Agentic RAG

Theoretical review

What is a Retrieval Augmented Generation (RAG)?

• Hybrid Model: Combines the strengths of retrieval-based and generation-based models.
• Purpose: Enhances LLM text generation by incorporating relevant external information, improving accuracy and context.

• Retriever:
  • Function: Searches a large corpus (e.g., documents, articles) to find relevant information based on the input query.
  • Techniques: Uses methods like BM25, dense retrieval, or neural retrievers.
• Generator:
  • Function: Generates a response using the information retrieved.
  • Model: Typically a transformer-based model like GPT-4 or Llama.

1. Input Query: User provides a query or prompt.
2. Document Retrieval:
   • The retriever fetches a set of relevant documents or passages.
   • These documents provide context and factual information.
3. Response Generation:
   • The generator uses the retrieved documents to produce a coherent and contextually accurate response.
   • Ensures the generated text is informed by the most relevant information available.

1. Enhanced Accuracy: By grounding responses in real-world data, RAG models significantly improve the accuracy of generated content.
2. Reduced Hallucinations: The integration of external knowledge helps mitigate the risk of generating incorrect or nonsensical responses.
3. Scalability: RAG systems can handle vast amounts of data, making them suitable for enterprise-level applications.

Pipeline

What is a Agentic-RAG?

• Enhanced RAG: Extends RAG by adding agent-like capabilities.
• Purpose: Designed to perform tasks autonomously, interacting with various tools and APIs to achieve specific goals.

• Retriever:
  • Function: Similar to RAG, it fetches relevant documents based on the input query.
• Generator:
  • Function: Generates an initial response using the retrieved documents.
• Agent Module:
  • Function: Evaluates the generated response, cross-references it with the knowledge base, and makes corrections if discrepancies are found.

1. Input Query: User provides a query/question/task.
2. Document Retrieval: The retriever fetches relevant documents to provide context.
3. Initial Response Generation: The generator creates a preliminary answer using the retrieved information.
4. Response Verification: The agent system assesses the initial response against the knowledge base to ensure accuracy.
5. Response Correction (if needed): If inaccuracies are detected, the agent system refines the response to align with verified information.

1. Improved Reliability: The agent system's verification process ensures responses are accurate and trustworthy.
2. Dynamic Correction: Enables real-time adjustments to responses, enhancing the system's adaptability to new information.
3. User Trust: By providing verified answers, the system builds greater user confidence in its outputs.

Pipeline

Frameworks recommended for Agents

Frameworks recommended to develop user interfaces.

Code Examples

• LangGraph Agentic RAG
• SmolAgent
• LangGraph ai agent for engineers
• Agentic RAG with LangChain:

References

1. Video: What is Agentic RAG?
2. Video: LangChain vs LangGraph
3. Video: Build Your Own AI Agent System from scratch!
4. Course: AI Agents in LangGraph
5. Course: Advanced Retrieval for AI with Chroma
6. Course: AI Agents in LangGraph
7. A Comprehensive Guide to Building Agentic RAG Systems with LangGraph
8. leewayhertz: Agentic RAG
9. Vectorize: How I finally got agentic RAG to work right
10. Simple Agentic RAG for Multi Vector stores with LangChain and LangGraph