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Text Representation Techniques for RAG Systems with Rust

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Overview

Learn key methods for representing text in RAG systems. Explore why text representation matters, implement a Bag-of-Words model, understand how embeddings capture deeper meaning, visualize embeddings with t-SNE, and compare BOW and embeddings in document retrieval and semantic search.

Syllabus

Unit 1: Introduction to Text Representation: Bag-of-Words Model

Start with a Clean-Up
Building a Vocabulary Dictionary
Transform Text into Numeric Vectors
Building a Bag-of-Words Text Processor

Unit 2: Generating and Comparing Sentence Embeddings

Creating Sentence Embeddings
Comparing Sentence Embeddings
Finding Most Similar Sentence Pairs
Exploring Sentence Similarity Changes
Ranking Sentences by Similarity

Unit 3: Visualizing Sentence Embeddings with t-SNE

Visualizing Sentence Embeddings
Optimizing t-SNE Parameters
Adding a New Category

Unit 4: Comparing Bag-of-Words and Embeddings-Based Semantic Search

Building a Bag of Words
Enhance Bag-of-Words with Bigrams
Bag of Words Search Task
Semantic Search with Embeddings

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