentially unsafe content in text, including hate speech, self-harm, sexual content, and violence. It allows customization of the severity threshold and the specific categories to check.
Google Cloud DLP PII Detection	Google DLP PII detects personally identifiable information in text, including phone numbers, email addresses, and social security numbers. It allows customization of the detection threshold and the specific types of PII to check.
Llama Guard	This evaluator is a special version of Llama trained strictly for acting as a guardrail, following customizable guidelines. It can work both as a safety evaluator and as policy enforcement.
OpenAI Moderation	This evaluator uses OpenAI’s moderation API to detect potentially harmful content in text, including harassment, hate speech, self-harm, sexual content, and violence.

Evaluator	Description
Competitor LLM check	This evaluator use an LLM-as-judge to check if the conversation is related to competitors, without having to name them explicitly
Off Topic Evaluator	This evaluator checks if the user message is concerning one of the allowed topics of the chatbot
Competitor Blocklist	This evaluator checks if any of the specified competitors was mentioned
Product Sentiment Polarity	For messages about products, this evaluator checks for the nuanced sentiment direction of the LLM output, either very positive, subtly positive, subtly negative, or very negative.

Evaluator	Description
Lingua Language Detection	This evaluator detects the language of the input and output text to check for example if the generated answer is in the same language as the prompt, or if it’s in a specific expected language.
Query Resolution	This evaluator checks if all the user queries in the conversation were resolved. Useful to detect when the bot doesn’t know how to answer or can’t help the user.
Ragas Context Recall	This evaluator measures the extent to which the retrieved context aligns with the annotated answer, treated as the ground truth. Higher values indicate better performance.
Ragas Faithfulness	This evaluator assesses the extent to which the generated answer is consistent with the provided context. Higher scores indicate better faithfulness to the context.
Ragas Context Utilization	This metric evaluates whether all of the output relevant items present in the contexts are ranked higher or not. Higher scores indicate better utilization.
Ragas Context Relevancy	This metric gauges the relevancy of the retrieved context, calculated based on both the question and contexts. The values fall within the range of (0, 1), with higher values indicating better relevancy.
Ragas Context Precision	This metric evaluates whether all of the ground-truth relevant items present in the contexts are ranked higher or not. Higher scores indicate better precision.
Ragas Answer Relevancy	This evaluator focuses on assessing how pertinent the generated answer is to the given prompt. Higher scores indicate better relevancy.

Evaluator	Description
Semantic Similarity Evaluator	Allows you to check for semantic similarity or dissimilarity between input and output and a target value, so you can avoid sentences that you don’t want to be present without having to match on the exact text.
Custom Basic Evaluator	Allows you to check for simple text matches or regex evaluation.
Custom LLM Boolean Evaluator	Use an LLM as a judge with a custom prompt to do a true/false boolean evaluation of the message.
Custom LLM Score Evaluator	Use an LLM as a judge with custom prompt to do a numeric score evaluation of the message.

Batch Evaluation Unit Tests

On this page

General