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* ⚡️ perf(image_utils): 优化图片哈希获取避免阻塞异步 * ✨ feat(llm): 增强 LLM 管理功能，支持纯文本列表输出，优化模型能力识别并新增提供商 - 【LLM 管理器】为 `llm list` 命令添加 `--text` 选项，支持以纯文本格式输出模型列表。 - 【LLM 配置】新增 `OpenRouter` LLM 提供商的默认配置。 - 【模型能力】增强 `get_model_capabilities` 函数的查找逻辑，支持模型名称分段匹配和更灵活的通配符匹配。 - 【模型能力】为 `Gemini` 模型能力注册表使用更通用的通配符模式。 - 【模型能力】新增 `GPT` 系列模型的详细能力定义，包括多模态输入输出和工具调用支持。 * ✨ feat(renderer): 添加 Jinja2 `inline_asset` 全局函数 - 新增 `RendererService._inline_asset_global` 方法，并注册为 Jinja2 全局函数 `inline_asset`。 - 允许模板通过 `{{ inline_asset('@namespace/path/to/asset.svg') }}` 直接内联已注册命名空间下的资源文件内容。 - 主要用于解决内联 SVG 时可能遇到的跨域安全问题。 - 【重构】优化 `ResourceResolver.resolve_asset_uri` 中对命名空间资源 (以 `@` 开头) 的解析逻辑，确保能够正确获取文件绝对路径并返回 URI。 - 改进 `RenderableComponent.get_extra_css`，使其在组件定义 `component_css` 时自动返回该 CSS 内容。 - 清理 `Renderable` 协议和 `RenderableComponent` 基类中已存在方法的 `[新增]` 标记。 * ✨ feat(tag): 添加标签克隆功能 - 新增 `tag clone <源标签名> <新标签名>` 命令，用于复制现有标签。 - 【优化】在 `tag create`, `tag edit --add`, `tag edit --set` 命令中，自动去重传入的群组ID，避免重复关联。 * ✨ feat(broadcast): 实现标签定向广播、强制发送及并发控制 - 【新功能】 - 新增标签定向广播功能，支持通过 `-t <标签名>` 或 `广播到 <标签名>` 命令向指定标签的群组发送消息 - 引入广播强制发送模式，允许绕过群组的任务阻断设置 - 实现广播并发控制，通过配置限制同时发送任务数量，避免API速率限制 - 优化视频消息处理，支持从URL下载视频内容并作为原始数据发送，提高跨平台兼容性 - 【配置】 - 添加 `DEFAULT_BROADCAST` 配置项，用于设置群组进群时广播功能的默认开关状态 - 添加 `BROADCAST_CONCURRENCY_LIMIT` 配置项，用于控制广播时的最大并发任务数 * ✨ feat(renderer): 支持组件变体样式收集 * ✨ feat(tag): 实现群组标签自动清理及手动清理功能 * 🐛 fix(gemini): 增加响应验证以处理内容过滤（promptFeedback） * 🐛 fix(codeql): 移除对 JavaScript 和 TypeScript 的分析支持 * 🚨 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>
604 lines
23 KiB
Python
604 lines
23 KiB
Python
"""
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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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from ..types.exceptions import LLMErrorCode, LLMException
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from ..utils import sanitize_schema_for_llm
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from .base import BaseAdapter, RequestData, ResponseData
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if TYPE_CHECKING:
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from ..config.generation import LLMGenerationConfig
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from ..service import LLMModel
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from ..types.content import LLMMessage
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from ..types.enums import EmbeddingTaskType
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from ..types.models import LLMToolCall
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from ..types.protocols import ToolExecutable
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class GeminiAdapter(BaseAdapter):
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"""Gemini API 适配器"""
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@property
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def api_type(self) -> str:
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return "gemini"
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@property
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def supported_api_types(self) -> list[str]:
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return ["gemini"]
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def get_base_headers(self, api_key: str) -> dict[str, str]:
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"""获取基础请求头"""
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from zhenxun.utils.user_agent import get_user_agent
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headers = get_user_agent()
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headers.update({"Content-Type": "application/json"})
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headers["x-goog-api-key"] = api_key
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return headers
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async def prepare_advanced_request(
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self,
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model: "LLMModel",
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api_key: str,
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messages: list["LLMMessage"],
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config: "LLMGenerationConfig | None" = None,
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tools: dict[str, "ToolExecutable"] | None = None,
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tool_choice: str | dict[str, Any] | None = None,
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) -> RequestData:
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"""准备高级请求"""
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effective_config = config if config is not None else model._generation_config
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endpoint = self._get_gemini_endpoint(model, effective_config)
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url = self.get_api_url(model, endpoint)
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headers = self.get_base_headers(api_key)
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gemini_contents: list[dict[str, Any]] = []
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system_instruction_parts: list[dict[str, Any]] | None = None
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for msg in messages:
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current_parts: list[dict[str, Any]] = []
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if msg.role == "system":
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if isinstance(msg.content, str):
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system_instruction_parts = [{"text": msg.content}]
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elif isinstance(msg.content, list):
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system_instruction_parts = [
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await part.convert_for_api_async("gemini")
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for part in msg.content
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]
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continue
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elif msg.role == "user":
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if isinstance(msg.content, str):
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current_parts.append({"text": msg.content})
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elif isinstance(msg.content, list):
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for part_obj in msg.content:
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current_parts.append(
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await part_obj.convert_for_api_async("gemini")
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)
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gemini_contents.append({"role": "user", "parts": current_parts})
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elif msg.role == "assistant" or msg.role == "model":
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if isinstance(msg.content, str) and msg.content:
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current_parts.append({"text": msg.content})
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elif isinstance(msg.content, list):
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for part_obj in msg.content:
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current_parts.append(
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await part_obj.convert_for_api_async("gemini")
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)
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if msg.tool_calls:
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import json
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for call in msg.tool_calls:
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current_parts.append(
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{
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"functionCall": {
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"name": call.function.name,
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"args": json.loads(call.function.arguments),
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}
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}
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)
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if current_parts:
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gemini_contents.append({"role": "model", "parts": current_parts})
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elif msg.role == "tool":
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if not msg.name:
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raise ValueError("Gemini 工具消息必须包含 'name' 字段（函数名）。")
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import json
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try:
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content_str = (
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msg.content
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if isinstance(msg.content, str)
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else str(msg.content)
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)
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tool_result_obj = json.loads(content_str)
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except json.JSONDecodeError:
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content_str = (
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msg.content
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if isinstance(msg.content, str)
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else str(msg.content)
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)
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logger.warning(
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f"工具 {msg.name} 的结果不是有效的 JSON: {content_str}. "
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f"包装为原始字符串。"
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)
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tool_result_obj = {"raw_output": content_str}
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if isinstance(tool_result_obj, list):
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logger.debug(
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f"工具 '{msg.name}' 的返回结果是列表，"
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f"正在为Gemini API包装为JSON对象。"
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)
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final_response_payload = {"result": tool_result_obj}
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elif not isinstance(tool_result_obj, dict):
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final_response_payload = {"result": tool_result_obj}
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else:
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final_response_payload = tool_result_obj
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current_parts.append(
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{
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"functionResponse": {
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"name": msg.name,
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"response": final_response_payload,
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}
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}
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)
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gemini_contents.append({"role": "function", "parts": current_parts})
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body: dict[str, Any] = {"contents": gemini_contents}
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if system_instruction_parts:
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body["systemInstruction"] = {"parts": system_instruction_parts}
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all_tools_for_request = []
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if tools:
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import asyncio
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from zhenxun.utils.pydantic_compat import model_dump
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definition_tasks = [
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executable.get_definition() for executable in tools.values()
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]
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tool_definitions = await asyncio.gather(*definition_tasks)
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function_declarations = []
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for tool_def in tool_definitions:
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tool_def.parameters = sanitize_schema_for_llm(
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tool_def.parameters, api_type="gemini"
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)
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function_declarations.append(model_dump(tool_def))
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if function_declarations:
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all_tools_for_request.append(
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{"functionDeclarations": function_declarations}
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)
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if effective_config:
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if getattr(effective_config, "enable_grounding", False):
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has_explicit_gs_tool = any(
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"googleSearch" in tool_item for tool_item in all_tools_for_request
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)
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if not has_explicit_gs_tool:
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all_tools_for_request.append({"googleSearch": {}})
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logger.debug("隐式启用 Google Search 工具进行信息来源关联。")
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if getattr(effective_config, "enable_code_execution", False):
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has_explicit_ce_tool = any(
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"codeExecution" in tool_item for tool_item in all_tools_for_request
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)
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if not has_explicit_ce_tool:
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all_tools_for_request.append({"codeExecution": {}})
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logger.debug("隐式启用代码执行工具。")
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if all_tools_for_request:
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body["tools"] = all_tools_for_request
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final_tool_choice = tool_choice
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if final_tool_choice is None and effective_config:
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final_tool_choice = getattr(effective_config, "tool_choice", None)
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if final_tool_choice:
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if isinstance(final_tool_choice, str):
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mode_upper = final_tool_choice.upper()
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if mode_upper in ["AUTO", "NONE", "ANY"]:
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body["toolConfig"] = {"functionCallingConfig": {"mode": mode_upper}}
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else:
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body["toolConfig"] = self._convert_tool_choice_to_gemini(
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final_tool_choice
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)
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else:
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body["toolConfig"] = self._convert_tool_choice_to_gemini(
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final_tool_choice
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)
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final_generation_config = self._build_gemini_generation_config(
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model, effective_config
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)
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if final_generation_config:
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body["generationConfig"] = final_generation_config
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safety_settings = self._build_safety_settings(effective_config)
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if safety_settings:
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body["safetySettings"] = safety_settings
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return RequestData(url=url, headers=headers, body=body)
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def apply_config_override(
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self,
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model: "LLMModel",
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body: dict[str, Any],
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config: "LLMGenerationConfig | None" = None,
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) -> dict[str, Any]:
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"""应用配置覆盖 - Gemini 不需要额外的配置覆盖"""
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return body
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def _get_gemini_endpoint(
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self, model: "LLMModel", config: "LLMGenerationConfig | None" = None
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) -> str:
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"""根据配置选择Gemini API端点"""
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if config:
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if getattr(config, "enable_code_execution", False):
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return f"/v1beta/models/{model.model_name}:generateContent"
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if getattr(config, "enable_grounding", False):
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return f"/v1beta/models/{model.model_name}:generateContent"
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return f"/v1beta/models/{model.model_name}:generateContent"
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def _convert_tool_choice_to_gemini(
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self, tool_choice_value: str | dict[str, Any]
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) -> dict[str, Any]:
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"""转换工具选择策略为Gemini格式"""
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if isinstance(tool_choice_value, str):
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mode_upper = tool_choice_value.upper()
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if mode_upper in ["AUTO", "NONE", "ANY"]:
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return {"functionCallingConfig": {"mode": mode_upper}}
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else:
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logger.warning(
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f"不支持的 tool_choice 字符串值: '{tool_choice_value}'。"
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f"回退到 AUTO。"
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)
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return {"functionCallingConfig": {"mode": "AUTO"}}
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elif isinstance(tool_choice_value, dict):
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if (
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tool_choice_value.get("type") == "function"
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and "function" in tool_choice_value
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):
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func_name = tool_choice_value["function"].get("name")
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if func_name:
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return {
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"functionCallingConfig": {
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"mode": "ANY",
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"allowedFunctionNames": [func_name],
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}
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}
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else:
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logger.warning(
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f"tool_choice dict 中的函数名无效: {tool_choice_value}。"
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f"回退到 AUTO。"
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)
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return {"functionCallingConfig": {"mode": "AUTO"}}
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elif "functionCallingConfig" in tool_choice_value:
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return {
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"functionCallingConfig": tool_choice_value["functionCallingConfig"]
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}
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else:
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logger.warning(
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f"不支持的 tool_choice dict 值: {tool_choice_value}。回退到 AUTO。"
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)
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return {"functionCallingConfig": {"mode": "AUTO"}}
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logger.warning(
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f"tool_choice 的类型无效: {type(tool_choice_value)}。回退到 AUTO。"
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)
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return {"functionCallingConfig": {"mode": "AUTO"}}
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def _build_gemini_generation_config(
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self, model: "LLMModel", config: "LLMGenerationConfig | None" = None
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) -> dict[str, Any]:
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"""构建Gemini生成配置"""
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effective_config = config if config is not None else model._generation_config
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if not effective_config:
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return {}
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generation_config = effective_config.to_api_params(
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api_type="gemini", model_name=model.model_name
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)
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if generation_config:
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param_keys = list(generation_config.keys())
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logger.debug(
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f"构建Gemini生成配置完成，包含 {len(generation_config)} 个参数: "
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f"{param_keys}"
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)
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return generation_config
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def _build_safety_settings(
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self, config: "LLMGenerationConfig | None" = None
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) -> list[dict[str, Any]] | None:
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"""构建安全设置"""
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if not config:
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return None
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safety_settings = []
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safety_categories = [
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"HARM_CATEGORY_HARASSMENT",
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"HARM_CATEGORY_HATE_SPEECH",
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"HARM_CATEGORY_SEXUALLY_EXPLICIT",
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"HARM_CATEGORY_DANGEROUS_CONTENT",
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]
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custom_safety_settings = getattr(config, "safety_settings", None)
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if custom_safety_settings:
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for category, threshold in custom_safety_settings.items():
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safety_settings.append({"category": category, "threshold": threshold})
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else:
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from ..config.providers import get_gemini_safety_threshold
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threshold = get_gemini_safety_threshold()
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for category in safety_categories:
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safety_settings.append({"category": category, "threshold": threshold})
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return safety_settings if safety_settings else None
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def validate_response(self, response_json: dict[str, Any]) -> None:
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"""验证 Gemini API 响应，增加对 promptFeedback 的检查"""
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super().validate_response(response_json)
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if prompt_feedback := response_json.get("promptFeedback"):
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if block_reason := prompt_feedback.get("blockReason"):
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logger.warning(
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f"Gemini 内容因 promptFeedback 被安全过滤: {block_reason}"
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)
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raise LLMException(
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f"内容被安全过滤: {block_reason}",
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code=LLMErrorCode.CONTENT_FILTERED,
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details={
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"block_reason": block_reason,
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"safety_ratings": prompt_feedback.get("safetyRatings"),
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},
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)
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def parse_response(
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self,
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model: "LLMModel",
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response_json: dict[str, Any],
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is_advanced: bool = False,
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) -> ResponseData:
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"""解析API响应"""
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return self._parse_response(model, response_json, is_advanced)
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def _parse_response(
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self,
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model: "LLMModel",
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response_json: dict[str, Any],
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is_advanced: bool = False,
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) -> ResponseData:
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"""解析 Gemini API 响应"""
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_ = is_advanced
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self.validate_response(response_json)
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try:
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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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candidate = candidates[0]
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if candidate.get("finishReason") in [
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"RECITATION",
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"OTHER",
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] and not candidate.get("content"):
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logger.warning(
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f"Gemini candidate finished with reason "
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f"'{candidate.get('finishReason')}' and no content."
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)
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return ResponseData(
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text="",
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raw_response=response_json,
|
||
usage_info=response_json.get("usageMetadata"),
|
||
)
|
||
|
||
content_data = candidate.get("content", {})
|
||
parts = content_data.get("parts", [])
|
||
|
||
text_content = ""
|
||
images_bytes: list[bytes] = []
|
||
parsed_tool_calls: list["LLMToolCall"] | None = None
|
||
thought_summary_parts = []
|
||
answer_parts = []
|
||
|
||
for part in parts:
|
||
if "text" in part:
|
||
answer_parts.append(part["text"])
|
||
elif "thought" in part:
|
||
thought_summary_parts.append(part["thought"])
|
||
elif "thoughtSummary" in part:
|
||
thought_summary_parts.append(part["thoughtSummary"])
|
||
elif "inlineData" in part:
|
||
inline_data = part["inlineData"]
|
||
if "data" in inline_data:
|
||
images_bytes.append(base64.b64decode(inline_data["data"]))
|
||
|
||
elif "functionCall" in part:
|
||
if parsed_tool_calls is None:
|
||
parsed_tool_calls = []
|
||
fc_data = part["functionCall"]
|
||
try:
|
||
import json
|
||
|
||
from ..types.models import LLMToolCall, LLMToolFunction
|
||
|
||
call_id = f"call_{model.provider_name}_{len(parsed_tool_calls)}"
|
||
parsed_tool_calls.append(
|
||
LLMToolCall(
|
||
id=call_id,
|
||
function=LLMToolFunction(
|
||
name=fc_data["name"],
|
||
arguments=json.dumps(fc_data["args"]),
|
||
),
|
||
)
|
||
)
|
||
except KeyError as e:
|
||
logger.warning(
|
||
f"解析Gemini functionCall时缺少键: {fc_data}, 错误: {e}"
|
||
)
|
||
except Exception as e:
|
||
logger.warning(
|
||
f"解析Gemini functionCall时出错: {fc_data}, 错误: {e}"
|
||
)
|
||
elif "codeExecutionResult" in part:
|
||
result = part["codeExecutionResult"]
|
||
if result.get("outcome") == "OK":
|
||
output = result.get("output", "")
|
||
answer_parts.append(f"\n[代码执行结果]:\n```\n{output}\n```\n")
|
||
else:
|
||
answer_parts.append(
|
||
f"\n[代码执行失败]: {result.get('outcome', 'UNKNOWN')}\n"
|
||
)
|
||
|
||
if thought_summary_parts:
|
||
full_thought_summary = "\n".join(thought_summary_parts).strip()
|
||
full_answer = "".join(answer_parts).strip()
|
||
|
||
formatted_parts = []
|
||
if full_thought_summary:
|
||
formatted_parts.append(f"🤔 **思考过程**\n\n{full_thought_summary}")
|
||
if full_answer:
|
||
separator = "\n\n---\n\n" if full_thought_summary else ""
|
||
formatted_parts.append(f"{separator}✅ **回答**\n\n{full_answer}")
|
||
|
||
text_content = "".join(formatted_parts)
|
||
else:
|
||
text_content = "".join(answer_parts)
|
||
|
||
usage_info = response_json.get("usageMetadata")
|
||
|
||
grounding_metadata_obj = None
|
||
if grounding_data := candidate.get("groundingMetadata"):
|
||
try:
|
||
from ..types.models import LLMGroundingMetadata
|
||
|
||
grounding_metadata_obj = LLMGroundingMetadata(**grounding_data)
|
||
except Exception as e:
|
||
logger.warning(f"无法解析Grounding元数据: {grounding_data}, {e}")
|
||
|
||
return ResponseData(
|
||
text=text_content,
|
||
tool_calls=parsed_tool_calls,
|
||
images=images_bytes if images_bytes else None,
|
||
usage_info=usage_info,
|
||
raw_response=response_json,
|
||
grounding_metadata=grounding_metadata_obj,
|
||
)
|
||
|
||
except Exception as e:
|
||
logger.error(f"解析 Gemini 响应失败: {e}", e=e)
|
||
raise LLMException(
|
||
f"解析API响应失败: {e}",
|
||
code=LLMErrorCode.RESPONSE_PARSE_ERROR,
|
||
cause=e,
|
||
)
|
||
|
||
def prepare_embedding_request(
|
||
self,
|
||
model: "LLMModel",
|
||
api_key: str,
|
||
texts: list[str],
|
||
task_type: "EmbeddingTaskType | str",
|
||
**kwargs: Any,
|
||
) -> RequestData:
|
||
"""准备文本嵌入请求"""
|
||
api_model_name = model.model_name
|
||
if not api_model_name.startswith("models/"):
|
||
api_model_name = f"models/{api_model_name}"
|
||
|
||
url = self.get_api_url(model, f"/{api_model_name}:batchEmbedContents")
|
||
headers = self.get_base_headers(api_key)
|
||
|
||
requests_payload = []
|
||
for text_content in texts:
|
||
request_item: dict[str, Any] = {
|
||
"content": {"parts": [{"text": text_content}]},
|
||
}
|
||
|
||
from ..types.enums import EmbeddingTaskType
|
||
|
||
if task_type and task_type != EmbeddingTaskType.RETRIEVAL_DOCUMENT:
|
||
request_item["task_type"] = str(task_type).upper()
|
||
if title := kwargs.get("title"):
|
||
request_item["title"] = title
|
||
if output_dimensionality := kwargs.get("output_dimensionality"):
|
||
request_item["output_dimensionality"] = output_dimensionality
|
||
|
||
requests_payload.append(request_item)
|
||
|
||
body = {"requests": requests_payload}
|
||
return RequestData(url=url, headers=headers, body=body)
|
||
|
||
def parse_embedding_response(
|
||
self, response_json: dict[str, Any]
|
||
) -> list[list[float]]:
|
||
"""解析文本嵌入响应"""
|
||
try:
|
||
embeddings_data = response_json["embeddings"]
|
||
return [item["values"] for item in embeddings_data]
|
||
except KeyError as e:
|
||
logger.error(f"解析Gemini嵌入响应时缺少键: {e}. 响应: {response_json}")
|
||
raise LLMException(
|
||
"Gemini嵌入响应格式错误",
|
||
code=LLMErrorCode.RESPONSE_PARSE_ERROR,
|
||
details={"error": str(e)},
|
||
)
|
||
except Exception as e:
|
||
logger.error(
|
||
f"解析Gemini嵌入响应时发生未知错误: {e}. 响应: {response_json}"
|
||
)
|
||
raise LLMException(
|
||
f"解析Gemini嵌入响应失败: {e}",
|
||
code=LLMErrorCode.RESPONSE_PARSE_ERROR,
|
||
cause=e,
|
||
)
|
||
|
||
def validate_embedding_response(self, response_json: dict[str, Any]) -> None:
|
||
"""验证嵌入响应"""
|
||
super().validate_embedding_response(response_json)
|
||
if "embeddings" not in response_json or not isinstance(
|
||
response_json["embeddings"], list
|
||
):
|
||
raise LLMException(
|
||
"Gemini嵌入响应缺少'embeddings'字段或格式不正确",
|
||
code=LLMErrorCode.RESPONSE_PARSE_ERROR,
|
||
details=response_json,
|
||
)
|
||
for item in response_json["embeddings"]:
|
||
if "values" not in item:
|
||
raise LLMException(
|
||
"Gemini嵌入响应的条目中缺少'values'字段",
|
||
code=LLMErrorCode.RESPONSE_PARSE_ERROR,
|
||
details=response_json,
|
||
)
|