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✨ feat(llm): 全面重构LLM服务模块，增强多模态与工具支持 (#1953)
* ✨ feat(llm): 全面重构LLM服务模块，增强多模态与工具支持 🚀 核心功能增强 - 多模型链式调用：新增 `pipeline_chat` 支持复杂任务流处理 - 扩展提供商支持：新增 ARK(火山方舟)、SiliconFlow(硅基流动) 适配器 - 多模态处理增强：支持URL媒体文件下载转换，提升输入灵活性 - 历史对话支持：AI.analyze 方法支持历史消息上下文和可选 UniMessage 参数 - 文本嵌入功能：新增 `embed`、`analyze_multimodal`、`search_multimodal` 等API - 模型能力系统：新增 `ModelCapabilities` 统一管理模型特性（多模态、工具调用等） 🔧 架构重构与优化 - MCP工具系统重构：配置独立化至 `data/llm/mcp_tools.json`，预置常用工具 - API调用逻辑统一：提取通用 `_perform_api_call` 方法，消除代码重复 - 跨平台兼容：Windows平台MCP工具npx命令自动包装处理 - HTTP客户端增强：兼容不同版本httpx代理配置（0.28+版本适配） 🛠️ API与配置完善 - 统一返回类型：`AI.analyze` 统一返回 `LLMResponse` 类型 - 消息转换工具：新增 `message_to_unimessage` 转换函数 - Gemini适配器增强：URL图片下载编码、动态安全阈值配置 - 缓存管理：新增模型实例缓存和管理功能 - 配置预设：扩展 CommonOverrides 预设配置选项 - 历史管理优化：支持多模态内容占位符替换，提升效率 📚 文档与开发体验 - README全面重写：新增完整使用指南、API参考和架构概览 - 文档内容扩充：补充嵌入模型、缓存管理、工具注册等功能说明 - 日志记录增强：支持详细调试信息输出 - API简化：移除冗余函数，优化接口设计 * 🎨 feat(llm): 统一LLM服务函数文档格式 * ✨ feat(llm): 添加新模型并简化提供者配置加载 * 🚨 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>
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webjoin111
pre-commit-ci[bot]
webjoin111
pre-commit-ci[bot]
parent
1e7ae38684
commit
48cbb2bf1d
@@ -4,15 +4,33 @@ LLM 提供商配置管理
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负责注册和管理 AI 服务提供商的配置项。
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"""
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from functools import lru_cache
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import json
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import sys
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from typing import Any
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from pydantic import BaseModel, Field
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from zhenxun.configs.config import Config
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from zhenxun.configs.path_config import DATA_PATH
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from zhenxun.configs.utils import parse_as
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from zhenxun.services.log import logger
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from zhenxun.utils.manager.priority_manager import PriorityLifecycle
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from ..types.models import ModelDetail, ProviderConfig
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class ToolConfig(BaseModel):
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"""MCP类型工具的配置定义"""
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type: str = "mcp"
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name: str = Field(..., description="工具的唯一名称标识")
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description: str | None = Field(None, description="工具功能的描述")
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mcp_config: dict[str, Any] | BaseModel = Field(
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..., description="MCP服务器的特定配置"
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)
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AI_CONFIG_GROUP = "AI"
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PROVIDERS_CONFIG_KEY = "PROVIDERS"
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@@ -38,6 +56,9 @@ class LLMConfig(BaseModel):
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providers: list[ProviderConfig] = Field(
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default_factory=list, description="配置多个 AI 服务提供商及其模型信息"
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)
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mcp_tools: list[ToolConfig] = Field(
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default_factory=list, description="配置可用的外部MCP工具"
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)
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def get_provider_by_name(self, name: str) -> ProviderConfig | None:
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"""根据名称获取提供商配置
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@@ -132,7 +153,7 @@ def get_default_providers() -> list[dict[str, Any]]:
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return [
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{
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"name": "DeepSeek",
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"api_key": "sk-******",
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"api_key": "YOUR_ARK_API_KEY",
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"api_base": "https://api.deepseek.com",
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"api_type": "openai",
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"models": [
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@@ -146,9 +167,30 @@ def get_default_providers() -> list[dict[str, Any]]:
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},
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],
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},
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{
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"name": "ARK",
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"api_key": "YOUR_ARK_API_KEY",
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"api_base": "https://ark.cn-beijing.volces.com",
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"api_type": "ark",
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"models": [
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{"model_name": "deepseek-r1-250528"},
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{"model_name": "doubao-seed-1-6-250615"},
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{"model_name": "doubao-seed-1-6-flash-250615"},
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{"model_name": "doubao-seed-1-6-thinking-250615"},
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],
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},
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{
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"name": "siliconflow",
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"api_key": "YOUR_ARK_API_KEY",
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"api_base": "https://api.siliconflow.cn",
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"api_type": "openai",
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"models": [
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{"model_name": "deepseek-ai/DeepSeek-V3"},
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],
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},
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{
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"name": "GLM",
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"api_key": "",
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"api_key": "YOUR_ARK_API_KEY",
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"api_base": "https://open.bigmodel.cn",
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"api_type": "zhipu",
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"models": [
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@@ -167,12 +209,41 @@ def get_default_providers() -> list[dict[str, Any]]:
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"api_type": "gemini",
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"models": [
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{"model_name": "gemini-2.0-flash"},
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{"model_name": "gemini-2.5-flash-preview-05-20"},
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{"model_name": "gemini-2.5-flash"},
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{"model_name": "gemini-2.5-pro"},
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{"model_name": "gemini-2.5-flash-lite-preview-06-17"},
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],
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},
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]
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def get_default_mcp_tools() -> dict[str, Any]:
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"""
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获取默认的MCP工具配置，用于在文件不存在时创建。
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包含了 baidu-map, Context7, 和 sequential-thinking.
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"""
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return {
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"mcpServers": {
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"baidu-map": {
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"command": "npx",
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"args": ["-y", "@baidumap/mcp-server-baidu-map"],
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"env": {"BAIDU_MAP_API_KEY": "<YOUR_BAIDU_MAP_API_KEY>"},
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"description": "百度地图工具，提供地理编码、路线规划等功能。",
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},
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"sequential-thinking": {
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"command": "npx",
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"args": ["-y", "@modelcontextprotocol/server-sequential-thinking"],
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"description": "顺序思维工具，用于帮助模型进行多步骤推理。",
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},
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"Context7": {
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"command": "npx",
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"args": ["-y", "@upstash/context7-mcp@latest"],
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"description": "Upstash 提供的上下文管理和记忆工具。",
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},
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}
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}
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def register_llm_configs():
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"""注册 LLM 服务的配置项"""
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logger.info("注册 LLM 服务的配置项")
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@@ -214,6 +285,19 @@ def register_llm_configs():
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help="LLM服务请求重试的基础延迟时间（秒）",
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type=int,
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)
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Config.add_plugin_config(
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AI_CONFIG_GROUP,
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"gemini_safety_threshold",
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"BLOCK_MEDIUM_AND_ABOVE",
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help=(
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"Gemini 安全过滤阈值 "
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"(BLOCK_LOW_AND_ABOVE: 阻止低级别及以上, "
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"BLOCK_MEDIUM_AND_ABOVE: 阻止中等级别及以上, "
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"BLOCK_ONLY_HIGH: 只阻止高级别, "
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"BLOCK_NONE: 不阻止)"
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),
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type=str,
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)
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Config.add_plugin_config(
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AI_CONFIG_GROUP,
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@@ -225,24 +309,111 @@ def register_llm_configs():
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)
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@lru_cache(maxsize=1)
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def get_llm_config() -> LLMConfig:
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"""获取 LLM 配置实例
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返回:
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LLMConfig: LLM 配置实例
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"""
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"""获取 LLM 配置实例，现在会从新的 JSON 文件加载 MCP 工具"""
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ai_config = get_ai_config()
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llm_data_path = DATA_PATH / "llm"
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mcp_tools_path = llm_data_path / "mcp_tools.json"
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mcp_tools_list = []
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mcp_servers_dict = {}
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if not mcp_tools_path.exists():
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logger.info(f"未找到 MCP 工具配置文件，将在 '{mcp_tools_path}' 创建一个。")
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llm_data_path.mkdir(parents=True, exist_ok=True)
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default_mcp_config = get_default_mcp_tools()
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try:
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with mcp_tools_path.open("w", encoding="utf-8") as f:
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json.dump(default_mcp_config, f, ensure_ascii=False, indent=2)
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mcp_servers_dict = default_mcp_config.get("mcpServers", {})
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except Exception as e:
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logger.error(f"创建默认 MCP 配置文件失败: {e}", e=e)
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mcp_servers_dict = {}
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else:
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try:
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with mcp_tools_path.open("r", encoding="utf-8") as f:
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mcp_data = json.load(f)
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mcp_servers_dict = mcp_data.get("mcpServers", {})
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if not isinstance(mcp_servers_dict, dict):
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logger.warning(
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f"'{mcp_tools_path}' 中的 'mcpServers' 键不是一个字典，"
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f"将使用空配置。"
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)
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mcp_servers_dict = {}
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except json.JSONDecodeError as e:
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logger.error(f"解析 MCP 配置文件 '{mcp_tools_path}' 失败: {e}", e=e)
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except Exception as e:
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logger.error(f"读取 MCP 配置文件时发生未知错误: {e}", e=e)
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mcp_servers_dict = {}
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if sys.platform == "win32":
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logger.debug("检测到Windows平台，正在调整MCP工具的npx命令...")
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for name, config in mcp_servers_dict.items():
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if isinstance(config, dict) and config.get("command") == "npx":
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logger.info(f"为工具 '{name}' 包装npx命令以兼容Windows。")
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original_args = config.get("args", [])
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config["command"] = "cmd"
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config["args"] = ["/c", "npx", *original_args]
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if mcp_servers_dict:
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mcp_tools_list = [
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{
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"name": name,
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"type": "mcp",
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"description": config.get("description", f"MCP tool for {name}"),
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"mcp_config": config,
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}
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for name, config in mcp_servers_dict.items()
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if isinstance(config, dict)
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]
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from ..tools.registry import tool_registry
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for tool_dict in mcp_tools_list:
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if isinstance(tool_dict, dict):
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tool_name = tool_dict.get("name")
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if not tool_name:
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continue
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config_model = tool_registry.get_mcp_config_model(tool_name)
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if not config_model:
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logger.debug(
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f"MCP工具 '{tool_name}' 没有注册其配置模型，"
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f"将跳过特定配置验证，直接使用原始配置字典。"
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)
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continue
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mcp_config_data = tool_dict.get("mcp_config", {})
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try:
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parsed_mcp_config = parse_as(config_model, mcp_config_data)
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tool_dict["mcp_config"] = parsed_mcp_config
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except Exception as e:
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raise ValueError(f"MCP工具 '{tool_name}' 的 `mcp_config` 配置错误: {e}")
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config_data = {
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"default_model_name": ai_config.get("default_model_name"),
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"proxy": ai_config.get("proxy"),
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"timeout": ai_config.get("timeout", 180),
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"max_retries_llm": ai_config.get("max_retries_llm", 3),
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"retry_delay_llm": ai_config.get("retry_delay_llm", 2),
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"providers": ai_config.get(PROVIDERS_CONFIG_KEY, []),
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PROVIDERS_CONFIG_KEY: ai_config.get(PROVIDERS_CONFIG_KEY, []),
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"mcp_tools": mcp_tools_list,
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}
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return LLMConfig(**config_data)
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return parse_as(LLMConfig, config_data)
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def get_gemini_safety_threshold() -> str:
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"""获取 Gemini 安全过滤阈值配置
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返回:
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str: 安全过滤阈值
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"""
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ai_config = get_ai_config()
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return ai_config.get("gemini_safety_threshold", "BLOCK_MEDIUM_AND_ABOVE")
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def validate_llm_config() -> tuple[bool, list[str]]:
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@@ -326,3 +497,17 @@ def set_default_model(provider_model_name: str | None) -> bool:
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logger.info("默认模型已清除")
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return True
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@PriorityLifecycle.on_startup(priority=10)
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async def _init_llm_config_on_startup():
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"""
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在服务启动时主动调用一次 get_llm_config，
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以触发必要的初始化操作，例如创建默认的 mcp_tools.json 文件。
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"""
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logger.info("正在初始化 LLM 配置并检查 MCP 工具文件...")
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try:
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get_llm_config()
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logger.info("LLM 配置初始化完成。")
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except Exception as e:
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logger.error(f"LLM 配置初始化时发生错误: {e}", e=e)
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