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所在平台: Udemy |
课程主页: https://www.udemy.com/course/qdrant-vector-database/
课程评论：没有评论
课程名称：使用Python的Qdrant（向量数据库）简介 课程概述：Qdrant是一款开源向量数据库，内置向量相似度搜索引擎。它使用Rust编写，经过验证在生产环境下即使在高负载情况下也表现出快速和可靠的特点。Qdrant提供了方便的API，以存储、搜索和管理向量及其相关的载荷。该课程将为您提供使用Python接口操作Qdrant的实用技能。学习之前，您需要具备基本的Python编程、Linux命令、Docker和Docker Compose的知识。 课程亮点： - 所有讲座均从基础开始设计，使复杂主题易于理解。 - 视频讲座中展示了大量的实用示例。 - 提供可下载的Python Notebook，包含课程中使用的示例。 - 视频讲座内容精准且信息丰富。 - 每个重要视频讲座后都有测验。 - 涵盖Qdrant的广泛基础主题。 完成该课程后，您将能够： - 使用Python安装和操作Qdrant。 - 管理Qdrant中的集合。 - 对存储在Qdrant集合中的向量进行向量搜索，过滤搜索结果。 - 创建和管理快照。 - 使用Qdrant构建可扩展的真实世界AI应用。 该课程将定期更新，现在注册可获得终身访问该课程的机会！
Qdrant is an Open Source vector database with in-built vector similarity search engine. Qdrant is written in Rust and is proven to be fast and reliable even under high load in production environment. Qdrant provides convenient API to store, search and manage vectors along with the associated payload for the vectors.This course will provide you with solid practical Skills in Qdrant using its Python interface. Before you begin, you are required to have basic knowledge onPython ProgrammingLinux CommandsDocker and Docker ComposeSome of the highlights of this course areAll lectures have been designed from the ground up to make the complex topics easy to understandAmple working examples demonstrated in the video lecturesDownloadable Python notebooks for the examples that were used in the coursePrecise and informative video lecturesQuiz at the end of every important video lecturesCovers a wide range of fundamental topics in Qdrant After completing this course, you will be able toInstall and work with Qdrant using PythonManage Collections in QdrantPerform vector search on vectors stored in Qdrant collection Filter the search resultsCreate and manage snapshotsUse Qdrant to build scalable real-world AI appsThis course will be updated periodically and enroll now to get lifelong access to this course!
