feat: 添加对 Gitee AI 的支持

pkg/provider/modelmgr/requesters/anthropicmsgs.py

@@ -0,0 +1,113 @@
+from __future__ import annotations
+
+import typing
+import traceback
+
+import anthropic
+
+from .. import entities, errors, requester
+
+from .. import entities, errors
+from ....core import entities as core_entities
+from ... import entities as llm_entities
+from ...tools import entities as tools_entities
+from ....utils import image
+
+
+@requester.requester_class("anthropic-messages")
+class AnthropicMessages(requester.LLMAPIRequester):
+    """Anthropic Messages API 请求器"""
+
+    client: anthropic.AsyncAnthropic
+
+    async def initialize(self):
+        self.client = anthropic.AsyncAnthropic(
+            api_key="",
+            base_url=self.ap.provider_cfg.data['requester']['anthropic-messages']['base-url'],
+            timeout=self.ap.provider_cfg.data['requester']['anthropic-messages']['timeout'],
+            proxies=self.ap.proxy_mgr.get_forward_proxies()
+        )
+
+    async def call(
+        self,
+        model: entities.LLMModelInfo,
+        messages: typing.List[llm_entities.Message],
+        funcs: typing.List[tools_entities.LLMFunction] = None,
+    ) -> llm_entities.Message:
+        self.client.api_key = model.token_mgr.get_token()
+
+        args = self.ap.provider_cfg.data['requester']['anthropic-messages']['args'].copy()
+        args["model"] = model.name if model.model_name is None else model.model_name
+
+        # 处理消息
+
+        # system
+        system_role_message = None
+
+        for i, m in enumerate(messages):
+            if m.role == "system":
+                system_role_message = m
+
+                messages.pop(i)
+                break
+
+        if isinstance(system_role_message, llm_entities.Message) \
+            and isinstance(system_role_message.content, str):
+            args['system'] = system_role_message.content
+
+        req_messages = []
+
+        for m in messages:
+            if isinstance(m.content, str) and m.content.strip() != "":
+                req_messages.append(m.dict(exclude_none=True))
+            elif isinstance(m.content, list):
+                # m.content = [
+                #     c for c in m.content if c.type == "text"
+                # ]
+
+                # if len(m.content) > 0:
+                #     req_messages.append(m.dict(exclude_none=True))
+
+                msg_dict = m.dict(exclude_none=True)
+
+                for i, ce in enumerate(m.content):
+                    if ce.type == "image_url":
+                        base64_image, image_format = await image.qq_image_url_to_base64(ce.image_url.url)
+                        alter_image_ele = {
+                            "type": "image",
+                            "source": {
+                                "type": "base64",
+                                "media_type": f"image/{image_format}",
+                                "data": base64_image
+                            }
+                        }
+                        msg_dict["content"][i] = alter_image_ele
+
+                req_messages.append(msg_dict)
+
+        args["messages"] = req_messages
+
+        # anthropic的tools处在beta阶段，sdk不稳定，故暂时不支持
+        #
+        # if funcs:
+        #     tools = await self.ap.tool_mgr.generate_tools_for_openai(funcs)
+
+        #     if tools:
+        #         args["tools"] = tools
+
+        try:
+            resp = await self.client.messages.create(**args)
+
+            return llm_entities.Message(
+                content=resp.content[0].text,
+                role=resp.role
+            )
+        except anthropic.AuthenticationError as e:
+            raise errors.RequesterError(f'api-key 无效: {e.message}')
+        except anthropic.BadRequestError as e:
+            raise errors.RequesterError(str(e.message))
+        except anthropic.NotFoundError as e:
+            if 'model: ' in str(e):
+                raise errors.RequesterError(f'模型无效: {e.message}')
+            else:
+                raise errors.RequesterError(f'请求地址无效: {e.message}')

pkg/provider/modelmgr/requesters/chatcmpl.py

@@ -0,0 +1,144 @@
+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 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
+
+
+@requester.requester_class("openai-chat-completions")
+class OpenAIChatCompletions(requester.LLMAPIRequester):
+    """OpenAI 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']['openai-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(
+                proxies=self.ap.proxy_mgr.get_forward_proxies()
+            )
+        )
+
+    async def _req(
+        self,
+        args: dict,
+    ) -> chat_completion.ChatCompletion:
+        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,
+        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_url":
+                        me["image_url"]['url'] = await self.get_base64_str(me["image_url"]['url'])
+
+        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,
+        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(req_messages, model, 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}')
+
+    @async_lru.alru_cache(maxsize=128)
+    async def get_base64_str(
+        self,
+        original_url: str,
+    ) -> str:
+        base64_image, image_format = await image.qq_image_url_to_base64(original_url)
+        return f"data:image/{image_format};base64,{base64_image}"

pkg/provider/modelmgr/requesters/deepseekchatcmpl.py

@@ -0,0 +1,53 @@
+from __future__ import annotations
+
+from ....core import app
+
+from . import chatcmpl
+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
+
+
+@requester.requester_class("deepseek-chat-completions")
+class DeepseekChatCompletions(chatcmpl.OpenAIChatCompletions):
+    """Deepseek ChatCompletion API 请求器"""
+
+    def __init__(self, ap: app.Application):
+        self.requester_cfg = ap.provider_cfg.data['requester']['deepseek-chat-completions']
+        self.ap = ap
+
+    async def _closure(
+        self,
+        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
+
+        # deepseek 不支持多模态，把content都转换成纯文字
+        for m in messages:
+            if 'content' in m and isinstance(m["content"], list):
+                m["content"] = " ".join([c["text"] for c in m["content"]])
+
+        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

pkg/provider/modelmgr/requesters/giteeaichatcmpl.py

@@ -0,0 +1,53 @@
+from __future__ import annotations
+
+import json
+
+import asyncio
+import aiohttp
+import typing
+
+from . import chatcmpl
+from .. import entities, errors, requester
+from ....core import app
+from ... import entities as llm_entities
+from ...tools import entities as tools_entities
+from .. import entities as modelmgr_entities
+
+
+@requester.requester_class("gitee-ai-chat-completions")
+class GiteeAIChatCompletions(chatcmpl.OpenAIChatCompletions):
+    """Gitee AI ChatCompletions API 请求器"""
+
+    def __init__(self, ap: app.Application):
+        self.ap = ap
+        self.requester_cfg = ap.provider_cfg.data['requester']['gitee-ai-chat-completions'].copy()
+
+    async def _closure(
+        self,
+        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
+
+        # gitee 不支持多模态，把content都转换成纯文字
+        for m in req_messages:
+            if 'content' in m and isinstance(m["content"], list):
+                m["content"] = " ".join([c["text"] for c in m["content"]])
+
+        args["messages"] = req_messages
+
+        resp = await self._req(args)
+
+        message = await self._make_msg(resp)
+
+        return message

pkg/provider/modelmgr/requesters/moonshotchatcmpl.py

@@ -0,0 +1,56 @@
+from __future__ import annotations
+
+from ....core import app
+
+from . import chatcmpl
+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
+
+
+@requester.requester_class("moonshot-chat-completions")
+class MoonshotChatCompletions(chatcmpl.OpenAIChatCompletions):
+    """Moonshot ChatCompletion API 请求器"""
+
+    def __init__(self, ap: app.Application):
+        self.requester_cfg = ap.provider_cfg.data['requester']['moonshot-chat-completions']
+        self.ap = ap
+
+    async def _closure(
+        self,
+        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
+
+        # deepseek 不支持多模态，把content都转换成纯文字
+        for m in messages:
+            if 'content' in m and isinstance(m["content"], list):
+                m["content"] = " ".join([c["text"] for c in m["content"]])
+
+        # 删除空的
+        messages = [m for m in messages if m["content"].strip() != ""]
+
+        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

pkg/provider/modelmgr/requesters/ollamachat.py

@@ -0,0 +1,105 @@
+from __future__ import annotations
+
+import asyncio
+import os
+import typing
+from typing import Union, Mapping, Any, AsyncIterator
+
+import async_lru
+import ollama
+
+from .. import entities, errors, requester
+from ... import entities as llm_entities
+from ...tools import entities as tools_entities
+from ....core import app
+from ....utils import image
+
+REQUESTER_NAME: str = "ollama-chat"
+
+
+@requester.requester_class(REQUESTER_NAME)
+class OllamaChatCompletions(requester.LLMAPIRequester):
+    """Ollama平台 ChatCompletion API请求器"""
+    client: ollama.AsyncClient
+    request_cfg: dict
+
+    def __init__(self, ap: app.Application):
+        super().__init__(ap)
+        self.ap = ap
+        self.request_cfg = self.ap.provider_cfg.data['requester'][REQUESTER_NAME]
+
+    async def initialize(self):
+        os.environ['OLLAMA_HOST'] = self.request_cfg['base-url']
+        self.client = ollama.AsyncClient(
+            timeout=self.request_cfg['timeout']
+        )
+
+    async def _req(self,
+                   args: dict,
+                   ) -> Union[Mapping[str, Any], AsyncIterator[Mapping[str, Any]]]:
+        return await self.client.chat(
+            **args
+        )
+
+    async def _closure(self, req_messages: list[dict], use_model: entities.LLMModelInfo,
+                       user_funcs: list[tools_entities.LLMFunction] = None) -> (
+            llm_entities.Message):
+        args: Any = self.request_cfg['args'].copy()
+        args["model"] = use_model.name if use_model.model_name is None else use_model.model_name
+
+        messages: list[dict] = req_messages.copy()
+        for msg in messages:
+            if 'content' in msg and isinstance(msg["content"], list):
+                text_content: list = []
+                image_urls: list = []
+                for me in msg["content"]:
+                    if me["type"] == "text":
+                        text_content.append(me["text"])
+                    elif me["type"] == "image_url":
+                        image_url = await self.get_base64_str(me["image_url"]['url'])
+                        image_urls.append(image_url)
+                msg["content"] = "\n".join(text_content)
+                msg["images"] = [url.split(',')[1] for url in image_urls]
+        args["messages"] = messages
+
+        resp: Mapping[str, Any] | AsyncIterator[Mapping[str, Any]] = await self._req(args)
+        message: llm_entities.Message = await self._make_msg(resp)
+        return message
+
+    async def _make_msg(
+            self,
+            chat_completions: Union[Mapping[str, Any], AsyncIterator[Mapping[str, Any]]]) -> llm_entities.Message:
+        message: Any = chat_completions.pop('message', None)
+        if message is None:
+            raise ValueError("chat_completions must contain a 'message' field")
+
+        message.update(chat_completions)
+        ret_msg: llm_entities.Message = llm_entities.Message(**message)
+        return ret_msg
+
+    async def call(
+            self,
+            model: entities.LLMModelInfo,
+            messages: typing.List[llm_entities.Message],
+            funcs: typing.List[tools_entities.LLMFunction] = None,
+    ) -> llm_entities.Message:
+        req_messages: list = []
+        for m in messages:
+            msg_dict: dict = m.dict(exclude_none=True)
+            content: Any = msg_dict.get("content")
+            if isinstance(content, list):
+                if all(isinstance(part, dict) and part.get('type') == 'text' for part in content):
+                    msg_dict["content"] = "\n".join(part["text"] for part in content)
+            req_messages.append(msg_dict)
+        try:
+            return await self._closure(req_messages, model)
+        except asyncio.TimeoutError:
+            raise errors.RequesterError('请求超时')
+
+    @async_lru.alru_cache(maxsize=128)
+    async def get_base64_str(
+            self,
+            original_url: str,
+    ) -> str:
+        base64_image, image_format = await image.qq_image_url_to_base64(original_url)
+        return f"data:image/{image_format};base64,{base64_image}"