Vector Databases for Large Language Models (LLMs)

Vector Databases for Large Language Models (LLMs)

Explore large language models (LLMs) and vector databases, including ANN search, similarity methods, and practical skills using BERT and ChromaDB embeddings.

Beginner

15 Lessons

2h 15min

Certificate of Completion

Explore large language models (LLMs) and vector databases, including ANN search, similarity methods, and practical skills using BERT and ChromaDB embeddings.

AI-POWERED

Explanations

AI-POWERED

Explanations

This course includes

16 Playgrounds

This course includes

16 Playgrounds

Course Overview

The course begins by introducing LLMs and their significance in modern generative AI. Learners will dive deep into vector databases, a vital tool for efficient data storage and querying in LLMs, and explore concepts like Approximate Nearest Neighbor (ANN) search, dense search, sparse search, hybrid search techniques, and similarity measures. You will then learn how to generate and store embeddings with BERT in ChromaDB, gaining hands-on experience handling complex queries and producing accurate recommendat...Show More

What You'll Learn

Knowledge of the fundamentals of vector databases and their necessity in efficiently handling high-dimensional data within LLMs

Hands-on experience implementing Approximate Nearest Neighbor (ANN) search techniques to improve data retrieval efficiency

The ability to generate embeddings using BERT and store them in ChromaDB, a leading vector database

The ability to query vector databases to generate recommendations, bridging the gap between theory and practice in AI-driven solutions

What You'll Learn

Knowledge of the fundamentals of vector databases and their necessity in efficiently handling high-dimensional data within LLMs

Show more

Course Content

1.

Getting Started

This chapter introduces the course, outlining its goals and providing an overview of large language models (LLMs), their functions, and their importance.
3.

Guide to Generate Embeddings and Store in ChromaDB

This chapter explores how to generate and store embeddings in ChromaDB using BERT, and how to query the database for generating recommendations.
4.

Conclusion

This chapter concludes this course and recaps the key concepts and skills covered.

Trusted by 1.4 million developers working at companies

Anthony Walker

@_webarchitect_

Evan Dunbar

ML Engineer

Carlos Matias La Borde

Software Developer

Souvik Kundu

Front-end Developer

Vinay Krishnaiah

Software Developer

Eric Downs

Musician/Entrepeneur

Kenan Eyvazov

DevOps Engineer

Souvik Kundu

Front-end Developer

Eric Downs

Musician/Entrepeneur

Anthony Walker

@_webarchitect_

Evan Dunbar

ML Engineer

Hands-on Learning Powered by AI

See how Educative uses AI to make your learning more immersive than ever before.

Instant Code Feedback

Evaluate and debug your code with the click of a button. Get real-time feedback on test cases, including time and space complexity of your solutions.

AI-Powered Mock Interviews

Adaptive Learning

Explain with AI

AI Code Mentor

FOR TEAMS

Interested in this course for your business or team?

Unlock this course (and 1,000+ more) for your entire org with DevPath