feat: add DashScope embedding provider with multimodal model support

astrbot/core/config/default.py

@@ -1884,6 +1884,20 @@
                         "timeout": 60,
                         "proxy": "",
                     },
+                    "DashScope Embedding": {
+                        "id": "dashscope_embedding",
+                        "type": "dashscope_embedding",
+                        "provider": "dashscope",
+                        "provider_type": "embedding",
+                        "hint": "provider_group.provider.dashscope_embedding.hint",
+                        "enable": True,
+                        "embedding_api_key": "",
+                        "embedding_api_base": "https://dashscope.aliyuncs.com/api/v1",
+                        "embedding_model": "text-embedding-v4",
+                        "embedding_dimensions": 1024,
+                        "timeout": 60,
+                        "proxy": "",
+                    },
                     "vLLM Rerank": {
                         "id": "vllm_rerank",
                         "type": "vllm_rerank",

astrbot/core/provider/manager.py

@@ -487,6 +487,10 @@ def dynamic_import_provider(self, type: str) -> None:
                 from .sources.ollama_embedding_source import (
                     OllamaEmbeddingProvider as OllamaEmbeddingProvider,
                 )
+            case "dashscope_embedding":
+                from .sources.dashscope_embedding_source import (
+                    DashScopeEmbeddingProvider as DashScopeEmbeddingProvider,
+                )
             case "vllm_rerank":
                 from .sources.vllm_rerank_source import (
                     VLLMRerankProvider as VLLMRerankProvider,

astrbot/core/provider/sources/dashscope_embedding_source.py

@@ -0,0 +1,139 @@
+import asyncio
+import os
+from http import HTTPStatus
+
+from dashscope import MultiModalEmbedding, TextEmbedding
+
+from astrbot import logger
+
+from ..entities import ProviderType
+from ..provider import EmbeddingProvider
+from ..register import register_provider_adapter
+
+_DEFAULT_API_BASE = "https://dashscope.aliyuncs.com/api/v1"
+_DEFAULT_MODEL = "text-embedding-v4"
+
+
+@register_provider_adapter(
+    "dashscope_embedding",
+    "阿里云百炼(DashScope) Embedding 提供商适配器",
+    provider_type=ProviderType.EMBEDDING,
+)
+class DashScopeEmbeddingProvider(EmbeddingProvider):
+    """DashScope (Aliyun Bailian) embedding provider via the native protocol.
+
+    Routes text embedding models (text-embedding-*) through TextEmbedding and
+    multimodal embedding models (qwen*-vl-embedding, multimodal-embedding-*,
+    tongyi-embedding-vision-*) through MultiModalEmbedding, so that models
+    unavailable in the OpenAI-compatible mode can be used.
+    """
+
+    def __init__(self, provider_config: dict, provider_settings: dict) -> None:
+        super().__init__(provider_config, provider_settings)
+        self.provider_config = provider_config
+
+        self.api_key = provider_config.get("embedding_api_key") or os.getenv(
+            "DASHSCOPE_API_KEY", ""
+        )
+        if not self.api_key:
+            raise ValueError("阿里云百炼(DashScope) Embedding API Key 不能为空。")
+
+        self.base_url = provider_config.get("embedding_api_base", _DEFAULT_API_BASE)
+        self.model = provider_config.get("embedding_model", _DEFAULT_MODEL)
+
+        provider_id = provider_config.get("id", "unknown_id")
+        logger.info(
+            f"[DashScope Embedding] {provider_id} Initialized via native SDK, "
+            f"base_url={self.base_url}, model={self.model}"
+        )
+        self.set_model(self.model)
+
+    async def get_embedding(self, text: str) -> list[float]:
+        """Get the embedding vector for a single text."""
+        embeddings = await self.get_embeddings([text])
+        return embeddings[0] if embeddings else []
+
+    async def get_embeddings(self, text: list[str]) -> list[list[float]]:
+        """Get the embedding vectors for a batch of texts via the dashscope SDK.
+
+        Multimodal models (e.g. qwen3-vl-embedding) use the
+        multimodal-embedding endpoint and accept text wrapped in content dicts;
+        text models use the text-embedding endpoint directly.
+        """
+        if not text:
+            return []
+
+        is_multimodal = (
+            "vl-embedding" in self.model
+            or self.model.startswith("multimodal-embedding")
+            or self.model.startswith("tongyi-embedding-vision")
+        )
+
+        kwargs: dict = {"base_address": self.base_url}
+        if "embedding_dimensions" in self.provider_config:
+            try:
+                dimensions = int(self.provider_config["embedding_dimensions"])
+                if dimensions > 0:
+                    kwargs["dimension"] = dimensions
+            except (ValueError, TypeError):
+                logger.warning(
+                    f"embedding_dimensions in embedding configs is not a valid integer: "
+                    f"'{self.provider_config['embedding_dimensions']}', ignored."
+                )
+
+        # The dashscope SDK is synchronous; run it in a worker thread.
+        # base_address is passed per-call to avoid racing on the module-level
+        # dashscope.base_http_api_url global under concurrent usage.
+        def _call():
+            if is_multimodal:
+                return MultiModalEmbedding.call(
+                    model=self.model,
+                    input=[{"text": t} for t in text],
+                    api_key=self.api_key,
+                    **kwargs,
+                )
+            return TextEmbedding.call(
+                model=self.model,
+                input=text,
+                api_key=self.api_key,
+                **kwargs,
+            )
+
+        resp = await asyncio.to_thread(_call)
+
+        if resp.status_code != HTTPStatus.OK:
+            task = "multimodal-embedding" if is_multimodal else "text-embedding"
+            request_url = (
+                self.base_url.rstrip("/") + f"/services/embeddings/{task}/{task}"
+            )
+            request_id = getattr(resp, "request_id", "") or ""
+            raise Exception(
+                f"DashScope Embedding API request failed (HTTP {resp.status_code}): "
+                f"{resp.code or '(no code)'} - {resp.message or '(no message)'}"
+                f" [url={request_url}]"
+                + (f" [request_id={request_id}]" if request_id else "")
+            )
+
+        embeddings = resp.output.get("embeddings", []) if resp.output else []
+        if not embeddings:
+            raise Exception(f"[DashScope Embedding] No embeddings returned: {resp}")
+
+        # Text embedding uses text_index; multimodal uses index.
+        return [
+            item["embedding"]
+            for item in sorted(
+                embeddings, key=lambda x: x.get("text_index", x.get("index", 0))
+            )
+        ]
+
+    def get_dim(self) -> int:
+        """Get the configured embedding dimension."""
+        if "embedding_dimensions" in self.provider_config:
+            try:
+                return int(self.provider_config["embedding_dimensions"])
+            except (ValueError, TypeError):
+                logger.warning(
+                    f"embedding_dimensions in embedding configs is not a valid integer: "
+                    f"'{self.provider_config['embedding_dimensions']}', ignored."
+                )
+        return 0

dashboard/src/i18n/locales/en-US/features/config-metadata.json

@@ -1393,6 +1393,9 @@
       "gemini_embedding": {
         "hint": "Gemini Embedding does not require manually adding /v1beta."
       },
+      "dashscope_embedding": {
+        "hint": "Aliyun DashScope Embedding. Default base URL: https://dashscope.aliyuncs.com/api/v1."
+      },
       "volcengine_cluster": {
         "description": "Volcengine cluster",
         "hint": "For voice cloning models, choose volcano_icl or volcano_icl_concurr; default is volcano_tts."

dashboard/src/i18n/locales/ru-RU/features/config-metadata.json

@@ -1390,6 +1390,9 @@
             "gemini_embedding": {
                 "hint": "Gemini Embedding не требует ручного добавления /v1beta."
             },
+            "dashscope_embedding": {
+                "hint": "Embedding Aliyun DashScope. URL по умолчанию: https://dashscope.aliyuncs.com/api/v1."
+            },
             "volcengine_cluster": {
                 "description": "Кластер Volcengine",
                 "hint": "Для моделей клонирования голоса выберите volcano_icl или volcano_icl_concurr; по умолчанию volcano_tts."

dashboard/src/i18n/locales/zh-CN/features/config-metadata.json

@@ -1395,6 +1395,9 @@
       "gemini_embedding": {
         "hint": "Gemini Embedding 无需手动添加 /v1beta。"
       },
+      "dashscope_embedding": {
+        "hint": "阿里云百炼 Embedding，默认地址为 https://dashscope.aliyuncs.com/api/v1。"
+      },
       "volcengine_cluster": {
         "description": "火山引擎集群",
         "hint": "若使用语音复刻大模型，可选volcano_icl或volcano_icl_concurr，默认使用volcano_tts"