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Vector Database Fundamentals
Rating: 4.5 out of 5(72 ratings)
1,491 students

Vector Database Fundamentals

Mastering RAG: Vector Search, Embeddings, and LLM Integration
Created byMichael Ryaboy
Last updated 11/2024
English
English [Auto],

What you'll learn

  • Implement Retrieval Augmented Generation (RAG) using KDB AI and OpenAI, including setting up a complete RAG pipeline.
  • Gain hands-on experience in data preparation, embedding generation, vector database operations, and integration with language models.
  • Master vector search techniques, advanced vector operations, and querying methods for efficient information retrieval.
  • Explore practical applications of RAG in AI-powered systems and NLP projects.

Course content

4 sections • 9 lectures • 1h 39m total length

  • Introduction to Vector Search10:21

    Explore how vector search converts unstructured data into semantic vectors with embedding models like Bert and GPT, enabling retrieval via cosine similarity, Euclidean distance, and multimodal Rag.

  • Introduction to Vector Databases9:51

    Learn how vector databases store high-dimensional vectors with metadata and use specialized indexes. Compare them to in-memory solutions, noting easier deployment and use cases like semantic search and recommendations.

  • Similarity Metrics12:01

    Explore vector database similarity metrics, including Euclidean distance, dot product, and cosine similarity. Learn how to choose the right measure for embeddings and applications like clustering, information retrieval, and recommendations.

Requirements

  • Basic knowledge of Python programming. Familiarity with machine learning and NLP concepts is helpful. A KDB AI account (free tier) and OpenAI API access are recommended for exercises.

Description

Dive into the world of vector databases and Retrieval Augmented Generation (RAG) with our comprehensive KDB AI course. Learn how to efficiently store, search, and retrieve high-dimensional data using cutting-edge techniques.

Key topics include:

  • Vector search fundamentals and applications

  • Advanced metadata filtering

  • Implementing RAG pipelines to enhance AI applications

  • Choosing and optimizing embedding models

  • Mastering similarity metrics: Euclidean distance, cosine similarity, and dot product

  • Leveraging indexes like HNSW and IVF-PQ for improved performance

  • Building sophisticated query systems with metadata filtering

Practical demonstrations cover:

  • Creating and managing tables

  • Implementing a RAG pipeline from scratch

  • Using metadata filters to make complex queries with groupings and aggregations

Some questions you will be able to answer after this course:

  • How do I choose an index? What are the right algorithm parameters for my data?

  • How do I choose an embedding model?

  • How do I optimize RAG performance?

  • How do I use a vector database to gain insights from my unstructured data


Whether you're a data scientist, ML engineer, or AI enthusiast, this course equips you with the skills to create powerful AI-driven applications. Learn to combine vector search with large language models, optimize query performance, and solve real-world problems across various industries.

Join us to unlock the full potential of semantic search and RAG with KDB AI Vector Database!

Gain hands-on experience with KDB AI Cloud instances. Master the intricacies of vector embeddings and learn to build scalable, efficient AI systems that push the boundaries of intelligent search and generation.

Who this course is for:

  • This course is ideal for software developers building AI applications, data scientists enhancing NLP projects, AI enthusiasts interested in vector databases and language models, and professionals implementing advanced search and question-answering systems.

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