✨ feat(llm): 增强LLM服务，支持图片生成、响应验证与OpenRouter集成

- 【新功能】统一图片生成与编辑API `create_image`，支持文生图、图生图及多图输入
- 【新功能】引入LLM响应验证机制，通过 `validation_policy` 和 `response_validator` 确保响应内容符合预期，例如强制返回图片
- 【新功能】适配OpenRouter API，扩展LLM服务提供商支持，并添加OpenRouter特定请求头
- 【重构】将日志净化逻辑重构至 `log_sanitizer` 模块，提供统一的净化入口，并应用于NoneBot消息、LLM请求/响应日志
- 【修复】优化Gemini适配器，正确解析图片生成响应中的Base64图片数据，并更新模型能力注册表

zhenxun/services/llm/service.py

@@ -12,6 +12,7 @@ from typing import Any, TypeVar
 from pydantic import BaseModel
 
 from zhenxun.services.log import logger
+from zhenxun.utils.log_sanitizer import sanitize_for_logging
 
 from .adapters.base import RequestData
 from .config import LLMGenerationConfig
@@ -34,7 +35,6 @@ from .types import (
     ToolExecutable,
 )
 from .types.capabilities import ModelCapabilities, ModelModality
-from .utils import _sanitize_request_body_for_logging
 
 T = TypeVar("T", bound=BaseModel)
 
@@ -187,7 +187,9 @@ class LLMModel(LLMModelBase):
             logger.debug(f"🔑 API密钥: {masked_key}")
             logger.debug(f"📋 请求头: {dict(request_data.headers)}")
 
-            sanitized_body = _sanitize_request_body_for_logging(request_data.body)
+            sanitized_body = sanitize_for_logging(
+                request_data.body, context="gemini_request"
+            )
             request_body_str = json.dumps(sanitized_body, ensure_ascii=False, indent=2)
             logger.debug(f"📦 请求体: {request_body_str}")
 
@@ -200,8 +202,11 @@ class LLMModel(LLMModelBase):
             logger.debug(f"📥 响应状态码: {http_response.status_code}")
             logger.debug(f"📄 响应头: {dict(http_response.headers)}")
 
+            response_bytes = await http_response.aread()
+            logger.debug(f"📦 响应体已完整读取 ({len(response_bytes)} bytes)")
+
             if http_response.status_code != 200:
-                error_text = http_response.text
+                error_text = response_bytes.decode("utf-8", errors="ignore")
                 logger.error(
                     f"❌ HTTP请求失败: {http_response.status_code} - {error_text} "
                     f"[{log_context}]"
@@ -232,13 +237,22 @@ class LLMModel(LLMModelBase):
                 )
 
             try:
-                response_json = http_response.json()
+                response_json = json.loads(response_bytes)
+
+                sanitizer_context_map = {"gemini": "gemini_response"}
+                sanitizer_context = sanitizer_context_map.get(
+                    self.api_type, "openai_response"
+                )
+
+                sanitized_for_log = sanitize_for_logging(
+                    response_json, context=sanitizer_context
+                )
+
                 response_json_str = json.dumps(
-                    response_json, ensure_ascii=False, indent=2
+                    sanitized_for_log, ensure_ascii=False, indent=2
                 )
                 logger.debug(f"📋 响应JSON: {response_json_str}")
                 parsed_data = parse_response_func(response_json)
-
             except Exception as e:
                 logger.error(f"解析 {log_context} 响应失败: {e}", e=e)
                 await self.key_store.record_failure(api_key, None, str(e))
@@ -376,6 +390,7 @@ class LLMModel(LLMModelBase):
             return LLMResponse(
                 text=response_data.text,
                 usage_info=response_data.usage_info,
+                image_bytes=response_data.image_bytes,
                 raw_response=response_data.raw_response,
                 tool_calls=response_tool_calls if response_tool_calls else None,
                 code_executions=response_data.code_executions,
@@ -390,6 +405,28 @@ class LLMModel(LLMModelBase):
             failed_keys=failed_keys,
             log_context="Generation",
         )
+
+        if config:
+            if config.response_validator:
+                try:
+                    config.response_validator(parsed_data)
+                except Exception as e:
+                    raise LLMException(
+                        f"响应内容未通过自定义验证器: {e}",
+                        code=LLMErrorCode.API_RESPONSE_INVALID,
+                        details={"validator_error": str(e)},
+                        cause=e,
+                    ) from e
+
+            policy = config.validation_policy
+            if policy:
+                if policy.get("require_image") and not parsed_data.image_bytes:
+                    raise LLMException(
+                        "响应验证失败：要求返回图片但未找到图片数据。",
+                        code=LLMErrorCode.API_RESPONSE_INVALID,
+                        details={"policy": policy, "text_response": parsed_data.text},
+                    )
+
         return parsed_data, api_key_used
 
     async def close(self):