Feb 13
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Back in 2014, everyone wanted to upload a PDF and talk to it. Simple idea. For almost a decade, it was a nightmare to build. The problem wasn't the AI models—it was orchestration. Connecting document loaders to embedding models to vector databases to LLMs. Spaghetti code everywhere. Then LangChain emerged. An open-source framework that solved the plumbing problem and democratized AI development. In this video, I break down how LangChain orchestrates the 6-component pipeline that powers modern AI applications. 🔧 What You'll Learn: - Why orchestration was the bottleneck (not the models) - The 6-component pipeline LangChain coordinates - How Chains streamline sequential operations - Why model agnosticism matters for production - How conversation memory enables natural interactions - The shift from knowledge assistants to AI agents - Why LlamaIndex and Haystack are alternatives 🔗 Resources: - LangChain Documentation: https://langchain.com - LlamaIndex (data-heavy alternative): https://llamaindex.ai - Haystack (retrieval pipelines): https://haystack.deepset.ai This is what I teach my team when we're building production systems with LangChain. The framework handles the plumbing so you can focus on building. Subscribe for more on AI orchestration, production engineering, and building with LangChain. #LangChain #AI #LLM #MachineLearning #RAG #VectorDatabase #AIEngineering #Python #Orchestration #OpenAI #Claude