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.
Open-source RAG infrastructure for building production-ready AI chat and search applications.
- Building research-grounded medical AI assistants
- Creating enterprise search systems for complex legal or municipal documents
- Replacing traditional keyword search with high-precision semantic search
- Developing AI chatbots with reliable answers for customer support
- Developers can start by signing up and using the JavaScript or Python SDKs to upload documents in various formats. After ingestion
- you can configure your preferred LLM and vector database. The platform then allows you to query your data via API to receive accurate answers with automatic citations
- or integrate it into external apps using the Model Context Protocol (MCP) server.
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