Built to demonstrate RAG (Retrieval-Augmented Generation) as a working product — not just a concept.

No setup. No sign-up. Just upload a PDF and start asking questions.

🌐 Live URL https://ai-research-assistant-five.vercel.app/

Reading through long PDFs — research papers, reports, contracts — to find specific answers is slow and frustrating.

AI Research Assistant lets you skip straight to the answer:

📄 Upload any PDF document

💬 Ask questions in plain language

🔍 Get answers traced back to the exact source chunks

📚 See which parts of the document the AI used to respond

Full RAG pipeline built from scratch — chunking, embeddings, vector search, LLM response

Source-attributed answers — every response is traceable to document sections

ChatGPT-style UI with persistent conversation history and auto-scroll

PDF preview with in-app navigation alongside the chat interface

Feature Description 📄 PDF Upload & Processing Extract, chunk, and embed document text on upload 💬 Conversational Chat Ask questions in natural language, get contextual answers 🔍 Semantic Search Vector embeddings power similarity-based retrieval 📚 Source Attribution Responses traced back to specific document chunks 🧠 RAG Pipeline Full retrieval-augmented generation implementation 💾 Persistent History Conversation saved across sessions ⚡ Real-Time Chat UI Auto-scroll, loading states, smooth interaction 👁️ PDF Preview In-app document viewer with page navigation

Layer Technology Framework Next.js (App Router) Styling Tailwind CSS Backend Next.js API Routes AI / ML OpenAI / Groq API + Embeddings Database Supabase (PostgreSQL + pgvector) Other PDF parsing, Vector similarity search Deployment Vercel

Create a .env.local file in the root:

Open http://localhost:3000 and you're in.

Implements a full RAG pipeline — not just an API call, but chunking, embedding, retrieval, and generation

Demonstrates backend thinking alongside frontend — vector DB, API routes, and data flow

Focuses on real usability — source attribution and PDF preview make it an actual tool, not a demo

Shows end-to-end AI product development from scratch to deployment

Multi-document querying

Highlight exact source text in PDF viewer

Streaming responses

User authentication + cloud session storage

Chat session management UI

🟢 Open to: Frontend Engineer · Product Engineer · Internal Tools Developer · Startup Software Engineer · AI Application Developer

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