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open-notebook/docs/5-CONFIGURATION/ai-providers.md
Luis Novo adc03e56bb feat: add DashScope (Qwen) and MiniMax provider support 
- Bump esperanto dependency to >=2.20.0 for new provider profiles
- Register both providers in credentials, key provider, connection tester, model discovery, and models router
- Add frontend provider entries (display names, modalities, docs links)
- Add documentation sections for both providers in ai-providers.md, environment-reference.md, and provider comparison
2026-04-06 10:54:37 -03:00

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AI Providers - Configuration Guide

Complete setup instructions for each AI provider via the Settings UI.

New in v1.2: All AI provider credentials are now managed through the Settings UI. Environment variables for API keys are deprecated.

How Provider Setup Works

Open Notebook uses a credential-based system for managing AI providers:

  1. Get your API key from the provider's website
  2. Open Settings → API Keys → Add Credential
  3. Test the connection to verify it works
  4. Discover & Register Models to make them available
  5. Start using the provider in your notebooks

Prerequisite: You must set OPEN_NOTEBOOK_ENCRYPTION_KEY in your docker-compose.yml before storing credentials. See API Configuration for details.

Cloud Providers (Recommended for Most)

OpenAI

Cost: ~$0.03-0.15 per 1K tokens (varies by model)

Get Your API Key:

  1. Go to https://platform.openai.com/api-keys
  2. Create account (if needed)
  3. Create new API key (starts with "sk-proj-")
  4. Add $5+ credits to account

Configure in Open Notebook:

  1. Go to Settings → API Keys
  2. Click Add Credential
  3. Select provider: OpenAI
  4. Give it a name (e.g., "My OpenAI Key")
  5. Paste your API key
  6. Click Save, then Test Connection
  7. Click Discover Models to find available models
  8. Click Register Models to make them available

Available Models (in Open Notebook):

  • gpt-4o — Best quality, fast (latest version)
  • gpt-4o-mini — Fast, cheap, good for testing
  • o1 — Advanced reasoning model (slower, more expensive)
  • o1-mini — Faster reasoning model

Recommended:

  • For general use: gpt-4o (best balance)
  • For testing/cheap: gpt-4o-mini (90% cheaper)
  • For complex reasoning: o1 (best for hard problems)

Cost Estimate:

Light use: $1-5/month
Medium use: $10-30/month
Heavy use: $50-100+/month

Troubleshooting:

  • "Invalid API key" → Check key starts with "sk-proj-" and test the connection in Settings
  • "Rate limit exceeded" → Wait or upgrade account
  • "Model not available" → Try gpt-4o-mini instead, or re-discover models

Anthropic (Claude)

Cost: ~$0.80-3.00 per 1M tokens (cheaper than OpenAI for long context)

Get Your API Key:

  1. Go to https://console.anthropic.com/
  2. Create account or login
  3. Go to API keys section
  4. Create new API key (starts with "sk-ant-")

Configure in Open Notebook:

  1. Go to Settings → API Keys
  2. Click Add Credential
  3. Select provider: Anthropic
  4. Give it a name, paste your API key
  5. Click Save, then Test Connection
  6. Click Discover Models → Register Models

Available Models:

  • claude-sonnet-4-5-20250929 — Latest, best quality (recommended)
  • claude-3-5-sonnet-20241022 — Previous generation, still excellent
  • claude-3-5-haiku-20241022 — Fast, cheap
  • claude-opus-4-5-20251101 — Most powerful, expensive

Recommended:

  • For general use: claude-sonnet-4-5 (best overall, latest)
  • For cheap: claude-3-5-haiku (80% cheaper)
  • For complex: claude-opus-4-5 (most capable)

Cost Estimate:

Sonnet: $3-20/month (typical use)
Haiku: $0.50-3/month
Opus: $10-50+/month

Advantages:

  • Great long-context support (200K tokens)
  • Excellent reasoning
  • Fast processing

Troubleshooting:

  • "Invalid API key" → Check it starts with "sk-ant-" and test in Settings
  • "Overloaded" → Anthropic is busy, retry later
  • "Model unavailable" → Re-discover models from the credential

Google Gemini

Cost: ~$0.075-0.30 per 1K tokens (competitive with OpenAI)

Get Your API Key:

  1. Go to https://aistudio.google.com/app/apikey
  2. Create account or login
  3. Create new API key

Configure in Open Notebook:

  1. Go to Settings → API Keys
  2. Click Add Credential
  3. Select provider: Google Gemini
  4. Give it a name, paste your API key
  5. Click Save, then Test Connection
  6. Click Discover Models → Register Models

Available Models:

  • gemini-2.0-flash-exp — Latest experimental, fastest (recommended)
  • gemini-2.0-flash — Stable version, fast, cheap

Recommended:

  • For general use: gemini-2.0-flash-exp (best value, latest)
  • For cheap: gemini-1.5-flash (very cheap)
  • For complex/long context: gemini-1.5-pro-latest (2M token context)

Advantages:

  • Very long context (1M tokens)
  • Multimodal (images, audio, video)
  • Good for podcasts

Troubleshooting:

  • "API key invalid" → Get fresh key from aistudio.google.com
  • "Quota exceeded" → Free tier limited, upgrade account
  • "Model not found" → Re-discover models from the credential

Groq

Cost: ~$0.05 per 1M tokens (cheapest, but limited models)

Get Your API Key:

  1. Go to https://console.groq.com/keys
  2. Create account or login
  3. Create new API key

Configure in Open Notebook:

  1. Go to Settings → API Keys
  2. Click Add Credential
  3. Select provider: Groq
  4. Give it a name, paste your API key
  5. Click Save, then Test Connection
  6. Click Discover Models → Register Models

Available Models:

  • llama-3.3-70b-versatile — Best on Groq (recommended)
  • llama-3.1-70b-versatile — Fast, capable
  • mixtral-8x7b-32768 — Good alternative
  • gemma2-9b-it — Small, very fast

Recommended:

  • For quality: llama-3.3-70b-versatile (best overall)
  • For speed: gemma2-9b-it (ultra-fast)
  • For balance: llama-3.1-70b-versatile

Advantages:

  • Ultra-fast inference
  • Very cheap
  • Great for transformations/batch work

Disadvantages:

  • Limited model selection
  • Smaller models than OpenAI/Anthropic

Troubleshooting:

  • "Rate limited" → Free tier has limits, upgrade
  • "Model not available" → Re-discover models from the credential

OpenRouter

Cost: Varies by model ($0.05-15 per 1M tokens)

Get Your API Key:

  1. Go to https://openrouter.ai/keys
  2. Create account or login
  3. Add credits to your account
  4. Create new API key

Configure in Open Notebook:

  1. Go to Settings → API Keys
  2. Click Add Credential
  3. Select provider: OpenRouter
  4. Give it a name, paste your API key
  5. Click Save, then Test Connection
  6. Click Discover Models → Register Models

Available Models (100+ options):

  • OpenAI: openai/gpt-4o, openai/o1
  • Anthropic: anthropic/claude-sonnet-4.5, anthropic/claude-3.5-haiku
  • Google: google/gemini-2.0-flash-exp, google/gemini-1.5-pro
  • Meta: meta-llama/llama-3.3-70b-instruct, meta-llama/llama-3.1-405b-instruct
  • Mistral: mistralai/mistral-large-2411
  • DeepSeek: deepseek/deepseek-chat
  • And many more...

Recommended:

  • For quality: anthropic/claude-sonnet-4.5 (best overall)
  • For speed/cost: google/gemini-2.0-flash-exp (very fast, cheap)
  • For open-source: meta-llama/llama-3.3-70b-instruct
  • For reasoning: openai/o1

Advantages:

  • One API key for 100+ models
  • Unified billing
  • Easy model comparison
  • Access to models that may have waitlists elsewhere

Cost Estimate:

Light use: $1-5/month
Medium use: $10-30/month
Heavy use: Depends on models chosen

Troubleshooting:

  • "Invalid API key" → Check it starts with "sk-or-"
  • "Insufficient credits" → Add credits at openrouter.ai
  • "Model not available" → Check model ID spelling (use full path)

DashScope (Qwen)

Cost: ~$0.01-0.06 per 1K tokens (varies by model)

Get Your API Key:

  1. Go to https://dashscope.console.aliyun.com/
  2. Create an Alibaba Cloud account (if needed)
  3. Navigate to API Keys section
  4. Create a new API key

Configure in Open Notebook:

  1. Go to Settings → API Keys
  2. Click Add Credential
  3. Select provider: DashScope (Qwen)
  4. Give it a name, paste your API key
  5. Click Save, then Test Connection
  6. Click Discover Models → Register Models

Available Models:

  • qwen-max — Most capable Qwen model
  • qwen-plus — Good balance of quality and speed
  • qwen-turbo — Fastest, cheapest

Recommended:

  • For quality: qwen-max (best overall)
  • For general use: qwen-plus (good balance)
  • For speed/cost: qwen-turbo (cheapest)

Troubleshooting:

  • "Invalid API key" → Check the key in the DashScope console
  • "Model not available" → Re-discover models from the credential

MiniMax

Cost: Varies by model

Get Your API Key:

  1. Go to https://platform.minimaxi.com/
  2. Create an account (if needed)
  3. Navigate to API Keys section
  4. Create a new API key

Configure in Open Notebook:

  1. Go to Settings → API Keys
  2. Click Add Credential
  3. Select provider: MiniMax
  4. Give it a name, paste your API key
  5. Click Save, then Test Connection
  6. Click Discover Models → Register Models

Available Models:

  • MiniMax-M2.5 — Most capable, 204K context
  • MiniMax-M2.5-highspeed — Faster variant, 204K context

Recommended:

  • For quality: MiniMax-M2.5 (best overall)
  • For speed: MiniMax-M2.5-highspeed (faster responses)

Advantages:

  • Very long context (204K tokens)
  • Competitive pricing

Troubleshooting:

  • "Invalid API key" → Check the key in the MiniMax platform
  • "Model not available" → Re-discover models from the credential

Self-Hosted / Local

Ollama (Recommended for Local)

Cost: Free (electricity only)

Setup Ollama:

  1. Install Ollama: https://ollama.ai
  2. Run Ollama in background: ollama serve
  3. Download a model: ollama pull mistral

Configure in Open Notebook:

  1. Go to Settings → API Keys
  2. Click Add Credential
  3. Select provider: Ollama
  4. Give it a name (e.g., "Local Ollama")
  5. Enter the base URL:
    • Same machine (non-Docker): http://localhost:11434
    • Docker with Ollama on host: http://host.docker.internal:11434
    • Docker with Ollama container: http://ollama:11434
  6. Click Save, then Test Connection
  7. Click Discover Models → Register Models

See Ollama Setup Guide for detailed network configuration.

Available Models:

  • llama3.3:70b — Best quality (requires 40GB+ RAM)
  • llama3.1:8b — Recommended, balanced (8GB RAM)
  • qwen2.5:7b — Excellent for code and reasoning
  • mistral:7b — Good general purpose
  • phi3:3.8b — Small, fast (4GB RAM)
  • gemma2:9b — Google's model, balanced
  • Many more: ollama list to see available

Recommended:

  • For quality (with GPU): llama3.3:70b (best)
  • For general use: llama3.1:8b (best balance)
  • For speed/low memory: phi3:3.8b (very fast)
  • For coding: qwen2.5:7b (excellent at code)

Hardware Requirements:

GPU (NVIDIA/AMD):
  8GB VRAM: Runs most models fine
  6GB VRAM: Works, slower
  4GB VRAM: Small models only

CPU-only:
  16GB+ RAM: Slow but works
  8GB RAM: Very slow
  4GB RAM: Not recommended

Advantages:

  • Completely private (runs locally)
  • Free (electricity only)
  • No API key needed
  • Works offline

Disadvantages:

  • Slower than cloud (unless on GPU)
  • Smaller models than cloud
  • Requires local hardware

Troubleshooting:

  • "Connection refused" → Ollama not running or wrong URL in credential
  • "Model not found" → Download it: ollama pull modelname
  • "Out of memory" → Use smaller model or add more RAM

LM Studio (Local Alternative)

Cost: Free

Setup LM Studio:

  1. Download LM Studio: https://lmstudio.ai
  2. Open app
  3. Download a model from library
  4. Go to "Local Server" tab
  5. Start server (default port: 1234)

Configure in Open Notebook:

  1. Go to Settings → API Keys
  2. Click Add Credential
  3. Select provider: OpenAI-Compatible
  4. Give it a name (e.g., "LM Studio")
  5. Enter the base URL: http://host.docker.internal:1234/v1 (Docker) or http://localhost:1234/v1 (local)
  6. API key: lm-studio (placeholder, LM Studio doesn't require one)
  7. Click Save, then Test Connection

Advantages:

  • GUI interface (easier than Ollama CLI)
  • Good model selection
  • Privacy-focused
  • Works offline

Disadvantages:

  • Desktop only (Mac/Windows/Linux)
  • Slower than cloud
  • Requires local GPU

Custom OpenAI-Compatible

For Text Generation UI, vLLM, or other OpenAI-compatible endpoints:

  1. Go to Settings → API Keys
  2. Click Add Credential
  3. Select provider: OpenAI-Compatible
  4. Enter the base URL for your endpoint (e.g., http://localhost:8000/v1)
  5. Enter API key if required
  6. Optionally configure per-service URLs (LLM, Embedding, TTS, STT)
  7. Click Save, then Test Connection

See OpenAI-Compatible Setup for detailed instructions.

Enterprise

Azure OpenAI

Cost: Same as OpenAI (usage-based)

Configure in Open Notebook:

  1. Create Azure OpenAI service in Azure portal
  2. Deploy GPT-4/3.5-turbo model
  3. Get your endpoint and key
  4. Go to Settings → API Keys
  5. Click Add Credential
  6. Select provider: Azure OpenAI
  7. Fill in: API Key, Endpoint, API Version
  8. Optionally configure service-specific endpoints (LLM, Embedding)
  9. Click Save, then Test Connection

Advantages:

  • Enterprise support
  • VPC integration
  • Compliance (HIPAA, SOC2, etc.)

Disadvantages:

  • More complex setup
  • Higher overhead
  • Requires Azure account

Embeddings (For Search/Semantic Features)

By default, Open Notebook uses the LLM provider's embeddings. Embedding models are discovered and registered through the same credential system — when you discover models from a credential, embedding models are included alongside language models.

Choosing Your Provider

1. Don't want to run locally and don't want to mess around with different providers:

Use OpenAI

  • Cloud-based
  • Good quality
  • Reasonable cost
  • Simplest setup, supports all modes (text, embedding, tts, stt, etc)

For budget-conscious: Groq, OpenRouter or Ollama

  • Groq: Super cheap cloud
  • Ollama: Free, but local
  • OpenRouter: many open source models very accessible

For privacy-first: Ollama or LM Studio and Speaches (TTS, STT)

  • Everything stays local
  • Works offline
  • No API keys sent anywhere

For enterprise: Azure OpenAI

  • Compliance
  • VPC integration
  • Support

Next Steps

  1. Choose your provider from above
  2. Get API key (if cloud) or install locally (if Ollama)
  3. Set OPEN_NOTEBOOK_ENCRYPTION_KEY in your docker-compose.yml (required for storing credentials)
  4. Open Settings → API Keys → Add Credential
  5. Test Connection to verify it works
  6. Discover & Register Models to make them available
  7. Verify it works with a test chat

Multiple providers: You can add credentials for as many providers as you want. Create separate credentials for different projects or team members.

Done!

Legacy: Environment Variables (Deprecated)

Deprecated: Configuring AI provider API keys via environment variables is deprecated. Use the Settings UI instead. Environment variables may still work as a fallback but are no longer the recommended approach.

If you are migrating from an older version that used environment variables, go to Settings → API Keys and click the Migrate to Database button to import your existing keys into the credential system.

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