Introduction to RAG Hey, folks, happy Saturday. In this session, Pavan Mantha from Orbcomm takes us through the technical foundations of Retrieval-Augmented Generation.
Key Takeaways
- RAG is crucial for grounding LLMs with domain-specific knowledge not present in their training data
- Effective RAG implementation requires addressing challenges at each stage: document processing, embedding, retrieval, and generation
- Advanced techniques like hybrid embeddings, fine-tuned embedding models, and diverse chunking strategies can significantly improve RAG performance
- Evaluation, observability, and continuous improvement are essential for production RAG systems
Introduction to RAG
- RAG allows incorporating domain-specific knowledge into LLM responses
- Basic RAG pipeline: document chunking → embedding → vector storage → retrieval → augmentation → LLM generation
- Key components: embedding models (dense/sparse vectors), vector databases, chunking strategies
RAG Implementation Challenges
- Document parsing: Handling unstructured data (images, tables, infographics)
- Chunking: Determining optimal chunk size and overlap
- Embedding: Selecting appropriate models and dimensions, handling multilingual content
- Vector databases: Indexing and tuning for retrieval performance
- Query processing: Addressing ambiguity and selecting effective search methodologies
- Augmentation: Crafting clear instructions for LLMs
- Generation: Ensuring high-quality, relevant responses
Advanced RAG Architectures
- Hybrid embedding methods: Combining dense and sparse vectors for improved retrieval
- Fine-tuned embedding models: Domain-specific training for better vector representations
- LLM ensembling: Using multiple LLMs and a "judge" LLM for more accurate responses
Chunking Strategies
- Token chunking: Basic splitting based on token count
- Sentence chunking: Splitting based on sentence boundaries and semantics
- Recursive chunking: Iterative splitting based on rules or document structure
- Semantic chunking: Grouping semantically related content across document sections
- Late chunking: Embedding full documents, then splitting embeddings
- Neural chunking: Using fine-tuned models for intelligent splitting
- Slumber chunking: Agent-based approach for adaptive chunking decisions
RAG vs. Fine-tuning
- Fine-tuning LLMs: Suitable for stable domain knowledge, less frequent updates
- RAG: Better for dynamic data, frequent updates, and maintaining up-to-date information
Structured Data in RAG
- Approaches: Using agents (e.g., Agno, QAI frameworks) or SQL Alchemy
- Process: Provide knowledge base of sample queries/answers, expose schema structure to LLM
- LLM infers table relationships, generates SQL queries, executes in REPL for validation
Observability and Evaluation
- Observability tools: MLflow, Langfuse, Aris Phoenix, Openlit
- Evaluation frameworks: Ragas, Deep Evals
- Key metrics: Context relevance, answer relevancy, faithfulness, retrieval accuracy
Continuous Improvement
- Collect user feedback on response quality
- Analyze traces of queries, retrievals, and contexts for dissatisfactory responses
- Tune hyperparameters: top-K, embedding model, chunking strategy, instruction sets, LLM settings
- Regularly update and refine the RAG pipeline based on performance metrics and user feedback
Next Steps
- Share additional resources: Medium articles, GitHub repositories, research papers
- Schedule follow-up session focused on practical code implementation of RAG concepts
- Explore advanced RAG architectures: context-enriched retrievals, fusion RAG, hide RAG
- Dive deeper into observability, deployment, and evaluation strategies for production RAG systems
Here's the entire recording of the session.