If you're interested in running AI models like NotebookLM and ChatGPT Enterprise locally rather than relying on cloud services, RAGFlow offers an impressive open-source solution. Having personally experimented with this platform, I can confirm that it provides a highly configurable environment that supports both small-scale personal projects and enterprise-level deployments. RAGFlow integrates Retrieval-Augmented Generation (RAG) with agent capabilities, enabling you to build powerful AI applications with contextual understanding. One standout feature is the ability to self-host all services via Docker, giving you full control over your data and infrastructure — a critical advantage if you have privacy or compliance concerns. During my setup, I appreciated RAGFlow’s support for different document ingestion methods like Mineru and Docling, which made it easy to load and query documents from sources such as Google Drive, Notion, and Confluence. The system architecture allows smooth switching between Elasticsearch and Infinity as the underlying document engine, tailoring search performance to your needs. The documentation is well-organized, guiding you through installing dependencies, launching backend services, and configuring API keys for large language models (LLMs). For those running on ARM64 platforms, some additional steps are required due to Docker image compatibility, but community support is improving steadily. Performance-wise, using a GPU to accelerate DeepDoc tasks drastically cuts down processing time. Configuring system parameters like vm.max_map_count was straightforward, helping the RAGFlow stack operate efficiently. Overall, running NotebookLM and ChatGPT Enterprise on your own machine via RAGFlow empowers developers to innovate freely while safeguarding sensitive data. Whether you’re a coder, researcher, or AI enthusiast, this platform offers a compelling mix of flexibility, scalability, and transparency in AI system deployment.