Marktechpost Unveils Innovative Agentic RAG System for Smarter Info Retrieval
A new tutorial has been released, demonstrating an innovative Agentic Retrieval-Augmented Generation (RAG) system. This system, developed by Marktechpost Media Inc. under the leadership of CEO Asif Razzaq, introduces agency to RAG, enhancing its adaptability and efficiency.
The system, showcased in a runnable demo, features an agent that actively decides when retrieval is needed. It selects the best retrieval strategy, such as semantic, multi-query, temporal, or hybrid, based on the query's requirements. The agent also synthesizes responses with contextual awareness, ensuring focused and relevant answers.
The system maintains efficient, transparent, and tightly aligned retrieval. It performs semantic search, branches into multi-query or temporal re-ranking when needed, and deduplicates results to provide the most accurate information. The tutorial uses embeddings and FAISS indexing for decision-making and retrieval, with a mock LLM serving as the foundation.
This Agentic RAG system, while currently using a mock LLM, can be extended in future iterations to include real LLMs, larger knowledge bases, and more sophisticated strategies. The system's adaptability and human-like decision-making capabilities highlight the potential of adding agency to RAG, making information retrieval smarter and more targeted.
