OpenAI_API

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)

\text{cosine similarity (θ)} = \frac{{a} \cdot {b}}{\|{a}\| \|{b}\|}

Explore the use of various distance metrics, including cosine similarity, dot product, and Manhattan distance to measure similarity search in vector databases.

Getting Started

Vector Databases

Guide to Generate Embeddings and Store in ChromaDB

Conclusion

Similarity Measures: Cosine, Dot Product, and Manhattan Distance