shuiyu1123
78fea7ccdd
- 重构 core/ 底层 (7文件): app/runner/engine/parser/history/safety/display - FC原生调用为主，prompt-JSON保底 - 移除所有工具返回截断 (ai_search/git_tools/cmd_executor/browser_auto/network_tools/sub_agent/tool_search) - 删除无用文件 (旧服务器/WebUI/彩蛋/启动器等21个文件) - 清理 constants.py 死代码 - 版本号更新为 7.0.0
491 lines
19 KiB
Python
491 lines
19 KiB
Python
import os
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import sys
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import json
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import requests
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import threading
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import time
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from colorama import Fore, Style
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# 延迟导入 ollama，避免程序启动时卡住
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# 如果导入失败，ollama_client 将为 None
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try:
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import ollama
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ollama_client = ollama
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except Exception as e:
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print(f"{Fore.YELLOW}警告: 无法导入 ollama 库: {e}{Style.RESET_ALL}")
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print(f"{Fore.YELLOW}请确保已安装 ollama 包 (pip install ollama){Style.RESET_ALL}")
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ollama_client = None
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# 添加项目根目录到sys.path
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current_dir = os.path.dirname(os.path.abspath(__file__))
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project_root = os.path.dirname(current_dir)
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sys.path.insert(0, project_root)
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def _safe_get(obj, key, default=None):
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"""兼容 dict 和 pydantic 对象的安全取值（新版 ollama 返回 pydantic 模型，不支持 .get()）"""
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if obj is None:
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return default
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# 1. 先尝试 dict 访问
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try:
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return obj[key]
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except (KeyError, TypeError):
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pass
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# 2. 尝试属性访问（pydantic 模型）
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val = getattr(obj, key, None)
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if val is not None:
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return val
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# 3. 尝试 model_dump 后的 dict 访问
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try:
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return obj.model_dump()[key]
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except (AttributeError, KeyError, TypeError):
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return default
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class OllamaAI:
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"""基于Ollama的本地AI引擎"""
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requires_api_key = False # Ollama是本地引擎，不需要API密钥
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def get_help_info(self):
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"""获取引擎帮助信息"""
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help_text = """
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Ollama AI引擎插件帮助信息
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========================
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引擎名称: Ollama
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描述: 基于Ollama的本地AI引擎,支持多种大型语言模型
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可用命令:
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/engine.ollama models - 列出本地可用的模型
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/engine.ollama remote-models - 显示可下载的模型列表
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/engine.ollama search <关键词> - 搜索可下载的模型
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/engine.ollama set <模型名> - 设置当前使用的模型
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/engine.ollama pull <模型名> - 从Ollama库拉取模型
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注意: 本引擎强制使用流式输出，AI回复会实时显示。
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使用说明:
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1. 确保Ollama服务已在本地运行
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2. 使用/engine.switch ollama切换到此引擎
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3. 使用上述命令管理模型和与AI交互
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思考模式:
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- 支持深度思考模型（如 QwQ、DeepSeek-R1）
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- 思考过程会自动以灰色文本显示
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"""
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return help_text
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def __init__(self):
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self.name = "ollama"
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self.cli = None # 引用CLI实例以访问插件
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# 初始化对话历史
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self.conversation_history = []
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# 直接从 config.json 读取配置
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config_file = os.path.join(project_root, "config.json")
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try:
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with open(config_file, 'r', encoding='utf-8') as f:
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config = json.load(f)
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engine_config = config.get("api", {}).get("engines", {}).get("ollama", {})
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self.api_key = engine_config.get("api_key", "")
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self.base_url = engine_config.get("base_url", "")
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self.model = engine_config.get("model", "")
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self.max_history = config.get("system", {}).get("max_history", 10)
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except Exception as e:
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# 如果读取失败，使用空值
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self.api_key = ""
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self.base_url = ""
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self.model = ""
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self.max_history = 10
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# 共享对话历史引用（由CLI设置）
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self.shared_conversation_history = None
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# 取消标志
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self._cancelled = False
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# 直接启用思考标记，由主程序自动处理深度思考模型的响应
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self.thinking_start_marker = "<thinking>"
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self.thinking_end_marker = "</thinking>"
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# 检测Ollama服务是否运行
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self.is_service_running = self.check_ollama_service()
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if self.is_service_running:
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print(f"{Fore.GREEN}Ollama服务检测成功,引擎已启用{Style.RESET_ALL}")
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else:
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print(f"{Fore.RED}Ollama服务未运行,引擎将不可用{Style.RESET_ALL}")
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def cancel(self):
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"""取消正在进行的请求"""
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self._cancelled = True
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def apply_config(self, base_url=None, api_key=None, model=None):
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"""应用新配置到当前引擎实例（内存），并持久化到 config.json"""
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if base_url is not None:
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self.base_url = base_url.rstrip('/')
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if api_key is not None:
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self.api_key = api_key
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if model is not None:
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self.model = model
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# 持久化到 config.json
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try:
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import os, json
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config_file = os.path.join(project_root, "config.json")
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with open(config_file, 'r', encoding='utf-8') as f:
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config = json.load(f)
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engine_cfg = config.setdefault("api", {}).setdefault("engines", {}).setdefault("ollama", {})
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engine_cfg["base_url"] = self.base_url
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engine_cfg["api_key"] = self.api_key
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engine_cfg["model"] = self.model
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with open(config_file, 'w', encoding='utf-8') as f:
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json.dump(config, f, ensure_ascii=False, indent=2)
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except Exception as e:
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logger.warning(f"持久化引擎配置失败: {e}")
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def check_ollama_service(self):
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"""检测Ollama服务是否运行"""
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# 检查 ollama 库是否可用
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if ollama_client is None:
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return False
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try:
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# 尝试调用Ollama API获取版本信息
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response = ollama_client.list()
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# 如果没有异常,说明服务运行正常
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return True
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except Exception as e:
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# 如果出现异常,说明服务未运行
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return False
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def generate_response(self, user_input, tool_results=None, system_prompt=None, stream_start_event=None):
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"""
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生成AI响应 - 使用Ollama API
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Args:
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user_input: 用户输入
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tool_results: 工具执行结果
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system_prompt: 系统提示词
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"""
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try:
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# 检查Ollama服务是否运行
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if not self.is_service_running:
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return "错误:Ollama服务未运行,请启动Ollama服务后再使用此引擎."
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# 检查模型是否已配置
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if not self.model or not self.model.strip():
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return ("错误:Ollama模型未配置。请执行以下操作:\n"
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" 1. /engine.ollama models → 查看本地可用模型\n"
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" 2. /engine.ollama set <模型名> → 设置要使用的模型\n"
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" 或编辑 config.json 中 api.engines.ollama.model 字段")
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# 如果有工具结果,将其作为上下文返回给AI处理
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if tool_results:
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tool_result_text = f"工具执行结果: {tool_results.get('result', '无结果')}"
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messages = self._build_messages_with_history(system_prompt, tool_result_text)
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else:
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messages = self._build_messages_with_history(system_prompt, user_input)
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# 构建调用参数
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# 重置取消标志
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self._cancelled = False
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# 获取流式回调
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stream_cb = None
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thinking_cb = None
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kwargs = {
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"model": self.model,
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"messages": messages,
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"stream": True if stream_cb else False
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}
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# 添加采样参数（从引擎配置读取）
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options = {}
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for key in ("temperature", "top_k", "top_p", "num_predict", "num_ctx"):
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val = self.engine_config.get(key)
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if val is not None:
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options[key] = val
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if options:
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kwargs["options"] = options
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# 使用Ollama Python库调用API
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response = ollama_client.chat(**kwargs)
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# ── 非流式模式 ──
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message = _safe_get(response, "message", {})
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# 普通文本响应
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content = _safe_get(message, "content", "") or ""
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# 处理深度思考：Ollama 把 thinking 和 content 分开返回
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# 合并到一起，用 <thinking> 标记包裹，让主程序处理
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thinking_content = _safe_get(message, "thinking", "") or ""
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if thinking_content:
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thinking_content = thinking_content.encode('utf-8', 'replace').decode('utf-8')
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content = content.encode('utf-8', 'replace').decode('utf-8')
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return f"<thinking>{thinking_content}</thinking>{content}"
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# 清理无效的 UTF-8 代理字符
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content = content.encode('utf-8', 'replace').decode('utf-8')
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return content
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except Exception as e:
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return f"Ollama API调用失败: {str(e)}"
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def _build_messages_with_history(self, system_prompt, user_input):
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"""构建包含历史记录的消息列表（支持 FC tool 消息）"""
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messages = []
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# 添加系统提示词
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if system_prompt:
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clean_prompt = system_prompt.encode('utf-8', 'replace').decode('utf-8')
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messages.append({
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"role": "system",
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"content": clean_prompt
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})
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# 添加历史对话记录（使用共享历史）
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if self.shared_conversation_history is not None:
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for msg in self.shared_conversation_history[-self.max_history:]:
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if not isinstance(msg, dict) or "role" not in msg:
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continue
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role = msg["role"]
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# 普通 user/assistant 消息
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if "content" in msg:
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clean_content = msg["content"]
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if isinstance(clean_content, str):
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clean_content = clean_content.encode('utf-8', 'replace').decode('utf-8')
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messages.append({"role": role, "content": clean_content})
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# 清理用户输入
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if user_input:
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clean_input = user_input.encode('utf-8', 'replace').decode('utf-8')
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messages.append({
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"role": "user",
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"content": clean_input
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})
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return messages
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def _call_api_with_history(self, content, system_prompt=None):
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"""使用对话历史调用API"""
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if not self.is_service_running:
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return "错误:Ollama服务未运行,请启动Ollama服务后再使用此引擎."
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# 构建消息列表
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messages = []
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# 添加系统提示词(如果提供)
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if system_prompt:
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messages.append({
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"role": "system",
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"content": system_prompt
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})
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# 添加历史对话记录（使用共享历史）
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if self.shared_conversation_history is not None:
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# 确保历史记录格式正确
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for msg in self.shared_conversation_history[-self.max_history:]:
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if isinstance(msg, dict) and "role" in msg and "content" in msg:
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messages.append(msg)
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else:
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# 如果格式不正确，跳过该条记录
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continue
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# 添加当前内容
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messages.append({
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"role": "tool",
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"content": content
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})
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# 使用Ollama Python库调用API
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response = ollama_client.chat(
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model=self.model,
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messages=messages,
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stream=False
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)
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return response["message"]["content"]
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def set_model(self, model_name):
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"""设置要使用的模型"""
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if not self.is_service_running:
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print(f"{Fore.RED}错误:Ollama服务未运行,无法切换模型{Style.RESET_ALL}")
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return
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self.model = model_name
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print(f"{Fore.GREEN}已切换Ollama模型为: {model_name}{Style.RESET_ALL}")
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def list_models(self):
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"""列出本地可用的模型"""
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if not self.is_service_running:
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return ["错误:Ollama服务未运行"]
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try:
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response = ollama_client.list()
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models_list = _safe_get(response, "models", [])
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result = []
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for model in models_list:
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name = _safe_get(model, "model", "") or _safe_get(model, "name", "")
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if name:
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result.append(name)
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return result if result else ["未找到本地模型"]
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except Exception as e:
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return [f"获取模型列表失败: {str(e)}"]
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def list_remote_models(self, limit=10):
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"""获取可下载的模型列表(仅展示前N个)"""
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if not self.is_service_running:
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return ["错误:Ollama服务未运行"]
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try:
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# 使用Ollama Python库获取可下载的模型列表
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# 注意:Ollama库本身没有直接获取可下载模型列表的API
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# 这里返回一些常见的模型作为示例
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# 实际应用中可能需要从Ollama官网或其他来源获取此信息
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common_models = [
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"llama3.1:latest",
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"llama3:latest",
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"mistral:latest",
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"gemma2:latest",
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"phi3:latest",
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"qwen2:latest",
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"command-r:latest",
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"dolphin-mistral:latest",
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"nous-hermes2:latest",
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"llava:latest",
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"codellama:latest",
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"mxbai-embed-large:latest",
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"all-minilm:latest",
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"stable-diffusion:latest",
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"moondream:latest"
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]
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return common_models[:limit]
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except Exception as e:
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return [f"获取远程模型列表失败: {str(e)}"]
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def search_models(self, query, limit=10):
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"""模糊搜索可下载的模型"""
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if not self.is_service_running:
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return ["错误:Ollama服务未运行"]
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try:
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# 使用Ollama Python库获取可下载的模型列表
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# 注意:Ollama库本身没有直接获取可下载模型列表的API
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all_models = [
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"llama3.1:latest", "llama3:latest", "llama2:latest",
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"mistral:latest", "mistral-openorca:latest",
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"gemma2:latest", "gemma:latest",
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"phi3:latest", "phi2:latest",
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"qwen2:latest", "qwen:latest",
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"command-r:latest", "command-r-plus:latest",
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"dolphin-mistral:latest", "dolphin-llama3:latest",
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"nous-hermes2:latest", "nous-hermes:latest",
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"llava:latest", "bakllava:latest",
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"codellama:latest", "codeup:latest",
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"mxbai-embed-large:latest", "nomic-embed-text:latest",
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"all-minilm:latest", "bge-m3:latest",
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"stable-diffusion:latest", "moondream:latest",
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"llama3-groq:latest", "eva-qwen2:latest"
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]
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# 进行模糊搜索(简单匹配,包含查询词的模型)
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matched_models = [model for model in all_models if query.lower() in model.lower()]
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# 限制返回数量
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return matched_models[:limit]
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except Exception as e:
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return [f"搜索模型失败: {str(e)}"]
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def pull_model(self, model_name):
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"""从Ollama库拉取模型"""
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if not self.is_service_running:
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print(f"{Fore.RED}错误:Ollama服务未运行,无法拉取模型{Style.RESET_ALL}")
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return False
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try:
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print(f"{Fore.YELLOW}正在拉取模型 {model_name}...{Style.RESET_ALL}")
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# 使用Ollama Python库拉取模型
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response = ollama_client.pull(model_name, stream=True)
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# 逐块处理响应
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for part in response:
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status = _safe_get(part, "status", None)
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if status:
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print(f"{Fore.YELLOW}{status}{Style.RESET_ALL}")
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# 检查是否完成
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completed = _safe_get(part, "completed", None)
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total = _safe_get(part, "total", None)
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if completed is not None and total is not None:
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if completed == total:
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print(f"{Fore.GREEN}模型 {model_name} 拉取完成!{Style.RESET_ALL}")
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print(f"{Fore.GREEN}模型 {model_name} 拉取成功!{Style.RESET_ALL}")
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return True
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except Exception as e:
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print(f"{Fore.RED}拉取模型 {model_name} 失败: {str(e)}{Style.RESET_ALL}")
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return False
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def handle_command(self, command):
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"""处理引擎特定命令"""
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if not self.is_service_running:
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print(f"{Fore.RED}Ollama服务未运行,无法执行命令{Style.RESET_ALL}")
|
||
return False
|
||
|
||
if command == "models":
|
||
# 显示本地可用模型
|
||
models = self.list_models()
|
||
print(f"{Fore.GREEN}本地可用的Ollama模型:{Style.RESET_ALL}")
|
||
for model in models:
|
||
status = " (当前使用)" if model == self.model else ""
|
||
print(f"{Fore.GREEN} - {model}{status}{Style.RESET_ALL}")
|
||
return True
|
||
|
||
elif command == "remote-models":
|
||
# 显示可下载的模型(前10个)
|
||
models = self.list_remote_models()
|
||
print(f"{Fore.GREEN}可下载的Ollama模型(前10个):{Style.RESET_ALL}")
|
||
for i, model in enumerate(models, 1):
|
||
print(f"{Fore.GREEN} {i}. {model}{Style.RESET_ALL}")
|
||
return True
|
||
|
||
elif command.startswith("search "):
|
||
# 搜索模型
|
||
query = command[7:].strip() # 移除 'search '
|
||
if query:
|
||
models = self.search_models(query)
|
||
if models:
|
||
print(f"{Fore.GREEN}搜索 '{query}' 的结果(前10个):{Style.RESET_ALL}")
|
||
for i, model in enumerate(models, 1):
|
||
print(f"{Fore.GREEN} {i}. {model}{Style.RESET_ALL}")
|
||
else:
|
||
print(f"{Fore.YELLOW}未找到与 '{query}' 匹配的模型{Style.RESET_ALL}")
|
||
return True
|
||
else:
|
||
print(f"{Fore.RED}请提供搜索关键词.用法: /engine.search <关键词>{Style.RESET_ALL}")
|
||
return False
|
||
|
||
elif command.startswith("set "):
|
||
# 设置模型
|
||
model_name = command[4:].strip() # 移除 'set '
|
||
if model_name:
|
||
self.set_model(model_name)
|
||
return True
|
||
else:
|
||
print(f"{Fore.RED}请提供模型名称.用法: /engine.set <模型名>{Style.RESET_ALL}")
|
||
return False
|
||
|
||
elif command.startswith("pull "):
|
||
# 拉取模型
|
||
model_name = command[5:].strip() # 移除 'pull '
|
||
if model_name:
|
||
success = self.pull_model(model_name)
|
||
return success
|
||
else:
|
||
print(f"{Fore.RED}请提供模型名称.用法: /engine.pull <模型名>{Style.RESET_ALL}")
|
||
return False
|
||
|
||
else:
|
||
print(f"{Fore.RED}未知的Ollama命令.可用命令: models, remote-models, search, set, pull{Style.RESET_ALL}")
|
||
return False |