Local RAG PDF Chatbot with LangChain, FAISS & Ollama | PDF Question Answering Demo
Автор: Neural Ascent
Загружено: 2026-01-01
Просмотров: 69
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
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Информация по загрузке:
![Как общаться с PDF-файлами, используя локальные большие языковые модели [Ollama RAG]](https://image.4k-video.ru/id-video/ztBJqzBU5kc)
