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Gain insights into vector databases for LLMs. Delve into BERT embeddings with ChromaDB, exploring ANN, dense, sparse, hybrid searches, and similarity measures to boost LLM performance efficiently.

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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 recommendations. By mastering these techniques, you will enhance the performance of LLMs, making them more powerful and efficient.

By the end of this course, you’ll be equipped with essential skills for working with vector databases and embeddings in Python, positioning you to excel in AI development.

Vector Databases for Large Language Models (LLMs)

Cannot be used for machine learning tasks

What is a key characteristic of vector data?

What is a key characteristic of vector data?

Embeddings are processed representations, while vectors can be raw data.

Embeddings are used for storage, while vectors are used for retrieval.

There is no difference; they are interchangeable terms.

Embeddings are for images only, while vectors are for text data.

How do vector embeddings differ from vector data? 

How do vector embeddings differ from vector data?

What is the process of converting data into numerical vectors for storage in a vector database called?

What is the process of converting data into numerical vectors for storage in a vector database called?

Which indexing technique is commonly used in vector databases for efficient similarity searches?

Which indexing technique is commonly used in vector databases for efficient similarity searches?

What is commonly used in sparse search to represent documents?

What is commonly used in sparse search to represent documents?

What is *not* a common indexing strategy used for vector search?

What is not a common indexing strategy used for vector search?

Interprets the similarity score as a distance metric (smaller = closer).

Offers the fastest computation time for all similarity calculations.

Measures the directional similarity between vectors, independent of their magnitude.

What is the primary advantage of using cosine similarity for comparing vectors?

What is the primary advantage of using cosine similarity for comparing vectors?

The overall cost of implementing and maintaining the database.


The nature of your data and the types of queries you need to perform.

The familiarity of your development team with different database technologies.

The scalability requirements for handling future growth in data volume.  (Both databases can be scalable)

When deciding between a vector database and a traditional relational database for your project, what factor should receive the highest priority?

When deciding between a vector database and a traditional relational database for your project, what factor should receive the highest priority?

Test your knowledge of vector databases for LLMs.

Getting Started

Vector Databases

Guide to Generate Embeddings and Store in ChromaDB

Conclusion

Quiz on Vector Databases