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✨ feat(llm): 增强LLM服务,支持图片生成、响应验证与OpenRouter集成 (#2054)
* ✨ 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 * ✨ feat(image): 优化图片生成响应并返回完整LLMResponse * ✨ feat(llm): 为 OpenAI 兼容请求体添加日志净化 * 🐛 fix(ui): 截断UI调试HTML日志中的长base64图片数据 --------- Co-authored-by: webjoin111 <455457521@qq.com>
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@@ -2,6 +2,7 @@
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Gemini API 适配器
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"""
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import base64
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from typing import TYPE_CHECKING, Any
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from zhenxun.services.log import logger
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@@ -373,7 +374,16 @@ class GeminiAdapter(BaseAdapter):
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self.validate_response(response_json)
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try:
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candidates = response_json.get("candidates", [])
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if "image_generation" in response_json and isinstance(
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response_json["image_generation"], dict
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):
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candidates_source = response_json["image_generation"]
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else:
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candidates_source = response_json
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candidates = candidates_source.get("candidates", [])
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usage_info = response_json.get("usageMetadata")
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if not candidates:
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logger.debug("Gemini响应中没有candidates。")
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return ResponseData(text="", raw_response=response_json)
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@@ -398,6 +408,7 @@ class GeminiAdapter(BaseAdapter):
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parts = content_data.get("parts", [])
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text_content = ""
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image_bytes: bytes | None = None
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parsed_tool_calls: list["LLMToolCall"] | None = None
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thought_summary_parts = []
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answer_parts = []
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@@ -409,6 +420,14 @@ class GeminiAdapter(BaseAdapter):
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thought_summary_parts.append(part["thought"])
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elif "thoughtSummary" in part:
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thought_summary_parts.append(part["thoughtSummary"])
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elif "inlineData" in part:
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inline_data = part["inlineData"]
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if "data" in inline_data:
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image_bytes = base64.b64decode(inline_data["data"])
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answer_parts.append(
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f"[图片已生成: {inline_data.get('mimeType', 'image')}]"
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)
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elif "functionCall" in part:
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if parsed_tool_calls is None:
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parsed_tool_calls = []
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@@ -475,6 +494,7 @@ class GeminiAdapter(BaseAdapter):
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return ResponseData(
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text=text_content,
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tool_calls=parsed_tool_calls,
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image_bytes=image_bytes,
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usage_info=usage_info,
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raw_response=response_json,
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grounding_metadata=grounding_metadata_obj,
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