Logo
Project Icon

vector-search-class-notes

向量搜索和数据库在人工智能长期记忆中的应用

向量搜索AI机器学习数据库嵌入Github开源项目

该项目深入探讨人工智能长期记忆技术中的向量搜索和数据库应用。课程内容涵盖向量搜索的理论基础和实际实现，包括文本和图像嵌入、低维向量搜索、降维技术、近似最近邻搜索、聚类和量化等关键主题。由Pinecone创始人Edo Liberty和FAISS主要开发者Matthijs Douze等行业专家主讲，为学习者提供全面而专业的向量搜索知识。

GithubGithub文档文档
介绍相关项目

Long Term Memory in AI - Vector Search and Databases

NOTE: COS 597A class times changed for Fall semester 2023. Classes will be held 9am-12noon.

Instructors

Overview

Long Term Memory is a foundational capability in the modern AI Stack. At their core, these systems use vector search. Vector search is also a basic tool for systems that manipulate large collections of media like search engines, knowledge bases, content moderation tools, recommendation systems, etc. As such, the discipline lays at the intersection of Artificial Intelligence and Database Management Systems. This course will cover the theoretical foundations and practical implementation of vector search applications, algorithms, and systems. The course will be evaluated with project and in-class presentation.

Contribute

All class materials are intended to be used freely by academics anywhere, students and professors alike. Please contribute in the form of pull requests or by opening issues.

https://github.com/edoliberty/vector-search-class-notes

On unix-like systems (e.g. macos) with bibtex and pdflatex available you should be able to run this:

git clone git@github.com:edoliberty/vector-search-class-notes.git
cd vector-search-class-notes
./build

Syllabus

  • 9/8 - Class 1 - Introduction to Vector Search [Matthijs + Edo + Nataly]

    • Intro to the course: Topic, Schedule, Project, Grading, ...

    • Embeddings as an information bottleneck. Instead of learning end-to-end, use embeddings as an intermediate representation

    • Advantages: scalability, instant updates, and explainability

    • Typical volumes of data and scalability. Embeddings are the only way to manage / access large databases

    • The embedding contract: the embedding extractor and embedding indexer agree on the meaning of the distance. Separation of concerns.

    • The vector space model in information retrieval

    • Vector embeddings in machine learning

    • Define vector, vector search, ranking, retrieval, recall

  • 9/15 - Class 2 - Text embeddings [Matthijs]

    • 2-layer word embeddings. Word2vec and fastText, obtained via a factorization of a co-occurrence matrix. Embedding arithmetic: king + woman - man = queen, (already based on similarity search)
    • Sentence embeddings: How to train, masked LM. Properties of sentence embeddings.
    • Large Language Models: reasoning as an emerging property of a LM. What happens when the training set = the whole web

  • 9/22 - Class 3 - Image embeddings [Matthijs]

    • Pixel structures of images. Early works on direct pixel indexing
    • Traditional CV models. Global descriptors (GIST). Local descriptors (SIFT and friends)Direct indexing of local descriptors for image matching, local descriptor pooling (Fisher, VLAD)
    • Convolutional Neural Nets. Off-the-shelf models. Trained specifically (contrastive learning, self-supervised learning)
    • Modern Computer Vision models

  • 9/29 - Class 4 - Low Dimensional Vector Search [Edo]

    • Vector search problem definition
    • k-d tree, space partitioning data structures
    • Worst case proof for kd-trees
    • Probabilistic inequalities. Recap of basic inequalities: Markov, Chernoof, Hoeffding
    • Concentration Of Measure phenomena. Orthogonality of random vectors in high dimensions
    • Curse of dimensionality and the failure of space partitioning

  • 10/6 - Class 5 - Dimensionality Reduction [Edo]

    • Singular Value Decomposition (SVD)
    • Applications of the SVD
    • Rank-k approximation in the spectral norm
    • Rank-k approximation in the Frobenius norm
    • Linear regression in the least-squared loss
    • PCA, Optimal squared loss dimension reduction
    • Closest orthogonal matrix
    • Computing the SVD: The power method
    • Random-projection
    • Matrices with normally distributed independent entries
    • Fast Random Projections

  • 10/13 - No Class - Midterm Examination Week

  • 10/20 - No Class - Fall Recess

  • 10/27 - Class 6 - Approximate Nearest Neighbor Search [Edo]

    • Definition of Approximate Nearest Neighbor Search (ANNS)
    • Criteria: Speed / accuracy / memory usage / updateability / index construction time
    • Definition of Locality Sensitive Hashing and examples
    • The LSH Algorithm
    • LSH Analysis, proof of correctness, and asymptotics

  • 11/3 - Class 7 - Clustering [Edo]

    • K-means clustering - mean squared error criterion.
    • Lloyd’s Algorithm
    • k-means and PCA
    • ε-net argument for fixed dimensions
    • Sampling based seeding for k-means
    • k-means++
    • The Inverted File Model (IVF)

  • 11/10 - Class 8 - Quantization for lossy vector compression This class will take place remotely via zoom, see the edstem message to get the link [Matthijs]

    • Python notebook corresponding to the class: Class_08_runbook_for_students.ipynb
    • Vector quantization is a topline (directly optimizes the objective)
    • Binary quantization and hamming comparison
    • Product quantization. Chunked vector quantization. Optimized vector quantization
    • Additive quantization. Extension of product quantization. Difficulty in training approximations (Residual quantization, CQ, TQ, LSQ, etc.)
    • Cost of coarse quantization vs. inverted list scanning

  • 11/17 - Class 9 - Graph based indexes by Guest lecturer Harsha Vardhan Simhadri.

    • Early works: hierarchical k-means
    • Neighborhood graphs. How to construct them. Nearest Neighbor Descent
    • Greedy search in Neighborhood graphs. That does not work -- need long jumps
    • HNSW. A practical hierarchical graph-based index
    • NSG. Evolving a graph k-NN graph

  • 11/24 - No Class - Thanksgiving Recess

  • 12/1 - Class 10 - Student project and paper presentations [Edo + Nataly]

Project

Class work includes a final project. It will be graded based on

  1. 50% - Project submission
  2. 50% - In-class presentation

Projects can be in three different flavors

  • Theory/Research: propose a new algorithm for a problem we explored in class (or modify an existing one), explain what it achieves, give experimental evidence or a proof for its behavior. If you choose this kind of project you are expected to submit a write up.
  • Data Science/AI: create an interesting use case for vector search using Pinecone, explain what data you used, what value your application brings, and what insights you gained. If you choose this kind of project you are expected to submit code (e.g. Jupyter Notebooks) and a writeup of your results and insights.
  • Engineering/HPC: adapt or add to FAISS, explain your improvements, show experimental results. If you choose this kind of project you are expected to submit a branch of FAISS for review along with a short writeup of your suggested improvement and experiments.

Project schedule  

  • 11/24 - One-page project proposal approved by the instructors
  • 12/1 - Final project submission
  • 12/1 - In-class presentation

Some more details

  • Project Instructor: Nataly nbrukhim@princeton.edu
  • Projects can be worked on individually, in teams of two or at most three students.
  • Expect to spend a few hours over the semester on the project proposal. Try to get it approved well ahead of the deadline.
  • Expect to spent 3-5 full days on the project itself (on par with preparing for a final exam)
  • In class project project presentation are 5 minutes per student (teams of two students present for 10 minutes. Teams of three, 15 minutes).  

Selected Literature

相关项目
项目侧边栏1项目侧边栏2
推荐项目
Project Cover

豆包MarsCode

豆包 MarsCode 是一款革命性的编程助手，通过AI技术提供代码补全、单测生成、代码解释和智能问答等功能，支持100+编程语言，与主流编辑器无缝集成，显著提升开发效率和代码质量。

Project Cover

AI写歌

Suno AI是一个革命性的AI音乐创作平台，能在短短30秒内帮助用户创作出一首完整的歌曲。无论是寻找创作灵感还是需要快速制作音乐，Suno AI都是音乐爱好者和专业人士的理想选择。

Project Cover

有言AI

有言平台提供一站式AIGC视频创作解决方案，通过智能技术简化视频制作流程。无论是企业宣传还是个人分享，有言都能帮助用户快速、轻松地制作出专业级别的视频内容。

Project Cover

Kimi

Kimi AI助手提供多语言对话支持，能够阅读和理解用户上传的文件内容，解析网页信息，并结合搜索结果为用户提供详尽的答案。无论是日常咨询还是专业问题，Kimi都能以友好、专业的方式提供帮助。

Project Cover

阿里绘蛙

绘蛙是阿里巴巴集团推出的革命性AI电商营销平台。利用尖端人工智能技术，为商家提供一键生成商品图和营销文案的服务，显著提升内容创作效率和营销效果。适用于淘宝、天猫等电商平台，让商品第一时间被种草。

Project Cover

吐司

探索Tensor.Art平台的独特AI模型，免费访问各种图像生成与AI训练工具，从Stable Diffusion等基础模型开始，轻松实现创新图像生成。体验前沿的AI技术，推动个人和企业的创新发展。

Project Cover

SubCat字幕猫

SubCat字幕猫APP是一款创新的视频播放器，它将改变您观看视频的方式！SubCat结合了先进的人工智能技术，为您提供即时视频字幕翻译，无论是本地视频还是网络流媒体，让您轻松享受各种语言的内容。

Project Cover

美间AI

美间AI创意设计平台，利用前沿AI技术，为设计师和营销人员提供一站式设计解决方案。从智能海报到3D效果图，再到文案生成，美间让创意设计更简单、更高效。

Project Cover

稿定AI

稿定设计 是一个多功能的在线设计和创意平台，提供广泛的设计工具和资源，以满足不同用户的需求。从专业的图形设计师到普通用户，无论是进行图片处理、智能抠图、H5页面制作还是视频剪辑，稿定设计都能提供简单、高效的解决方案。该平台以其用户友好的界面和强大的功能集合，帮助用户轻松实现创意设计。

使用协议隐私政策广告服务
投诉举报邮箱: service@vectorlightyear.com
@2024 懂AI·鲁ICP备2024100362号-6·鲁公网安备37021002001498号