Merge pull request #725 from lfnovo/feat/dashscope-minimax-providers
feat: add DashScope (Qwen) and MiniMax provider support
This commit is contained in:
Luis Novo
GitHub
commit
6274358b21
16 changed files with 227 additions and 16 deletions
11
CHANGELOG.md
11
CHANGELOG.md
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@ -7,6 +7,17 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
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## [Unreleased]
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## [1.8.2] - 2026-04-06
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### Added
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- DashScope (Qwen) provider support — Alibaba Cloud's Qwen models (qwen-turbo, qwen-plus, qwen-max)
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- MiniMax provider support — MiniMax models with 204K context (MiniMax-M2.5, MiniMax-M2.5-highspeed)
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- Model discovery, connection testing, and credential management for both new providers
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- Documentation for DashScope and MiniMax in AI providers guide, environment reference, and provider comparison
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### Dependencies
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- Bump esperanto to >= 2.20.0
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## [1.8.1] - 2026-03-10
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### Added
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10
README.md
10
README.md
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@ -58,7 +58,7 @@ In a world dominated by Artificial Intelligence, having the ability to think
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**Open Notebook empowers you to:**
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- 🔒 **Control your data** - Keep your research private and secure
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- 🤖 **Choose your AI models** - Support for 16+ providers including OpenAI, Anthropic, Ollama, LM Studio, and more
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- 🤖 **Choose your AI models** - Support for 18+ providers including OpenAI, Anthropic, Ollama, LM Studio, and more
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- 📚 **Organize multi-modal content** - PDFs, videos, audio, web pages, and more
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- 🎙️ **Generate professional podcasts** - Advanced multi-speaker podcast generation
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- 🔍 **Search intelligently** - Full-text and vector search across all your content
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@ -74,7 +74,7 @@ Learn more about our project at [https://www.open-notebook.ai](https://www.open-
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| Feature | Open Notebook | Google Notebook LM | Advantage |
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|---------|---------------|--------------------|-----------|
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| **Privacy & Control** | Self-hosted, your data | Google cloud only | Complete data sovereignty |
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| **AI Provider Choice** | 16+ providers (OpenAI, Anthropic, Ollama, LM Studio, etc.) | Google models only | Flexibility and cost optimization |
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| **AI Provider Choice** | 18+ providers (OpenAI, Anthropic, Ollama, LM Studio, etc.) | Google models only | Flexibility and cost optimization |
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| **Podcast Speakers** | 1-4 speakers with custom profiles | 2 speakers only | Extreme flexibility |
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| **Content Transformations** | Custom and built-in | Limited options | Unlimited processing power |
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| **API Access** | Full REST API | No API | Complete automation |
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@ -213,6 +213,8 @@ Thanks to the [Esperanto](https://github.com/lfnovo/esperanto) library, we suppo
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| Voyage | ❌ | ✅ | ❌ | ❌ |
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| xAI | ✅ | ❌ | ❌ | ❌ |
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| OpenRouter | ✅ | ❌ | ❌ | ❌ |
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| DashScope (Qwen) | ✅ | ❌ | ❌ | ❌ |
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| MiniMax | ✅ | ❌ | ❌ | ❌ |
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| OpenAI Compatible* | ✅ | ❌ | ❌ | ❌ |
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*Supports LM Studio and any OpenAI-compatible endpoint
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@ -223,7 +225,7 @@ Thanks to the [Esperanto](https://github.com/lfnovo/esperanto) library, we suppo
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- **🔒 Privacy-First**: Your data stays under your control - no cloud dependencies
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- **🎯 Multi-Notebook Organization**: Manage multiple research projects seamlessly
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- **📚 Universal Content Support**: PDFs, videos, audio, web pages, Office docs, and more
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- **🤖 Multi-Model AI Support**: 16+ providers including OpenAI, Anthropic, Ollama, Google, LM Studio, and more
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- **🤖 Multi-Model AI Support**: 18+ providers including OpenAI, Anthropic, Ollama, Google, LM Studio, and more
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- **🎙️ Professional Podcast Generation**: Advanced multi-speaker podcasts with Episode Profiles
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- **🔍 Intelligent Search**: Full-text and vector search across all your content
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- **💬 Context-Aware Chat**: AI conversations powered by your research materials
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@ -280,7 +282,7 @@ Thanks to the [Esperanto](https://github.com/lfnovo/esperanto) library, we suppo
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### Recently Completed ✅
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- **Next.js Frontend**: Modern React-based frontend with improved performance
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- **Comprehensive REST API**: Full programmatic access to all functionality
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- **Multi-Model Support**: 16+ AI providers including OpenAI, Anthropic, Ollama, LM Studio
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- **Multi-Model Support**: 18+ AI providers including OpenAI, Anthropic, Ollama, LM Studio
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- **Advanced Podcast Generator**: Professional multi-speaker podcasts with Episode Profiles
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- **Content Transformations**: Powerful customizable actions for content processing
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- **Enhanced Citations**: Improved layout and finer control for source citations
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@ -57,6 +57,8 @@ PROVIDER_ENV_CONFIG: Dict[str, dict] = {
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"openai_compatible": {
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"required_any": ["OPENAI_COMPATIBLE_BASE_URL", "OPENAI_COMPATIBLE_API_KEY"],
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},
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"dashscope": {"required": ["DASHSCOPE_API_KEY"]},
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"minimax": {"required": ["MINIMAX_API_KEY"]},
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}
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PROVIDER_MODALITIES: Dict[str, List[str]] = {
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@ -74,6 +76,8 @@ PROVIDER_MODALITIES: Dict[str, List[str]] = {
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"vertex": ["language", "embedding"],
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"azure": ["language", "embedding", "speech_to_text", "text_to_speech"],
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"openai_compatible": ["language", "embedding", "speech_to_text", "text_to_speech"],
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"dashscope": ["language"],
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"minimax": ["language"],
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}
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@ -512,6 +516,8 @@ async def discover_with_config(provider: str, config: dict) -> List[dict]:
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"deepseek": "https://api.deepseek.com/models",
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"xai": "https://api.x.ai/v1/models",
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"openrouter": "https://openrouter.ai/api/v1/models",
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"dashscope": "https://dashscope.aliyuncs.com/compatible-mode/v1/models",
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"minimax": "https://api.minimax.io/v1/models",
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}
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if provider == "ollama":
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@ -96,6 +96,8 @@ PROVIDER_PRIORITY = [
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"ollama",
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"azure",
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"openai_compatible",
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"dashscope",
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"minimax",
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]
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# Model preference patterns (preferred models within each provider)
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@ -105,6 +107,8 @@ MODEL_PREFERENCES = {
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"google": ["gemini-2.0", "gemini-1.5-pro", "gemini-pro"],
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"mistral": ["mistral-large", "mixtral"],
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"groq": ["llama-3.3", "llama-3.1", "mixtral"],
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"dashscope": ["qwen-max", "qwen-plus", "qwen-turbo"],
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"minimax": ["MiniMax-M2.5", "MiniMax-M2.5-highspeed"],
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}
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@ -378,6 +382,8 @@ async def get_provider_availability():
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"voyage": "VOYAGE_API_KEY",
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"elevenlabs": "ELEVENLABS_API_KEY",
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"ollama": "OLLAMA_API_BASE",
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"dashscope": "DASHSCOPE_API_KEY",
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"minimax": "MINIMAX_API_KEY",
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}
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provider_status = {}
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@ -12,7 +12,7 @@
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---
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### ☁️ I want to use other cloud AI (Anthropic, Google, OpenRouter, etc.)
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**5 minutes to running.** Choose from 15+ AI providers.
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**5 minutes to running.** Choose from 17+ AI providers.
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→ [Cloud Providers Quick Start](quick-start-cloud.md)
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@ -39,7 +39,7 @@
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| Feature | Open Notebook | Notebook LM |
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|---------|---|---|
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| **Privacy** | Self-hosted, your control | Cloud, Google's servers |
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| **AI Choice** | 15+ providers | Google's models only |
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| **AI Choice** | 17+ providers | Google's models only |
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| **Podcast Speakers** | 1-4 customizable | 2 only |
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| **Cost** | Completely free | Free (but your data) |
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| **Offline** | Yes | No |
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@ -1,6 +1,6 @@
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# AI Providers - Comparison & Selection Guide
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Open Notebook supports 15+ AI providers. This guide helps you **choose the right provider** for your needs.
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Open Notebook supports 17+ AI providers. This guide helps you **choose the right provider** for your needs.
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> 💡 **Just want to set up a provider?** Skip to the [Configuration Guide](../5-CONFIGURATION/ai-providers.md) for detailed setup instructions.
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@ -54,6 +54,24 @@ Open Notebook supports 15+ AI providers. This guide helps you **choose the right
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→ [Setup Guide](../5-CONFIGURATION/ai-providers.md#openrouter)
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**DashScope (Qwen)**
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- Cost: ~$0.01-0.06 per 1K tokens
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- Speed: Fast
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- Quality: Good
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- Best for: Users in Asia, Alibaba Cloud ecosystem
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- Advantage: Competitive pricing, strong multilingual support
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→ [Setup Guide](../5-CONFIGURATION/ai-providers.md#dashscope-qwen)
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**MiniMax**
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- Cost: Varies by model
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- Speed: Fast
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- Quality: Good
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- Best for: Long context tasks (204K tokens)
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- Advantage: Very long context window
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→ [Setup Guide](../5-CONFIGURATION/ai-providers.md#minimax)
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### Local / Self-Hosted (Free)
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**Ollama (Recommended for Local)**
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@ -98,6 +116,8 @@ Open Notebook supports 15+ AI providers. This guide helps you **choose the right
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| **Google** | Very Fast | $$ | Good-Excellent | Low | 5 min | 2M |
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| **Groq** | Ultra Fast | $ | Good | Low | 5 min | 32K |
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| **OpenRouter** | Varies | Varies | Varies | Low | 5 min | Varies |
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| **DashScope** | Fast | $ | Good | Low | 5 min | Varies |
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| **MiniMax** | Fast | $$ | Good | Low | 5 min | 204K |
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| **Ollama** | Slow-Medium | Free | Good | Max | 10 min | Varies |
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| **LM Studio** | Slow-Medium | Free | Good | Max | 15 min | Varies |
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| **Azure** | Very Fast | $$ | Excellent | High | 10 min | 128K |
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@ -249,6 +249,76 @@ Heavy use: Depends on models chosen
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---
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### DashScope (Qwen)
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**Cost:** ~$0.01-0.06 per 1K tokens (varies by model)
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**Get Your API Key:**
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1. Go to https://dashscope.console.aliyun.com/
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2. Create an Alibaba Cloud account (if needed)
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3. Navigate to API Keys section
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4. Create a new API key
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**Configure in Open Notebook:**
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1. Go to **Settings** → **API Keys**
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2. Click **Add Credential**
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3. Select provider: **DashScope (Qwen)**
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4. Give it a name, paste your API key
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5. Click **Save**, then **Test Connection**
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6. Click **Discover Models** → **Register Models**
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**Available Models:**
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- `qwen-max` — Most capable Qwen model
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- `qwen-plus` — Good balance of quality and speed
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- `qwen-turbo` — Fastest, cheapest
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**Recommended:**
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- For quality: `qwen-max` (best overall)
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- For general use: `qwen-plus` (good balance)
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- For speed/cost: `qwen-turbo` (cheapest)
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**Troubleshooting:**
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- "Invalid API key" → Check the key in the DashScope console
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- "Model not available" → Re-discover models from the credential
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---
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### MiniMax
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**Cost:** Varies by model
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**Get Your API Key:**
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1. Go to https://platform.minimaxi.com/
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2. Create an account (if needed)
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3. Navigate to API Keys section
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4. Create a new API key
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**Configure in Open Notebook:**
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1. Go to **Settings** → **API Keys**
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2. Click **Add Credential**
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3. Select provider: **MiniMax**
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4. Give it a name, paste your API key
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5. Click **Save**, then **Test Connection**
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6. Click **Discover Models** → **Register Models**
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**Available Models:**
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- `MiniMax-M2.5` — Most capable, 204K context
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- `MiniMax-M2.5-highspeed` — Faster variant, 204K context
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**Recommended:**
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- For quality: `MiniMax-M2.5` (best overall)
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- For speed: `MiniMax-M2.5-highspeed` (faster responses)
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**Advantages:**
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- Very long context (204K tokens)
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- Competitive pricing
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**Troubleshooting:**
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- "Invalid API key" → Check the key in the MiniMax platform
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- "Model not available" → Re-discover models from the credential
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---
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## Self-Hosted / Local
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### Ollama (Recommended for Local)
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@ -263,6 +263,8 @@ If you have these variables configured from a previous installation, click the *
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| `OPENAI_COMPATIBLE_API_KEY_STT` | OpenAI-Compatible | Configure per-service key in credential |
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| `OPENAI_COMPATIBLE_BASE_URL_TTS` | OpenAI-Compatible | Configure per-service URL in credential |
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| `OPENAI_COMPATIBLE_API_KEY_TTS` | OpenAI-Compatible | Configure per-service key in credential |
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| `DASHSCOPE_API_KEY` | DashScope (Qwen) | Settings → API Keys → Add DashScope Credential |
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| `MINIMAX_API_KEY` | MiniMax | Settings → API Keys → Add MiniMax Credential |
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| `AZURE_OPENAI_API_KEY` | Azure OpenAI | Settings → API Keys → Add Azure OpenAI Credential |
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| `AZURE_OPENAI_ENDPOINT` | Azure OpenAI | Configure in Azure OpenAI credential |
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| `AZURE_OPENAI_API_VERSION` | Azure OpenAI | Configure in Azure OpenAI credential |
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@ -18,7 +18,7 @@ Open Notebook is an open-source, privacy-focused alternative to Google's Noteboo
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### How is it different from Google Notebook LM?
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**Privacy**: Your data stays local by default. Only your chosen AI providers receive queries.
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**Flexibility**: Support for 15+ AI providers (OpenAI, Anthropic, Google, local models, etc.)
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**Flexibility**: Support for 17+ AI providers (OpenAI, Anthropic, Google, local models, etc.)
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**Customization**: Open source, so you can modify and extend functionality
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**Control**: You control your data, models, and processing
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@ -86,7 +86,7 @@ Open Notebook aims to be a **privacy-focused, self-hosted alternative to Google'
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- Graceful degradation when providers are unavailable
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**Example Decisions**:
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- ✅ Support for 16+ AI providers
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- ✅ Support for 18+ AI providers
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- ✅ Per-feature model selection (chat, embeddings, TTS)
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- ❌ Features that only work with OpenAI
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- ❌ Hard-coded API endpoints for specific providers
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@ -75,12 +75,14 @@ const PROVIDER_DISPLAY_NAMES: Record<string, string> = {
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azure: 'Azure OpenAI',
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vertex: 'Google Vertex AI',
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openai_compatible: 'OpenAI Compatible',
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dashscope: 'DashScope (Qwen)',
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minimax: 'MiniMax',
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}
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// All providers in display order
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const ALL_PROVIDERS = [
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'openai', 'anthropic', 'google', 'groq', 'mistral', 'deepseek',
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'xai', 'openrouter', 'voyage', 'elevenlabs', 'ollama',
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'xai', 'openrouter', 'dashscope', 'minimax', 'voyage', 'elevenlabs', 'ollama',
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'azure', 'vertex', 'openai_compatible',
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]
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@ -100,6 +102,8 @@ const PROVIDER_MODALITIES: Record<string, ModelType[]> = {
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azure: ['language', 'embedding', 'text_to_speech', 'speech_to_text'],
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vertex: ['language', 'embedding', 'text_to_speech'],
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openai_compatible: ['language', 'embedding', 'text_to_speech', 'speech_to_text'],
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dashscope: ['language'],
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minimax: ['language'],
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}
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// Documentation links
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@ -117,6 +121,8 @@ const PROVIDER_DOCS: Record<string, string> = {
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azure: 'https://portal.azure.com/#view/Microsoft_Azure_ProjectOxford/CognitiveServicesHub/~/OpenAI',
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vertex: 'https://cloud.google.com/vertex-ai/docs/start/cloud-environment',
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openai_compatible: 'https://github.com/lfnovo/open-notebook/blob/main/docs/5-CONFIGURATION/openai-compatible.md',
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dashscope: 'https://help.aliyun.com/zh/model-studio/getting-started/',
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minimax: 'https://platform.minimaxi.com/document/Guides',
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}
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const TYPE_ICONS: Record<ModelType, React.ReactNode> = {
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@ -34,6 +34,8 @@ TEST_MODELS = {
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"vertex": ("gemini-2.0-flash", "language"), # Uses Google Vertex AI
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"azure": ("gpt-35-turbo", "language"), # Azure OpenAI deployment name
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"openai_compatible": (None, "language"), # Dynamic - will use first available model
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"dashscope": ("qwen-plus", "language"),
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"minimax": ("MiniMax-M2.5", "language"),
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}
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@ -62,6 +62,12 @@ PROVIDER_CONFIG = {
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"ollama": {
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"env_var": "OLLAMA_API_BASE",
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},
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"dashscope": {
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"env_var": "DASHSCOPE_API_KEY",
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},
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"minimax": {
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"env_var": "MINIMAX_API_KEY",
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},
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}
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@ -131,6 +131,14 @@ ELEVENLABS_MODEL_TYPES = {
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"text_to_speech": ["eleven"],
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}
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DASHSCOPE_MODEL_TYPES = {
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"language": ["qwen"],
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}
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MINIMAX_MODEL_TYPES = {
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"language": ["minimax", "abab"],
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}
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def classify_model_type(model_name: str, provider: str) -> str:
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"""
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@ -150,6 +158,8 @@ def classify_model_type(model_name: str, provider: str) -> str:
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"xai": XAI_MODEL_TYPES,
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"voyage": VOYAGE_MODEL_TYPES,
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"elevenlabs": ELEVENLABS_MODEL_TYPES,
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"dashscope": DASHSCOPE_MODEL_TYPES,
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"minimax": MINIMAX_MODEL_TYPES,
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}
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mapping = type_mappings.get(provider, {})
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@ -518,6 +528,74 @@ async def discover_elevenlabs_models() -> List[DiscoveredModel]:
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]
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||||
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||||
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||||
async def discover_dashscope_models() -> List[DiscoveredModel]:
|
||||
"""Fetch available models from DashScope (Qwen) API."""
|
||||
api_key = os.environ.get("DASHSCOPE_API_KEY")
|
||||
if not api_key:
|
||||
return []
|
||||
|
||||
models = []
|
||||
try:
|
||||
async with httpx.AsyncClient() as client:
|
||||
response = await client.get(
|
||||
"https://dashscope.aliyuncs.com/compatible-mode/v1/models",
|
||||
headers={"Authorization": f"Bearer {api_key}"},
|
||||
timeout=30.0,
|
||||
)
|
||||
response.raise_for_status()
|
||||
data = response.json()
|
||||
|
||||
for model in data.get("data", []):
|
||||
model_id = model.get("id", "")
|
||||
if model_id:
|
||||
model_type = classify_model_type(model_id, "dashscope")
|
||||
models.append(
|
||||
DiscoveredModel(
|
||||
name=model_id,
|
||||
provider="dashscope",
|
||||
model_type=model_type,
|
||||
)
|
||||
)
|
||||
except Exception as e:
|
||||
logger.warning(f"Failed to discover DashScope models: {e}")
|
||||
|
||||
return models
|
||||
|
||||
|
||||
async def discover_minimax_models() -> List[DiscoveredModel]:
|
||||
"""Fetch available models from MiniMax API."""
|
||||
api_key = os.environ.get("MINIMAX_API_KEY")
|
||||
if not api_key:
|
||||
return []
|
||||
|
||||
models = []
|
||||
try:
|
||||
async with httpx.AsyncClient() as client:
|
||||
response = await client.get(
|
||||
"https://api.minimax.io/v1/models",
|
||||
headers={"Authorization": f"Bearer {api_key}"},
|
||||
timeout=30.0,
|
||||
)
|
||||
response.raise_for_status()
|
||||
data = response.json()
|
||||
|
||||
for model in data.get("data", []):
|
||||
model_id = model.get("id", "")
|
||||
if model_id:
|
||||
model_type = classify_model_type(model_id, "minimax")
|
||||
models.append(
|
||||
DiscoveredModel(
|
||||
name=model_id,
|
||||
provider="minimax",
|
||||
model_type=model_type,
|
||||
)
|
||||
)
|
||||
except Exception as e:
|
||||
logger.warning(f"Failed to discover MiniMax models: {e}")
|
||||
|
||||
return models
|
||||
|
||||
|
||||
async def discover_openai_compatible_models() -> List[DiscoveredModel]:
|
||||
"""
|
||||
Fetch available models from an OpenAI-compatible API endpoint.
|
||||
|
|
@ -600,6 +678,8 @@ PROVIDER_DISCOVERY_FUNCTIONS = {
|
|||
"voyage": discover_voyage_models,
|
||||
"elevenlabs": discover_elevenlabs_models,
|
||||
"openai_compatible": discover_openai_compatible_models,
|
||||
"dashscope": discover_dashscope_models,
|
||||
"minimax": discover_minimax_models,
|
||||
"azure": None, # Azure requires credential-based discovery (different auth)
|
||||
"vertex": None, # Vertex requires credential-based discovery (service account)
|
||||
}
|
||||
|
|
|
|||
|
|
@ -1,6 +1,6 @@
|
|||
[project]
|
||||
name = "open-notebook"
|
||||
version = "1.8.1"
|
||||
version = "1.8.2"
|
||||
description = "An open source implementation of a research assistant, inspired by Google Notebook LM"
|
||||
authors = [
|
||||
{name = "Luis Novo", email = "lfnovo@gmail.com"}
|
||||
|
|
@ -34,7 +34,7 @@ dependencies = [
|
|||
"httpx[socks]>=0.27.0",
|
||||
"content-core>=1.14.1,<2",
|
||||
"ai-prompter>=0.3,<1",
|
||||
"esperanto>=2.19.7,<3",
|
||||
"esperanto>=2.20.0,<3",
|
||||
"surrealdb>=1.0.4",
|
||||
"podcast-creator>=0.12.0,<1",
|
||||
"surreal-commands>=1.3.1,<2",
|
||||
|
|
|
|||
8
uv.lock
8
uv.lock
|
|
@ -648,15 +648,15 @@ wheels = [
|
|||
|
||||
[[package]]
|
||||
name = "esperanto"
|
||||
version = "2.19.7"
|
||||
version = "2.20.0"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
dependencies = [
|
||||
{ name = "httpx" },
|
||||
{ name = "pydantic" },
|
||||
]
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/da/e0/d7c7f91a7b86744ce040385a4c81f8eaa147a140f299995c1fb8a59c4238/esperanto-2.19.7.tar.gz", hash = "sha256:83b2b1683361fc019444305a34ae398da71724f76b9563c0cf5a7c4d54fb08eb", size = 833887, upload-time = "2026-03-11T20:25:41.47Z" }
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/bb/8c/eba93a2b7b1a4fc6812bcf0a83a69a3376fcaf5831fae04a0ad539baeff6/esperanto-2.20.0.tar.gz", hash = "sha256:53a16c539ed2f83e3400b4e56d5a20b7873df6cb926c1e6696a9611b3be928fb", size = 845084, upload-time = "2026-03-21T14:32:04.589Z" }
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/fb/ab/6d81682bfd3b2f5db283c302c4695623f282ae08c99c667609467c10bc22/esperanto-2.19.7-py3-none-any.whl", hash = "sha256:9a853a51f59495ac2487c3d7a12942c931e4daf6185e45247b715938a4597685", size = 202436, upload-time = "2026-03-11T20:25:40.089Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/13/83/476dd4d7ee9f1b013ccd66e2518d150e66d3537736aed735b27230af0eb4/esperanto-2.20.0-py3-none-any.whl", hash = "sha256:767f95d08dd29e7d1950f4cc2ec908084e93ec61e945ce9446f001c54b86bb63", size = 203977, upload-time = "2026-03-21T14:32:05.676Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
|
|
@ -2129,7 +2129,7 @@ requires-dist = [
|
|||
{ name = "ai-prompter", specifier = ">=0.3,<1" },
|
||||
{ name = "babel", specifier = ">=2.18.0" },
|
||||
{ name = "content-core", specifier = ">=1.14.1,<2" },
|
||||
{ name = "esperanto", specifier = ">=2.19.7,<3" },
|
||||
{ name = "esperanto", specifier = ">=2.20.0,<3" },
|
||||
{ name = "fastapi", specifier = ">=0.104.0" },
|
||||
{ name = "httpx", extras = ["socks"], specifier = ">=0.27.0" },
|
||||
{ name = "ipykernel", marker = "extra == 'dev'", specifier = ">=6.29.5" },
|
||||
|
|
|
|||
Loading…
Reference in a new issue