Agentic Retrieval-Augmented Generation (Agentic RAG): A Survey

What is the project about?

This project is a comprehensive survey and resource repository focused on Agentic Retrieval-Augmented Generation (Agentic RAG). Agentic RAG is a new approach in AI that combines the strengths of large language models (LLMs) with the capabilities of autonomous AI agents within a Retrieval-Augmented Generation (RAG) pipeline. It's an evolution of traditional RAG, incorporating agentic principles.

What problem does it solve?

Traditional RAG systems, while good at retrieving information and generating text, struggle with:

Dynamic, multi-step reasoning tasks: They aren't great at problems that require breaking down a complex task into smaller steps and reasoning through them.
Adaptability: They have difficulty adjusting to changing information or task requirements.
Complex workflows: Orchestrating multiple steps and tools is challenging.
Document-centric workflows: Automating the process.

Agentic RAG addresses these limitations by introducing AI agents that can plan, reflect, use tools, and collaborate.

What are the features of the project?

The project itself (the repository and survey) provides:

Foundational Principles: Explanations of core agentic patterns like reflection, planning, tool use, and multi-agent collaboration.
Taxonomy: A classification of different Agentic RAG system architectures (single-agent, multi-agent, hierarchical, corrective, adaptive, graph-based, and Agentic Document Workflows).
Comparative Analysis: A comparison of traditional RAG, Agentic RAG, and Agentic Document Workflows (ADW), highlighting their strengths and weaknesses.
Applications: Discussion of real-world use cases across various industries.
Challenges and Future Directions: An overview of the open problems and research opportunities in the field.
Implementation Resources: Links to notebooks, blogs, tutorials, and related concepts for practical implementation.
Agentic Workflow Patterns: Description of adaptive strategies.

The Agentic RAG systems described in the project feature:

Dynamic Adaptation: Agents can adjust their strategies based on the task.
Multi-Step Reasoning: Breaking down complex problems into manageable steps.
Tool Use: Interacting with external tools and APIs.
Multi-Agent Collaboration: Multiple agents working together.
Reflection: Agents evaluating their own performance and improving.
State Maintenance: (Especially in ADW) Keeping track of context across multiple steps.

What are the technologies used in the project?

The project discusses the use of the following technologies (it's a survey, not a software project itself):

Large Language Models (LLMs): The foundation for text generation and understanding.
Retrieval Mechanisms: Databases, vector stores (FAISS, Chroma, Weaviate, Redis, Vertex AI Vector Store), search APIs (Tavily), and knowledge graphs.
Agent Frameworks: LangChain, LlamaIndex, LangGraph, AutoGen, Semantic Kernel.
Cloud Platforms: AWS (Amazon Bedrock), Google Cloud (Vertex AI), IBM Watsonx.ai.
Inference Services: NVIDIA Inference Microservices (NIMs), Groq.
Document Processing Tools: LlamaParse.

What are the benefits of the project?

The project (the survey) benefits researchers and practitioners by:

Providing a comprehensive overview of the Agentic RAG field.
Clarifying the different approaches and architectures.
Highlighting the potential applications and challenges.
Offering resources for implementation.

The Agentic RAG systems discussed offer benefits such as:

Improved Accuracy: Through reflection, correction, and multi-step reasoning.
Greater Flexibility: Adapting to different tasks and data sources.
Enhanced Scalability: Handling complex, multi-step tasks and large datasets.
Domain-Specific Expertise: Integrating external tools and knowledge bases.
Automation of Complex Workflows: Streamlining processes, especially in document-heavy tasks.

What are the use cases of the project?

Agentic RAG has applications in:

Healthcare: Clinical decision support, medical report generation.
Education: Personalized tutoring, customized content creation.
Legal: Contract analysis, legal research.
Finance: Risk analysis, financial report generation, fraud detection.
Customer Support: AI-powered virtual assistants.
Multimodal Applications: Combining text, images, and structured data.
Document-Centric Workflows: Invoice processing, contract review, claims analysis.
Scientific Research: Knowledge management and discovery.