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
Choosing a Vector Database: Considerations
New · Open Source
Kerb - LLM Development Toolkit
Python toolkit for building production-ready LLM applications. Modular utilities for prompts, RAG, agents, structured outputs, and multi-provider support.
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- Efficient and Robust Approximate Nearest Neighbor Search Using Hierarchical Navigable Small World Graphs, Yury Malkov, Dmitry Yashunin, 2018 IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 40 (IEEE) DOI: 10.1109/TPAMI.2018.2889476 - Foundational paper on a key Approximate Nearest Neighbor (ANN) algorithm, aiding in the understanding of recall-speed trade-offs.
- A Survey on Retrieve-Augmented Generation, Lifan Yuan, Chenhao Wang, Wenhao Huang, Wenjie Li, Fandong Meng, Huixing Shao, 2024 arXiv preprint arXiv:2402.19473 DOI: arXiv:2402.19473 - An overview of Retrieve-Augmented Generation (RAG) systems, outlining the role of retrieval components like vector databases.
- A Comprehensive Survey of Approximate Nearest Neighbor Search Algorithms and Systems, Jianqiu Chen, Qi Mao, Kaiwei Li, Mengyan Hu, Chenghao Liu, Yanlei Shang, Xiaoting Shi, Ruobing Huang, Qingyuan Zhang, Yulei Li, Wei Xu, Jinheng Bao, Xiaopeng Li, 2023 arXiv preprint arXiv:2308.08632 DOI: arXiv:2308.08632 - A survey covering various Approximate Nearest Neighbor (ANN) search algorithms and systems, with discussions on scalability and performance.
- Milvus Architecture Overview, Zilliz, 2024 (Zilliz) - Official documentation detailing the architecture, deployment, and scalability of a prominent open-source vector database.
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