Adding Agentic Layers to RAG
TLDRJerry, CEO of Llama Index, discusses enhancing Retrieval-Augmented Generation (RAG) with agentic layers for more sophisticated data querying. He introduces the concept of agents that use LLMs for reasoning and tool selection to handle complex questions beyond RAG's capabilities. The talk covers agent types, from simple routing to advanced query planning and tool use, emphasizing the need for dynamic QA systems. Jerry also highlights the importance of agent architectures for long-term planning and system optimization, suggesting the future of agents in data frameworks.
Takeaways
- 😀 Llama Index is a data framework for building LM applications over your data, used by large enterprises and startups alike.
- 🔍 The talk focuses on enhancing Retrieval-Augmented Generation (RAG) by integrating agentic layers to handle more complex queries.
- 📈 RAG is a popular method for building applications that involve retrieving information from a database and using it to answer questions.
- 🚧 Limitations of RAG include struggles with summarization, comparison, structured analytics, and multi-part questions, which are addressed by introducing agents.
- 🤖 The concept of 'agents' in this context refers to using LLMs for automated reasoning and tool selection to enhance RAG's capabilities.
- 🛠️ Agents can be added at various stages of the RAG pipeline to make it more sophisticated and capable of handling a broader range of questions.
- 🔄 The speaker discusses different types of agents, from simple routing to more complex query planning and tool use.
- 🔄🔄 The 'React' paradigm is highlighted as a popular method for agents to iteratively approach complex tasks by breaking them down into smaller steps.
- 💡 The future of agents may involve more advanced architectures like LM Compiler, which allows for long-term planning and system-level optimization.
- ⚙️ As agent technology progresses, the need for observability, control, and customizability in agent systems will become increasingly important.
Q & A
What is Llama Index and who is its co-founder and CEO?
-Llama Index is a data framework for building LM applications over your data, used by large Enterprises to startups alike. The co-founder and CEO of Llama Index is Jerry.
What does RAG stand for and what is its basic function?
-RAG stands for Retrieval-Augmented Generation. Its basic function involves taking documents, chunking them up, and putting them into a vector database for retrieval, and then using LLM logic to pull that data out to build applications.
What are the limitations of RAG prototypes?
-RAG prototypes are limited as they work well for simple questions over a small set of documents but struggle with more complex queries, summarization, comparison, structured analytics, semantic search, and general multi-part questions.
What is an agent in the context of RAG and what role does it play?
-An agent, in the context of RAG, uses an LLM for automated reasoning and tool selection. It is a higher-level abstraction that can decide to use RAG as one of many tools to access data, interface with it, and synthesize the right answer.
How can agents be incorporated into the RAG pipeline?
-Agents can be added at the beginning, middle, or end of the RAG pipeline to make any part of it more agentic, thus creating a more sophisticated and dynamic question answering system.
What is routing in the context of agentic reasoning?
-Routing is the simplest form of agentic reasoning where an LLM is used to decide which underlying query engine or tool to route a given question to, based on the input task or question.
How does query planning differ from simple routing?
-Query planning involves breaking down a complex query into smaller, more manageable subqueries that can be executed against relevant data sources to obtain the desired answer, whereas routing simply directs the query to a pre-determined tool or engine.
What is tool use and how does it relate to agents?
-Tool use refers to the ability of an LLM to call an API and decide the parameters to use in order to interact with a given tool. This concept allows agents to translate user queries into actions that can be taken using various tools and APIs.
What is the REACT paradigm and how does it enhance agent capabilities?
-REACT is a paradigm where an agent executes tasks in a while loop, planning the next step ahead and maintaining a conversation history. It includes capabilities like tool use, query planning, and routing, and can continue executing in a loop until the task is complete.
What are some additional requirements for building effective agents?
-Additional requirements for building effective agents include observability for transparency and debugging, control for guiding intermediate agent steps, and customizability to adapt agent behavior to specific needs.
Outlines
📚 Introduction to Llama Index and Advanced RAG
Jerry, the co-founder and CEO of Llama, introduces the company's data framework for building LM applications over enterprise and startup data. He discusses the limitations of RAG (Retrieval-Augmented Generation) prototypes, which are effective for simple questions but not for complex queries over large document sets. Jerry proposes moving beyond RAG to build dynamic question-answering systems capable of handling any type of question. He outlines the challenges of naive RAG, such as failing on summarization and comparison questions, and the need for a more sophisticated approach involving agents.
🤖 Enhancing RAG with Agents
The concept of agents in the context of RAG is explained, where agents use LLMs for automated reasoning and tool selection to enhance the RAG pipeline. Agents can be added at various stages of the RAG process to make it more dynamic and capable of handling complex queries. The speaker outlines the spectrum of agent sophistication, from simple routing and query planning to more advanced capabilities like tool use and dynamic query planning. Examples are given, such as routing questions to different query engines based on the question type and breaking down complex questions into sub-queries for execution against relevant data sources.
🔍 Deep Dive into Agentic Capabilities
This section delves deeper into agentic capabilities, such as using LLMs to call APIs and interact with various tools, which can be more precise and effective than human-driven queries. The idea of tool use is explored, where an LLM decides the parameters for a tool based on a user query. The speaker also discusses the potential for agents to tackle sequential, multi-part problems through iterative loops and maintaining state over time. The concept of a data agent with an execution pipeline and agentic loops, like the popular REACT paradigm, is introduced, allowing for more complex and dynamic question answering.
🚀 Future of Agents and Closing Thoughts
The final paragraph discusses the future of agents, emphasizing the need for observability, control, and customizability as agents become more sophisticated. The speaker anticipates that as LLMs improve and costs decrease, more people will build agents. He mentions the importance of being able to see the full trace of agent execution for transparency and the ability to guide agents step by step. The speaker encourages the implementation of agent paradigms and highlights the composability of agents through Llama's query syntax, which allows for step-by-step execution and user input. The talk concludes with a thank you and a mention of sharing the slides publicly.
Mindmap
Keywords
💡Llama Index
💡RAG
💡Agentic Layers
💡Query Orchestration
💡Challenges with Naive RAG
💡Dynamic Question Answering System
💡Agents
💡Tool Use
💡React
💡Observability
Highlights
Llama Index is a data framework for building LM applications over your data.
RAG (Retrieval-Augmented Generation) is a method for building applications that can retrieve information from documents.
RAG prototypes are limited for complex questions over large sets of documents.
Challenges with naive RAG include failure in summarization, comparison, structured analytics, and multi-part questions.
Agents can be added to RAG to create a more dynamic question answering system.
Agents use LLMs for automated reasoning and tool selection.
RAG is a lookup tool, and agents are higher-level abstractions that can use RAG and other tools.
Agents can be added at the beginning, middle, or end of the RAG pipeline to enhance functionality.
Routing is a simple form of agentic reasoning where an LLM decides which tool or pipeline to use for a given question.
Query planning involves breaking down a complex question into subqueries that can be answered independently.
Tool use allows an LLM to call APIs with the appropriate parameters to retrieve information.
Agents can tackle sequential multi-part problems with iterative reasoning and maintain state over time.
React is a popular agentic loop that allows for ongoing execution and planning until a task is complete.
Llama Index implements React and other agentic features to enable advanced question answering.
Long-term planning and system-level optimization are emerging areas for enhancing agent capabilities.
Observability, control, and customizability are essential for building effective agents.
Llama Index provides a query syntax for implementing agentic pipelines with observability and control.
