Building a RAG-Based PDF Reader Web App with Node.js, LangChain, Ollama, and Hugging Face
The author built a web app that allows users to upload PDFs and ask questions about their content, using Retrieval-Augmented Generation (RAG) to provide relevant context and generate answers.
Why it matters
This project showcases how AI can be used to make document-centric workflows more efficient and accessible, by providing a natural language interface to explore PDF content.
Key Points
- 1Allows users to upload PDFs, extract and process the content, and generate embeddings to store in a vector store
- 2Retrieves relevant sections based on user questions and generates answers using a local large language model (LLM)
- 3Combines traditional web development with modern AI application design, demonstrating how RAG works in practice
- 4Uses a tech stack including Node.js, LangChain, Ollama, and Hugging Face Transformers
Details
The project aims to provide an interactive AI-powered PDF reader, where users can upload documents and ask questions about their content in natural language. The app uses Retrieval-Augmented Generation (RAG) to combine retrieval of relevant information from the PDF with generation of answers by a local LLM. The key steps include extracting text from the PDF, splitting it into manageable chunks, generating embeddings, storing them in a vector store, retrieving the most relevant chunks based on user questions, and using Ollama (a local LLM) to generate the final answers. This project demonstrates how modern AI techniques like RAG can be applied to enhance traditional document-based workflows and provide a more intelligent and interactive user experience.
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