How Agentic RAG solves problem with current RAG limitations

In this volume 4 of coffee break concept, we will understand how AgenticRAG helps solve limitations of traditional RAG.

RAG Framework

The RAG (Retrieval Augmented Generation) framework operates in a specific sequence:

Document -> Chunks-> Vector DB -> Chunk Retrieval (Top K) -> LLM

However, this sequence encounters obstacles when dealing with certain types of queries.

Traditional RAG Pipeline

Problem 1: Summarization

Consider a query like “Summarize the document”.

• The conventional RAG approach retrieves the top K chunks and summarizes them.
• But wouldn’t it be more comprehensive if it retrieved all chunks of the document and summarized them?

Summarization problem with RAG

Problem 2: Comparing Documents

• When tasked with comparing Document A and Document B, the basic RAG retrieves random chunks and attempts to compare these top K chunks.
• This doesn’t paint an accurate picture as it doesn’t represent the full scope of the documents.

Problem 2: Comparing Documents

Problem 3: Structured Data Analysis

Consider a question like “When is the next leave?”.

• The first step is to retrieve the region to which the employee belongs from a structured table.
• Based on the region, the next leave for that region is extracted from the leave policy document.
• This process isn’t as straight forward with the current RAG framework.

Problem 3: Structured Data Analysis

Problem 4: The Multi-part Question

Consider a question like “Identify common leave across all regions?”.

• Imagine you have a leave policy document of a company present in 120 countries.
• Since you are passing the top K contexts, the maximum number of regions that can be compared is limited to K, where K is the number of chunks passed to LLM.

Problem 4: The Multi-part Question

Look into our AgenticRAG with LlamaIndex Course with 5 real-time case studies.

Course link: https://www.masteringllm.com/course/agentic-retrieval-augmented-generation-agenticrag

New course on AgenticRAG with LlamaIndex

Agentic RAG

Agentic RAG can solve this 4 problems by replacing via custom agents.

• Agents will interact with multiple systems.
• RAG is now one part of this system which agents can use.

Agentic RAG

• Agents uses LLMs to automate the reasoning and tool selection
• RAG is just another tool which Agent may decides to use.

Routing Agent

• Routing agents are simple agents which routes the queries.
• An agent can route query in one or multiple tools.
• Remember our question “Summarize the document” or a question if we want to combine “Summarization + Sematic search” can be solved using below example routing

Routing Agent

Query Planning Agent

• Query planning agent breaks down the queries into sub-queries.
• Each of the sub-queries can be executed against RAG pipeline.

Query Planning Agent

Tools For Agents

• LLMs can have multiple tools like calling an API, infer parameters for API.
• RAG is now a tool which LLM might use.

Tools For Agents

Summary

• RAG has limitations when represented with complex questions.
• Few of the use cases like summarization, comparison etc. can’t be solve with just RAG.
• Agentic RAG can help overcome limitation of RAG.
• Agentic RAG treats RAG as a tool which it can use for semantic search.
• Agents equipped with routing, query planning and tools can out perform traditional RAG applications.

Look into our AgenticRAG with LlamaIndex Course with 5 real-time case studies.

Agentic Retrieval Augmented Generation (AgenticRAG) with LlamaIndex

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