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

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Explanations

This course includes

15 Playgrounds

This course includes

15 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

Course Content

Getting Started

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

Introduction to the Course

Introduction to Large Language Models (LLMs)

Vector Databases

This chapter introduces vector databases and explains their significance for LLMs, their working principles, and key search techniques.

Introduction to Vector Databases Need of Vector Databases for LLMs Working of Vector Databases Approximate Nearest Neighbor (ANN) Search Implementation Dense, Sparse, and Hybrid Search Techniques Similarity Measures: Cosine, Dot Product, and Manhattan Distance Similarity Measures: Hamming, Euclidean, and Squared Euclidean Chroma Vector Database Applications of Vector Databases Quiz on Vector Databases

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.

Generating and Storing Embeddings in ChromaDB Using BERT Querying the Database to Generate Recommendations

Conclusion

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

Wrapping Up

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