DocMind AI
Chat with your documents — grounded, cited, and honest about what it doesn't know.
DocMind AI is a multi-tenant RAG (Retrieval-Augmented Generation) SaaS. Users
upload their own PDFs, Word docs, or text files, and get an instant AI
chatbot that answers questions using only that content — with citations
pointing back to the exact document and passage behind every claim. If the
answer isn't in the uploaded documents, it says so instead of guessing.
Live demo: [add your Vercel URL here after deploying]
Demo video: [add a Loom/YouTube link here]
Why this exists
Most "chat with your PDF" demos are a thin wrapper around a single API call
with no real multi-tenancy, no verifiable citations, and no guardrails
against hallucination. This project is built to look and behave like an
actual product: isolated per-user workspaces enforced at the database level,
streaming answers with clickable source citations, usage analytics, and a
real (test-mode) billing flow with plan limits.
Tech stack
| Layer | Choice | Why |
|---|
| Framework | Next.js 16 (App Router) + TypeScript | Server components, API routes, and the frontend in one deployable unit |
| Styling | Tailwind CSS | Fast iteration, small bundle |
| Auth + DB | Supabase (Postgres + Auth) | Email/password + Google OAuth, Row Level Security for multi-tenancy, free tier |
| Vector search | Supabase pgvector | No separate vector database to run or pay for — cosine similarity search lives in Postgres |
| Chunking | LangChain.js (RecursiveCharacterTextSplitter) | Battle-tested chunk/overlap strategy |
| Embeddings | Google Gemini (gemini-embedding-001, pinned to 768 dims) | Free tier, 768-dim vectors |
| Chat LLM | Groq (openai/gpt-oss-120b by default, set via GROQ_MODEL) | Extremely fast streaming inference, free tier |
| Billing | Stripe (test mode) | Real checkout flow, no real charges |
| Hosting | Vercel | Zero-config Next.js deploys, free tier |
Monthly cost: $0, using each provider's free tier.
Architecture
┌────────────────────────────────────────┐
│ Browser (Next.js) │
│ Landing · Auth · Dashboard · Chat UI │
└───────────────┬──────────────────────────┘
│
▼
┌────────────────────────────────────────┐
│ Next.js API Routes (Vercel) │
│ │
│ /api/upload ──► parse → chunk → embed │
│ /api/chat ──► embed Q → retrieve → │
│ stream Groq answer │
│ /api/usage, /api/documents │
│ /api/stripe/checkout, /webhook │
└───┬─────────────┬─────────────┬─────────┘
│ │ │
┌───────────▼──┐ ┌────────▼───────┐ ┌────▼──────────┐
│ Supabase │ │ Gemini API │ │ Groq API │
│ Postgres + │ │ (embeddings) │ │ (chat, LLM) │
│ pgvector + │ └────────────────┘ └───────────────┘
│ Auth + RLS │
└───────┬───────┘
│
┌───────▼───────┐
│ Stripe (test) │
│ subscriptions│
└────────────────┘
RAG pipeline, step by step:
- Upload → file is parsed (
pdf-parse / mammoth / raw text) into plain text.
- Chunk → text is split into ~1000-character overlapping chunks (LangChain.js).
- Embed → each chunk is embedded via Gemini into a 768-dim vector, stored in
document_chunks.embedding (pgvector column).
- Ask → the user's question is embedded the same way.
- Retrieve → a Postgres RPC (
match_document_chunks) does a cosine-similarity search scoped to that user's rows only, returning the top 5 matches above a relevance threshold.
- Generate → the matched chunks are injected into a system prompt that instructs the model to answer only from that context, or say it doesn't know. Groq streams the answer token-by-token back to the client.
- Cite → each answer carries structured citation metadata (document, chunk index, snippet, similarity score) rendered as clickable chips under the message.
Multi-tenancy is enforced at the database layer with Postgres Row Level
Security — every table (documents, document_chunks, chats, messages)
has policies scoping all reads/writes to auth.uid(), so one user's data is
structurally unreachable by another, not just hidden by the UI.
This is the beginning of the README. Read the rest at the original. Read the full README ↗
