setup

You can configure a modular RAG project by interactively selecting LLM, embedding, vision, and rerank providers. The setup process then generates the required YAML settings, installs dependencies, and launches the dashboard automatically.

The best way to bootstrap a RAG environment is through an interactive setup flow that validates your Python runtime and virtual environment. It generates the correct YAML settings file, prepares folders, and verifies critical imports for a reliable launch.

Yes, you can scaffold unsupported providers into your project's plugin architecture. The setup process supports provider onboarding by generating the necessary scaffolding to integrate new LLM or embedding services into your workflow.

Dashboard startup failures often stem from missing dependencies or invalid credentials. The setup process automatically diagnoses launch issues using retry loops, validates your configuration, and applies automated recovery to resolve startup errors.

To complete project bootstrap, you need a validated Python runtime and a virtual environment. The setup process requires these prerequisites to install dependencies reliably, generate YAML settings, and verify critical imports before launch.

Yes, the setup process supports automated recovery from environment initialization failures. It diagnoses startup issues by running retry loops, re-validating credentials, and checking generated configurations to ensure the dashboard launches correctly.