open-notebook/docs/1-INSTALLATION/index.md
Luis Novo 3f352cfcce
feat: credential-based API key management (#477) (#540)
* feat: replace provider config with credential-based system (#477)

Introduce a new credential management system replacing the old
ProviderConfig singleton and standalone Models page. Each credential
stores encrypted API keys and provider-specific configuration with
full CRUD support via a unified settings UI.

Backend:
- Add Credential domain model with encrypted API key storage
- Add credentials API router (CRUD, discovery, registration, testing)
- Add encryption utilities for secure key storage
- Add key_provider for DB-first env-var fallback provisioning
- Add connection tester and model discovery services
- Integrate ModelManager with credential-based config
- Add provider name normalization for Esperanto compatibility
- Add database migrations 11-12 for credential schema

Frontend:
- Rewrite settings/api-keys page with credential management UI
- Add model discovery dialog with search and custom model support
- Add compact default model assignments (primary/advanced layout)
- Add inline model testing and credential connection testing
- Add env-var migration banner
- Update navigation to unified settings page
- Remove standalone models page and old settings components

i18n:
- Update all 7 locale files with credential and model management keys

Closes #477

Co-Authored-By: JFMD <git@jfmd.us>
Co-Authored-By: OraCatQAQ <570768706@qq.com>

* fix: address PR #540 review comments

- Fix docs referencing removed Models page
- Fix error-handler returning raw messages instead of i18n keys
- Fix auth.py misleading docstring and missing no-password guard
- Fix connection_tester using wrong env var for openai_compatible
- Add provision_provider_keys before model discovery/sync
- Update CLAUDE.md to reflect credential-based system
- Fix missing closing brace in api-keys page useEffect

* fix: add logging to credential migration and surface errors in UI

- Add comprehensive logging to migrate-from-env and
  migrate-from-provider-config endpoints (start, per-provider
  progress, success/failure with stack traces, final summary)
- Fix frontend migration hooks ignoring errors array from response
- Show error toast when migration fails instead of "nothing to migrate"
- Invalidate status/envStatus queries after migration so banner updates

* docs: update CLAUDE.md files for credential system

Replace stale ProviderConfig and /api-keys/ references across 8 CLAUDE.md
files to reflect the new Credential-based system from PR #540.

* docs: update user documentation for credential-based system

Replace env var API key instructions with Settings UI credential
workflow across all user-facing documentation. The new flow is:
set OPEN_NOTEBOOK_ENCRYPTION_KEY → start services → add credential
in Settings UI → test → discover models → register.

- Rewrite ai-providers.md, api-configuration.md, environment-reference.md
- Update all quick-start guides and installation docs
- Update ollama.md, openai-compatible.md, local-tts/stt networking sections
- Update reverse-proxy.md, development-setup.md, security.md
- Fix broken links to non-existent docs/deployment/ paths
- Add credentials endpoints to api-reference.md
- Move all API key env vars to deprecated/legacy sections

* chore: bump version to 1.7.0-rc1

Release candidate for credential-based provider management system.

* fix: initialize provider before try block in test_credential

Prevents UnboundLocalError when Credential.get() throws (e.g.,
invalid credential_id) before provider is assigned.

* fix: reorder down migration to drop index before table

Removes duplicate REMOVE FIELD statement and reorders so the index
is dropped before the table, preventing rollback failures.

* refactor: simplify encryption key to always derive via SHA-256

Remove the dual code path in _ensure_fernet_key() that detected native
Fernet keys. Since the credential system is new, always deriving via
SHA-256 removes unnecessary complexity. Also removes the generate_key()
function and Fernet.generate_key() references from docs.

* fix: correct mock patch targets in embedding tests and URL validation

Fix embedding tests patching wrong module path for model_manager
(was targeting open_notebook.utils.embedding.model_manager but it's
imported locally from open_notebook.ai.models). Also fix URL validation
to allow unresolvable hostnames since they may be valid in the
deployment environment (e.g., Azure endpoints, internal DNS).

* feat: add global setup banner for encryption and migration status

Show a persistent banner in AppShell when encryption key is missing
(red) or env var API keys can be migrated (amber), so users see
these prompts on every page instead of only on Settings > API Keys.

Includes a docs link for the encryption banner and i18n support
across all 7 locales.

* docs: several improvements to docker-compose e env examples

* Update README.md

Co-authored-by: cubic-dev-ai[bot] <191113872+cubic-dev-ai[bot]@users.noreply.github.com>

* docs: fix env var format in README and update model setup instructions

Align the encryption key snippet in README Step 2 with the list
format used in the compose file. Replace deprecated "Settings →
Models" instructions with credential-based Discover Models flow.

* fix: address credential system review issues

- Fix SSRF bypass via IPv4-mapped IPv6 addresses (::ffff:169.254.x.x)
- Fix TTS connection test missing config parameter
- Add Azure-specific model discovery using api-key auth header
- Add Vertex static model list for credential-based discovery
- Fix PROVIDER_DISCOVERY_FUNCTIONS incorrect azure/vertex mapping
- Extract business logic to api/credentials_service.py (service layer)
- Move credential Pydantic schemas to api/models.py
- Update tests to use new service imports and ValueError assertions

* fix: sanitize error responses and migrate key_provider to Credential

- Replace raw exception messages in all credential router 500 responses
  with generic error strings (internal details logged server-side only)
- Refactor key_provider.py to use Credential.get_by_provider() instead
  of deprecated ProviderConfig.get_instance()
- Remove unused functions (get_provider_configs, get_default_api_key,
  get_provider_config) that were dead code

---------

Co-authored-by: JFMD <git@jfmd.us>
Co-authored-by: OraCatQAQ <570768706@qq.com>
2026-02-10 08:30:22 -03:00

4.3 KiB

Installation Guide

Choose your installation route based on your setup and use case.

Quick Decision: Which Route?

Docker Compose - Multi-container setup, production-ready

  • All features working
  • Clear separation of services
  • Easy to scale
  • Works on Mac, Windows, Linux
  • ⏱️ 5 minutes to running

🏠 I want everything in one container (Simplified)

Single Container - All-in-one for simple deployments

  • Minimal configuration
  • Lower resource usage
  • Good for shared hosting
  • Works on PikaPods, Railway, etc.
  • ⏱️ 3 minutes to running

👨‍💻 I want to develop/contribute (Developers only)

From Source - Clone repo, set up locally

  • Full control over code
  • Easy to debug
  • Can modify and test
  • ⚠️ Requires Python 3.11+, Node.js
  • ⏱️ 10 minutes to running

System Requirements

Minimum

  • RAM: 4GB
  • Storage: 2GB for app + space for documents
  • CPU: Any modern processor
  • Network: Internet (optional for offline setup)
  • RAM: 8GB+
  • Storage: 10GB+ for documents and models
  • CPU: Multi-core processor
  • GPU: Optional (speeds up local AI models)

AI Provider Options

Cloud-Based (Pay-as-you-go)

  • OpenAI - GPT-4, GPT-4o, fast and capable
  • Anthropic (Claude) - Claude 3.5 Sonnet, excellent reasoning
  • Google Gemini - Multimodal, cost-effective
  • Groq - Ultra-fast inference
  • Others: Mistral, DeepSeek, xAI, OpenRouter

Cost: Usually $0.01-$0.10 per 1K tokens Speed: Fast (sub-second) Privacy: Your data sent to cloud

Local (Free, Private)

  • Ollama - Run open-source models locally
  • LM Studio - Desktop app for local models
  • Hugging Face models - Download and run

Cost: $0 (just electricity) Speed: Depends on your hardware (slow to medium) Privacy: 100% offline


Choose a Route

Already know which way to go? Pick your installation path:

Privacy-first? Any installation method works with Ollama for 100% local AI. See Local Quick Start.


Pre-Installation Checklist

Before installing, you'll need:

  • Docker (for Docker routes) or Node.js 18+ (for source)
  • AI Provider API key (OpenAI, Anthropic, etc.) OR willingness to use free local models
  • At least 4GB RAM available
  • Stable internet (or offline setup with Ollama)

Detailed Installation Instructions

For Docker Users

  1. Install Docker Desktop
  2. Choose: Docker Compose or Single Container
  3. Follow the step-by-step guide
  4. Access at http://localhost:8502

For Source Installation (Developers)

  1. Have Python 3.11+, Node.js 18+, Git installed
  2. Follow From Source
  3. Run make start-all
  4. Access at http://localhost:8502 (frontend) or http://localhost:5055 (API)

After Installation

Once you're up and running:

  1. Configure Models - Choose your AI provider in Settings
  2. Create First Notebook - Start organizing research
  3. Add Sources - PDFs, web links, documents
  4. Explore Features - Chat, search, transformations
  5. Read Full Guide - User Guide

Troubleshooting During Installation

Having issues? Check the troubleshooting section in your chosen installation guide, or see Quick Fixes.


Need Help?


Production Deployment

Installing for production use? See additional resources:


Ready to install? Pick a route above! ⬆️