About This Site
Open-source RAG infrastructure for building production-ready AI chat and search applications. Agentset is an open-source RAG (Retrieval-Augmented Generation) infrastructure designed to help developers build production-grade AI chat and search applications. It handles the complex aspects of RAG, including document extraction, chunking, hybrid search, and agentic reasoning, ensuring that AI apps remain reliable even with large-scale data and high user traffic. The platform is model-agnostic, allowing users to choose their own LLMs, vector databases, and embedding models. It supports over 22 file formats and multimodal data like images and tables, providing cited answers out of the box for sectors like medical AI, legal tech, and enterprise search.
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LocalMode UI - Local-First AI Components
Open-source browser AI components for private, offline applications
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Outter
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Key Features AI
Core Features
Production-grade RAG infrastructure with hybrid search and reranking
Multimodal support for text, images, graphs, and tables
Model-agnostic architecture (choose your own LLM, Vector DB, and Embeddings)
Automatic source citations and metadata filtering
Developer-friendly SDKs and MCP server integration
Deep research and agentic reasoning capabilities
Advantages
Ready for production use out of the box
Supports 22+ file formats including multimodal data
Model-agnostic flexibility avoids vendor lock-in
Open-source core with easy scalability
Includes built-in citations for answer verification
缺点：Connectors in the Pro plan are relatively expensive ($100 each)
缺点：Self-hosted and on-premise options are limited to Enterprise tier
缺点：Custom file formats require an Enterprise subscription
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