Introduction
Agentic RAG is an evolution of the traditional Retrieval-Augmented Generation (RAG) framework, where large language models (LLMs) act as intelligent agents. This enhances query handling by introducing contextual decision-making, improving response accuracy, and making the system more dynamic.
Key Features
- Intelligent Query Routing: The LLM can decide which vector database to query based on the context of the query.
- Failsafe Handling: Out-of-scope queries are routed to a failsafe mechanism for graceful responses.
- Dynamic Response Types: The agent determines the type of response (e.g., text, charts, or code) based on query context.
- Enhanced Context Understanding: Leverages natural language understanding to interpret queries and select appropriate actions.
Advantages
- Improved relevance by routing queries to the most suitable database.
- Adaptability with multiple data sources and real-time external integrations.
- Scalability for complex fields like customer support, healthcare, and legal tech.
Use Cases
Agentic RAG can be applied to various domains. For example:
- Customer Support: Route product-related queries to internal documentation, industry-related queries to external knowledge bases, and handle out-of-scope queries with a failsafe response.
- Legal Tech: Lawyers can query internal legal briefs for case-specific details or public caseload databases for general precedents.
Summary
Agentic RAG extends the traditional RAG pipeline by allowing LLMs to act as decision-making agents. This enables nuanced query handling, enhanced context awareness, and more dynamic responses, paving the way for more powerful applications in diverse fields.