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Explore I Built a Complex RAG App Using Warp, the Agentic Development Environment 🤖🧠 with 11,961 views, 602 likes, and 71 comments. Experience the impact of this video content that has captured audience attention.

What if Python is no longer the closest thing to plain English? We’ve entered the era where we can build serious, full-stack software using nothing but natural language! 👉 Try Warp for free today → https://go.warp.dev/simplifiedytev In this tutorial, we’re using a professional Agentic Development Environment (ADE) to build a production-ready RAG application from scratch! 🏗️💻 By the end of this video, you’ll see exactly how to take a tiny language model like Qwen and turn it into a world-class expert on any research paper or PDF you throw at it. No more manual debugging for hours—we’re using AI agents to architect our pipeline, handle our vector database, and even design a beautiful user interface! 🎨🤖 📚 What you'll learn: • What RAG is (The "Open Book Exam" for AI) • Setting up Warp ADE on Windows • Building a minimal RAG pipeline via natural language prompts • Creating a Vector Database from PDF research papers (AlexNet, DeepSeek, etc.) • Natural Language Debugging: How to fix LLM hallucinations without code • Designing a Custom GUI with Flask using a layout sketch • Scaling your app to handle multiple documents 🛠️ Tools used: • Warp ADE • Python 3.12 • LangChain • Qwen Instruct LLM • FAISS • Flask 🔎 Helpful Resources: ⭐ Full code on GitHub: https://github.com/MariyaSha/RAG_GUI_GenAIApp.git ⭐ Hugging Face RAG Pipeline: https://huggingface.co/learn/cookbook/en/rag_with_unstructured_data ⭐ AlexNet Research Paper: https://arxiv.org/abs/1803.01164 ⏰ Timestamps ⏰ 01:13 - What exactly is RAG? (Retrieval-Augmented Generation) 01:52 - Setting Up Warp ADE 03:02 - Prompting the Agent for a Specific RAG Pipeline 04:34 - Reviewing and Manually Adjusting the Agent’s Code 05:35 - Simplifying the LLM Chat Interface 07:45 - Debugging LLM Hallucinations with Natural Language 09:31 - Designing the GUI with Flask using a Wireframe Sketch 10:51 - Final UI Polishing and Testing with Multiple Research Papers 📝 Prompts 📝 1. Initial RAG Pipeline Build [04:01] Build a minimal RAG app from this guide: https://huggingface.co/learn/cookbook/en/rag_with_unstructured_data PDF-only Extract and chunk Alexnet.pdf text Use LangChain Use a very small Qwen Instruct Save pipeline as app.py 2. Automatic Module Installation [05:18] suggest which modules to install in rag_env to match the requirements of app.py 3. Debugging Accuracy & Conciseness [07:53] the pipeline works, but not perfectly. - LLM gives wrong answers (AlexNet has 60 million parameters and was trained on a single GTX 580 GPU). - LLM provides too much information. - LLMs answers must be short and concise. - review attached output and fix these issues. 4. Refactoring to vectorize.py [10:14] please move the code that generates the vector database into a separate file - vectorize.py. We will run vectorize.py independently and get app.py to read from it. 5. GUI Design with Flask [10:51] Design GUI for app.py. - use Flask. - use attached app layout image as inspiration. - use logo.png. - don't change the RAG pipeline itself - just add a GUI in app.py. 6. UI Refinements & Sidebar Scaling [12:05] remove "RAG Chat" text next to the logo. make logo bigger. make sidebar 30% smaller in width and justify side bar content to the center. 7. Multi-PDF Embedding [12:44] instead of embedding AlexNet.pdf only - please embed all the PDF files from the local folder /rag_app/research. don't change anything else. #Python #AI #RAG #MachineLearning #Coding #ArtificialIntelligence #LLM #SoftwareEngineering #Flask #VectorDatabase

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