Skip To Main Content
Import Langchain on Muck Rack

Import Langchain

As seen in: Towards Data Science, CSDN
Is this you? As a journalist, you can create a free Muck Rack account to customize your profile, list your contact preferences, and upload a portfolio of your best work. Claim your profile
Back to Import Langchain’s profile

Articles by Import Langchain

A Visual Exploration of Semantic Text Chunking

|
By Robert Martin-Short, Import Langchain, StrOutput Parser
| Towards Data Science
Automatic information retrieval and summarization of large volumes of text has many useful applications. One of the most well developed is Retrieval Augmented Generation (RAG), which involves extraction of relevant chunks of text from a large corpus — typically via semantic search or some other filtering step — in response to a user question. Then, the chunks are interpreted or summarized by an LLM with the aim of providing a high quality, accurate answer.
Open in Who Shared Issue with article?

LangChain大型语言模型(LLM)应用开发(五)：评估_-派神-的博客-CSDN博客

|
By Import Langchain
| CSDN
LangChain是一个基于大语言模型（如ChatGPT）用于构建端到端语言模型应用的 Python 框架。它提供了一套工具、组件和接口，可简化创建由大型语言模型 (LLM) 和聊天模型提供支持的应用程序的过程。LangChain 可以轻松管理与语言模型的交互，将多个组件链接在一起，以便在不同的应用程序中使用。 今天我们来学习DeepLearning.AI的在线课程:LangChain for LLM Application Development的第五门课：Evaluation(评估)，所谓评估是指检验LLM回答的问题是否正确的方法，在上一篇博客Q&A over Documents中我们解释了如何通过langchain来实现对文档的问答功能，在文档的问答过程中LLM会就用户提出的关于文档内容的相关问题进行回答，那么今天我们需要研究的就是如何来检验LLM的回答是否正确？ 要评估LLM回答问题的准确性大致需要下面几个步骤： 需要创建一组关于相关的问答测试集(包含了问题和标准答案) 让LLM回答测试集中的所有问题，并收集LLM给出的所有答案...
Open in Who Shared Issue with article?
Show More