feat: 新增 dashscope rerank/embeddings 模型支持并重构 reranker 基类 #341

- 在 models.py 中添加 dashscope 的 text-embedding-v4 和 gte-rerank-v2/qwen3-rerank 模型配置
- 重构 reranker 模块，提取 BaseReranker 抽象基类并实现 OpenAIReranker 和 DashscopeReranker
- 在 embed.py 中优化错误提示信息并统一测试连接方法
- 更新 roadmap.md 文档记录新增功能

docs/latest/changelog/roadmap.md

@@ -27,7 +27,7 @@
 - 新增基于知识库文件生成思维导图功能（[#335](https://github.com/xerrors/Yuxi-Know/pull/335#issuecomment-3530976425)）
 - 新增基于知识库文件生成示例问题功能（[#335](https://github.com/xerrors/Yuxi-Know/pull/335#issuecomment-3530976425)）
 - 新增知识库支持文件夹/压缩包上传的功能（[#335](https://github.com/xerrors/Yuxi-Know/pull/335#issuecomment-3530976425)）
-- 新增自定义模型支持
+- 新增自定义模型支持、新增 dashscope rerank/embeddings 模型的支持
 
 ### 修复
 - 修复重排序模型实际未生效的问题

src/config/static/models.py

@@ -129,6 +129,29 @@ DEFAULT_CHAT_MODEL_PROVIDERS: dict[str, ChatModelProvider] = {
             "anthropic/claude-sonnet-4",
         ],
     ),
+    # "moonshot": ChatModelProvider(
+    #     name="月之暗面",
+    #     url="https://platform.moonshot.cn/docs/overview",
+    #     base_url="https://api.moonshot.cn/v1",
+    #     default="kimi-latest",
+    #     env="MOONSHOT_API_KEY",
+    #     models=[
+    #         "kimi-latest",
+    #         "kimi-k2-thinking",
+    #         "kimi-k2-0905-preview",
+    #     ],
+    # ), # 目前适配有问题 Error code: 400 - {'error': {'message': 'Invalid request: function name is invalid, must start with a letter and can contain letters, numbers, underscores, and dashes', 'type': 'invalid_request_error'}}   # noqa: E501
+    "modelscope": ChatModelProvider(
+        name="ModelScope",
+        url="https://www.modelscope.cn/docs/model-service/API-Inference/intro",
+        base_url="https://api-inference.modelscope.cn/v1/",
+        default="deepseek-ai/DeepSeek-V3.2-Exp",
+        env="MODELSCOPE_ACCESS_TOKEN",
+        models=[
+            "Qwen/Qwen3-32B",
+            "deepseek-ai/DeepSeek-V3.2-Exp"
+        ],
+    ),
 }
 
 
@@ -173,6 +196,12 @@ DEFAULT_EMBED_MODELS: dict[str, EmbedModelInfo] = {
         base_url="http://localhost:11434/api/embed",
         api_key="no_api_key",
     ),
+    "dashscope/text-embedding-v4": EmbedModelInfo(
+        name="text-embedding-v4",
+        dimension=1024,
+        base_url="https://dashscope.aliyuncs.com/compatible-mode/v1/embeddings",
+        api_key="DASHSCOPE_API_KEY",
+    ),
 }
 
 
@@ -191,6 +220,16 @@ DEFAULT_RERANKERS: dict[str, RerankerInfo] = {
         base_url="https://api.siliconflow.cn/v1/rerank",
         api_key="SILICONFLOW_API_KEY",
     ),
+    "dashscope/gte-rerank-v2": RerankerInfo(
+        name="gte-rerank-v2",
+        base_url="https://dashscope.aliyuncs.com/api/v1/services/rerank/text-rerank/text-rerank",
+        api_key="DASHSCOPE_API_KEY",
+    ),
+    "dashscope/qwen3-rerank": RerankerInfo(
+        name="qwen3-rerank",
+        base_url="https://dashscope.aliyuncs.com/api/v1/services/rerank/text-rerank/text-rerank",
+        api_key="DASHSCOPE_API_KEY",
+    ),
     "vllm/BAAI/bge-reranker-v2-m3": RerankerInfo(
         name="BAAI/bge-reranker-v2-m3",
         base_url="http://localhost:8000/v1/rerank",

src/models/embed.py

@@ -89,6 +89,23 @@ class BaseEmbeddingModel(ABC):
 
         return data
 
+    async def test_connection(self) -> tuple[bool, str]:
+        """
+        测试embedding模型的连接性
+
+        Returns:
+            tuple: (success: bool, message: str)
+        """
+        try:
+            # 使用简单的测试文本
+            test_text = ["Hello world"]
+            await self.aencode(test_text)
+            return True, "连接正常"
+        except Exception as e:
+            error_msg = str(e)
+            error_msg += f", maybe you can check the `{self.base_url}` end with /embeddings as examples."
+            logger.error(error_msg)
+            return False, error_msg
 
 class OllamaEmbedding(BaseEmbeddingModel):
     """
@@ -129,24 +146,7 @@ class OllamaEmbedding(BaseEmbeddingModel):
                     raise ValueError(f"Ollama Embedding failed: Invalid response format {result}")
                 return result["embeddings"]
             except (httpx.RequestError, json.JSONDecodeError) as e:
-                logger.error(f"Ollama Embedding async request failed: {e}, {payload}")
+                raise ValueError(f"Ollama Embedding async request failed: {e}, {payload}, {self.base_url=}")
-                raise ValueError(f"Ollama Embedding async request failed: {e}")
-
-    async def test_connection(self) -> tuple[bool, str]:
-        """
-        测试embedding模型的连接性
-
-        Returns:
-            tuple: (success: bool, message: str)
-        """
-        try:
-            # 使用简单的测试文本
-            test_text = ["Hello world"]
-            await self.aencode(test_text)
-            return True, "连接正常"
-        except Exception as e:
-            error_msg = str(e)
-            return False, error_msg
 
 
 class OtherEmbedding(BaseEmbeddingModel):
@@ -181,24 +181,8 @@ class OtherEmbedding(BaseEmbeddingModel):
                     raise ValueError(f"Other Embedding failed: Invalid response format {result}")
                 return [item["embedding"] for item in result["data"]]
             except (httpx.RequestError, json.JSONDecodeError) as e:
-                logger.error(f"Other Embedding async request failed: {e}, {payload}")
+                raise ValueError(f"Other Embedding async request failed: {e}, {payload}, {self.base_url=}")
-                raise ValueError(f"Other Embedding async request failed: {e}")
 
-    async def test_connection(self) -> tuple[bool, str]:
-        """
-        测试embedding模型的连接性
-
-        Returns:
-            tuple: (success: bool, message: str)
-        """
-        try:
-            # 使用简单的测试文本
-            test_text = ["Hello world"]
-            await self.aencode(test_text)
-            return True, "连接正常"
-        except Exception as e:
-            error_msg = str(e)
-            return False, error_msg
 
 
 async def test_embedding_model_status(model_id: str) -> dict:

src/models/rerank.py

@@ -1,5 +1,6 @@
 import asyncio
 import os
+from abc import ABC, abstractmethod
 from collections.abc import Iterable, Sequence
 from typing import Any
 
@@ -14,7 +15,7 @@ def sigmoid(x):
     return 1 / (1 + np.exp(-x))
 
 
-class OnlineReranker:
+class BaseReranker(ABC):
     def __init__(self, model_name, api_key, base_url, **kwargs):
         self.url = get_docker_safe_url(base_url)
         self.model = model_name
@@ -22,11 +23,20 @@ class OnlineReranker:
         self.headers = {"Authorization": f"Bearer {api_key}", "Content-Type": "application/json"}
         self.session: aiohttp.ClientSession | None = None
         self.timeout = aiohttp.ClientTimeout(total=30)
+        self.parameters: dict[str, Any] = dict(kwargs.get("parameters", {}))
 
     async def _ensure_session(self) -> None:
         if self.session is None or self.session.closed:
             self.session = aiohttp.ClientSession(headers=self.headers, timeout=self.timeout)
 
+    @abstractmethod
+    def _build_payload(self, query: str, documents: list[str], max_length: int) -> dict[str, Any]:
+        raise NotImplementedError
+
+    @abstractmethod
+    def _extract_results(self, result: dict[str, Any]) -> list[dict[str, Any]]:
+        raise NotImplementedError
+
     async def acompute_score(
         self,
         sentence_pairs: Sequence[Sequence[str]],
@@ -65,16 +75,12 @@ class OnlineReranker:
         return all_scores
 
     async def _batch_rerank(self, query: str, documents: Iterable[str], max_length: int) -> list[float]:
-        payload = {
+        docs = list(documents)
-            "model": self.model,
+        if not docs:
-            "query": query,
-            "documents": list(documents),
-            "max_chunks_per_doc": max_length,
-        }
-
-        if not payload["documents"]:
             return []
 
+        payload = self._build_payload(query, docs, max_length)
+
         await self._ensure_session()
         assert self.session is not None
 
@@ -90,11 +96,10 @@ class OnlineReranker:
             logger.error(f"Reranking request failed: {exc}")
             raise exc
 
-        processed = sorted(result.get("results", []), key=lambda item: item.get("index", 0))
+        processed = sorted(self._extract_results(result), key=lambda item: item.get("index", 0))
         return [float(entry.get("relevance_score", 0.0)) for entry in processed]
 
     def compute_score(self, sentence_pairs, batch_size=256, max_length=512, normalize=False):
-        """Synchronous helper retained for backwards compatibility."""
         try:
             _ = asyncio.get_running_loop()
         except RuntimeError:
@@ -119,6 +124,35 @@ class OnlineReranker:
                 loop.run_until_complete(self.aclose())
 
 
+class OpenAIReranker(BaseReranker):
+    def _build_payload(self, query: str, documents: list[str], max_length: int) -> dict[str, Any]:
+        return {
+            "model": self.model,
+            "query": query,
+            "documents": documents,
+            "max_chunks_per_doc": max_length,
+        }
+
+    def _extract_results(self, result: dict[str, Any]) -> list[dict[str, Any]]:
+        return list(result.get("results", []))
+
+
+class DashscopeReranker(BaseReranker):
+    def _build_payload(self, query: str, documents: list[str], max_length: int) -> dict[str, Any]:
+        params = {"top_n": len(documents), "return_documents": False}
+        instruct = self.parameters.get("instruct")
+        if instruct:
+            params["instruct"] = instruct
+        return {
+            "model": self.model,
+            "input": {"query": query, "documents": documents},
+            "parameters": params,
+        }
+
+    def _extract_results(self, result: dict[str, Any]) -> list[dict[str, Any]]:
+        return list(result.get("output", {}).get("results", []))
+
+
 def get_reranker(model_id, **kwargs):
     support_rerankers = config.reranker_names.keys()
     assert model_id in support_rerankers, f"Unsupported Reranker: {model_id}, only support {support_rerankers}"
@@ -127,4 +161,9 @@ def get_reranker(model_id, **kwargs):
     base_url = model_info.base_url
     api_key = os.getenv(model_info.api_key) or model_info.api_key
     assert api_key, f"{model_info.name} api_key is required"
-    return OnlineReranker(model_name=model_info.name, api_key=api_key, base_url=base_url, **kwargs)
+    provider = model_id.split("/", maxsplit=1)[0] if "/" in model_id else ""
+    if provider in {"siliconflow", "vllm"}:
+        return OpenAIReranker(model_name=model_info.name, api_key=api_key, base_url=base_url, **kwargs)
+    if provider == "dashscope":
+        return DashscopeReranker(model_name=model_info.name, api_key=api_key, base_url=base_url, **kwargs)
+    return OpenAIReranker(model_name=model_info.name, api_key=api_key, base_url=base_url, **kwargs)