Master LangChain #20 | Document Loaders Complete Overview (PDF, Web, Cloud, API) #openai

#langchain #aidevelopment #generativeai #machinelearning #llm #python #openai #azureopenai #langgraph #artificialintelligence #chatgpt #documentloaders LangChain Document Loaders are the foundation of every **RAG (Retrieval-Augmented Generation)** pipeline. In this video, you’ll get a **complete overview of LangChain document loaders**, including **how they work, when to use each one, and real Python examples**. ### 🔍 What you’ll learn in this tutorial: ✔️ What are **Document Loaders in LangChain** ✔️ Difference between **load() vs lazy_load()** ✔️ PDF loaders (PyPDF, Unstructured, OCR, Math PDFs) ✔️ Website loaders (WebBase, Recursive URL, Sitemap, Firecrawl, Spider) ✔️ Cloud loaders (AWS S3, Azure Blob, Google Drive, GCS) ✔️ Social & messaging loaders (Twitter, Reddit, Slack, WhatsApp, Discord) ✔️ Productivity tool loaders (Notion, GitHub, Trello) ✔️ Common file loaders (CSV, JSON, HTML, Unstructured) ✔️ Best practices & limitations for each loader ✔️ How document loaders connect to **Azure OpenAI, embeddings, and vector databases** 📌 This video is perfect for: * LangChain beginners * RAG application developers * LLM engineers * Azure OpenAI users * AI engineers building knowledge bots ### 🧠 Tech Stack Used * LangChain (Python) * Azure OpenAI * FAISS / Vector Databases * PDFs, Websites, Cloud Storage