feat: Function Calling 兼容
新增 xcli_core/fc_tools.py: - MCP 定义 → OpenAI FC 格式转换 - FC 响应解析 (tool_calls 提取) - 自动收集所有插件 FC 定义 - 400 错误自动降级为普通模式 引擎层改动 (openai_engine + ollama_engine): - generate_response 新增 tools 参数 - 请求体自动注入 tools/tool_choice - 响应检测 tool_calls 并返回 FC 标记 - 对话历史支持 tool 角色消息和 assistant.tool_calls - _build_messages_with_history 兼容 FC 消息格式 cli_core.py 集成: - _handle_fc_response: FC 响应 → 工具执行 → 结果回传 - _execute_tool_by_name: FC 调用路径的安全检查+工具分发 - process_conversation 循环自动处理 FC 工具调用 - TUI/CLI 双模式 FC 支持
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@@ -107,74 +107,136 @@ Ollama AI引擎插件帮助信息
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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):
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"""生成AI响应 - 使用Ollama API"""
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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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# 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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# 构建包含历史记录的消息列表
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messages = self._build_messages_with_history(system_prompt, tool_result_text)
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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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# 返回AI的回复
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return response["message"]["content"]
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# 构建包含历史记录的消息列表
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messages = self._build_messages_with_history(system_prompt, user_input)
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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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kwargs = {
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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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# ── 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(
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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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# 返回AI的回复
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content = response["message"]["content"]
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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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# 如果API调用失败,返回错误信息
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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"""
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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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# 清理代理字符
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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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# 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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# 清理内容中的代理字符
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clean_content = msg["content"].encode('utf-8', 'replace').decode('utf-8')
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messages.append({"role": msg["role"], "content": clean_content})
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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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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）
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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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if "name" in msg:
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tool_msg["name"] = msg["name"]
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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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@@ -182,7 +244,7 @@ Ollama AI引擎插件帮助信息
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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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+83
-13
@@ -125,8 +125,16 @@ OpenAI 兼容格式引擎帮助信息
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url = url.rstrip('/') + '/chat/completions'
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return url
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def generate_response(self, user_input, tool_results=None, system_prompt=None):
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"""生成 AI 响应"""
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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
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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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if not self.base_url:
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return "错误: openai 引擎的 base_url 未配置，请在 config.json 中设置"
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@@ -145,6 +153,11 @@ OpenAI 兼容格式引擎帮助信息
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"stream": False
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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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body["tools"] = tools
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body["tool_choice"] = "auto"
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# 发送请求
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api_url = self._get_api_url()
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response = requests.post(
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@@ -157,18 +170,49 @@ OpenAI 兼容格式引擎帮助信息
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if response.status_code == 200:
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response_data = response.json()
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if "choices" in response_data and len(response_data["choices"]) > 0:
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ai_response = response_data["choices"][0]["message"]["content"]
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# 清理无效的 UTF-8 代理字符
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ai_response = ai_response.encode('utf-8', 'replace').decode('utf-8')
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# 处理思考内容
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ai_response = self._process_thinking_content(ai_response)
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return ai_response
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message = response_data["choices"][0].get("message", {})
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# ── Function Calling: 检查 tool_calls ──
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tool_calls_raw = message.get("tool_calls")
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if tool_calls_raw:
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# 返回 FC 结果（由调用方处理工具执行）
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from xcli_core.fc_tools import parse_fc_response
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fc_calls = parse_fc_response(response_data)
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if fc_calls:
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# 返回一个特殊的 JSON 字符串，标记为 FC 调用
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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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"raw_message": message
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}, ensure_ascii=False)
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# 普通文本响应
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ai_response = message.get("content", "")
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if ai_response:
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# 清理无效的 UTF-8 代理字符
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ai_response = ai_response.encode('utf-8', 'replace').decode('utf-8')
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# 处理思考内容
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ai_response = self._process_thinking_content(ai_response)
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return ai_response or ""
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else:
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return f"API 返回格式错误: {response_data}"
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elif response.status_code == 401:
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return "API 认证失败，请检查 api_key 是否正确"
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elif response.status_code == 404:
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return f"API 端点不存在，请检查 base_url 是否正确: {api_url}"
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elif response.status_code == 400:
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# 工具定义可能不兼容，降级为无工具重试
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if tools and "tools" in str(response.text).lower():
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print(f"{Fore.YELLOW}FC 不支持，降级为普通模式{Style.RESET_ALL}")
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body.pop("tools", None)
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body.pop("tool_choice", None)
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retry = requests.post(url=api_url, json=body, headers=self._get_headers(), timeout=None)
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if retry.status_code == 200:
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rd = retry.json()
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if "choices" in rd and rd["choices"]:
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ai_response = rd["choices"][0].get("message", {}).get("content", "")
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return ai_response.encode('utf-8', 'replace').decode('utf-8') if ai_response else ""
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return f"API 调用失败: {response.status_code} - {response.text}"
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else:
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return f"API 调用失败: {response.status_code} - {response.text}"
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@@ -178,7 +222,7 @@ OpenAI 兼容格式引擎帮助信息
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return f"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"""
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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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@@ -192,12 +236,38 @@ OpenAI 兼容格式引擎帮助信息
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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 isinstance(msg, dict) and "role" in msg and "content" in msg:
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clean_content = msg["content"].encode('utf-8', 'replace').decode('utf-8')
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messages.append({"role": msg["role"], "content": clean_content})
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else:
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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）
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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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if "name" in msg:
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tool_msg["name"] = msg["name"]
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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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# 添加当前用户输入
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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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