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https://github.com/zhenxun-org/zhenxun_bot.git
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bba90e62db
- 【重构】LLM 服务核心架构：
- 引入中间件管道，统一处理请求生命周期（重试、密钥选择、日志、网络请求）。
- 适配器重构为组件化设计，分离配置映射、消息转换、响应解析和工具序列化逻辑。
- 移除 `with_smart_retry` 装饰器，其功能由中间件接管。
- 移除 `LLMToolExecutor`，工具执行逻辑集成到 `ToolInvoker`。
- 【功能】增强配置系统：
- `LLMGenerationConfig` 采用组件化结构（Core, Reasoning, Visual, Output, Safety, ToolConfig）。
- 新增 `GenConfigBuilder` 提供语义化配置构建方式。
- 新增 `LLMEmbeddingConfig` 用于嵌入专用配置。
- `CommonOverrides` 迁移并更新至新配置结构。
- 【功能】强化工具系统：
- 引入 `ToolInvoker` 实现更灵活的工具执行，支持回调与结构化错误。
- `function_tool` 装饰器支持动态 Pydantic 模型创建和依赖注入 (`ToolParam`, `RunContext`)。
- 平台原生工具支持 (`GeminiCodeExecution`, `GeminiGoogleSearch`, `GeminiUrlContext`)。
- 【功能】高级生成与嵌入：
- `generate_structured` 方法支持 In-Context Validation and Repair (IVR) 循环和 AutoCoT (思维链) 包装。
- 新增 `embed_query` 和 `embed_documents` 便捷嵌入 API。
- `OpenAIImageAdapter` 支持 OpenAI 兼容的图像生成。
- `SmartAdapter` 实现模型名称智能路由。
- 【重构】消息与类型系统：
- `LLMContentPart` 扩展支持更多模态和代码执行相关内容。
- `LLMMessage` 和 `LLMResponse` 结构更新，支持 `content_parts` 和思维链签名。
- 统一 `LLMErrorCode` 和用户友好错误消息，提供更详细的网络/代理错误提示。
- `pyproject.toml` 移除 `bilireq`，新增 `json_repair`。
- 【优化】日志与调试：
- 引入 `DebugLogOptions`，提供细粒度日志脱敏控制。
- 增强日志净化器，处理更多敏感数据和长字符串。
- 【清理】删除废弃模块：
- `zhenxun/services/llm/memory.py`
- `zhenxun/services/llm/executor.py`
- `zhenxun/services/llm/config/presets.py`
- `zhenxun/services/llm/types/content.py`
- `zhenxun/services/llm/types/enums.py`
- `zhenxun/services/llm/tools/__init__.py`
- `zhenxun/services/llm/tools/manager.py`
348 lines
15 KiB
Python
348 lines
15 KiB
Python
import copy
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import re
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from typing import Any
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from nonebot.adapters import Message, MessageSegment
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def _truncate_base64_string(value: str, threshold: int = 256) -> str:
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"""如果字符串是超长的base64或data URI，则截断它。"""
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if not isinstance(value, str):
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return value
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prefixes = ("base64://", "data:image", "data:video", "data:audio")
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if value.startswith(prefixes) and len(value) > threshold:
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prefix = next((p for p in prefixes if value.startswith(p)), "base64")
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return f"[{prefix}_data_omitted_len={len(value)}]"
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if len(value) > 1000:
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return f"[long_string_omitted_len={len(value)}] {value[:20]}...{value[-20:]}"
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if len(value) > 2000:
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return f"[long_string_omitted_len={len(value)}] {value[:50]}...{value[-20:]}"
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return value
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def _truncate_vector_list(vector: list, threshold: int = 10) -> list:
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"""如果列表过长（通常是embedding向量），则截断它用于日志显示。"""
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if isinstance(vector, list) and len(vector) > threshold:
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return [*vector[:3], f"...({len(vector)} floats omitted)...", *vector[-3:]]
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return vector
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def _recursive_sanitize_any(obj: Any) -> Any:
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"""递归清洗任何对象中的长字符串"""
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if isinstance(obj, dict):
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return {k: _recursive_sanitize_any(v) for k, v in obj.items()}
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elif isinstance(obj, list):
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return [_recursive_sanitize_any(v) for v in obj]
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elif isinstance(obj, str):
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return _truncate_base64_string(obj)
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return obj
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def _sanitize_ui_html(html_string: str) -> str:
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"""
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专门用于净化UI渲染调试HTML的函数。
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它会查找所有内联的base64数据（如字体、图片）并将其截断。
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"""
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if not isinstance(html_string, str):
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return html_string
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pattern = re.compile(r"(data:[^;]+;base64,)[A-Za-z0-9+/=\s]{100,}")
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def replacer(match):
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prefix = match.group(1)
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original_len = len(match.group(0)) - len(prefix)
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return f"{prefix}[...base64_omitted_len={original_len}...]"
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return pattern.sub(replacer, html_string)
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def _sanitize_nonebot_message(message: Message) -> Message:
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"""净化nonebot.adapter.Message对象，用于日志记录。"""
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sanitized_message = copy.deepcopy(message)
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for seg in sanitized_message:
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seg: MessageSegment
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if seg.type in ("image", "record", "video"):
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file_info = seg.data.get("file", "")
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if isinstance(file_info, str):
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seg.data["file"] = _truncate_base64_string(file_info)
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return sanitized_message
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def _sanitize_openai_response(response_json: dict) -> dict:
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"""净化OpenAI兼容API的响应体。"""
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try:
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sanitized_json = copy.deepcopy(response_json)
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if "choices" in sanitized_json and isinstance(sanitized_json["choices"], list):
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for choice in sanitized_json["choices"]:
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if "message" in choice and isinstance(choice["message"], dict):
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message = choice["message"]
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if "images" in message and isinstance(message["images"], list):
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for i, image_info in enumerate(message["images"]):
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if "image_url" in image_info and isinstance(
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image_info["image_url"], dict
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):
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url = image_info["image_url"].get("url", "")
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message["images"][i]["image_url"]["url"] = (
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_truncate_base64_string(url)
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)
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if "reasoning_details" in message and isinstance(
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message["reasoning_details"], list
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):
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for detail in message["reasoning_details"]:
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if isinstance(detail, dict):
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if "data" in detail and isinstance(detail["data"], str):
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if len(detail["data"]) > 100:
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detail["data"] = (
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f"[encrypted_data_omitted_len={len(detail['data'])}]"
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)
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if "text" in detail and isinstance(detail["text"], str):
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detail["text"] = _truncate_base64_string(
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detail["text"], threshold=2000
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)
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if "data" in sanitized_json and isinstance(sanitized_json["data"], list):
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for item in sanitized_json["data"]:
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if "embedding" in item and isinstance(item["embedding"], list):
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item["embedding"] = _truncate_vector_list(item["embedding"])
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if "b64_json" in item and isinstance(item["b64_json"], str):
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if len(item["b64_json"]) > 256:
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item["b64_json"] = (
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f"[base64_json_omitted_len={len(item['b64_json'])}]"
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)
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if "input" in sanitized_json and isinstance(sanitized_json["input"], list):
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for item in sanitized_json["input"]:
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if "content" in item and isinstance(item["content"], list):
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for part in item["content"]:
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if isinstance(part, dict) and part.get("type") == "input_image":
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image_url = part.get("image_url")
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if isinstance(image_url, str):
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part["image_url"] = _truncate_base64_string(image_url)
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return sanitized_json
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except Exception:
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return response_json
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def _sanitize_openai_request(body: dict) -> dict:
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"""净化OpenAI兼容API的请求体，主要截断图片base64。"""
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from zhenxun.services.llm.config.providers import (
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DebugLogOptions,
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get_llm_config,
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)
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debug_conf = get_llm_config().debug_log
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if isinstance(debug_conf, bool):
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debug_conf = DebugLogOptions(
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show_tools=debug_conf, show_schema=debug_conf, show_safety=debug_conf
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)
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try:
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sanitized_json = _recursive_sanitize_any(copy.deepcopy(body))
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if "tools" in sanitized_json and not debug_conf.show_tools:
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tools = sanitized_json["tools"]
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if isinstance(tools, list):
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tool_names = []
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for t in tools:
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if isinstance(t, dict):
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name = None
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if "function" in t and isinstance(t["function"], dict):
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name = t["function"].get("name")
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if not name and "name" in t:
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name = t.get("name")
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tool_names.append(name or "unknown")
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sanitized_json["tools"] = (
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f"<{len(tool_names)} tools hidden: {', '.join(tool_names)}>"
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)
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if "response_format" in sanitized_json and not debug_conf.show_schema:
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response_format = sanitized_json["response_format"]
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if isinstance(response_format, dict):
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if response_format.get("type") == "json_schema":
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sanitized_json["response_format"] = {
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"type": "json_schema",
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"json_schema": "<JSON Schema Hidden>",
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}
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return sanitized_json
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except Exception:
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return body
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def _sanitize_gemini_response(response_json: dict) -> dict:
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"""净化Gemini API的响应体，处理文本和图片生成两种格式。"""
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from zhenxun.services.llm.config.providers import get_llm_config
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debug_mode = get_llm_config().debug_log
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try:
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sanitized_json = copy.deepcopy(response_json)
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def _process_candidates(candidates_list: list):
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"""辅助函数，用于处理任何 candidates 列表。"""
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if not isinstance(candidates_list, list):
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return
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for candidate in candidates_list:
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if "content" in candidate and isinstance(candidate["content"], dict):
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content = candidate["content"]
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if "parts" in content and isinstance(content["parts"], list):
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for i, part in enumerate(content["parts"]):
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if "inlineData" in part and isinstance(
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part["inlineData"], dict
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):
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data = part["inlineData"].get("data", "")
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if isinstance(data, str) and len(data) > 256:
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content["parts"][i]["inlineData"]["data"] = (
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f"[base64_data_omitted_len={len(data)}]"
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)
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if "thoughtSignature" in part:
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signature = part.get("thoughtSignature", "")
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if isinstance(signature, str) and len(signature) > 256:
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content["parts"][i]["thoughtSignature"] = (
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f"[signature_omitted_len={len(signature)}]"
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)
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if not debug_mode and isinstance(candidate, dict):
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if "safetyRatings" in candidate:
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candidate["safetyRatings"] = "<Safety Ratings Hidden>"
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if "candidates" in sanitized_json:
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_process_candidates(sanitized_json["candidates"])
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if "image_generation" in sanitized_json and isinstance(
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sanitized_json["image_generation"], dict
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):
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if "candidates" in sanitized_json["image_generation"]:
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_process_candidates(sanitized_json["image_generation"]["candidates"])
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if "embeddings" in sanitized_json and isinstance(
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sanitized_json["embeddings"], list
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):
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for embedding in sanitized_json["embeddings"]:
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if "values" in embedding and isinstance(embedding["values"], list):
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embedding["values"] = _truncate_vector_list(embedding["values"])
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if not debug_mode and "promptFeedback" in sanitized_json:
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prompt_feedback = sanitized_json.get("promptFeedback") or {}
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if isinstance(prompt_feedback, dict) and "safetyRatings" in prompt_feedback:
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prompt_feedback["safetyRatings"] = "<Safety Ratings Hidden>"
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sanitized_json["promptFeedback"] = prompt_feedback
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return sanitized_json
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except Exception:
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return response_json
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def _sanitize_gemini_request(body: dict) -> dict:
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"""净化Gemini API的请求体，进行结构转换和总结。"""
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from zhenxun.services.llm.config.providers import (
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DebugLogOptions,
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get_llm_config,
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)
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debug_conf = get_llm_config().debug_log
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if isinstance(debug_conf, bool):
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debug_conf = DebugLogOptions(
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show_tools=debug_conf, show_schema=debug_conf, show_safety=debug_conf
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)
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try:
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sanitized_body = copy.deepcopy(body)
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if "tools" in sanitized_body and not debug_conf.show_tools:
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tool_summary = []
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for tool_group in sanitized_body["tools"]:
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if (
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isinstance(tool_group, dict)
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and "functionDeclarations" in tool_group
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):
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declarations = tool_group["functionDeclarations"]
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if isinstance(declarations, list):
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for func in declarations:
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if isinstance(func, dict):
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tool_summary.append(func.get("name", "unknown"))
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sanitized_body["tools"] = (
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f"<{len(tool_summary)} functions hidden: {', '.join(tool_summary)}>"
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)
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if not debug_conf.show_safety and "safetySettings" in sanitized_body:
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sanitized_body["safetySettings"] = "<Safety Settings Hidden>"
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if not debug_conf.show_schema and "generationConfig" in sanitized_body:
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generation_config = sanitized_body["generationConfig"]
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if (
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isinstance(generation_config, dict)
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and "responseJsonSchema" in generation_config
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):
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generation_config["responseJsonSchema"] = "<JSON Schema Hidden>"
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if "contents" in sanitized_body and isinstance(
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sanitized_body["contents"], list
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):
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for content_item in sanitized_body["contents"]:
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if "parts" in content_item and isinstance(content_item["parts"], list):
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media_summary = []
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new_parts = []
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for part in content_item["parts"]:
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if "inlineData" in part and isinstance(
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part["inlineData"], dict
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):
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data = part["inlineData"].get("data")
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if isinstance(data, str):
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mime_type = part["inlineData"].get(
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"mimeType", "unknown"
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)
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media_summary.append(f"{mime_type} ({len(data)} chars)")
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continue
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new_parts.append(part)
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if "thoughtSignature" in part:
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sig = part["thoughtSignature"]
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if isinstance(sig, str) and len(sig) > 64:
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part["thoughtSignature"] = (
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f"[signature_omitted_len={len(sig)}]"
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)
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if media_summary:
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summary_text = (
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f"[多模态内容: {len(media_summary)}个文件 - "
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f"{', '.join(media_summary)}]"
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)
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new_parts.insert(0, {"text": summary_text})
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content_item["parts"] = new_parts
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return sanitized_body
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except Exception:
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return body
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def sanitize_for_logging(data: Any, context: str | None = None) -> Any:
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"""
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统一的日志净化入口。
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Args:
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data: 需要净化的数据 (dict, Message, etc.).
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context: 净化场景的上下文标识，例如 'gemini_request', 'openai_response'.
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Returns:
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净化后的数据。
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"""
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if context == "nonebot_message":
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if isinstance(data, Message):
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return _sanitize_nonebot_message(data)
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elif context == "openai_response":
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if isinstance(data, dict):
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return _sanitize_openai_response(data)
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elif context == "gemini_response":
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if isinstance(data, dict):
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return _sanitize_gemini_response(data)
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elif context == "gemini_request":
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if isinstance(data, dict):
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return _sanitize_gemini_request(data)
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elif context == "openai_request":
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if isinstance(data, dict):
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return _sanitize_openai_request(data)
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elif context == "ui_html":
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if isinstance(data, str):
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return _sanitize_ui_html(data)
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return _recursive_sanitize_any(data)
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