Instructor: Lance Martin, Software Engineer at Langchain
Focus: Implementation of Retrieval-Augmented Generation (RAG) from scratch.
Motivation: Most data is private; LLMs are trained on publicly available data.
Objective: Combine custom data with LLMs using RAG.
Key Concepts
1. Introduction to RAG
Definition: Retrieval-Augmented Generation (RAG) combines LLMs with external data for better contextual responses.
Context Windows: Recent advancements have increased context windows from 4-8k tokens to millions of tokens, allowing extensive private data utilization.
Motivation: Importance of integrating external data into LLMs for processing private and corporate information.
2. Process of RAG
Three Main Steps:
Indexing: Create a database of documents for retrieval.
Retrieval: Extract relevant data from indexed documents based on input queries.
Generation: Use relevant documents to generate answers via LLM.
3. Query Translation Techniques
Goal: Improve retrieval by modifying user queries.
Methods:
Query Rewriting: Reformulate questions for better retrieval.
Sub-question Decomposition: Break down complex questions into simpler sub-questions.
Step-back Prompting: Generate more abstract questions that are easier to answer.
4. Indexing Techniques
Document Indexing: Processing documents to allow easy retrieval.
Embedding Methods: Converting documents into numerical representations (vectors) for comparison.
Multi-Representation Indexing: Utilizing summarized representations of documents for optimized retrieval.
5. Retrieval Techniques
K-Nearest Neighbor Search: Finding relevant documents by comparing the embedded question to the stored document vectors.
Locational Proximity: Documents close in the semantic space are retrieved based on their relevance.
6. Generation Phase
Prompt Construction: Building prompts that combine context and user questions for accurate answers.
Active RAG: Implementing feedback loops to iteratively improve retrieval and generation quality based on relevance and accuracy checks.
Recent Developments in RAG
1. Document-Centric RAG
Overview: Focus on retrieving full documents instead of chunks to avoid issues related to chunking and ensure holistic context is preserved.
Implications: Reduced latency, increased accuracy, and enhanced retrieval quality.
2. Adaptive RAG Framework
Concept: Incorporating testing and validation in RAG workflows to improve the reliability of retrieved documents and generated answers.
Techniques: Utilizing existing query analysis, routing to appropriate sources, and applying grading techniques to ensure relevant and accurate outputs.
Conclusion
Future of RAG: As LLMs evolve, the integration of robust retrieval methods with flexible document management will become increasingly critical.
Encouragement: Engage with the course, experiment with the techniques discussed, and provide feedback for continuous improvement in RAG implementations.