Lecture Notes: Beyond Naive RAG and Adding Agents to RAG

Introduction

• Presenter: Jerry, co-founder and CEO of Llama Index
• Topic: Enhancing RAG (Retrieval-Augmented Generation) with agents
• Context: Suitable for large enterprises to startups

What is Llama Index?

• Purpose: Framework for building LLM (Large Language Model) applications on your data
• **Features:
  • Data loaders
  • Data indexing
  • Query orchestration including retrieval, prompt orchestration with LLMs, and agent abstractions

Understanding RAG (Retrieval-Augmented Generation)

• Concept: Involves taking documents, chunking them, storing in vector database, and retrieving data via LLM logic
• **Basic Workflow:
  • Load documents
  • Chunk documents
  • Store chunks in vector database
  • Retrieve data using LLM logic
• Limitations: Effective for simple questions over limited datasets; struggles with complex queries and large datasets

Challenges with Naive RAG

• Summarization Questions: Simple top-k retrieval may fail
• Comparison Questions: Requires deeper analysis and breakdown of queries
• Complex Multi-part Questions: Needs sequential reasoning and planning
• Structured Analytics Combined with Semantic Search: Combining SQL and unstructured text searches

Advancing Beyond Naive RAG with Agents

• Move Beyond RAG Buzzword: Focus on dynamic question-answering systems
• Agents: Allow complex task handling by employing advanced pipelines

Introduction to Agents

• Definition: Using LLM for automated reasoning and tool selection
• Role in RAG: Enhances pipeline's intelligence and flexibility
• **Simple to Advanced Agent Spectrum:
  • Routing: Basic agentic reasoning
  • Query Planning: Breaks down queries into parallel subqueries
  • Tool Use: LLM uses APIs for database querying and other tasks

Detailed Discussion on Agents

Routing

• Concept: Selects relevant tool/pipeline based on input question
• Use Case: Decide between summarization pipeline and traditional rag pipeline

Query Planning

• Concept: Breaks down complex questions into simpler, parallelizable subqueries
• Example: Comparing revenue growth of different companies

Tool Use

• Concept: Uses LLM to call various APIs (e.g., vector databases, SQL queries, other APIs)
• Example: Auto-retrieval with metadata filters, converting text to SQL

Adding Iterative and Stateful Reasoning to Agents

• Challenge: Tackling sequential, multi-part problems and maintaining state
• Solution: Introduce loops and memory to agent execution

React Model

• Concept: Iterative reasoning in a loop with intermediate steps
• Features: Combines routing, query planning, and tool use in a while loop

Advanced Agent Models

• Example: LM Compiler
  • Approach: Plans entire dependency graph and optimizes execution in parallel
  • Features: Analogous to an operating system executing tasks efficiently

Practical Considerations and Future Directions

• Observability: Trace agent behaviors for debugging and understanding execution
• Control: Human-in-the-loop feedback and guidance
• Customizability: Frameworks should enable custom implementations at various levels of complexity

Conclusion

• Key Takeaway: Advanced agents can transform simple RAG pipelines into sophisticated dynamic question-answering systems
• Next Steps: Encouragement to explore and implement custom agent architectures

Additional Resources

• Documentation: Publicly available soon