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Continue reading →: #30DaysOfLangChain – Day 20: Building Bulletproof AI: Error Handling & Resilience in LangGraph
Welcome to Day 20 of #30DaysOfLangChain – LangChain 0.3 Edition! We’ve built powerful, intelligent agents capable of complex tasks. But in the real world, things don’t always go as planned. External APIs fail, data formats are unexpected, LLMs hallucinate, and network issues arise. Without robust error handling, your sophisticated AI…
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Continue reading →: #30DaysOfLangChain – Day 19: Giving AI Memory: State Persistence in LangGraph
Welcome to Day 19 of #30DaysOfLangChain – LangChain 0.3 Edition! So far, we’ve built intelligent agents and collaborative workflows. But what happens when a conversation ends, or an agent’s task is paused? Without a mechanism to remember its past, the agent would lose all context and start fresh every time,…
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Continue reading →: #30DaysOfLangChain – Day 18: Collaborative AI: Building a Writer-Editor Workflow with LangGraph
Welcome to Day 18 of #30DaysOfLangChain – LangChain 0.3 Edition! After conceptually exploring multi-agent architectures yesterday, today we get hands-on. We’ll build a practical, collaborative multi-agent system using LangGraph, demonstrating how different AI “agents” can work together, iteratively refining a shared task. The power of LangGraph truly shines here. Its…
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Continue reading →: #30DaysOfLangChain – Day 17: Multi-Agent Architectures: The Power of AI Collaboration
Welcome to Day 17 of #30DaysOfLangChain – LangChain 0.3 Edition! We’ve built sophisticated agents capable of complex reasoning, tool use, and even iterative RAG. But as tasks become more intricate and nuanced, a single agent, no matter how powerful, might not be the most efficient or robust solution. This is…
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Continue reading →: #30DaysOfLangChain – Day 16: Building an Iterative RAG Application with LangGraph
Welcome to Day 16 of #30DaysOfLangChain – LangChain 0.3 Edition! Retrieval-Augmented Generation (RAG) has revolutionized how LLMs access external knowledge. However, basic RAG, where a single query fetches documents and an LLM answers, often falls short in real-world scenarios. What if the initial query is ambiguous? What if the first…
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Continue reading →: #30DaysOfLangChain – Day 15: Intelligent Workflows: Advanced LangGraph Routing & Conditional Logic
Welcome to Day 15 of #30DaysOfLangChain – LangChain 0.3 Edition! So far, our LangGraph agents have demonstrated impressive sequential reasoning and tool-use. But what happens when a single agent needs to handle vastly different types of requests? How do we build a workflow that intelligently adapts to diverse user intents?…
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Continue reading →: #30DaysOfLangChain – Day 14: Expanding Agent Intelligence with Custom Tools & Observability with Callbacks
Welcome to Day 14 of #30DaysOfLangChain – LangChain 0.3 Edition! We’ve seen our LangGraph agents come alive, making decisions and using tools. Today, we’re taking their capabilities to the next level by focusing on two crucial aspects: By mastering custom tools, you grant your agents access to the entire digital…
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Continue reading →: #30DaysOfLangChain – Day 13: Fortifying Your Flows: Error Handling & Debugging in LangGraph
Welcome to Day 13 of #30DaysOfLangChain! We’ve made great strides in building dynamic agents with LangGraph, but what happens when an LLM hallucinates, a tool fails, or an external API returns an unexpected response? In the real world, errors are inevitable. Today, we’ll equip ourselves with strategies for Error Handling…
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Continue reading →: #30DaysOfLangChain – Day 12: Building Your First Autonomous Agent with LangGraph
Welcome to Day 12 of #30DaysOfLangChain! Over the past two days, we’ve explored the foundations of LangGraph: understanding nodes, edges, state, and the crucial ability to create conditional paths and loops. Today, we’re bringing it all together to build our first truly autonomous agent powered by LangGraph. An autonomous agent,…
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Continue reading →: #30DaysOfLangChain – Day 11: Dynamic Workflows: Conditional Edges & Looping in LangGraph
Welcome to Day 11 of #30DaysOfLangChain! On Day 10, we got a foundational understanding of LangGraph, defining static flows with nodes and basic edges. Today, we’re unlocking LangGraph’s true power: Conditional Edges and the ability to create Loops. These features are what enable complex, adaptive, and intelligent behaviors in your…
Hello,
I’m Arpan
I’m a Software Engineer driven by curiosity and a deep interest in Generative AI Technologies. I believe we’re standing at the frontier of a new era—where machines not only learn but create, and I’m excited to explore what’s possible at this intersection of intelligence and imagination.
When I’m not writing code or experimenting with new AI models, you’ll probably find me travelling, soaking in new cultures, or reading a book that challenges how I think. I thrive on new ideas—especially ones that can be turned into meaningful, impactful projects. If it’s bold, innovative, and GenAI-related, I’m all in.
“The future belongs to those who believe in the beauty of their dreams.” – Eleanor Roosevelt
“Imagination is more important than knowledge. For knowledge is limited, whereas imagination embraces the entire world.” – Albert Einstein
This blog, MLVector, is my space to share technical insights, project breakdowns, and explorations in GenAI—from the models shaping tomorrow to the code powering today.
Let’s build the future, one vector at a time.
ML Vector 