bash-A: RAG Chatbot

A Retrieval-Augmented Generation (RAG) chatbot powered by Next.js and Google Gemini. The application uses PDF documents, uploaded by an administrator, as a knowledge base to provide contextually aware, streaming responses to user queries.

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Features

• Role-Based Access: Differentiates between admin and user roles.
• Admin-Only PDF Ingestion: Admins can upload and process PDF files to build the knowledge base.
• RAG-Powered Chat: The LLM uses a vector search over the document knowledge base to generate relevant answers.
• Streaming UI: Responses are streamed token-by-token for a fluid chat experience.
• Vector Search: Uses Neon serverless Postgres with an HNSW index for efficient semantic search.

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Tech Stack

• Framework: Next.js 15 (App Router)
• Language: TypeScript
• Authentication: Clerk (handles user roles)
• Database: Neon Serverless Postgres + pgvector
• ORM: Drizzle ORM
• AI & Embeddings: Google Gemini (gemini-2.5-flash-lite, text-embedding-004) via Vercel AI SDK
• Styling: Tailwind CSS
• UI: Shadcn UI & Vercel AI Elements

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Local Setup

Prerequisites

• Node.js (v18+)
• Bun
• Neon Account & Database URL
• Clerk Account & Credentials
• Google AI API Key

Installation

1. Clone the repo:

   git clone https://github.com/moroii69/rag-bash-a.git
   cd rag-bash-a

2. Install dependencies:

   bun install

3. Configure environment:

   • Copy .env.example to .env.local.
   • Add your credentials for Neon, Clerk, and Google AI.

   cp .env.example .env.local

4. Sync database schema:

   • Ensure the pgvector extension is enabled on your Neon database.
   • Push the schema.

   bun run db:push

5. Run the dev server:

   bun run dev

   The application will be available at http://localhost:3000.

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Environment Variables

Your .env.local must contain the following keys:

# Neon Database
NEON_DATABASE_URL="postgresql://..."

# Clerk Authentication
NEXT_PUBLIC_CLERK_PUBLISHABLE_KEY="..."
CLERK_SECRET_KEY="..."

# Google AI
GOOGLE_GENERATIVE_AI_API_KEY="..."

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Project Structure

Key files and directories in the project.

src
├── app/
│   ├── api/chat/route.ts   # Chat streaming endpoint
│   ├── chat/page.tsx       # Main chat UI for users
│   ├── upload/page.tsx     # Admin UI for PDF uploads
│   ├── upload/actions.ts   # Server action for PDF processing (admin only)
│   └── layout.tsx          # Root layout with auth provider
├── components/             # Reusable UI components
└── lib/
├── db-config.ts        # Database connection
├── db-schema.ts        # documents table schema
├── embeddings.ts       # Embedding generation logic
└── search.ts           # Vector search implementation

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CI Status & Tests

The project includes integration tests and CI checks for linting, database connectivity, and build steps.

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Scripts

• bun run dev: Start the dev server.
• bun run build: Create a production build.
• bun run db:push: Push Drizzle schema to the database.
• bun run lint: Run the Biome linter.