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Discover cutting-edge methods to perform retrieval-augmented generation (RAG) with a vector database.
Overview
Syllabus
Introduction
- RAG with vector databases: Advanced strategies for AI optimization
- What you should know
- Setting up your exercise files
- Introduction to preprocessing for RAG
- Chunking considerations
- Chunking examples
- Introduction to embeddings
- Embedding examples
- Metadata
- Demo: Chunking
- Demo: Metadata
- Demo: Embed and store
- Demo: Querying
- Demo: Adding the LLM
- Challenge: Cite your document sources
- Solution: Cite your document sources
- Challenge: Change the chunk size
- Solution: Change the chunk size
- Introduction to vector embeddings for images
- Vision models 101
- Demo: Getting semantic vectors
- Demo: Storing image vectors
- Demo: Comparing images semantically
- Challenge: Find the dog most similar to a cat
- Solution: Find the dog most similar to a cat
- Introduction to the types of multimodality
- Ways to do multimodal RAG
- Introduction to multimodal embedding models
- Demo: Embedding and storing data
- Demo: Query images with text
- Challenge: Find anomalies in your embeddings
- Solution: Find anomalies in your embeddings
- Next steps
Taught by
Yujian Tang
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