LangChain Evaluator

LangChain Evaluator refers to langchain.evaluation, the module that scores large language model workflows — chains, agents, and retrieval-augmented 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 scores such as correctness, relevance, embedding distance, and string distance. 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 land in LangSmith, where prompt versions, models, and retrieval settings can be compared across runs. Continuous assessment feeds into CI pipelines — failing a new pull request if accuracy drops — while cost trackers flag token spikes. Standardized benchmarks make operational tuning repeatable. The module is now legacy: LangChain points evaluation work to LangSmith and to the separate openevals and agentevals packages, and in LangChain 1.0 it moved to langchain-classic, the package that holds v0.x functionality for backward compatibility.

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Related services: LangChain development company, Agentic AI consulting.

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