Learn advanced Retrieval-Augmented Generation (RAG), focusing on techniques such as metadata augmentation and metadata filtering using LlamaIndex. This will help you improve your RAG models for better performance and accuracy.
## Links
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- Notebook: https://colab.research.google.com/gist/alejandro-ao/75d18a00f0fbe6bacb57083c210178a1/llamaindex-vector-stores.ipynb
- AI Engineering Bootcamp (join the waitlist): http://aibootcamp.dev/
- Buy me a coffee (or a beer): https://buymeacoffee.com/alejandro.ao
## What You'll Learn
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- Advanced RAG Techniques: Understand the intricacies of Retrieval-Augmented Generation and how it can boost your AI models.
- Metadata Augmentation: Learn how to enrich your data with metadata to improve retrieval and generation processes. Also, control what metadata is sent to your LLM and embedding model.
- Hands-On Notebook: Follow along as we explore a detailed Jupyter notebook, showcasing practical applications of these techniques.
- LlamaIndex Integration: See how LlamaIndex can be integrated into your workflow to streamline RAG processes.
## Timestamps
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0:00 Intro
2:21 Quick Explanation of RAG
6:21 Setup
7:07 Extract text from File
12:26 Metadata Selection
22:40 Metadata Extraction
35:46 Create An Index
43:55 Use a Vector Store
50:15 Conclusion