shuiyu67
a63b9d2f97
根因: cli_tools.process_tool_call 在启用 UnifiedToolManager 时直接返回
ToolResult 对象，而 7 个调用点全部按 dict 用 result.get('result')，触发
AttributeError；FC 路径 _execute_tool_by_name 也可能把 ToolResult 透传出去，
后续 result[:5000] 会再炸 TypeError。
- tool_result.py: 新增 normalize_tool_text / normalize_tool_dict，
统一收敛 ToolResult / dict / str / None 三种历史返回形态。
- cli_tools.process_tool_call: 统一返回 dict（附带 success/error_code/
tool_result），回退 handler 路径同样归一。
- cli_core._execute_tool_by_name: handler / memory / scheduler 返回值
归一成 str，保证 FC 路径可安全切片。
- openai/ollama/manual 引擎、websocket_server、cli_clawli: 取工具结果改用
normalize_tool_text（带本地兜底实现，避免循环导入）。
- cli_clawli: 顺带修复只回传最后一个工具结果的 bug，改为汇总全部。
- 测试: 23 项全过；纯格式/讯飞 500 降级回归冒烟通过。
513 lines
20 KiB
Python
513 lines
20 KiB
Python
import os
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import re
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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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# <input> 特殊语法解析（配置模型时可交互式询问用户）
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try:
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from xcli_core.config import resolve_input_value
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except Exception:
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def resolve_input_value(value, field_name="", default_prompt=None):
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return value
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try:
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from xcli_core.tool_result import normalize_tool_text
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except Exception:
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def normalize_tool_text(result, default="无结果"):
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"""兜底归一化：ToolResult / dict / str 统一成字符串"""
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if result is None:
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return default
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if isinstance(result, dict):
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return str(result.get("result", default))
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return str(result)
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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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# 模型最大 token 大小（单位 K，如 32 = 32K = 32768 token）
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# 支持 <input> 语法：配置里写 <input:提示语> 会在配置模型时询问用户
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max_token_k = "<input:请输入模型最大token大小(K)>"
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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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max_token_k = engine_config.get("max_token_k", max_token_k)
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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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# 解析 <input> 语法（询问用户）并换算成 num_ctx（token 数）
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resolved_k = resolve_input_value(max_token_k, field_name="模型最大token大小(K)")
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self.num_ctx = self._parse_num_ctx(resolved_k)
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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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# 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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@staticmethod
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def _parse_num_ctx(value):
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"""把用户输入的 K 值换算成 token 数：32 / 32K → 32768。非法/空 → 4096"""
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if value is None:
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return 4096
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s = str(value).strip().lower()
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digits = re.sub(r"[^\d.]", "", s)
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if not digits:
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return 4096
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try:
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k = float(digits)
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except ValueError:
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return 4096
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if k <= 0:
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return 4096
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return max(1024, int(k * 1024))
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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, tools=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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tools: OpenAI FC 工具定义列表 (可选)
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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: {normalize_tool_text(tool_results)}"
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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（num_ctx: 模型上下文窗口大小，由配置的 max_token_k 决定）
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kwargs = {
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"model": self.model,
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"messages": messages,
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"stream": False,
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"options": {"num_ctx": self.num_ctx}
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}
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# ── Function Calling: 注入工具定义 ──
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if tools:
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kwargs["tools"] = tools
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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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# ── Function Calling: 检查 tool_calls ──
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message = response.get("message", {})
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tool_calls_raw = message.get("tool_calls")
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if tool_calls_raw:
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# Ollama 的 tool_calls 格式: [{"function": {"name": ..., "arguments": {...}}}]
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fc_calls = []
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for i, tc in enumerate(tool_calls_raw):
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func = tc.get("function", {})
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name = func.get("name", "")
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args = func.get("arguments", {})
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call_id = f"call_{name}_{i}"
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# 统一为小狸内部格式
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if "args" in args and isinstance(args["args"], str):
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final_args = args["args"]
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elif "args" in args:
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final_args = json.dumps(args["args"], ensure_ascii=False)
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else:
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parts = []
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for k, v in args.items():
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parts.append(str(v) if isinstance(v, str) else json.dumps(v, ensure_ascii=False))
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final_args = " ".join(parts) if parts else ""
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fc_calls.append({
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"id": call_id,
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"name": name,
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"arguments": args,
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"action": "use_tool",
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"tool": name,
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"args": final_args,
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})
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if fc_calls:
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return json.dumps({
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"_fc": True,
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"tool_calls": fc_calls,
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}, ensure_ascii=False)
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# 普通文本响应
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content = message.get("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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# tool 角色消息（FC 工具结果）—— 官方格式不含 name 字段
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if role == "tool":
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tool_msg = {"role": "tool", "content": msg.get("content", "")}
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if "tool_call_id" in msg:
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tool_msg["tool_call_id"] = msg["tool_call_id"]
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messages.append(tool_msg)
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continue
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# assistant 消息可能包含 tool_calls
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if role == "assistant" and "tool_calls" in msg:
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assistant_msg = {
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"role": "assistant",
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"content": msg.get("content"),
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"tool_calls": msg["tool_calls"]
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}
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messages.append(assistant_msg)
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continue
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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 = [model["name"] for model in response["models"]]
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return models
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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)}"]
|
||
|
||
def pull_model(self, model_name):
|
||
"""从Ollama库拉取模型"""
|
||
if not self.is_service_running:
|
||
print(f"{Fore.RED}错误:Ollama服务未运行,无法拉取模型{Style.RESET_ALL}")
|
||
return False
|
||
|
||
try:
|
||
print(f"{Fore.YELLOW}正在拉取模型 {model_name}...{Style.RESET_ALL}")
|
||
|
||
# 使用Ollama Python库拉取模型
|
||
response = ollama_client.pull(model_name, stream=True)
|
||
|
||
# 逐块处理响应
|
||
for part in response:
|
||
if 'status' in part:
|
||
status = part['status']
|
||
print(f"{Fore.YELLOW}{status}{Style.RESET_ALL}")
|
||
# 检查是否完成
|
||
if 'completed' in part and 'total' in part:
|
||
if part['completed'] == part['total']:
|
||
print(f"{Fore.GREEN}模型 {model_name} 拉取完成!{Style.RESET_ALL}")
|
||
|
||
print(f"{Fore.GREEN}模型 {model_name} 拉取成功!{Style.RESET_ALL}")
|
||
return True
|
||
except Exception as e:
|
||
print(f"{Fore.RED}拉取模型 {model_name} 失败: {str(e)}{Style.RESET_ALL}")
|
||
return False
|
||
|
||
def handle_command(self, command):
|
||
"""处理引擎特定命令"""
|
||
if not self.is_service_running:
|
||
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 |