* 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>
9.1 KiB
AI & Chat Issues - Model Configuration & Quality
Problems with AI models, chat, and response quality.
"Failed to send message" Error
Symptom: Chat shows "Failed to send message" toast. Logs show:
Error executing chat: Model is not a LanguageModel: None
Cause: No valid language model configured for chat
Solutions:
Solution 1: Check Default Model Configuration
1. Go to Settings → Models
2. Scroll to "Default Models" section
3. Verify "Default Chat Model" has a model selected
4. If empty, select an available language model
5. Click Save
Solution 2: Verify Model Names (Ollama Users)
# Get exact model names
ollama list
# Example output:
# NAME SIZE MODIFIED
# gemma3:12b 8.1 GB 2 months ago
# The model name in Open Notebook must be EXACTLY "gemma3:12b"
# NOT "gemma3" or "gemma3-12b"
Solution 3: Re-add Missing Models
1. Note the exact model names from your provider
2. Go to Settings → Models
3. Delete any misconfigured models
4. Add models with exact names
5. Set new defaults
Solution 4: Check Model Still Exists
# For Ollama: verify model is installed
ollama list
# For cloud providers: verify API key is valid
# and you have access to the model
Tip: This error often occurs when you delete a model from Ollama but forget to update the default models in Open Notebook. Always re-configure defaults after removing models.
"Models not available" or "Models not showing"
Symptom: Settings → Models shows empty, or "No models configured"
Cause: No credential configured, or credential has invalid API key
Solutions:
Solution 1: Add Credential via Settings UI
1. Go to Settings → API Keys
2. Click "Add Credential"
3. Select your provider (e.g., OpenAI, Anthropic, Google)
4. Enter your API key
5. Click Save, then Test Connection
6. Click Discover Models → Register Models
7. Go to Settings → Models to verify
Solution 2: Check Key is Valid
1. Go to Settings → API Keys
2. Click "Test Connection" on your credential
3. If it shows "Invalid API key":
- Get a fresh key from the provider's website
- Delete the credential and create a new one
Solution 3: Switch Provider
1. Go to Settings → API Keys
2. Add a credential for a different provider
3. Test Connection → Discover Models → Register Models
4. Go to Settings → Models to select the new provider's models
"Invalid API key" or "Unauthorized"
Symptom: Error when trying to chat: "Invalid API key"
Cause: Credential has wrong, expired, or revoked API key
Solutions:
Step 1: Test Your Credential
1. Go to Settings → API Keys
2. Click "Test Connection" on your credential
3. If it fails, proceed to Step 2
Step 2: Get Fresh Key
Go to provider's dashboard:
- OpenAI: https://platform.openai.com/api-keys (starts with sk-proj-)
- Anthropic: https://console.anthropic.com/ (starts with sk-ant-)
- Google: https://aistudio.google.com/app/apikey (starts with AIzaSy)
Generate new key and copy exactly (no extra spaces)
Step 3: Update Credential
1. Go to Settings → API Keys
2. Delete the old credential
3. Click "Add Credential" → select provider
4. Paste the new key
5. Click Save, then Test Connection
6. Re-discover and register models if needed
Step 4: Verify in UI
1. Go to Settings → Models
2. Verify models are available
3. Try a test chat
Chat Returns Generic/Bad Responses
Symptom: AI responses are shallow, generic, or wrong
Cause: Bad context, vague question, or wrong model
Solutions:
Solution 1: Check Context
1. In Chat, click "Select Sources"
2. Verify sources you want are CHECKED
3. Set them to "Full Content" (not "Summary Only")
4. Click "Save"
5. Try chat again
Solution 2: Ask Better Question
Bad: "What do you think?"
Good: "Based on the paper's methodology, what are 3 limitations?"
Bad: "Tell me about X"
Good: "Summarize X in 3 bullet points with page citations"
Solution 3: Use Stronger Model
OpenAI:
Current: gpt-4o-mini → Switch to: gpt-4o
Anthropic:
Current: claude-3-5-haiku → Switch to: claude-3-5-sonnet
To change:
1. Settings → Models
2. Select model
3. Try chat again
Solution 4: Add More Sources
If: "Response seems incomplete"
Try: Add more relevant sources to provide context
Chat is Very Slow
Symptom: Chat responses take minutes
Cause: Large context, slow model, or overloaded API
Solutions:
Solution 1: Use Faster Model
Fastest: Groq (any model)
Fast: OpenAI gpt-4o-mini
Medium: Anthropic claude-3-5-haiku
Slow: Anthropic claude-3-5-sonnet
Switch in: Settings → Models
Solution 2: Reduce Context
1. Chat → Select Sources
2. Uncheck sources you don't need
3. Or switch to "Summary Only" for background sources
4. Save and try again
Solution 3: Increase Timeout
# In .env:
API_CLIENT_TIMEOUT=600 # 10 minutes
# Restart:
docker compose restart
Solution 4: Check System Load
# See if API is overloaded:
docker stats
# If CPU >80% or memory >90%:
# Reduce: SURREAL_COMMANDS_MAX_TASKS=2
# Restart: docker compose restart
Chat Doesn't Remember History
Symptom: Each message treated as separate, no context between questions
Cause: Chat history not saved or new chat started
Solution:
1. Make sure you're in same Chat (not new Chat)
2. Check Chat title at top
3. If it's blank, start new Chat with a title
4. Each named Chat keeps its history
5. If you start new Chat, history is separate
"Rate limit exceeded"
Symptom: Error: "Rate limit exceeded" or "Too many requests"
Cause: Hit provider's API rate limit
Solutions:
For Cloud Providers (OpenAI, Anthropic, etc.)
Immediate:
- Wait 1-2 minutes
- Try again
Short term:
- Use cheaper/smaller model
- Reduce concurrent operations
- Space out requests
Long term:
- Upgrade your account
- Switch to different provider
- Use Ollama (local, no limits)
Check Account Status
OpenAI: https://platform.openai.com/account/usage/overview
Anthropic: https://console.anthropic.com/account/billing/overview
Google: Google Cloud Console
For Ollama (Local)
- No rate limits
- Use
ollama pull mistralfor best model - Restart if hitting resource limits
"Context length exceeded" or "Token limit"
Symptom: Error about too many tokens
Cause: Sources too large for model
Solutions:
Solution 1: Use Model with Longer Context
Current: GPT-4o (128K tokens) → Switch to: Claude (200K tokens)
Current: Claude Haiku (200K) → Switch to: Gemini (1M tokens)
To change: Settings → Models
Solution 2: Reduce Context
1. Select fewer sources
2. Or use "Summary Only" instead of "Full Content"
3. Or split large documents into smaller pieces
Solution 3: For Ollama (Local)
# Use smaller model:
ollama pull phi # Very small
# Instead of: ollama pull neural-chat # Large
"API call failed" or Timeout
Symptom: Generic API error, response times out
Cause: Provider API down, network issue, or slow service
Solutions:
Check Provider Status
OpenAI: https://status.openai.com/
Anthropic: Check website
Google: Google Cloud Status
Groq: Check website
Retry Operation
1. Wait 30 seconds
2. Try again
Use Different Model/Provider
1. Settings → Models
2. Try different provider
3. If OpenAI down, use Anthropic
Check Network
# Verify internet working:
ping google.com
# Test API endpoint directly:
curl https://api.openai.com/v1/models \
-H "Authorization: Bearer YOUR_KEY"
Responses Include Hallucinations
Symptom: AI makes up facts that aren't in sources
Cause: Sources not in context, or model guessing
Solutions:
Solution 1: Verify Context
1. Click citation in response
2. Check source actually says that
3. If not, sources weren't in context
4. Add source to context and try again
Solution 2: Request Citations
Ask: "Answer this with citations to specific pages"
The AI will be more careful if asked for citations
Solution 3: Use Stronger Model
Weaker models hallucinate more
Switch to: GPT-4o or Claude Sonnet
High API Costs
Symptom: API bills are higher than expected
Cause: Using expensive model, large context, many requests
Solutions:
Use Cheaper Model
Expensive: gpt-4o
Cheaper: gpt-4o-mini (10x cheaper)
Expensive: Claude Sonnet
Cheaper: Claude Haiku (5x cheaper)
Groq: Ultra cheap but fewer models
Reduce Context
In Chat:
1. Select fewer sources
2. Use "Summary Only" for background
3. Ask more specific questions
Switch to Ollama (Free)
# Install Ollama
# Run: ollama serve
# Download: ollama pull mistral
# Set: OLLAMA_API_BASE=http://localhost:11434
# Cost: Free!
Still Having Chat Issues?
- Try Quick Fixes
- Try Chat Effectively Guide
- Check logs:
docker compose logs api | grep -i "error" - Ask for help: Troubleshooting Index