Vane: Your Private AI Answering Engine That Puts You in Control

Vane is an open-source, privacy-focused AI answering engine that allows users to self-host their searches and integrate with various LLM providers for accurate, cited answers while maintaining user privacy.

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Why it matters

Vane provides a privacy-focused, customizable AI assistant that empowers users and developers to take control of their data and workflow, without sacrificing capability.

Key Points

  • 1Vane is a privacy-focused, self-hosted AI answering engine that runs on your own hardware
  • 2It offers flexibility by supporting both local LLMs (Ollama) and major cloud providers (OpenAI, Claude, Groq)
  • 3Get accurate answers with cited sources, ensuring transparency and trustworthiness in your research
  • 4Enhance your workflow with smart search modes, diverse source selection, and the ability to upload and query files
  • 5Developers gain full control over their data, models, and search experience for secure and customized information gathering

Details

Vane is an innovative, open-source AI answering engine designed to run entirely on your own hardware. It combines the vast knowledge of the internet with the analytical power of large language models, while ensuring your searches remain confidential. Vane achieves this by intelligently leveraging a privacy-focused web search component, like SearxNG, to gather information from various online sources, and then processing the data using your chosen Large Language Model. The flexibility of Vane allows you to run powerful local LLMs through Ollama for ultimate privacy and control, or integrate with major cloud-based providers like OpenAI, Anthropic Claude, Google Gemini, and Groq. This mix-and-match approach enables you to tailor the AI's capabilities to your specific needs. Beyond just answering questions, Vane offers a suite of powerful features to enhance your research and information gathering, including search mode selection, targeted domain searches, and the ability to upload and query documents locally.

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