* docs: update CHANGELOG for v1.6.0 release * fix: improve error logging for chat model configuration issues (#358) - Add detailed error logging in provision.py when model lookup fails - Add warning logging in models.py when default model is not configured - Add traceback logging in chat router exception handler - Update Ollama docs with model name configuration guidance - Update troubleshooting docs with "Failed to send message" solutions - Bump version to 1.6.1 * chore: uvlock
8.9 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: Missing or invalid API key
Solutions:
Solution 1: Add API Key
# Check .env has your API key:
cat .env | grep -i "OPENAI\|ANTHROPIC\|GOOGLE"
# Should see something like:
# OPENAI_API_KEY=sk-proj-...
# If missing, add it:
OPENAI_API_KEY=sk-proj-your-key-here
# Save and restart:
docker compose restart api
# Wait 10 seconds, then refresh browser
Solution 2: Check Key is Valid
# Test API key directly:
curl https://api.openai.com/v1/models \
-H "Authorization: Bearer sk-proj-..."
# Should return list of models
# If error: key is invalid
Solution 3: Switch Provider
# Try a different provider:
# Remove: OPENAI_API_KEY
# Add: ANTHROPIC_API_KEY=sk-ant-...
# Restart and check Settings → Models
"Invalid API key" or "Unauthorized"
Symptom: Error when trying to chat: "Invalid API key"
Cause: API key wrong, expired, or revoked
Solutions:
Step 1: Verify Key Format
# OpenAI: Should start with sk-proj-
# Anthropic: Should start with sk-ant-
# Google: Should be AIzaSy...
# Check in .env:
cat .env | grep OPENAI_API_KEY
Step 2: Get Fresh Key
# Go to provider's dashboard:
# - OpenAI: https://platform.openai.com/api-keys
# - Anthropic: https://console.anthropic.com/
# - Google: https://aistudio.google.com/app/apikey
# Generate new key
# Copy exactly (no extra spaces)
Step 3: Update .env
# Edit .env:
OPENAI_API_KEY=sk-proj-new-key-here
# No quotes needed, no spaces
# Save and restart:
docker compose restart api
Step 4: Verify in UI
1. Open Open Notebook
2. Go to Settings → Models
3. Select your provider
4. Should show available models
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