LangChain Evaluator

LangChain Evaluator is the framework’s built-in benchmark tool that evaluates large language model workflows — chains, agents, and augmented extraction generation (RAG) — for quality, cost, and latency. Developers put any callable into the Evaluator, pass it a dataset of prompts and valid answers, and then get metrics like accuracy, relevance, answer similarity, and pass@k. Evaluators come in three flavors: string-based (exact match, ROUGE, BLEU), embedded (cosine similarity of sentence vectors), and LLM-based (a judge’s model scores output by rubric). Results are delivered to CSV, Weights & Biases, or OpenTelemetry panels, allowing for A/B testing of prompts, model replacement, and vector storage adjustments. Continuous assessment feeds into CI pipelines — failing a new pull request if accuracy drops — while cost trackers flag token spikes. By standardizing benchmarks, LangChain assessment turns subjective operational tuning into a repeatable science, accelerating secure, data-driven deployment of generative AI.