Local RAG PDF Chatbot with LangChain, FAISS & Ollama | PDF Question Answering Demo скачать в хорошем качестве

Local RAG PDF Chatbot with LangChain, FAISS & Ollama | PDF Question Answering Demo

In this video, I demonstrate a PDF‑based Question Answering system built using a Retrieval‑Augmented Generation (RAG) pipeline. ​ The app lets you upload any PDF and ask questions whose answers are generated only from the document content, powered by a local LLM running through Ollama. ​ 🔗 GitHub Repository https://github.com/Aakash109-hub/loca... 🧠 What this RAG app does Upload a PDF and chat with it using natural language Retrieves the most relevant chunks from the document using vector search Generates grounded answers from a local LLM instead of calling cloud APIs ​ ​🛠 Tech stack used PyPDFLoader for loading PDF documents RecursiveCharacterTextSplitter for smart text chunking HuggingFace sentence‑transformer embeddings for vector representations ​- FAISS as the vector database for fast similarity search ​- LangChain to orchestrate the RAG pipeline (retrieval + generation) ​- Ollama for local LLM‑based answer generation ​- Streamlit for the web user interface 🎯 Who this video is for Developers and students exploring RAG and PDF Question Answering Anyone interested in running local LLMs for privacy‑friendly document QA ​- Recruiters reviewing my AI/ML projects and practical GenAI skills