chore: release v3.4.13 (#1284)

Add ModelScope Support

README.md

@@ -116,6 +116,7 @@
 | [SiliconFlow](https://siliconflow.cn/) | ✅ | 大模型聚合平台 |
 | [阿里云百炼](https://bailian.console.aliyun.com/) | ✅ | 大模型聚合平台, LLMOps 平台 |
 | [火山方舟](https://console.volcengine.com/ark/region:ark+cn-beijing/model?vendor=Bytedance&view=LIST_VIEW) | ✅ | 大模型聚合平台, LLMOps 平台 |
+| [ModelScope](https://modelscope.cn/docs/model-service/API-Inference/intro) | ✅ | 大模型聚合平台 |
 | [MCP](https://modelcontextprotocol.io/) | ✅ | 支持通过 MCP 协议获取工具 |
 
 ### TTS

README_EN.md

@@ -113,6 +113,7 @@ Directly use the released version to run, see the [Manual Deployment](https://do
 | [SiliconFlow](https://siliconflow.cn/) | ✅ | LLM gateway(MaaS) |
 | [Aliyun Bailian](https://bailian.console.aliyun.com/) | ✅ | LLM gateway(MaaS), LLMOps platform |
 | [Volc Engine Ark](https://console.volcengine.com/ark/region:ark+cn-beijing/model?vendor=Bytedance&view=LIST_VIEW) | ✅ | LLM gateway(MaaS), LLMOps platform |
+| [ModelScope](https://modelscope.cn/docs/model-service/API-Inference/intro) | ✅ | LLM gateway(MaaS) |
 | [MCP](https://modelcontextprotocol.io/) | ✅ | Support tool access through MCP protocol |
 
 ## 🤝 Community Contribution

README_JP.md

@@ -112,6 +112,7 @@ LangBotはBTPanelにリストされています。BTPanelをインストール
 | [SiliconFlow](https://siliconflow.cn/) | ✅ | LLMゲートウェイ(MaaS) |
 | [Aliyun Bailian](https://bailian.console.aliyun.com/) | ✅ | LLMゲートウェイ(MaaS), LLMOpsプラットフォーム |
 | [Volc Engine Ark](https://console.volcengine.com/ark/region:ark+cn-beijing/model?vendor=Bytedance&view=LIST_VIEW) | ✅ | LLMゲートウェイ(MaaS), LLMOpsプラットフォーム |
+| [ModelScope](https://modelscope.cn/docs/model-service/API-Inference/intro) | ✅ | LLMゲートウェイ(MaaS) |
 | [MCP](https://modelcontextprotocol.io/) | ✅ | MCPプロトコルをサポート |
 
 ## 🤝 コミュニティ貢献

pkg/core/migrations/m039_modelscope_cfg_completion.py

@@ -0,0 +1,30 @@
+from __future__ import annotations
+
+from .. import migration
+
+
+@migration.migration_class("modelscope-config-completion", 4)
+class ModelScopeConfigCompletionMigration(migration.Migration):
+    """OpenAI配置迁移
+    """
+
+    async def need_migrate(self) -> bool:
+        """判断当前环境是否需要运行此迁移
+        """
+        return 'modelscope-chat-completions' not in self.ap.provider_cfg.data['requester'] \
+            or 'modelscope' not in self.ap.provider_cfg.data['keys']
+
+    async def run(self):
+        """执行迁移
+        """
+        if 'modelscope-chat-completions' not in self.ap.provider_cfg.data['requester']:
+            self.ap.provider_cfg.data['requester']['modelscope-chat-completions'] = {
+                'base-url': 'https://api-inference.modelscope.cn/v1',
+                'args': {},
+                'timeout': 120,
+            }
+
+        if 'modelscope' not in self.ap.provider_cfg.data['keys']:
+            self.ap.provider_cfg.data['keys']['modelscope'] = []
+
+        await self.ap.provider_cfg.dump_config()

pkg/core/stages/migrate.py

@@ -12,7 +12,7 @@ from ..migrations import m020_wecom_config, m021_lark_config, m022_lmstudio_conf
 from ..migrations import m026_qqofficial_config, m027_wx_official_account_config, m028_aliyun_requester_config
 from ..migrations import m029_dashscope_app_api_config, m030_lark_config_cmpl, m031_dingtalk_config, m032_volcark_config
 from ..migrations import m033_dify_thinking_config, m034_gewechat_file_url_config, m035_wxoa_mode, m036_wxoa_loading_message
-from ..migrations import m037_mcp_config, m038_tg_dingtalk_markdown
+from ..migrations import m037_mcp_config, m038_tg_dingtalk_markdown, m039_modelscope_cfg_completion
 
 
 @stage.stage_class("MigrationStage")

pkg/provider/modelmgr/modelmgr.py

@@ -6,7 +6,7 @@ from . import entities, requester
 from ...core import app
 from ...discover import engine
 from . import token
-from .requesters import bailianchatcmpl, chatcmpl, anthropicmsgs, moonshotchatcmpl, deepseekchatcmpl, ollamachat, giteeaichatcmpl, volcarkchatcmpl, xaichatcmpl, zhipuaichatcmpl, lmstudiochatcmpl, siliconflowchatcmpl, volcarkchatcmpl
+from .requesters import bailianchatcmpl, chatcmpl, anthropicmsgs, moonshotchatcmpl, deepseekchatcmpl, ollamachat, giteeaichatcmpl, volcarkchatcmpl, xaichatcmpl, zhipuaichatcmpl, lmstudiochatcmpl, siliconflowchatcmpl, volcarkchatcmpl, modelscopechatcmpl
 
 FETCH_MODEL_LIST_URL = "https://api.qchatgpt.rockchin.top/api/v2/fetch/model_list"
 

pkg/provider/modelmgr/requesters/modelscopechatcmpl.py

@@ -0,0 +1,207 @@
+from __future__ import annotations
+ 
+import asyncio
+import typing
+import json
+import base64
+from typing import AsyncGenerator
+
+import openai
+import openai.types.chat.chat_completion as chat_completion
+import openai.types.chat.chat_completion_message_tool_call as chat_completion_message_tool_call
+import httpx
+import aiohttp
+import async_lru
+
+from .. import entities, errors, requester
+from ....core import entities as core_entities, app
+from ... import entities as llm_entities
+from ...tools import entities as tools_entities
+from ....utils import image
+
+
+class ModelScopeChatCompletions(requester.LLMAPIRequester):
+    """ModelScope ChatCompletion API 请求器"""
+
+    client: openai.AsyncClient
+
+    requester_cfg: dict
+
+    def __init__(self, ap: app.Application):
+        self.ap = ap
+
+        self.requester_cfg = self.ap.provider_cfg.data['requester']['modelscope-chat-completions']
+
+    async def initialize(self):
+
+        self.client = openai.AsyncClient(
+            api_key="",
+            base_url=self.requester_cfg['base-url'],
+            timeout=self.requester_cfg['timeout'],
+            http_client=httpx.AsyncClient(
+                trust_env=True,
+                timeout=self.requester_cfg['timeout']
+            )
+        )
+
+    async def _req(
+        self,
+        args: dict,
+    ) -> chat_completion.ChatCompletion:
+        args["stream"] = True
+
+        chunk = None
+
+        pending_content = ""
+
+        tool_calls = []
+
+        resp_gen: openai.AsyncStream = await self.client.chat.completions.create(**args)
+
+        async for chunk in resp_gen:
+            # print(chunk)
+            if not chunk or not chunk.id or not chunk.choices or not chunk.choices[0] or not chunk.choices[0].delta:
+                continue
+
+            if chunk.choices[0].delta.content is not None:
+                pending_content += chunk.choices[0].delta.content
+
+            if chunk.choices[0].delta.tool_calls is not None:
+                for tool_call in chunk.choices[0].delta.tool_calls:
+                    for tc in tool_calls:
+                        if tc.index == tool_call.index:
+                            tc.function.arguments += tool_call.function.arguments
+                            break
+                    else:
+                        tool_calls.append(tool_call)
+            
+            if chunk.choices[0].finish_reason is not None:
+                break
+
+        real_tool_calls = []
+
+        for tc in tool_calls:
+            function = chat_completion_message_tool_call.Function(
+                name=tc.function.name,
+                arguments=tc.function.arguments
+            )
+            real_tool_calls.append(chat_completion_message_tool_call.ChatCompletionMessageToolCall(
+                id=tc.id,
+                function=function,
+                type="function"
+            ))
+
+        return chat_completion.ChatCompletion(
+            id=chunk.id,
+            object="chat.completion",
+            created=chunk.created,
+            choices=[
+                chat_completion.Choice(
+                    index=0,
+                    message=chat_completion.ChatCompletionMessage(
+                        role="assistant",
+                        content=pending_content,
+                        tool_calls=real_tool_calls if len(real_tool_calls) > 0 else None
+                    ),
+                    finish_reason=chunk.choices[0].finish_reason if hasattr(chunk.choices[0], 'finish_reason') and chunk.choices[0].finish_reason is not None else 'stop',
+                    logprobs=chunk.choices[0].logprobs,
+                )
+            ],
+            model=chunk.model,
+            service_tier=chunk.service_tier if hasattr(chunk, 'service_tier') else None,
+            system_fingerprint=chunk.system_fingerprint if hasattr(chunk, 'system_fingerprint') else None,
+            usage=chunk.usage if hasattr(chunk, 'usage') else None
+        ) if chunk else None
+        return await self.client.chat.completions.create(**args)
+
+    async def _make_msg(
+        self,
+        chat_completion: chat_completion.ChatCompletion,
+    ) -> llm_entities.Message:
+        chatcmpl_message = chat_completion.choices[0].message.dict()
+
+        # 确保 role 字段存在且不为 None
+        if 'role' not in chatcmpl_message or chatcmpl_message['role'] is None:
+            chatcmpl_message['role'] = 'assistant'
+
+        message = llm_entities.Message(**chatcmpl_message)
+
+        return message
+
+    async def _closure(
+        self,
+        query: core_entities.Query,
+        req_messages: list[dict],
+        use_model: entities.LLMModelInfo,
+        use_funcs: list[tools_entities.LLMFunction] = None,
+    ) -> llm_entities.Message:
+        self.client.api_key = use_model.token_mgr.get_token()
+
+        args = self.requester_cfg['args'].copy()
+        args["model"] = use_model.name if use_model.model_name is None else use_model.model_name
+
+        if use_funcs:
+            tools = await self.ap.tool_mgr.generate_tools_for_openai(use_funcs)
+
+            if tools:
+                args["tools"] = tools
+
+        # 设置此次请求中的messages
+        messages = req_messages.copy()
+
+        # 检查vision
+        for msg in messages:
+            if 'content' in msg and isinstance(msg["content"], list):
+                for me in msg["content"]:
+                    if me["type"] == "image_base64":
+                        me["image_url"] = {
+                            "url": me["image_base64"]
+                        }
+                        me["type"] = "image_url"
+                        del me["image_base64"]
+
+        args["messages"] = messages
+
+        # 发送请求
+        resp = await self._req(args)
+
+        # 处理请求结果
+        message = await self._make_msg(resp)
+
+        return message
+
+    async def call(
+        self,
+        query: core_entities.Query,
+        model: entities.LLMModelInfo,
+        messages: typing.List[llm_entities.Message],
+        funcs: typing.List[tools_entities.LLMFunction] = None,
+    ) -> llm_entities.Message:
+        req_messages = []  # req_messages 仅用于类内，外部同步由 query.messages 进行
+        for m in messages:
+            msg_dict = m.dict(exclude_none=True)
+            content = msg_dict.get("content")
+            if isinstance(content, list):
+                # 检查 content 列表中是否每个部分都是文本
+                if all(isinstance(part, dict) and part.get("type") == "text" for part in content):
+                    # 将所有文本部分合并为一个字符串
+                    msg_dict["content"] = "\n".join(part["text"] for part in content)
+            req_messages.append(msg_dict)
+
+        try:
+            return await self._closure(query=query, req_messages=req_messages, use_model=model, use_funcs=funcs)
+        except asyncio.TimeoutError:
+            raise errors.RequesterError('请求超时')
+        except openai.BadRequestError as e:
+            if 'context_length_exceeded' in e.message:
+                raise errors.RequesterError(f'上文过长，请重置会话: {e.message}')
+            else:
+                raise errors.RequesterError(f'请求参数错误: {e.message}')
+        except openai.AuthenticationError as e:
+            raise errors.RequesterError(f'无效的 api-key: {e.message}')
+        except openai.NotFoundError as e:
+            raise errors.RequesterError(f'请求路径错误: {e.message}')
+        except openai.RateLimitError as e:
+            raise errors.RequesterError(f'请求过于频繁或余额不足: {e.message}')
+        except openai.APIError as e:
+            raise errors.RequesterError(f'请求错误: {e.message}')

pkg/provider/modelmgr/requesters/modelscopechatcmpl.yaml

@@ -0,0 +1,34 @@
+apiVersion: v1
+kind: LLMAPIRequester
+metadata:
+  name: modelscope-chat-completions
+  label:
+    en_US: ModelScope
+    zh_CN: 魔搭社区
+spec:
+  config:
+    - name: base-url
+      label:
+        en_US: Base URL
+        zh_CN: 基础 URL
+      type: string
+      required: true
+      default: "https://api-inference.modelscope.cn/v1"
+    - name: args
+      label:
+        en_US: Args
+        zh_CN: 附加参数
+      type: object
+      required: true
+      default: {}
+    - name: timeout
+      label:
+        en_US: Timeout
+        zh_CN: 超时时间
+      type: int
+      required: true
+      default: 120
+execution:
+  python:
+    path: ./modelscopechatcmpl.py
+    attr: ModelScopeChatCompletions

pkg/utils/constants.py

@@ -1,4 +1,4 @@
-semantic_version = "v3.4.12.1"
+semantic_version = "v3.4.13"
 
 debug_mode = False
 

templates/platform.json

@@ -99,7 +99,7 @@
         },
         {
             "adapter": "slack",
-            "enable":true,
+            "enable": false,
             "bot_token": "",
             "signing_secret": "",
             "port": 2288

templates/provider.json

@@ -31,6 +31,9 @@
         ],
         "volcark": [
             "xxxxxxxx"
+        ],
+        "modelscope": [
+            "xxxxxxxx"
         ]
     },
     "requester": {
@@ -95,6 +98,11 @@
             "args": {},
             "base-url": "https://ark.cn-beijing.volces.com/api/v3",
             "timeout": 120
+        },
+        "modelscope-chat-completions": {
+            "base-url": "https://api-inference.modelscope.cn/v1",
+            "args": {},
+            "timeout": 120
         }
     },
     "model": "gpt-4o",