open-notebook/docs/6-TROUBLESHOOTING/ai-chat-issues.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

9.1 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 mistral for 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?