open-notebook/open_notebook/CLAUDE.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

9.9 KiB

Open Notebook - Root CLAUDE.md

This file provides architectural guidance for contributors working on Open Notebook at the project level.

Project Overview

Open Notebook is an open-source, privacy-focused alternative to Google's Notebook LM. It's an AI-powered research assistant enabling users to upload multi-modal content (PDFs, audio, video, web pages), generate intelligent notes, search semantically, chat with AI models, and produce professional podcasts—all with complete control over data and choice of AI providers.

Key Values: Privacy-first, multi-provider AI support, fully self-hosted option, open-source transparency.


Three-Tier Architecture

┌─────────────────────────────────────────────────────────┐
│              Frontend (React/Next.js)                    │
│              frontend/ @ port 3000                       │
├─────────────────────────────────────────────────────────┤
│ - Notebooks, sources, notes, chat, podcasts, search UI  │
│ - Zustand state management, TanStack Query (React Query)│
│ - Shadcn/ui component library with Tailwind CSS         │
└────────────────────────┬────────────────────────────────┘
                         │ HTTP REST
┌────────────────────────▼────────────────────────────────┐
│              API (FastAPI)                              │
│              api/ @ port 5055                           │
├─────────────────────────────────────────────────────────┤
│ - REST endpoints for notebooks, sources, notes, chat    │
│ - LangGraph workflow orchestration                      │
│ - Job queue for async operations (podcasts)             │
│ - Multi-provider AI provisioning via Esperanto          │
└────────────────────────┬────────────────────────────────┘
                         │ SurrealQL
┌────────────────────────▼────────────────────────────────┐
│         Database (SurrealDB)                            │
│         Graph database @ port 8000                      │
├─────────────────────────────────────────────────────────┤
│ - Records: Notebook, Source, Note, ChatSession, Credential│
│ - Relationships: source-to-notebook, note-to-source     │
│ - Vector embeddings for semantic search                 │
└─────────────────────────────────────────────────────────┘

Useful sources

User documentation is at @docs/

Tech Stack

Frontend (frontend/)

  • Framework: Next.js 16 (React 19)
  • Language: TypeScript
  • State Management: Zustand
  • Data Fetching: TanStack Query (React Query)
  • Styling: Tailwind CSS + Shadcn/ui
  • Build Tool: Webpack (via Next.js)
  • i18n compatible: All front-end changes must also consider the translation keys

API Backend (api/ + open_notebook/)

  • Framework: FastAPI 0.104+
  • Language: Python 3.11+
  • Workflows: LangGraph state machines
  • Database: SurrealDB async driver
  • AI Providers: Esperanto library (8+ providers: OpenAI, Anthropic, Google, Groq, Ollama, Mistral, DeepSeek, xAI)
  • Job Queue: Surreal-Commands for async jobs (podcasts)
  • Logging: Loguru
  • Validation: Pydantic v2
  • Testing: Pytest

Database

  • SurrealDB: Graph database with built-in embedding storage and vector search
  • Schema Migrations: Automatic on API startup via AsyncMigrationManager

Additional Services

  • Content Processing: content-core library (file/URL extraction)
  • Prompts: AI-Prompter with Jinja2 templating
  • Podcast Generation: podcast-creator library
  • Embeddings: Multi-provider via Esperanto

Architecture Highlights

1. Async-First Design

  • All database queries, graph invocations, and API calls are async (await)
  • SurrealDB async driver with connection pooling
  • FastAPI handles concurrent requests efficiently

2. LangGraph Workflows

  • source.py: Content ingestion (extract → embed → save)
  • chat.py: Conversational agent with message history
  • ask.py: Search + synthesis (retrieve relevant sources → LLM)
  • transformation.py: Custom transformations on sources
  • All use provision_langchain_model() for smart model selection

3. Multi-Provider AI

  • Esperanto library: Unified interface to 8+ AI providers
  • Credential system: Individual encrypted credential records per provider; models link to credentials for direct config
  • ModelManager: Factory pattern with fallback logic; uses credential config when available, env vars as fallback
  • Smart selection: Detects large contexts, prefers long-context models
  • Override support: Per-request model configuration

4. Database Schema

  • Automatic migrations: AsyncMigrationManager runs on API startup
  • SurrealDB graph model: Records with relationships and embeddings
  • Vector search: Built-in semantic search across all content
  • Transactions: Repo functions handle ACID operations

5. Authentication

  • Current: Simple password middleware (insecure, dev-only)
  • Production: Replace with OAuth/JWT (see CONFIGURATION.md)

Important Quirks & Gotchas

API Startup

  • Migrations run automatically on startup; check logs for errors
  • Must start API before UI: UI depends on API for all data
  • SurrealDB must be running: API fails without database connection

Frontend-Backend Communication

  • Base API URL: Configured in .env.local (default: http://localhost:5055)
  • CORS enabled: Configured in api/main.py (allow all origins in dev)
  • Rate limiting: Not built-in; add at proxy layer for production

LangGraph Workflows

  • Blocking operations: Chat/podcast workflows may take minutes; no timeout
  • State persistence: Uses SQLite checkpoint storage in /data/sqlite-db/
  • Model fallback: If primary model fails, falls back to cheaper/smaller model

Podcast Generation

  • Async job queue: podcast_service.py submits jobs but doesn't wait
  • Track status: Use /commands/{command_id} endpoint to poll status
  • TTS failures: Fall back to silent audio if speech synthesis fails

Content Processing

  • File extraction: Uses content-core library; supports 50+ file types
  • URL handling: Extracts text + metadata from web pages
  • Large files: Content processing is sync; may block API briefly

Component References

See dedicated CLAUDE.md files for detailed guidance:


Documentation Map


Testing Strategy

  • Unit tests: tests/test_domain.py, test_models_api.py
  • Graph tests: tests/test_graphs.py (workflow integration)
  • Utils tests: tests/test_utils.py, tests/test_chunking.py, tests/test_embedding.py
  • Run all: uv run pytest tests/
  • Coverage: Check with pytest --cov

Common Tasks

Add a New API Endpoint

  1. Create router in api/routers/feature.py
  2. Create service in api/feature_service.py
  3. Define schemas in api/models.py
  4. Register router in api/main.py
  5. Test via http://localhost:5055/docs

Add a New LangGraph Workflow

  1. Create open_notebook/graphs/workflow_name.py
  2. Define StateDict and node functions
  3. Build graph with .add_node() / .add_edge()
  4. Invoke in service: graph.ainvoke({"input": ...}, config={"..."})
  5. Test with sample data in tests/

Add Database Migration

  1. Create migrations/XXX_description.surql
  2. Write SurrealQL schema changes
  3. Create migrations/XXX_description_down.surql (optional rollback)
  4. API auto-detects on startup; migration runs if newer than recorded version

Deploy to Production

  1. Review CONFIGURATION.md for security settings
  2. Use make docker-release for multi-platform image
  3. Push to Docker Hub / GitHub Container Registry
  4. Deploy docker compose --profile multi up
  5. Verify migrations via API logs

Support & Community


Last Updated: January 2026 | Project Version: 1.2.4+