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A technical case study detailing the implementation of an AI chatbot agent in a project management platform. Learn how the team leveraged LangChain, LangGraph, and GPT-4 to build a multi-agent system using the supervisor-worker pattern.

As large language models (LLMs) continue to evolve, their parameter counts grow exponentially, with some models reaching trillions of parameters. This exponential growth presents significant challenges for deployment on edge devices and in resource-constrained environments due to extensive memory and computational requirements. Quantization emerges as a crucial technique to reduce model footprint while preserving acceptable performance.

In baseline Retrieval Augmented Generation (RAG), sometimes the result might not be accurate as expected since the query itself have multiple layers of reasoning or the answer requires traversing disparate pieces of information through their shared attributes in order to provide new synthesized insights. In this post, we will explore a new approach called GraphRAG which combines the strengths of knowledge graphs and large language models to improve the accuracy of RAG systems