The Archive — AI Document Intelligence

The Archive is a RAG-powered (Retrieval-Augmented Generation) document analysis platform. Upload PDFs, DOCX, or TXT files and chat with them using AI. The system extracts and chunks your documents, generates embeddings, and answers questions by citing only what's in your files — no hallucination.

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What It Does

• Upload PDF, DOCX, and TXT files via drag-and-drop
• AI-powered Q&A chat grounded in your uploaded documents
• Auto-generates a TL;DR (executive summary) on upload
• Citation tracking — every AI answer references its source document
• Document library with search and filtering
• In-browser PDF preview modal
• Brutalist UI design with dark mode support

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Tech Stack

Layer	Technology
Framework	Next.js 15 (App Router) + React 19 + TypeScript
Styling	Tailwind CSS + Radix UI (headless) + shadcn/ui
AI Models	OpenAI gpt-4o-mini (chat) + text-embedding-3-small (embeddings)
AI SDK	Vercel AI SDK v6 (generateText, embed)
AI Flows	Google Genkit 1.16
File Parsing	pdf-parse for PDF text extraction
Forms	React Hook Form + Zod
File Upload	React Dropzone
Deployment	Firebase App Hosting

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Project Structure

the-archive-ai/
├── src/
│   ├── app/
│   │   ├── page.tsx                  # Landing page
│   │   ├── layout.tsx                # Root layout & metadata
│   │   ├── globals.css               # Global styles & CSS variables
│   │   ├── dashboard/
│   │   │   ├── layout.tsx            # Dashboard shell with sidebar
│   │   │   ├── page.tsx              # Overview / stats
│   │   │   ├── chat/page.tsx         # Chat interface
│   │   │   ├── documents/page.tsx    # Document library & upload
│   │   │   └── settings/page.tsx     # Settings
│   │   └── api/
│   │       └── extract-text/route.ts # PDF/text extraction API route
│   ├── ai/
│   │   ├── genkit.ts                 # OpenAI client config
│   │   ├── dev.ts                    # Genkit dev server entry
│   │   └── flows/
│   │       ├── rag-query-response-generation.ts    # RAG chat flow
│   │       └── document-embedding-processing.ts    # Chunking & embedding flow
│   ├── components/
│   │   ├── chat/chat-window.tsx      # Main chat UI
│   │   ├── documents/
│   │   │   ├── upload-zone.tsx       # Drag-and-drop uploader
│   │   │   └── document-list.tsx     # Document library list
│   │   ├── layout/                   # Header & footer
│   │   └── ui/                       # Radix-based UI primitives (37 components)
│   ├── hooks/                        # use-mobile, use-toast
│   └── lib/
│       ├── types.ts                  # Shared TypeScript interfaces
│       └── utils.ts                  # cn() and other helpers
├── .env                              # Environment variables (see below)
├── next.config.ts
├── tailwind.config.ts
├── apphosting.yaml                   # Firebase App Hosting config
└── package.json

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How It Works (RAG Pipeline)

1. Upload — file is sent to /api/extract-text, which uses pdf-parse to extract raw text
2. Chunk — document-embedding-processing.ts splits text into ~1000-char segments at paragraph/sentence boundaries
3. Embed — each chunk is embedded with text-embedding-3-small
4. Query — user message is embedded and matched against stored chunk embeddings (cosine similarity)
5. Generate — top matching chunks are passed as context to gpt-4o-mini via the RAG flow, which is instructed to answer only from the provided documents

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Setup

Prerequisites

• Node.js 18+
• An OpenAI API key

1. Clone & install

git clone <repo-url>
cd the-archive-ai
npm install

2. Configure environment variables

Create a .env file in the project root:

OPENAI_API_KEY=your_openai_api_key_here

3. Run the development server

npm run dev

The app runs at http://localhost:9002

4. (Optional) Start the Genkit AI dev server

npm run genkit:dev

This starts the Genkit developer UI for inspecting and testing AI flows locally.

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Available Scripts

Script	Description
npm run dev	Start Next.js dev server on port 9002 (Turbopack)
npm run build	Production build
npm start	Start production server
npm run lint	Run ESLint
npm run typecheck	TypeScript type check (no emit)
npm run genkit:dev	Start Genkit AI dev UI
npm run genkit:watch	Genkit dev UI in watch mode

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Environment Variables

Variable	Required	Description
OPENAI_API_KEY	Yes	OpenAI API key used for GPT-4o-mini and text embeddings

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Deployment

The project is configured for Firebase App Hosting via apphosting.yaml.

npm run build
# then deploy via Firebase CLI or push to connected repo

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License

MIT License

Copyright (c) 2026 Saw Simon Linn

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.