Files
next-ai-draw-io/docs/en/ai-providers.md
T
dayuan.jiang b2cadfa5bf feat(providers): an OpenAI-compatible preset for any vLLM, LM Studio or gateway URL
Step 8 of the provider refactor plan, a new provider made of one record.
'custom' runs on @ai-sdk/openai-compatible with <think> extraction, merges
system messages (open-source chat templates reject several), has no default
URL (so a key alone does not count as connected, and the chat endpoint is
shown under the URL field) and lists models from its /models. Server side:
CUSTOM_API_KEY and CUSTOM_BASE_URL, ADMIN_CUSTOM_* in the admin panel.
env.example and the three provider docs gained a section; the picker count
in the e2e test is 25 and a new e2e test walks through the preset. The
behavior snapshot gained the new provider's 146 rows; its two 'No AI provider
configured' rows list CUSTOM_API_KEY now.
2026-10-12 21:17:51 +09:00

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13 KiB
Markdown

# AI Provider Configuration
This guide explains how to configure different AI model providers for next-ai-draw-io.
## Quick Start
1. Copy `.env.example` to `.env.local`
2. Set your API key for your chosen provider
3. Set `AI_MODEL` to your desired model
4. Run `npm run dev`
## Supported Providers
### Doubao (ByteDance Volcengine)
```bash
DOUBAO_API_KEY=your_api_key
AI_MODEL=doubao-seed-1-8-251215 # or other Doubao model
```
### Google Gemini
```bash
GOOGLE_GENERATIVE_AI_API_KEY=your_api_key
AI_MODEL=gemini-2.0-flash
```
Optional custom endpoint:
```bash
GOOGLE_BASE_URL=https://your-custom-endpoint
```
### Google Vertex AI (Enterprise GCP)
Google Vertex AI offers enterprise-grade features and data residency. **Express Mode** allows for simple API key authentication, making it compatible with edge runtimes like Vercel and Cloudflare.
```bash
GOOGLE_VERTEX_API_KEY=your_api_key
AI_MODEL=gemini-2.0-flash
```
Optional custom endpoint:
```bash
GOOGLE_VERTEX_BASE_URL=https://your-custom-endpoint
```
### OpenAI
```bash
OPENAI_API_KEY=your_api_key
AI_MODEL=gpt-4o
```
Optional custom endpoint (for OpenAI-compatible services):
```bash
OPENAI_BASE_URL=https://your-custom-endpoint/v1
```
LM Studio and other local OpenAI-compatible servers work the same way: set the base URL to `http://localhost:1234/v1` (LM Studio's default port) and use any non-empty API key, such as `lm-studio`. This works both in the environment variables above and in the model settings.
### AIHubMix
AIHubMix provides access to Claude, GPT, Gemini, DeepSeek, and other models through a single API key.
```bash
AIHUBMIX_API_KEY=your_api_key
AI_MODEL=claude-sonnet-4-5-20250929
```
Optional custom endpoint:
```bash
AIHUBMIX_BASE_URL=https://aihubmix.com/v1
```
### Anthropic
```bash
ANTHROPIC_API_KEY=your_api_key
AI_MODEL=claude-sonnet-4-5-20250514
```
Or use a Bearer auth token instead of an API key (e.g. when going through a gateway that issues OAuth-style tokens). `ANTHROPIC_AUTH_TOKEN` is sent as `Authorization: Bearer <token>`, while `ANTHROPIC_API_KEY` is sent as `x-api-key`. The two are mutually exclusive — set only one:
```bash
ANTHROPIC_AUTH_TOKEN=your_auth_token
AI_MODEL=claude-sonnet-4-5-20250514
```
Optional custom endpoint:
```bash
ANTHROPIC_BASE_URL=https://your-custom-endpoint
```
### DeepSeek
```bash
DEEPSEEK_API_KEY=your_api_key
AI_MODEL=deepseek-chat
```
Optional custom endpoint:
```bash
DEEPSEEK_BASE_URL=https://your-custom-endpoint
```
### SiliconFlow (OpenAI-compatible)
```bash
SILICONFLOW_API_KEY=your_api_key
AI_MODEL=deepseek-ai/DeepSeek-V3 # example; use any SiliconFlow model id
```
Optional custom endpoint (defaults to the recommended domain):
```bash
SILICONFLOW_BASE_URL=https://api.siliconflow.com/v1 # or https://api.siliconflow.cn/v1
```
### SGLang
```bash
SGLANG_API_KEY=your_api_key
AI_MODEL=your_model_id
```
Optional custom endpoint:
```bash
SGLANG_BASE_URL=https://your-custom-endpoint/v1
```
### Azure OpenAI
```bash
AZURE_API_KEY=your_api_key
AZURE_RESOURCE_NAME=your-resource-name # Required: your Azure resource name
AI_MODEL=your-deployment-name
```
Or use a custom endpoint instead of resource name:
```bash
AZURE_API_KEY=your_api_key
AZURE_BASE_URL=https://your-resource.openai.azure.com # Alternative to AZURE_RESOURCE_NAME
AI_MODEL=your-deployment-name
```
Optional reasoning configuration:
```bash
AZURE_REASONING_EFFORT=low # Optional: low, medium, high
AZURE_REASONING_SUMMARY=detailed # Optional: none, brief, detailed
```
### AWS Bedrock
```bash
AWS_REGION=us-west-2
AWS_ACCESS_KEY_ID=your_access_key_id
AWS_SECRET_ACCESS_KEY=your_secret_access_key
AI_MODEL=global.anthropic.claude-sonnet-5-5
```
Recent Claude models answer only through an inference profile id, which starts with a region prefix such as `global.` or `us.`. The plain id (`anthropic.claude-sonnet-5-5`) fails with "on-demand throughput isn't supported".
Note: On AWS (Lambda, EC2 with IAM role), credentials are automatically obtained from the IAM role.
### OpenRouter
```bash
OPENROUTER_API_KEY=your_api_key
AI_MODEL=anthropic/claude-sonnet-4
```
Optional custom endpoint:
```bash
OPENROUTER_BASE_URL=https://your-custom-endpoint
```
### Ollama (Local)
```bash
AI_PROVIDER=ollama
AI_MODEL=llama3.2
```
Optional custom URL:
```bash
OLLAMA_BASE_URL=http://localhost:11434/api
```
### ModelScope
```bash
MODELSCOPE_API_KEY=your_api_key
AI_MODEL=Qwen/Qwen3-235B-A22B-Instruct-2507
```
Optional custom endpoint:
```bash
MODELSCOPE_BASE_URL=https://your-custom-endpoint
```
### Vercel AI Gateway
Vercel AI Gateway provides unified access to multiple AI providers through a single API key. This simplifies authentication and allows you to switch between providers without managing multiple API keys.
**Basic Usage (Vercel-hosted Gateway):**
```bash
AI_GATEWAY_API_KEY=your_gateway_api_key
AI_MODEL=openai/gpt-4o
```
**Custom Gateway URL (for local development or self-hosted Gateway):**
```bash
AI_GATEWAY_API_KEY=your_custom_api_key
AI_GATEWAY_BASE_URL=https://your-custom-gateway.com/v1/ai
AI_MODEL=openai/gpt-4o
```
Model format uses `provider/model` syntax:
- `openai/gpt-4o` - OpenAI GPT-4o
- `anthropic/claude-sonnet-4-5` - Anthropic Claude Sonnet 4.5
- `google/gemini-2.0-flash` - Google Gemini 2.0 Flash
**Configuration notes:**
- If `AI_GATEWAY_BASE_URL` is not set, the default Vercel Gateway URL (`https://ai-gateway.vercel.sh/v1/ai`) is used
- Custom base URL is useful for:
- Local development with a custom Gateway instance
- Self-hosted AI Gateway deployments
- Enterprise proxy configurations
- When using a custom base URL, you must also provide `AI_GATEWAY_API_KEY`
Get your API key from the [Vercel AI Gateway dashboard](https://vercel.com/ai-gateway).
### MiniMax
MiniMax supports two API formats:
- **Anthropic-compatible** (`/anthropic` endpoint) — recommended, supports interleaved thinking
- **OpenAI-compatible** (`/v1` endpoint) — standard OpenAI chat completions format
```bash
MINIMAX_API_KEY=your_api_key
AI_MODEL=MiniMax-M3
```
Optional configuration:
```bash
# China mainland, Anthropic-compatible (default)
MINIMAX_BASE_URL=https://api.minimaxi.com/anthropic
# China mainland, OpenAI-compatible
MINIMAX_BASE_URL=https://api.minimaxi.com/v1
# International, Anthropic-compatible
MINIMAX_BASE_URL=https://api.minimax.io/anthropic
# International, OpenAI-compatible
MINIMAX_BASE_URL=https://api.minimax.io/v1
```
### GLM (Zhipu AI)
```bash
GLM_API_KEY=your_api_key
AI_MODEL=glm-4
```
Optional custom endpoint:
```bash
GLM_BASE_URL=https://your-custom-endpoint
```
### Qwen (Alibaba Cloud)
```bash
QWEN_API_KEY=your_api_key
AI_MODEL=qwen-turbo
```
Optional custom endpoint:
```bash
QWEN_BASE_URL=https://your-custom-endpoint
```
### Kimi (Moonshot AI)
```bash
KIMI_API_KEY=your_api_key
AI_MODEL=kimi-latest
```
Optional custom endpoint:
```bash
KIMI_BASE_URL=https://your-custom-endpoint
```
### Qiniu (Qiniu Cloud)
```bash
QINIU_API_KEY=your_api_key
AI_MODEL=your_model_id
```
Optional custom endpoint:
```bash
QINIU_BASE_URL=https://your-custom-endpoint
```
### MiMo (Xiaomi)
```bash
MIMO_API_KEY=your_api_key
AI_MODEL=mimo-v2.5-pro
```
Optional custom endpoint (Token Plan subscribers should set their dedicated Base URL):
```bash
MIMO_BASE_URL=https://token-plan-cn.xiaomimimo.com/v1
```
### Any OpenAI-compatible API (custom)
For a server not listed above (vLLM, LM Studio, LiteLLM, an API gateway), pick **OpenAI-compatible** in the settings and enter its base URL and a key; a server that ignores keys takes any non-empty value. Several system messages are merged into one, as open-source chat templates expect, and `<think>` reasoning is split from the reply.
```bash
CUSTOM_API_KEY=your_api_key
CUSTOM_BASE_URL=http://127.0.0.1:8000/v1
AI_MODEL=your_model_id
```
---
## Auto-Detection
If you only configure **one** provider's API key, the system will automatically detect and use that provider. No need to set `AI_PROVIDER`.
If you configure **multiple** API keys, you must explicitly set `AI_PROVIDER`:
```bash
AI_PROVIDER=google # or: openai, anthropic, aihubmix, deepseek, siliconflow, doubao, azure, bedrock, openrouter, ollama, gateway, sglang, modelscope, minimax, glm, qwen, kimi, qiniu, mimo
```
## Server-Side Multi-Model Configuration
Administrators can configure multiple server-side models that are available to all users without requiring personal API keys.
### Configuration Methods
**Option 1: Environment Variable** (recommended for cloud deployments)
Set `AI_MODELS_CONFIG` as a JSON string:
```bash
AI_MODELS_CONFIG='{"providers":[{"name":"OpenAI","provider":"openai","models":["gpt-4o"],"default":true}]}'
```
**Option 2: Config File**
Create an `ai-models.json` file in the project root (or set `AI_MODELS_CONFIG_PATH` to a custom location).
**Option 3: Comma-separated `AI_MODEL`** (quick setup, single provider)
If you only need multiple models from one provider, list them in `AI_MODEL` separated by commas. The first model is treated as the default.
```bash
AI_PROVIDER=doubao
AI_MODEL=doubao-seed-1-8-251215,doubao-seed-1-6-flash,doubao-seed-1-6-pro
```
This is shorthand for the equivalent `ai-models.json`. For multiple providers or custom `apiKeyEnv` / `baseUrlEnv`, use Option 1 or 2 instead.
### Example Configuration
```json
{
"providers": [
{
"name": "OpenAI Production",
"provider": "openai",
"models": ["gpt-4o", "gpt-4o-mini"],
"default": true
},
{
"name": "Custom DeepSeek",
"provider": "deepseek",
"models": ["deepseek-chat"],
"apiKeyEnv": "MY_DEEPSEEK_KEY",
"baseUrlEnv": "MY_DEEPSEEK_URL"
}
]
}
```
### Field Reference
| Field | Required | Description |
|-------|----------|-------------|
| `name` | Yes | Display name (supports multiple configs for same provider) |
| `provider` | Yes | Provider type (`openai`, `anthropic`, `google`, `bedrock`, etc.) |
| `models` | Yes | List of model IDs |
| `default` | No | Set to `true` to auto-select this provider's first model as default |
| `apiKeyEnv` | No | Custom API key env var name (defaults to provider's standard var like `OPENAI_API_KEY`). May also be an array of env var names: each request picks one that has a value at random, spreading load over several keys |
| `baseUrlEnv` | No | Custom base URL env var name |
### Notes
- API keys and credentials are provided via environment variables. By default, standard var names are used (e.g., `OPENAI_API_KEY`), but you can specify custom var names with `apiKeyEnv`.
- The `name` field allows multiple configurations for the same provider (e.g., "OpenAI Production" and "OpenAI Staging" both using `provider: "openai"` but with different `apiKeyEnv` values).
- If config is not present, the app falls back to `AI_PROVIDER`/`AI_MODEL` environment variable configuration.
## Model Capability Requirements
This task requires exceptionally strong model capabilities, as it involves generating long-form text with strict formatting constraints (draw.io XML).
**Recommended models**:
- Claude Sonnet 4.5 / Opus 4.5
**Note on Ollama**: While Ollama is supported as a provider, it's generally not practical for this use case unless you're running high-capability models like DeepSeek R1 or Qwen3-235B locally.
## Temperature Setting
You can optionally configure the temperature via environment variable:
```bash
TEMPERATURE=0 # More deterministic output (recommended for diagrams)
```
**Important**: Leave `TEMPERATURE` unset for models that don't support temperature settings, such as:
- GPT-5.1 and other reasoning models
- Some specialized models
When unset, the model uses its default behavior.
## Recommendations
- **Best experience**: Use models with vision support (GPT-4o, Claude, Gemini) for image-to-diagram features
- **Budget-friendly**: DeepSeek offers competitive pricing
- **Privacy**: Use Ollama for fully local, offline operation (requires powerful hardware)
- **Flexibility**: OpenRouter provides access to many models through a single API
## Adding a Provider
Every fact about a provider lives in one record in `lib/providers.ts`: which AI SDK runs it (`sdk`), what it signs in with (`credential`), the prefix of its `<PREFIX>_API_KEY` / `<PREFIX>_BASE_URL` variables (`envPrefix`), its official URL, the picker group, and optional notes. The settings UI, the connection test, the chat route and the admin panel all read that record, so adding an OpenAI-compatible provider is:
1. Add a record to `lib/providers.ts`, for example:
```ts
acme: {
label: "Acme AI",
sdk: "openai-compatible",
credential: "apiKey",
group: "aggregators",
envPrefix: "ACME",
defaultBaseUrl: "https://api.acme.example/v1",
apiKeyUrl: "https://acme.example/keys",
modelList: "openai",
},
```
A provider that needs a hint under its base URL field, or a note in the picker, gets a `baseUrlHint` or `note` object with all four UI languages (`en`, `zh`, `ja`, `zh-Hant`). Providers that cannot list their models online keep a short `models` list instead of `modelList`.
2. Add `ACME_API_KEY` and `ACME_BASE_URL` to `env.example` (a unit test checks that every record's variables appear there).
3. Add a section for it to this document in `docs/en`, `docs/cn` and `docs/ja`.