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Continue reading →: #30DaysOfLangChain – Day 10: Building Brains with Graphs: Introduction to LangGraph
Welcome to Day 10 of #30DaysOfLangChain! We’ve spent the first week mastering LangChain Expression Language (LCEL), building RAG pipelines, and even creating autonomous agents. As we enter the advanced stages, sometimes complex, multi-step LLM applications need more than just linear chains or simple agent loops. They need explicit state management,…
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Continue reading →: #30DaysOfLangChain – Day 9: Smart Agents: Agent Executors & Memory Management
Welcome to Day 9 of #30DaysOfLangChain! On Day 8, we introduced the powerful concept of LangChain Agents, autonomous LLMs that can reason and use tools. While an agent can perform a single task, real-world interactions often involve multi-turn conversations where the agent needs to remember past turns. This is where…
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Continue reading →: #30DaysOfLangChain – Day 8: Unleashing Autonomy: Introduction to Agents & Tools
Welcome to Day 8 of #30DaysOfLangChain and the start of Week 2! We’ve spent the first week mastering LCEL, building robust chains, and setting up our RAG pipeline. Now, prepare to elevate your LLM applications to a new level of intelligence: Agents. While a LangChain chain executes a predefined sequence…
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Continue reading →: #30DaysOfLangChain – Day 7: Building Robust Chains: Parallelism & Fallbacks with LCEL
Welcome to Day 7 of #30DaysOfLangChain! We’ve covered the fundamentals of LCEL, integrated various LLMs, and even built a basic RAG chain. Today, we’re going to elevate our LCEL mastery by exploring advanced patterns that make your applications more resilient, efficient, and versatile: Parallelism and Fallbacks. These patterns are key…
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Continue reading →: #30DaysOfLangChain – Day 6: Assembling Your First RAG Chain with LCEL
Welcome to Day 6 of #30DaysOfLangChain! We’ve come a long way. We’ve mastered LCEL (Runnables), fine-tuned prompts and parsers, flexibly integrated LLMs, and meticulously prepared our data by loading, splitting, embedding, and storing it in a vector database. Today, we bring all these components together to build our first end-to-end…
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Continue reading →: 30DaysOfLangChain – Day 5: The Brain of RAG: Embeddings & Vector Stores
Welcome to Day 5 of #30DaysOfLangChain! On Day 4, we learned how to prepare our raw data by loading documents and splitting them into manageable chunks. Now, how do we make these chunks searchable by an LLM based on meaning, not just keywords? The answer lies in Embeddings and Vector…
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Continue reading →: 30DaysOfLangChain – Day 4: Preparing Data for RAG: Document Loaders & Text Splitters
Welcome to Day 4 of #30DaysOfLangChain! Over the past three days, we’ve built a solid foundation with Runnables, LCEL, prompt engineering, output parsing, and flexible LLM integration. Today, we begin our journey into Retrieval-Augmented Generation (RAG), a powerful technique that allows LLMs to access and utilize external, up-to-date information. The…
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Continue reading →: 30DaysOfLangChain – Day 3: Flexible LLMs: Integrating Remote & Local Models with LCEL
Welcome to Day 3 of #30DaysOfLangChain! After establishing our LCEL foundation on Day 1 and mastering prompts and parsers on Day 2, it’s time to talk about the core intelligence of our applications: the Large Language Models themselves. Today, we’ll learn how to integrate various LLMs into our LCEL pipelines,…
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Continue reading →: 30DaysOfLangChain – Day 2: Precision with Prompts & Parsers in LCEL
Welcome back to #30DaysOfLangChain! On Day 1, we established the foundational concepts of Runnables and built our first simple LCEL pipeline. Today, we’re going to enhance our control over LLM interactions by diving into Prompt Templates and Output Parsers. These two components are essential for steering the LLM’s generation process…
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Continue reading →: 30DaysOfLangChain – Day 1: The Core of LangChain 0.3: Understanding Runnables & LCEL
Welcome to #30DaysOfLangChain – LangChain 0.3 Edition! Over the next month, we’ll embark on a journey to demystify the modern LangChain ecosystem, focusing purely on the latest best practices, the powerful LangChain Expression Language (LCEL), and LangGraph. Today, on Day 1, we’re laying the groundwork by diving into the single…
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 