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Retrieval Augmented Generation with LlamaIndex

In this session you'll learn how to get started with Chroma and perform Q&A on some documents using Llama 2, the RAG technique, and LlamaIndex.
Dan Becker
Dan Becker

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Large language models (LLMs) like Llama 2 are the must-have technology of the year. Unfortunately, LLMs can't accurately answer questions about your business because they lack enough domain knowledge. The solution is to combine the LLM with a vector database like Chroma—a technique known as retrieval augmented generation (RAG). Beyond this, incorporating AI into products is best done with an AI application framework, like LlamaIndex.

In this session you'll learn how to get started with Chroma and perform Q&A on some documents using Llama 2, the RAG technique, and LlamaIndex.

Key Takeaways:

  • Learn how to store text in the Chroma vector database.
  • Learn how to use retrieval augmented generation to combine LLama 2 and Chroma.
  • Learn how to develop AI applications using LlamaIndex

Additional Resources

[COURSE] Dan's course: Introduction to Deep Learning in Python

[CODE-ALONG SERIES] Become a Generative AI developer

[SKILL TRACK] OpenAI Fundamentals

[SKILL TRACK] Deep Learning in Python

[BLOG] The Top 5 Vector Databases

[TUTORIAL] Mastering Vector Databases with Pinecone: A Comprehensive Guide

Sujets

Machine LearningArtificial IntelligenceData Scientist

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