Retrieval-Augmented Generation (RAG) is becoming the go-to pattern for building AI systems that can fetch real-time or domain-specific knowledge on demand. But RAG alone doesn’t make your chatbot smart. With LangGraph, you can build stateful, agent-l...
Introduction In the era of advanced AI applications, Retrieval-Augmented Generation (RAG) stands out as a game-changing approach. By combining retrieval techniques with generative models, RAG enhances the quality, accuracy, and relevance of generated...
Introduction At FutureSmart AI, we've helped many clients improve their support systems with AI customer service solutions. Now, we're sharing our proven approach to creating a custom AI assistant for customer service using Freshdesk. This tutorial s...
Introduction In the last three blogs in our Ultimate Langraph Tutorial Series, we highlighted different components of LangGraph for beginners, Long-term Memory Support, and building an AI agent with custom tools support. After implementing these syst...
Introduction In this blog post, we will walk you through the process of creating a custom AI agent with three powerful tools: Web Search, Retrieval-Augmented Generation (RAG), and Natural Language to SQL (NL2SQL), all integrated within the LangGraph ...
Introduction In our previous article, we delved into the fundamentals of Retrieval-Augmented Generation (RAG) using LangChain. We explored the core concepts, built a basic RAG system, and demonstrated its capabilities in a Jupyter notebook environmen...
