Programming Tutorials

  The debate between LangChain and LlamaIndex is no longer about "which library is more popular." It is an architectural decision about where complexity lives in your application. In 2025, the most common anti-pattern I see in production RAG pipelines is the  Abstraction Mismatch . Teams choose LangChain for its ecosystem but spend weeks reinventing data parsing logic that LlamaIndex provides out of the box. Conversely, teams choose LlamaIndex for retrieval but end up writing unmaintainable spaghetti code to handle complex, multi-turn agentic behaviors that LangChain’s  LangGraph  handles natively. This post dissects the architectural trade-offs and provides a unified, production-grade pattern that leverages the specific strengths of both frameworks. The Root Cause: Data-First vs. Flow-First The friction arises because these two libraries solve fundamentally different problems, despite their overlapping feature sets. 1. LangChain is Flow-First (Control Plane) LangCha...

  It is the most common deployment issue in LLM engineering today. You build a sophisticated RAG pipeline or a chat interface on your local machine. It works flawlessly. You push to Vercel. You type a prompt. The AI responds for exactly 10 or 15 seconds, and then the stream abruptly dies mid-sentence. No error appears in the browser console other than a generic network disconnect or JSON parsing error. The server logs usually show nothing because the execution context was essentially "kill -9'd" by the platform. Here is the architectural root cause and the specific configuration patterns required to fix it in Next.js App Router. The Architecture of a Timeout To fix this, you must understand the discrepancy between your local environment and Vercel’s serverless infrastructure. Localhost (Node.js):  Your local server is a long-lived process. When you initiate a stream with  streamText  or  OpenAIStream , the connection remains open indefinitely until the LLM finis...