feat(providers): add ModelScope provider for LLM and image generation
This commit is contained in:
yrk
Xubin Ren
@@ -254,6 +254,7 @@ Tracing covers the providers that go through nanobot's OpenAI-compatible client
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> - **OpenCode Zen / Go**: `providers.opencode` (canonical Zen), the legacy-compatible `providers.opencodeZen`, and `providers.opencodeGo` use the same `OPENCODE_API_KEY`, but route to different OpenCode gateways. These providers use OpenCode's OpenAI-compatible `chat/completions` endpoints; choose model IDs from that endpoint family.
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> - **Zhipu Coding Plan**: If you're on Zhipu's coding plan, set `"apiBase": "https://open.bigmodel.cn/api/coding/paas/v4"` in your zhipu provider config.
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> - **Alibaba Cloud BaiLian**: If you're using Alibaba Cloud BaiLian's OpenAI-compatible endpoint, set `"apiBase": "https://dashscope.aliyuncs.com/compatible-mode/v1"` in your dashscope provider config.
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> - **ModelScope (魔搭社区)**: If you're using ModelScope's OpenAI-compatible endpoint, set `"apiBase": "https://api-inference.modelscope.cn/v1"` in your modelscope provider config.
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> - **StepFun Step Plan**: If you're on StepFun's Step Plan subscription, set `"apiBase": "https://api.stepfun.ai/step_plan/v1"` in your stepfun provider config. Supported models include `step-3.5-flash`, `step-3.5-flash-2603`, and `step-router-v1`.
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> - **Step Fun (Mainland China)**: If your API key is from Step Fun's mainland China platform (stepfun.com), set `"apiBase": "https://api.stepfun.com/v1"` in your stepfun provider config.
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> - **Xiaomi MiMo thinking mode**: MiMo models (e.g. `mimo-v2.5-pro`) default to enabled thinking. Use `agents.defaults.reasoningEffort: "none"` to disable it, or `"low"` / `"medium"` / `"high"` to keep it on. Omitting the field preserves the provider's per-model default.
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@@ -288,6 +289,7 @@ Tracing covers the providers that go through nanobot's OpenAI-compatible client
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| `siliconflow` | LLM (SiliconFlow/硅基流动) | [siliconflow.cn](https://siliconflow.cn) |
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| `novita` | LLM (Novita AI OpenAI-compatible gateway) | [novita.ai](https://novita.ai) |
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| `dashscope` | LLM (Qwen) | [dashscope.console.aliyun.com](https://dashscope.console.aliyun.com) |
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| `modelscope` | LLM (ModelScope/魔搭) + Image generation | [modelscope.cn](https://modelscope.cn) |
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| `moonshot` | LLM (Moonshot/Kimi) | [platform.kimi.com](https://platform.kimi.com?aff=nanobot) |
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| `kimi_coding` | LLM (Kimi Coding Plan, Anthropic Messages API) | [platform.kimi.com](https://platform.kimi.com?aff=nanobot) |
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| `zhipu` | LLM (Zhipu GLM) | [open.bigmodel.cn](https://open.bigmodel.cn) |
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@@ -34,7 +34,7 @@ This snippet uses the current built-in image-generation default so the JSON has
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}
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```
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See [Provider Notes](#provider-notes) for Custom, AIHubMix, MiniMax, Gemini, Ollama, StepFun, and Zhipu configuration examples.
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See [Provider Notes](#provider-notes) for Custom, AIHubMix, MiniMax, Gemini, Ollama, StepFun, Zhipu, and ModelScope configuration examples.
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> [!TIP]
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> Prefer environment variables for API keys. nanobot resolves `${VAR_NAME}` values from the environment at startup.
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@@ -55,7 +55,7 @@ The WebUI hides provider storage details from the user. The agent sees the saved
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| Option | Type | Default | Description |
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|--------|------|---------|-------------|
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| `tools.imageGeneration.enabled` | boolean | `false` | Register the `generate_image` tool |
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| `tools.imageGeneration.provider` | string | `"openrouter"` | Current built-in image provider default. Supported values: `openrouter`, `openai`, `openai_codex`, `custom`, `aihubmix`, `minimax`, `gemini`, `ollama`, `stepfun`, `zhipu` |
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| `tools.imageGeneration.provider` | string | `"openrouter"` | Current built-in image provider default. Supported values: `openrouter`, `openai`, `openai_codex`, `custom`, `aihubmix`, `minimax`, `gemini`, `ollama`, `stepfun`, `zhipu`, `modelscope` |
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| `tools.imageGeneration.model` | string | `"openai/gpt-5.4-image-2"` | Provider model name |
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| `tools.imageGeneration.defaultAspectRatio` | string | `"1:1"` | Default ratio when the prompt/tool call does not specify one |
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| `tools.imageGeneration.defaultImageSize` | string | `"1K"` | Default size hint, for example `1K`, `2K`, `4K`, or `1024x1024` |
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@@ -319,6 +319,29 @@ Supported aspect ratios: `1:1`, `16:9`, `9:16`, `3:4`, `4:3`. Sizes can be speci
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Other supported models: `cogview-4`, `cogview-4-250304`, `cogview-3-flash`. Reference images are not supported by this integration.
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### ModelScope
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ModelScope (魔搭社区) API-Inference supports text-to-image generation and image editing via an async task pattern.
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Supported aspect ratios: `1:1`, `16:9`, `9:16`, `3:4`, `4:3`. Sizes can be specified as `WIDTHxHEIGHT` (e.g. `1024x1024`, `1536x1024`) or using aspect ratio presets.
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```json
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{
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"providers": {
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"modelscope": {
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"apiKey": "${MODELSCOPE_API_KEY}"
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}
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},
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"tools": {
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"imageGeneration": {
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"enabled": true,
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"provider": "modelscope",
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"model": "Qwen/Qwen-Image-2512"
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}
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}
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}
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```
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## Artifacts
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Generated images are stored under the active nanobot instance's media directory:
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@@ -373,7 +396,7 @@ Use the reference image. Keep the same robot and composition, change the palette
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|---------|-------|
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| `generate_image` is not available | Set `tools.imageGeneration.enabled` to `true` and restart the gateway |
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| Missing API key error | Configure `providers.<provider>.apiKey`; if using `${VAR_NAME}`, confirm the environment variable is visible to the gateway process |
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| `unsupported image generation provider` | Use `openrouter`, `openai`, `openai_codex`, `custom`, `aihubmix`, `minimax`, `gemini`, `ollama`, `stepfun`, or `zhipu` |
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| `unsupported image generation provider` | Use `openrouter`, `openai`, `openai_codex`, `custom`, `aihubmix`, `minimax`, `gemini`, `ollama`, `stepfun`, `zhipu`, or `modelscope` |
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| AIHubMix says `Incorrect model ID` | Use `model: "gpt-image-2-free"`; nanobot expands it to the required `openai/gpt-image-2-free` model path internally |
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| Generation times out | Try a smaller/default image size, set AIHubMix `extraBody.quality` to `"low"`, or retry later |
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| Reference image rejected | Reference image paths must be inside the workspace or nanobot media directory and must be valid image files |
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@@ -245,6 +245,7 @@ class ProvidersConfig(Base):
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groq: ProviderConfig = Field(default_factory=ProviderConfig)
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zhipu: ProviderConfig = Field(default_factory=ProviderConfig)
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dashscope: ProviderConfig = Field(default_factory=ProviderConfig)
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modelscope: ProviderConfig = Field(default_factory=ProviderConfig)
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vllm: ProviderConfig = Field(default_factory=ProviderConfig)
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ollama: ProviderConfig = Field(default_factory=ProviderConfig) # Ollama local models
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lm_studio: ProviderConfig = Field(default_factory=ProviderConfig) # LM Studio local models
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@@ -1752,6 +1752,192 @@ async def _zhipu_images_from_payload(
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return images
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# ---------------------------------------------------------------------------
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# ModelScope (魔搭) image generation
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# ---------------------------------------------------------------------------
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_MODELSCOPE_TIMEOUT_S = 300.0
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_MODELSCOPE_POLL_INTERVAL_S = 5.0
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_MODELSCOPE_POLL_MAX_ATTEMPTS = 60 # 5 min at 5s intervals
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_MODELSCOPE_ASPECT_RATIOS = {
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"1:1": "1024x1024",
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"16:9": "1536x1024",
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"9:16": "1024x1536",
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"3:4": "1024x1536",
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"4:3": "1536x1024",
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}
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def _modelscope_size(
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aspect_ratio: str | None,
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image_size: str | None,
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) -> str:
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"""Resolve aspect ratio / image_size to a ModelScope size string."""
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if image_size and "x" in image_size.lower():
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return image_size
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if aspect_ratio and aspect_ratio in _MODELSCOPE_ASPECT_RATIOS:
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return _MODELSCOPE_ASPECT_RATIOS[aspect_ratio]
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return "1024x1024"
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class ModelScopeImageGenerationClient(ImageGenerationProvider):
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"""Async client for ModelScope (魔搭) AIGC image generation.
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ModelScope uses an async task pattern: POST submits the job and returns
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a task_id, then the client polls GET /tasks/{task_id} until
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task_status is SUCCEED or FAILED.
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"""
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provider_name = "modelscope"
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missing_key_message = (
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"ModelScope API key is not configured. Set providers.modelscope.apiKey."
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)
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default_timeout = _MODELSCOPE_TIMEOUT_S
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def _default_base_url(self) -> str:
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return "https://api-inference.modelscope.cn/v1"
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async def generate(
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self,
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*,
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prompt: str,
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model: str,
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reference_images: list[str] | None = None,
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aspect_ratio: str | None = None,
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image_size: str | None = None,
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) -> GeneratedImageResponse:
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if not self.api_key:
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raise ImageGenerationError(self.missing_key_message)
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headers = {
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"Authorization": f"Bearer {self.api_key}",
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"Content-Type": "application/json",
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"X-ModelScope-Async-Mode": "true",
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**self.extra_headers,
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}
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body: dict[str, Any] = {
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"model": model,
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"prompt": prompt,
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}
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size = _modelscope_size(aspect_ratio, image_size)
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if size:
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body["size"] = size
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refs = list(reference_images or [])
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if refs:
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image_refs = [image_path_to_data_url(path) for path in refs]
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body["image_url"] = image_refs[0] if len(image_refs) == 1 else image_refs
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body.update(self.extra_body)
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url = f"{self.api_base}/images/generations"
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client = self._client or httpx.AsyncClient(timeout=self.timeout)
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try:
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return await self._generate_with_client(
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client,
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url=url,
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headers=headers,
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body=body,
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)
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finally:
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if self._client is None:
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await client.aclose()
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async def _generate_with_client(
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self,
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client: httpx.AsyncClient,
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*,
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url: str,
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headers: dict[str, str],
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body: dict[str, Any],
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) -> GeneratedImageResponse:
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try:
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response = await client.post(url, headers=headers, json=body)
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except httpx.TimeoutException as exc:
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raise ImageGenerationError("ModelScope image generation request timed out") from exc
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except httpx.RequestError as exc:
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raise ImageGenerationError(f"ModelScope image generation request failed: {exc}") from exc
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try:
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response.raise_for_status()
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except httpx.HTTPStatusError as exc:
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detail = _http_error_detail(response)
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raise ImageGenerationError(f"ModelScope image generation failed: {detail}") from exc
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task_data = response.json()
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task_id = task_data.get("task_id")
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if not task_id:
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raise ImageGenerationError(
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f"ModelScope did not return a task_id: {response.text[:500]}"
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)
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images = await self._poll_task(client, task_id, headers)
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self._require_images(images, task_data)
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return GeneratedImageResponse(images=images, content="", raw=task_data)
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async def _poll_task(
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self,
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client: httpx.AsyncClient,
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task_id: str,
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submit_headers: dict[str, str],
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) -> list[str]:
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poll_headers = {
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"Authorization": submit_headers["Authorization"],
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"X-ModelScope-Task-Type": "image_generation",
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**self.extra_headers,
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}
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poll_url = f"{self.api_base}/tasks/{task_id}"
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for _ in range(_MODELSCOPE_POLL_MAX_ATTEMPTS):
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try:
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response = await client.get(poll_url, headers=poll_headers)
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except httpx.RequestError:
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await asyncio.sleep(_MODELSCOPE_POLL_INTERVAL_S)
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continue
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try:
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response.raise_for_status()
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except httpx.HTTPStatusError as exc:
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detail = _http_error_detail(response)
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raise ImageGenerationError(
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f"ModelScope task polling failed: {detail}"
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) from exc
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data = response.json()
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status = data.get("task_status")
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if status == "SUCCEED":
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return await self._collect_images(client, data)
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if status == "FAILED":
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raise ImageGenerationError(
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f"ModelScope image generation task failed: {data}"
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)
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await asyncio.sleep(_MODELSCOPE_POLL_INTERVAL_S)
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raise ImageGenerationError(
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f"ModelScope image generation timed out after "
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f"{_MODELSCOPE_POLL_MAX_ATTEMPTS} polls"
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)
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@staticmethod
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async def _collect_images(
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client: httpx.AsyncClient,
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data: dict[str, Any],
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) -> list[str]:
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images: list[str] = []
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for url in data.get("output_images") or []:
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if isinstance(url, str) and url:
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if url.startswith("data:image/"):
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images.append(url)
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else:
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images.append(await _download_image_data_url(client, url))
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return images
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# ---------------------------------------------------------------------------
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# Provider registration
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# ---------------------------------------------------------------------------
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@@ -1766,3 +1952,4 @@ register_image_gen_provider(OpenAIImageGenerationClient)
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register_image_gen_provider(OpenRouterImageGenerationClient)
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register_image_gen_provider(StepFunImageGenerationClient)
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register_image_gen_provider(ZhipuImageGenerationClient)
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register_image_gen_provider(ModelScopeImageGenerationClient)
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@@ -471,6 +471,19 @@ PROVIDERS: tuple[ProviderSpec, ...] = (
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default_api_base="https://dashscope.aliyuncs.com/compatible-mode/v1",
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thinking_style="enable_thinking",
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),
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# ModelScope (魔搭社区): OpenAI-compatible API
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ProviderSpec(
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name="modelscope",
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keywords=("modelscope",),
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env_key="MODELSCOPE_API_KEY",
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display_name="ModelScope",
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backend="openai_compat",
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is_gateway=True,
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detect_by_base_keyword="modelscope",
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default_api_base="https://api-inference.modelscope.cn/v1",
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strip_model_prefixes=("modelscope",),
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thinking_style="enable_thinking",
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),
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# Moonshot (月之暗面): Kimi K2.5/K2.6 choose temperature from thinking mode;
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# the OpenAI-compatible provider omits it. K2.7 models require 1.0.
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ProviderSpec(
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@@ -15,6 +15,7 @@ from nanobot.providers.image_generation import (
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GeneratedImageResponse,
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ImageGenerationError,
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MiniMaxImageGenerationClient,
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ModelScopeImageGenerationClient,
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OllamaImageGenerationClient,
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OpenAIImageGenerationClient,
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OpenRouterImageGenerationClient,
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@@ -1524,3 +1525,232 @@ async def test_zhipu_image_generation_rejects_reference_images() -> None:
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model="glm-image",
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reference_images=["ref.png"],
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)
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# ---------------------------------------------------------------------------
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# ModelScope (魔搭) image generation tests
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# ---------------------------------------------------------------------------
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class ModelScopeFakeClient:
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"""Fake httpx client for ModelScope async task pattern.
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Returns submit_response for POST, and serves poll_responses in sequence
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for GET /tasks/{id} calls. Image download GETs return PNG content.
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"""
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def __init__(
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self,
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submit_response: FakeResponse,
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poll_responses: list[FakeResponse],
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download_content: bytes = PNG_BYTES,
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) -> None:
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self.submit_response = submit_response
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self.poll_responses = poll_responses
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self.poll_idx = 0
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self.download_content = download_content
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self.calls: list[dict[str, Any]] = []
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self.get_calls: list[dict[str, Any]] = []
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async def post(self, url: str, **kwargs: Any) -> FakeResponse:
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self.calls.append({"url": url, **kwargs})
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return self.submit_response
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async def get(self, url: str, **kwargs: Any) -> FakeResponse:
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self.get_calls.append({"url": url, **kwargs})
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if "/tasks/" in url:
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idx = min(self.poll_idx, len(self.poll_responses) - 1)
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resp = self.poll_responses[idx]
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self.poll_idx += 1
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return resp
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return FakeResponse({}, content=self.download_content)
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@pytest.fixture(autouse=True)
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def _modelscope_fast_poll(monkeypatch) -> None:
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"""Skip the real asyncio.sleep between ModelScope poll attempts."""
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monkeypatch.setattr(
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"nanobot.providers.image_generation._MODELSCOPE_POLL_INTERVAL_S", 0.0
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)
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@pytest.mark.asyncio
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async def test_modelscope_image_generation_submit_and_poll() -> None:
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submit = FakeResponse({"task_id": "abc123"})
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poll_responses = [
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FakeResponse({"task_status": "PENDING"}),
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FakeResponse({
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"task_status": "SUCCEED",
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"output_images": ["https://cdn.example/image.png"],
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}),
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]
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fake = ModelScopeFakeClient(submit, poll_responses)
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client = ModelScopeImageGenerationClient(
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api_key="ms-token",
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api_base="https://api-inference.modelscope.cn/v1",
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client=fake, # type: ignore[arg-type]
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)
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response = await client.generate(
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prompt="A golden cat",
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model="Qwen/Qwen-Image",
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)
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assert response.images[0].startswith("data:image/png;base64,")
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# Verify POST request
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post_call = fake.calls[0]
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assert post_call["url"] == "https://api-inference.modelscope.cn/v1/images/generations"
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assert post_call["headers"]["Authorization"] == "Bearer ms-token"
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assert post_call["headers"]["X-ModelScope-Async-Mode"] == "true"
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body = post_call["json"]
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assert body["model"] == "Qwen/Qwen-Image"
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assert body["prompt"] == "A golden cat"
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# Verify task polling GET
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assert "/tasks/abc123" in fake.get_calls[0]["url"]
|
||||
poll_headers = fake.get_calls[0]["headers"]
|
||||
assert poll_headers["X-ModelScope-Task-Type"] == "image_generation"
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_modelscope_image_generation_with_size() -> None:
|
||||
submit = FakeResponse({"task_id": "t1"})
|
||||
poll = [FakeResponse({"task_status": "SUCCEED", "output_images": ["https://cdn/img.png"]})]
|
||||
fake = ModelScopeFakeClient(submit, poll)
|
||||
client = ModelScopeImageGenerationClient(
|
||||
api_key="ms-token",
|
||||
client=fake, # type: ignore[arg-type]
|
||||
)
|
||||
|
||||
await client.generate(
|
||||
prompt="test",
|
||||
model="Qwen/Qwen-Image-2512",
|
||||
image_size="768x1024",
|
||||
)
|
||||
|
||||
body = fake.calls[0]["json"]
|
||||
assert body["size"] == "768x1024"
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_modelscope_image_generation_aspect_ratio_mapping() -> None:
|
||||
submit = FakeResponse({"task_id": "t1"})
|
||||
poll = [FakeResponse({"task_status": "SUCCEED", "output_images": ["https://cdn/img.png"]})]
|
||||
fake = ModelScopeFakeClient(submit, poll)
|
||||
client = ModelScopeImageGenerationClient(
|
||||
api_key="ms-token",
|
||||
client=fake, # type: ignore[arg-type]
|
||||
)
|
||||
|
||||
await client.generate(prompt="test", model="m", aspect_ratio="16:9")
|
||||
|
||||
assert fake.calls[0]["json"]["size"] == "1536x1024"
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_modelscope_image_generation_task_failed() -> None:
|
||||
submit = FakeResponse({"task_id": "bad-task"})
|
||||
poll = [FakeResponse({"task_status": "FAILED", "errors": "oom"})]
|
||||
fake = ModelScopeFakeClient(submit, poll)
|
||||
client = ModelScopeImageGenerationClient(
|
||||
api_key="ms-token",
|
||||
client=fake, # type: ignore[arg-type]
|
||||
)
|
||||
|
||||
with pytest.raises(ImageGenerationError, match="task failed"):
|
||||
await client.generate(prompt="test", model="m")
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_modelscope_image_generation_requires_api_key() -> None:
|
||||
client = ModelScopeImageGenerationClient(api_key=None)
|
||||
|
||||
with pytest.raises(ImageGenerationError, match="API key"):
|
||||
await client.generate(prompt="draw", model="m")
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_modelscope_image_generation_missing_task_id() -> None:
|
||||
submit = FakeResponse({"unexpected": "response"})
|
||||
fake = ModelScopeFakeClient(submit, [])
|
||||
client = ModelScopeImageGenerationClient(
|
||||
api_key="ms-token",
|
||||
client=fake, # type: ignore[arg-type]
|
||||
)
|
||||
|
||||
with pytest.raises(ImageGenerationError, match="task_id"):
|
||||
await client.generate(prompt="draw", model="m")
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_modelscope_image_generation_with_reference_image() -> None:
|
||||
"""Reference images are converted to base64 data URLs for image editing models."""
|
||||
submit = FakeResponse({"task_id": "t1"})
|
||||
poll = [FakeResponse({"task_status": "SUCCEED", "output_images": ["https://cdn/img.png"]})]
|
||||
fake = ModelScopeFakeClient(submit, poll)
|
||||
client = ModelScopeImageGenerationClient(
|
||||
api_key="ms-token",
|
||||
client=fake, # type: ignore[arg-type]
|
||||
)
|
||||
|
||||
# Create a temporary image file
|
||||
import tempfile
|
||||
with tempfile.NamedTemporaryFile(suffix=".png", delete=False) as f:
|
||||
f.write(PNG_BYTES)
|
||||
ref_path = f.name
|
||||
|
||||
try:
|
||||
await client.generate(
|
||||
prompt="edit this image",
|
||||
model="Qwen/Qwen-Image-Edit",
|
||||
reference_images=[ref_path],
|
||||
)
|
||||
|
||||
body = fake.calls[0]["json"]
|
||||
assert "image_url" in body
|
||||
assert body["image_url"].startswith("data:image/png;base64,")
|
||||
finally:
|
||||
Path(ref_path).unlink(missing_ok=True)
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_modelscope_image_generation_extra_body_passthrough() -> None:
|
||||
"""Extra body fields like loras are passed through to the API."""
|
||||
submit = FakeResponse({"task_id": "t1"})
|
||||
poll = [FakeResponse({"task_status": "SUCCEED", "output_images": ["https://cdn/img.png"]})]
|
||||
fake = ModelScopeFakeClient(submit, poll)
|
||||
client = ModelScopeImageGenerationClient(
|
||||
api_key="ms-token",
|
||||
extra_body={"loras": "lora-repo-1", "seed": 42},
|
||||
client=fake, # type: ignore[arg-type]
|
||||
)
|
||||
|
||||
await client.generate(prompt="test", model="m")
|
||||
|
||||
body = fake.calls[0]["json"]
|
||||
assert body["loras"] == "lora-repo-1"
|
||||
assert body["seed"] == 42
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_modelscope_image_generation_poll_timeout(monkeypatch) -> None:
|
||||
"""Polling that never reaches SUCCEED/FAILED raises a timeout error."""
|
||||
monkeypatch.setattr(
|
||||
"nanobot.providers.image_generation._MODELSCOPE_POLL_MAX_ATTEMPTS", 3
|
||||
)
|
||||
submit = FakeResponse({"task_id": "t1"})
|
||||
# Always PENDING — never resolves.
|
||||
poll = [FakeResponse({"task_status": "PENDING"})]
|
||||
fake = ModelScopeFakeClient(submit, poll)
|
||||
client = ModelScopeImageGenerationClient(
|
||||
api_key="ms-token",
|
||||
client=fake, # type: ignore[arg-type]
|
||||
)
|
||||
|
||||
with pytest.raises(ImageGenerationError, match="timed out"):
|
||||
await client.generate(prompt="test", model="m")
|
||||
|
||||
# Should have polled up to the (patched) attempt limit.
|
||||
assert len(fake.get_calls) == 3
|
||||
|
||||
|
||||
@@ -0,0 +1,122 @@
|
||||
"""Tests for the ModelScope (魔搭) provider registration."""
|
||||
|
||||
from unittest.mock import patch
|
||||
|
||||
from nanobot.config.schema import Config, ProvidersConfig
|
||||
from nanobot.providers.openai_compat_provider import OpenAICompatProvider
|
||||
from nanobot.providers.registry import PROVIDERS, find_by_name
|
||||
|
||||
|
||||
def test_modelscope_config_field_exists() -> None:
|
||||
config = ProvidersConfig()
|
||||
|
||||
assert hasattr(config, "modelscope")
|
||||
|
||||
|
||||
def test_modelscope_provider_in_registry() -> None:
|
||||
specs = {spec.name: spec for spec in PROVIDERS}
|
||||
|
||||
assert "modelscope" in specs
|
||||
ms = specs["modelscope"]
|
||||
assert ms.backend == "openai_compat"
|
||||
assert ms.env_key == "MODELSCOPE_API_KEY"
|
||||
assert ms.display_name == "ModelScope"
|
||||
assert ms.is_gateway is True
|
||||
assert ms.default_api_base == "https://api-inference.modelscope.cn/v1"
|
||||
assert ms.strip_model_prefixes == ("modelscope",)
|
||||
assert ms.thinking_style == "enable_thinking"
|
||||
|
||||
|
||||
def test_find_by_name_modelscope() -> None:
|
||||
spec = find_by_name("modelscope")
|
||||
|
||||
assert spec is not None
|
||||
assert spec.name == "modelscope"
|
||||
|
||||
|
||||
def test_modelscope_forced_provider_uses_default_api_base() -> None:
|
||||
config = Config.model_validate(
|
||||
{
|
||||
"providers": {
|
||||
"modelscope": {
|
||||
"apiKey": "ms-token",
|
||||
},
|
||||
},
|
||||
"agents": {
|
||||
"defaults": {
|
||||
"model": "Qwen/Qwen3.5-35B-A3B",
|
||||
"provider": "modelscope",
|
||||
},
|
||||
},
|
||||
}
|
||||
)
|
||||
|
||||
assert config.get_provider_name("Qwen/Qwen3.5-35B-A3B") == "modelscope"
|
||||
assert config.get_api_key("Qwen/Qwen3.5-35B-A3B") == "ms-token"
|
||||
assert config.get_api_base("Qwen/Qwen3.5-35B-A3B") == "https://api-inference.modelscope.cn/v1"
|
||||
|
||||
|
||||
def test_modelscope_keyword_matches_prefixed_model() -> None:
|
||||
config = Config.model_validate(
|
||||
{
|
||||
"providers": {
|
||||
"modelscope": {
|
||||
"apiKey": "ms-token",
|
||||
},
|
||||
},
|
||||
"agents": {
|
||||
"defaults": {
|
||||
"model": "modelscope/Qwen/Qwen3.5-35B-A3B",
|
||||
},
|
||||
},
|
||||
}
|
||||
)
|
||||
|
||||
assert config.get_provider_name("modelscope/Qwen/Qwen3.5-35B-A3B") == "modelscope"
|
||||
assert config.get_api_key("modelscope/Qwen/Qwen3.5-35B-A3B") == "ms-token"
|
||||
|
||||
|
||||
def test_modelscope_strips_prefix_in_request_model() -> None:
|
||||
spec = find_by_name("modelscope")
|
||||
with patch("nanobot.providers.openai_compat_provider.AsyncOpenAI"):
|
||||
provider = OpenAICompatProvider(
|
||||
api_key="ms-token",
|
||||
default_model="modelscope/Qwen/Qwen3.5-35B-A3B",
|
||||
spec=spec,
|
||||
)
|
||||
|
||||
kwargs = provider._build_kwargs(
|
||||
messages=[{"role": "user", "content": "hi"}],
|
||||
tools=None,
|
||||
model="modelscope/Qwen/Qwen3.5-35B-A3B",
|
||||
max_tokens=1024,
|
||||
temperature=0.7,
|
||||
reasoning_effort=None,
|
||||
tool_choice=None,
|
||||
)
|
||||
|
||||
# strip_model_prefixes removes "modelscope/" → "Qwen/Qwen3.5-35B-A3B"
|
||||
assert kwargs["model"] == "Qwen/Qwen3.5-35B-A3B"
|
||||
assert kwargs["max_tokens"] == 1024
|
||||
assert "max_completion_tokens" not in kwargs
|
||||
|
||||
|
||||
def test_modelscope_routes_unprefixed_models_when_configured() -> None:
|
||||
config = Config.model_validate(
|
||||
{
|
||||
"providers": {
|
||||
"modelscope": {
|
||||
"apiKey": "ms-token",
|
||||
"apiBase": "https://api-inference.modelscope.cn/v1",
|
||||
},
|
||||
},
|
||||
"agents": {
|
||||
"defaults": {
|
||||
"model": "Qwen/Qwen3.5-35B-A3B",
|
||||
},
|
||||
},
|
||||
}
|
||||
)
|
||||
|
||||
name = config.get_provider_name("Qwen/Qwen3.5-35B-A3B")
|
||||
assert name == "modelscope"
|
||||
@@ -232,6 +232,7 @@ const DEFERRED_MODEL_LIST_PROVIDERS = new Set([
|
||||
"byteplus_coding_plan",
|
||||
"huggingface",
|
||||
"lm_studio",
|
||||
"modelscope",
|
||||
"novita",
|
||||
"ollama",
|
||||
"openrouter",
|
||||
@@ -7889,6 +7890,7 @@ const PROVIDER_ICONS: Record<string, LucideIcon> = {
|
||||
deepseek: Waves,
|
||||
zhipu: Grid3X3,
|
||||
dashscope: Cloud,
|
||||
modelscope: Layers,
|
||||
moonshot: Moon,
|
||||
minimax: Zap,
|
||||
minimax_anthropic: Brain,
|
||||
|
||||
@@ -137,6 +137,7 @@ const PROVIDER_BRANDS: Record<string, ProviderBrand> = {
|
||||
]),
|
||||
minimax: brand("minimax.io", "#111827", "MM"),
|
||||
mistral: brand("mistral.ai", "#FA520F", "M"),
|
||||
modelscope: brand("modelscope.cn", "#5B5BF6", "MS"),
|
||||
moonshot: brand("moonshot.ai", "#111827", "MS"),
|
||||
novita: brand("novita.ai", "#7C3AED", "N"),
|
||||
olostep: brand("olostep.com", "#111827", "O"),
|
||||
@@ -189,6 +190,7 @@ export function inferProviderFromModelName(modelName: string | null | undefined)
|
||||
if (/gpt-|^o\d|chatgpt|openai/.test(normalized)) return "openai";
|
||||
if (/deepseek/.test(normalized)) return "deepseek";
|
||||
if (/gemini/.test(normalized)) return "gemini";
|
||||
if (/modelscope/.test(normalized)) return "modelscope";
|
||||
if (/qwen|dashscope/.test(normalized)) return "dashscope";
|
||||
if (/kimi|moonshot/.test(normalized)) return "moonshot";
|
||||
if (/minimax/.test(normalized)) return "minimax";
|
||||
|
||||
Reference in New Issue
Block a user