mirror of
https://github.com/zhenxun-org/zhenxun_bot.git
synced 2026-09-28 16:20:56 +08:00
* ♻️ refactor(core): 重构 AI 能力与定时任务调度系统 - 【AI 能力与工具】重构 Capability 注册与管理机制,引入 CapabilityManager 统一管理 - 移除全局能力注册表,改用声明式装饰器 `@capability` 进行解耦注册 - 重构工具解析器链,使用统一的 BaseToolResolver 代替原有的多个特定解析器 - 增强工具查询过滤,支持通配符匹配、工具箱过滤和排除标签 - 【定时任务调度】重构定时任务管理器,引入 SchedulerRegistry 统一管理任务元数据 - 引入 JobConfig 聚合定时任务配置,支持用户维度的定时任务调度 - 重构执行分发器,支持并发限制、串行间隔和随机延迟打散 - 【运行上下文】引入 ScheduledDeps 以支持后台和定时任务环境下的依赖注入 - 优化 RunContext,支持从定时任务上下文快速构造,并提供 emit 辅助方法 - 【日志与监控】引入 AILoggerProxy,实现 AI 各模块的专属日志输出 - 将各模块的全局 logger 替换为对应的模块专属日志代理 - 【其他优化】修复 Pydantic V1 兼容层中 model_validator 的装饰器兼容性问题 - 在非交互式环境(如定时任务)中自动隐藏 HITL 交互工具以节省 Token * ♻️ refactor(core): 优化内部导入路径并提升 Pydantic 兼容性 - 【重构】将 `services/ai` 模块内的绝对导入重构为相对导入,优化包结构 - 【重构】移除不必要的 `if TYPE_CHECKING` 保护,通过 `from __future__ import annotations` 直接导入类型 - 【清理】清理 `core/messages/types.py` 中未使用的 `AssistantContentUnion` 等联合类型定义 - 【优化】在 `utils/pydantic_compat.py` 中新增 `model_rebuild` 兼容函数,统一 Pydantic V1/V2 的模型重建逻辑 - 【优化】将部分函数内部的延迟导入提升至模块顶部,规范代码结构 * ♻️ refactor(imports): 优化导入路径为相对导入并清理冗余导入 - 【重构】将 AI 服务相关模块中的绝对导入路径修改为相对导入,提升模块内聚性与可移植性 - 【清理】移除多处函数内部或类方法中未使用的冗余导入,避免循环引用和资源浪费 - 【格式化】微调部分工具装饰器和返回语句的格式与尾随逗号 * 🚨 auto fix by pre-commit hooks --------- Co-authored-by: webjoin111 <455457521@qq.com> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
204 lines
7.0 KiB
Python
204 lines
7.0 KiB
Python
import base64
|
|
from pathlib import Path
|
|
from typing import Any
|
|
|
|
from zhenxun.services.ai.core.exceptions import LLMException
|
|
from zhenxun.services.ai.core.messages import (
|
|
ImagePart,
|
|
ImageRequest,
|
|
LLMMessage,
|
|
ThoughtPart,
|
|
)
|
|
from zhenxun.services.ai.core.models import ModelIdentity
|
|
|
|
from .base import (
|
|
BaseAdapter,
|
|
RequestData,
|
|
ResponseData,
|
|
process_image_data,
|
|
)
|
|
from .handlers.base import BaseImageHandler
|
|
from .handlers.openai_handlers import (
|
|
OpenAIMessageConverter,
|
|
OpenAITextHandler,
|
|
)
|
|
from .openai import OpenAIAdapter
|
|
|
|
|
|
class OpenRouterMessageConverter(OpenAIMessageConverter):
|
|
"""OpenRouter 专有消息转换器:处理 reasoning_details 的无损回传"""
|
|
|
|
async def convert_messages_async(
|
|
self, messages: list[LLMMessage]
|
|
) -> list[dict[str, Any]]:
|
|
openai_messages = await super().convert_messages_async(messages)
|
|
|
|
assistant_msgs = [m for m in messages if getattr(m, "role", "") == "assistant"]
|
|
ast_idx = 0
|
|
|
|
for o_msg in openai_messages:
|
|
if o_msg.get("role") == "assistant":
|
|
if ast_idx < len(assistant_msgs):
|
|
orig_ast = assistant_msgs[ast_idx]
|
|
ast_idx += 1
|
|
|
|
thought_parts = [
|
|
p for p in orig_ast.content if isinstance(p, ThoughtPart)
|
|
]
|
|
if thought_parts:
|
|
part = thought_parts[0]
|
|
raw_details = (
|
|
part.metadata.get("raw_reasoning_details")
|
|
if part.metadata
|
|
else None
|
|
)
|
|
|
|
if raw_details:
|
|
o_msg["reasoning_details"] = raw_details
|
|
o_msg.pop("reasoning_content", None)
|
|
o_msg.pop("reasoning", None)
|
|
|
|
return openai_messages
|
|
|
|
|
|
class OpenRouterTextHandler(OpenAITextHandler):
|
|
"""OpenRouter 专有文本处理器,挂载专有 Converter"""
|
|
|
|
def __init__(self, api_type: str = "openrouter"):
|
|
super().__init__(api_type=api_type)
|
|
self.converter = OpenRouterMessageConverter(api_type=api_type)
|
|
|
|
|
|
class OpenRouterImageHandler(BaseImageHandler):
|
|
"""OpenRouter 专有的图像生成处理器"""
|
|
|
|
def prepare_image_request(
|
|
self,
|
|
adapter: BaseAdapter,
|
|
identity: ModelIdentity,
|
|
api_key: str,
|
|
request: ImageRequest,
|
|
) -> RequestData:
|
|
headers = adapter.get_base_headers(api_key)
|
|
|
|
endpoint = "/v1/chat/completions"
|
|
url = adapter.get_api_url(identity, endpoint)
|
|
|
|
body: dict[str, Any] = {
|
|
"model": identity.model_name,
|
|
"modalities": ["image", "text"],
|
|
}
|
|
|
|
if request.images:
|
|
content_list: list[dict[str, Any]] = [
|
|
{"type": "text", "text": request.prompt}
|
|
]
|
|
for img_source in request.images:
|
|
img_bytes = None
|
|
if isinstance(img_source, bytes):
|
|
img_bytes = img_source
|
|
elif hasattr(img_source, "read_bytes"):
|
|
img_bytes = img_source.read_bytes()
|
|
elif isinstance(img_source, str) and img_source.startswith(
|
|
"data:image"
|
|
):
|
|
content_list.append(
|
|
{"type": "image_url", "image_url": {"url": img_source}}
|
|
)
|
|
continue
|
|
else:
|
|
raise LLMException(
|
|
"OpenRouter 图像生成仅支持 bytes/Path/base64 URI"
|
|
)
|
|
|
|
if img_bytes:
|
|
mime_type = "image/jpeg"
|
|
if img_bytes.startswith(b"\x89PNG\r\n\x1a\n"):
|
|
mime_type = "image/png"
|
|
elif img_bytes.startswith(b"GIF87a") or img_bytes.startswith(
|
|
b"GIF89a"
|
|
):
|
|
mime_type = "image/gif"
|
|
elif img_bytes.startswith(b"RIFF") and img_bytes[8:12] == b"WEBP":
|
|
mime_type = "image/webp"
|
|
|
|
b64_str = base64.b64encode(img_bytes).decode("utf-8")
|
|
content_list.append(
|
|
{
|
|
"type": "image_url",
|
|
"image_url": {"url": f"data:{mime_type};base64,{b64_str}"},
|
|
}
|
|
)
|
|
body["messages"] = [{"role": "user", "content": content_list}]
|
|
else:
|
|
body["messages"] = [{"role": "user", "content": request.prompt}]
|
|
|
|
if request.config:
|
|
image_config = {}
|
|
if request.config.media.aspect_ratio:
|
|
image_config["aspect_ratio"] = str(request.config.media.aspect_ratio)
|
|
if request.config.media.resolution:
|
|
image_config["image_size"] = str(
|
|
request.config.media.resolution
|
|
).upper()
|
|
|
|
if image_config:
|
|
body["image_config"] = image_config
|
|
|
|
return RequestData(url=url, headers=headers, body=body)
|
|
|
|
def parse_image_response(
|
|
self, adapter: BaseAdapter, response_json: dict[str, Any]
|
|
) -> ResponseData:
|
|
adapter.validate_response(response_json)
|
|
|
|
images_data = []
|
|
choices = response_json.get("choices", [])
|
|
|
|
if choices:
|
|
message = choices[0].get("message", {})
|
|
if "images" in message:
|
|
for img_data in message["images"]:
|
|
img_url_obj = img_data.get("image_url", {})
|
|
url_str = img_url_obj.get("url", "")
|
|
if url_str.startswith("data:image"):
|
|
try:
|
|
b64_data = url_str.split(",", 1)[1]
|
|
decoded = base64.b64decode(b64_data)
|
|
images_data.append(process_image_data(decoded))
|
|
except Exception:
|
|
pass
|
|
elif url_str:
|
|
images_data.append(url_str)
|
|
|
|
content_parts = []
|
|
for img in images_data:
|
|
if isinstance(img, str) and img.startswith("http"):
|
|
content_parts.append(ImagePart(url=img))
|
|
elif isinstance(img, bytes):
|
|
content_parts.append(ImagePart(raw=img))
|
|
else:
|
|
content_parts.append(ImagePart(path=Path(img)))
|
|
|
|
if not content_parts:
|
|
raise LLMException("OpenRouter 图像生成响应中未找到有效的图片数据")
|
|
|
|
return ResponseData(content_parts=content_parts, raw_response=response_json)
|
|
|
|
|
|
class OpenRouterAdapter(OpenAIAdapter):
|
|
"""OpenRouter 平台适配器"""
|
|
|
|
def __init__(self):
|
|
super().__init__()
|
|
self.text_handler = OpenRouterTextHandler(api_type=self.api_type)
|
|
self.image_handler = OpenRouterImageHandler()
|
|
|
|
@property
|
|
def api_type(self) -> str:
|
|
return "openrouter"
|
|
|
|
@property
|
|
def supported_api_types(self) -> list[str]:
|
|
return ["openrouter"]
|