langsmith-evaluator

To create custom evaluation metrics in LangSmith, you define unique evaluation logic using Python functions and upload them to the platform. This supports both custom code-based evaluators for deterministic checks and LLM-as-Judge for grading outputs.

LLM as Judge evaluates chatbot responses by using an LLM to grade model outputs based on defined criteria. This allows you to assess subjective qualities like professional tone or factual accuracy automatically within LangSmith.

Yes, you can use custom Python code to check AI model output formats in LangSmith. Code-based evaluators allow you to develop deterministic checks for specific output formats or sequences to ensure strict adherence.

To deploy LangSmith evaluators, you need a Python environment with the langsmith, langchain-openai, and python-dotenv libraries installed. You also require a valid LangSmith API key to authenticate and manage your evaluators.

The best way to evaluate AI model accuracy and completeness is combining LLM-as-Judge and custom code evaluators. This handles both factual correctness and structural adherence, providing robust performance measurement for your models.

You upload a custom evaluator script to a LangSmith dataset by using the skill to push your Python functions. This integrates your custom metrics directly with LangSmith datasets and experiments for testing.