Democratizing reliable knowledge-seeking with MyGPT: a privacy-first, open-source retrieval-augmented generation platform

Large language models (LLMs) have transformed knowledge discovery, but their use in sensitive domains is limited by privacy concerns and risks of data leakage. Moreover, LLMs may hallucinate when queried about information outside their training data, undermining their reliability for scientific, clinical, or policy-related tasks. We present MyGPT, a fully open-source, privacy-preserving retrieval-augmented-generation (RAG) platform that uses open-source LLMs and operates locally or within secure institutional environments. MyGPT empowers users to query user-defined libraries such as biomedical literature, confidential clinical documents, and multilingual global health policies, while ensuring that sensitive data never leaves the secure environment. We introduce confidence metrics, including “relevance scores” and “hallucination index,” and provide explicit source citation from the library within a single interface to assess contextual support for generated answers. MyGPT offers a cost-effective, modular, open-source alternative to commercial LLM services and democratizes knowledge-seeking across multiple domains, including use at remote facilities without internet access. Patel et al. report MyGPT, an open-source, locally deployable retrieval-augmented generation (RAG) solution that provides confidence metrics and highlights text sources to assess the reliability of answers, offering a secure and cost-effective alternative to commercial LLM services.