2025-04-25 –, Helium3
Retrieval Augmented Generation (RAG) has become a cornerstone in enriching GenAI outputs with external data, yet traditional frameworks struggle with challenges like data noise, domain specialization, and scalability. In this talk, Tuhin will dive into open-source frameworks Fast GraphRAG and InstructLab, which addresses these limitations by combining knowledge graphs with the classical PageRank algorithm and Fine-tuning, delivering a precision-focused, scalable, and interpretable solution. By leveraging the structured context of knowledge graphs, Fast GraphRAG enhances data adaptability, handles dynamic datasets efficiently, and provides traceable, explainable outputs while InstructLab adds domain depth to the LLM through Fine-tuning. Designed for real-world applications, it bridges the gap between raw data and actionable insights, redefining intelligent retrieval for developers, researchers, and enterprises. This talk will showcase Fast GraphRAG’s transformative features coupled with domain specific Fine-tuning leveraging InstructLab and demonstrate its potential to elevate RAG’s capabilities in handling the evolving demands of large language models (LLMs) for developers, researchers, and businesses.
Retrieval Augmented Generation (RAG) has changed the way AI systems incorporate external knowledge, but it often falls short when faced with real-world challenges like adapting to new data, managing complexity, or delivering reliable answers. Fast GraphRAG steps in to address these gaps with a refreshing approach that blends the structure of knowledge graphs with the proven efficiency of algorithms like PageRank. By focusing on interpretability, scalability, and adaptability, Fast GraphRAG creates a pathway for building AI systems that don’t just retrieve data but leverage it in a meaningful way.
The agenda for the talk is as follows
Challenges in Traditional RAG
- Lack of interpretability leads to untrustworthy outputs.
- High computational costs limit scalability.
- Inflexibility makes adapting to evolving data cumbersome.
Fast GraphRAG’s Core Innovations
- Interpretability: Knowledge graphs provide clear, traceable reasoning.
- Scalability: Efficient query resolution with minimal overhead.
- Adaptability: Dynamic updates ensure relevance in changing domains.
- Precision: PageRank sharpens focus on high-value information.
- Robust Workflows: Typed and asynchronous handling for complex scenarios.
How Fast GraphRAG Works
- Architecture and algorithmic innovations.
- Knowledge graphs for intelligent reasoning.
- PageRank for multi-hop exploration and precise retrieval.
- Entity extraction, incremental updates, and graph exploration.
- Role of InstructLab and Fine-tuning.
Demo and Practical Takeaways
- Building a knowledge graph and resolving queries.
- Open-source tools for scaling Fast GraphRAG.
- Real-World applications
Fast GraphRAG isn’t just another tool. It's a game-changer for anyone frustrated by the limitations of traditional RAG systems. By combining the structured clarity of knowledge graphs with the power of algorithms like PageRank and fine-tuning by InstructLab, it makes retrieval smarter, faster, and the LLM more adaptable. This session will leave you with a clear understanding of how to build/train AI systems that deliver meaningful results while being transparent and trustworthy. Whether you’re a developer, researcher, or just someone passionate about AI, Fast GraphRAG is a framework that sparks possibilities and redefines what intelligent retrieval can achieve.
Novice
Expected audience expertise: Python:Novice
Tuhin Sharma is Senior Principal Data Scientist at Redhat in the Data Development Insights & Strategy AI team. Prior to that, he worked at Hypersonix as an AI architect. He also co-founded and has been CEO of Binaize (backed by Techstars), a website conversion intelligence product for e-commerce SMBs. Previously, he was part of IBM Watson where he worked on NLP and ML projects featured on Star Sports and CNN-IBN. He received a master's degree from IIT Roorkee and a bachelor's degree from IIEST Shibpur in Computer Science. He loves to code and collaborate on open-source projects. He is one of the top 20 contributors of pandas. He has 4 research papers and 5 patents in the fields of AI and NLP. He is a reviewer of the IEEE MASS conference, Springer nature and Packt publication in the AI track. He writes deep learning articles for O'Reilly in collaboration with the AWS MXNET team. He is a regular speaker at prominent AI conferences like O'Reilly Strata & AI, ODSC, GIDS, Devconf, Datahack Summit etc.