How to Deploy an Open-Source Version of NotebookLM on Your Own Server

How to Deploy an Open-Source Version of NotebookLM on Your Own Server

NotebookLM, Google’s AI-powered note-taking and research tool, is gaining traction — but it’s proprietary and cloud-based. Fortunately, there are open-source alternatives like Open Notebook that you can self-host within your own network for improved privacy and control.

Why Self-Host an Alternative?

  • Privacy & Security: Running Open Notebook locally means your data never leaves your infrastructure.
  • Flexibility: Supports multiple LLM providers, such as OpenAI, Google Gemini, and others.
  • Source Control: You control which AI models are used, their configuration, and how they interact with your documents.

What You’ll Need to Deploy

  • A machine (e.g., server or workstation) with Docker installed.
  • An API key for an LLM provider if you plan to use proprietary services.
  • Sufficient storage and memory depending on the size of your document corpus.

Steps to Deploy Open Notebook

  • Clone the Open Notebook GitHub repository to your server.
  • Copy and configure the docker.env file based on which LLM(s) you intend to use.
  • Edit settings such as API keys, host IPs, and default models.
  • Run docker compose up -d to start services in detached mode.
  • Open http://<server-ip>:8502 in your browser to access the web UI.

Configuring Models and LLM Providers

Once you’re in the Open Notebook UI, head to the “Models” section. Here you can:

  • Select default LLM providers.
  • Configure behavior per model (e.g., summarization vs. Q&A).
  • Add or remove sources and document collections.

Who Should Consider This Setup?

This deployment model works best for:

  • Researchers and students who want a personal AI workspace.
  • Organizations needing a secure, on-premises research and note-taking tool.
  • AI enthusiasts who want to experiment with different LLMs locally.

Limitations & Considerations

While powerful, running Open Notebook comes with trade-offs:

  • It requires technical setup (Docker, environment configuration).
  • Costs depend on the LLMs you use — local free models may be slower or less capable.
  • Maintaining and updating the service is now your responsibility.

Source: PromakAI. Compiled by PromakAI News.

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