Sam Nündel's Agentic RAG Brings Human-Like Intelligence to Information Retrieval
A new Agentic AI Retrieval-Augmented Generation (RAG) system has been developed by Sam Nündel. This innovative system, wrapped into a runnable demo, showcases the potential of adding agency to RAG, making information retrieval smarter and more human-like.
The Agentic AI RAG system uses embeddings and FAISS indexing for efficient information retrieval. It employs a mock LLM for decision-making, allowing the AI agent to actively determine when retrieval is needed. The AI agent selects the most suitable strategy from semantic, multi-query, temporal, or hybrid approaches.
Once relevant information is retrieved, the system performs semantic search, multi-query or temporal re-ranking, and deduplication. It then synthesizes a focused answer from the retrieved context, demonstrating contextual awareness in its responses.
The Agentic AI RAG system, developed by Sam Nündel, highlights the potential of adding agency to RAG. By actively deciding when retrieval is needed and selecting the best strategy, the system makes information retrieval smarter, more targeted, and more human-like in its adaptability.
