RAG Chatbot for Policy Documents using BGE, FAISS & LLaMA 3 | Tamil Explanation скачать в хорошем качестве

RAG Chatbot for Policy Documents using BGE, FAISS & LLaMA 3 | Tamil Explanation

In this Tamil-explained (தமிழில் விளக்கம்) Generative AI project, we build an enterprise-grade RAG chatbot that can answer questions from large policy and compliance documents using BGE embeddings, FAISS vector database, and LLaMA 3 language model. This video is perfect for Tamil AI students, LLM learners, final year project students, and AI engineers who want to build real-world Document AI & compliance assistants. 🔍 What You’ll Learn (Tamil-Friendly Explanation) ✅ What is RAG (Retrieval-Augmented Generation) ✅ Policy document ingestion & intelligent chunking ✅ How BGE embeddings improve semantic search ✅ Build fast vector search using FAISS ✅ Integrate LLaMA 3 for context-aware answers ✅ End-to-end policy chatbot system implementation By the end of this video, you’ll have a production-ready policy document chatbot. 🧠 Applications of Policy RAG Chatbots • Compliance & Policy Assistants • HR & Company Rule Chatbots • Legal Document Q&A • Enterprise Knowledge Systems • Regulatory AI Tools • Final Year AI / NLP Projects 🛠️ Tech Stack & Techniques Used 1️⃣ Retrieval-Augmented Generation (RAG) 2️⃣ BGE Embedding Model 3️⃣ FAISS Vector Database 4️⃣ LLaMA 3 Large Language Model 5️⃣ LangChain 6️⃣ Prompt Engineering 7️⃣ Python ⏱️ RAG Policy Chatbot – Timeline 00:00–00:40 → Project Outcome Final system result, features implemented, and use-case overview. 00:40–02:10 → Introduction Problem statement, motivation, and application scope. 02:10–04:20 → System Architecture Overview Overall workflow, modules, and data flow. 04:20–06:20 → System Requirements Hardware, software stack, libraries, and environment setup. 06:20–09:30 → Environment Setup Python installation, dependency setup, IDE configuration, project structure. 09:30–12:50 → Dataset Overview Dataset source, classes, annotation format, preprocessing steps. 12:50–16:30 → Data Preparation & Processing Data cleaning, augmentation, train–test split. 16:30–20:30 → Model Setup & Configuration Model loading, configuration files, parameter tuning. 20:30–23:40 → Model Training & Testing Training workflow, basic evaluation, performance checks. 23:40–26:56 → Conclusion Final results, improvements, and future scope. ⭐ Get Full Source Code + 21 AI / Computer Vision Projects (For Tamil Students) 💡 Want this complete Policy RAG chatbot source code, sample documents & documentation AND 21+ real-world AI / CV projects with certificate? 👉 Unlock everything here → https://www.udemy.com/course/generati... You’ll get: ✔ All source codes ✔ Datasets ✔ Project reports ✔ 21 AI & Computer Vision projects ✔ Certificate ✔ Lifetime access 🔥 Limited-time offer – ideal for Tamil engineering students. 👍 Don’t forget to: 👍 Like 🔁 Share 🔔 Subscribe for more Tamil-explained AI, Python & LLM Projects 🔖 Hashtags (Tamil SEO Optimized) #RAG #LLaMA3 #FAISS #BGE #GenerativeAI #AITamil #AIProjects #DocumentAI #LearnAI #ScratchLearn #TamilTech #FinalYearProject #BuildInPublic