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LarkupRAG gives you two ways to chat with your knowledge base: a CLI for developers who prefer the terminal, and a full Web UI with a built-in chat interface. Both paths support multiple LLM providers out of the box.

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Supported Providers

LarkupRAG isn’t locked to OpenAI. You can choose from any of the following embedding and LLM providers, all configurable from the UI or CLI:
  • Vercel AI Gateway (Recommended): unified gateway with automatic routing
  • OpenAI: GPT-4o, GPT-4o Mini, and embedding models
  • DeepSeek: cost-effective alternative models
  • Google: Gemini models
  • Anthropic / Cohere / Mistral / Voyage AI / Jina AI / Nomic
  • Custom: bring your own OpenAI-compatible endpoint
Supported LLM and embedding providers

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Getting Started

The CLI is the fastest way to go from documents to a working chatbot, no browser required.

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1. Initialize your project

Open your terminal and create a new chatbot workspace:
npx @larkup-rag/cli init my-first-chatbot

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2. Configure your provider

Set your embedding provider and API key:
npx @larkup-rag/cli config
You’ll be prompted to select a provider (OpenAI, Vercel AI Gateway, DeepSeek, etc.) and paste your API key.

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3. Add your documents

Feed documents into the pipeline: PDF, text, markdown, or URLs:
npx @larkup-rag/cli add-doc --file /path/to/your/document.pdf
Run this command multiple times to add as many files as you need.

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4. Index your data

Process and embed your documents into the vector store:
npx @larkup-rag/cli index

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5. Start chatting

Launch the interactive chat directly in your terminal:
npx @larkup-rag/cli chat
The AI pulls facts directly from your indexed documents to answer questions. Type exit to quit.

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Vector Store Options

LarkupRAG supports multiple vector store backends. Choose the one that fits your deployment:
  • LanceDB: embedded, file-based (great for local/offline use)
  • Pinecone: fully-managed cloud vector database
  • Chroma: open-source, developer-friendly
  • Weaviate / Qdrant / pgvector / Supabase: coming soon
Vector store providers