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Scaling up RAG with Vector Databases in Rust

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Overview

Scale up your RAG system by building and querying a vector database. Learn to preprocess documents, store chunk embeddings in ChromaDB, retrieve relevant chunks, and construct prompts that can handle multiple context chunks. Additionally, see how to manage updates to your collection and how to approach large-scale ingestion using batch strategies.

Syllabus

Unit 1: Chunking Text for Retrieval-Augmented Generation Systems

Mastering Document Chunking Basics
Preserve Sentence Boundaries in Chunks
Enhance Text Chunking Skills
Organize Chunks with Metadata
Enhance Text with Keyword Detection

Unit 2: Storing and Managing Text Chunks in Vector Databases

Loading and Chunking text
Building a ChromaDB Collection
Dynamic Chunk Management in ChromaDB
Dynamic Document Management in ChromaDB

Unit 3: Retrieving and Prompt Building in RAG Systems

Adding Query Options
Complete the Retrieval
Crafting Context-Rich LLM Prompts
Metadata-Based Retrieval Enhancement
Adding Distance-Based Filtering

Unit 4: Metadata-Based Filtering in RAG Systems

Where Clauses
Crafting Metadata Enhanced Search
Adding Date-Based Filtering
Adding a simple Fallback

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