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Text Representation Techniques for RAG Systems

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Overview

Learn essential text representation methods for RAG systems, from Bag-of-Words to embeddings. Explore how these techniques enhance understanding and retrieval, 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

Text Cleaning with Python
Building a Vocabulary Dictionary
Transform Text into Numeric Vectors
Bag-of-Words Vectorization Task

Unit 2: Generating and Comparing Sentence Embeddings

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

Unit 3: Visualizing Sentence Embeddings with t-SNE

Visualize Sentence Clusters
Explore t-SNE Perplexity Effects
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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