Getting Started with Retrieve-Augmented Generation (RAG)
Chapter 1: Introduction to Retrieve-Augmented Generation
Limitations of Standard Large Language Models
What is Retrieve-Augmented Generation (RAG)?
The Core Architecture of a RAG System
RAG vs. Fine-tuning: Understanding the Differences
Benefits of Using RAG
Quiz for Chapter 1
Chapter 2: The Retrieval Component
Role of the Retriever in RAG
Introduction to Vector Embeddings
Common Embedding Models
Similarity Search: Finding Relevant Vectors
Introduction to Vector Databases
Choosing a Vector Database: Considerations
Practice: Generating Text Embeddings
Quiz for Chapter 2
Chapter 3: Preparing Data for Retrieval
Loading Documents from Various Sources
The Need for Document Chunking
Fixed-Size Chunking Strategies
Content-Aware Chunking Approaches
Metadata Association with Chunks
Storing Processed Data in a Vector Database
Hands-on Practical: Chunking Documents
Quiz for Chapter 3
Chapter 4: The Generation Component and Augmentation
Role of the Generator (LLM) in RAG
Structuring Prompts for RAG
Context Injection Methods
Managing Context Length Limitations
Generating the Final Response
Attributing Sources in Generated Output
Quiz for Chapter 4
Chapter 5: Building a Basic RAG Pipeline
Overview of RAG Frameworks (e.g., LangChain, LlamaIndex)
Setting up the Environment
Implementing the Retriever
Implementing the Generator Integration
Combining Retrieval and Generation
Running Queries Through the Pipeline
Hands-on Practical: End-to-End RAG System
Quiz for Chapter 5
Chapter 6: Evaluating and Improving RAG Systems
Challenges in Evaluating RAG
Component-Level Evaluation: Retrieval
Component-Level Evaluation: Generation
End-to-End RAG Evaluation Frameworks
Common Failure Modes
Basic Strategies for Improvement
Practice: Analyzing RAG Output Quality
Quiz for Chapter 6
Common Embedding Models
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- Attention Is All You Need, Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Łukasz Kaiser, Illia Polosukhin, 2017 Advances in Neural Information Processing Systems, Vol. 30 (Curran Associates, Inc.) DOI: 10.48550/arXiv.1706.03762 - This paper introduced the Transformer architecture, which underpins modern NLP models like BERT and SBERT.
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding, Jacob Devlin, Ming-Wei Chang, Kenton Lee, Kristina Toutanova, 2019 Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), Vol. 1 (Association for Computational Linguistics) DOI: 10.18653/v1/N19-1423 - Presents the BERT model, a foundational pre-trained language model that later served as a base for specialized models like SBERT.
- Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks, Nils Reimers, Iryna Gurevych, 2019 Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) (Association for Computational Linguistics) DOI: 10.18653/v1/D19-1410 - The seminal paper introducing Sentence-BERT, specifically designed for generating high-quality sentence embeddings suitable for similarity tasks.
- Sentence-Transformers Documentation, Nils Reimers, 2024 (Hugging Face) - Official documentation for the
sentence-transformerslibrary, offering practical guides, model examples, and implementation details.
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