142 lines
5.3 KiB
Python
142 lines
5.3 KiB
Python
import json
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import requests
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from config.logger import setup_logging
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from core.providers.llm.base import LLMProviderBase
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TAG = __name__
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logger = setup_logging()
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class LLMProvider(LLMProviderBase):
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"""
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蚂蚁阿福 LLM Provider
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通过 Frida HTTP Bridge (port 18900) 对接蚂蚁阿福 App 的文字对话 API。
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Bridge 运行在手机上，通过 adb forward 或网络暴露 SSE 流式接口。
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"""
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def __init__(self, config):
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self.bridge_url = config.get("bridge_url", "http://127.0.0.1:18900")
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self.timeout = config.get("timeout", 60)
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self.should_idle = False # signal to send system idle after TTS
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logger.bind(tag=TAG).info(
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f"AntafLLM 初始化: bridge={self.bridge_url}, timeout={self.timeout}s"
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)
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@staticmethod
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def _is_thinking(text):
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"""检测蚂蚁阿福的内心思考/推理过程，这些不应该发给用户"""
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thinking_patterns = [
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"用户问", "用户说", "用户的", "用户可能", "用户真正",
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"我得", "我会", "我在想", "我决定", "我要",
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"语气比较", "感觉他", "让他知道", "让他觉得",
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"先安抚", "得先", "不想表现",
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"整体语气", "这样能", "这样他",
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"所以我", "还带了个",
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]
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for p in thinking_patterns:
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if p in text:
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return True
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return False
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@staticmethod
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def _clean_text(text):
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"""清理阿福返回文本中的脏数据"""
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# 去掉阿福内部状态文本
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junk = [
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"完成资料引用", "内容生成", "正在思考", "正在搜索",
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]
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for j in junk:
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text = text.replace(j, "")
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return text.strip()
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@staticmethod
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def _is_system_injected(content):
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"""检测是否为系统注入的消息（非用户真实输入）"""
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if not content:
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return True
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markers = [
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"[系统提示]", "tool_call", "<tool_call>", "TOOL USE",
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"系统提示", "工具调用", "function_call",
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"handle_exit_intent", "你有以下工具", "You have access",
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]
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for m in markers:
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if m in content:
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return True
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# 超过200字的 user 消息大概率是系统注入的
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if len(content) > 200:
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return True
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return False
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def response(self, session_id, dialogue, **kwargs):
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# 从 dialogue 中提取真正的用户消息（跳过系统注入的 user 消息）
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query = ""
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for msg in reversed(dialogue):
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if msg.get("role") == "user":
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content = msg.get("content", "")
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if not self._is_system_injected(content):
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# ASR 结果可能是 JSON: {"content":"...", "language":"zh", "emotion":"..."}
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try:
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parsed = json.loads(content)
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if isinstance(parsed, dict) and "content" in parsed:
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query = parsed["content"]
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else:
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query = content
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except (json.JSONDecodeError, TypeError):
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query = content
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break
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if not query:
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logger.bind(tag=TAG).warning("对话中没有用户消息")
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yield "抱歉，我没有收到您的问题。"
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return
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self.should_idle = False
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logger.bind(tag=TAG).info(f"AntafLLM 请求: {query[:50]}...")
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try:
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url = f"{self.bridge_url}/chat"
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resp = requests.get(
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url,
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params={"q": query},
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stream=True,
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timeout=self.timeout,
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)
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resp.encoding = "utf-8"
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seen_texts = set()
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for line in resp.iter_lines(decode_unicode=True):
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if not line:
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continue
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if line.startswith("data: "):
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data = line[6:]
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if data == "[DONE]":
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break
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if not data or len(data.strip()) == 0:
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continue
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# 去重：跳过完全相同的文本块
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if data in seen_texts:
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continue
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seen_texts.add(data)
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# 过滤思考过程
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if self._is_thinking(data):
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logger.bind(tag=TAG).debug(f"过滤思考内容: {data[:50]}...")
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continue
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# 清理脏数据
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data = self._clean_text(data)
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if not data:
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continue
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yield data
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except requests.exceptions.ConnectionError:
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logger.bind(tag=TAG).error("无法连接蚂蚁阿福 Bridge，请检查手机和 Frida 状态")
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self.should_idle = True
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yield "抱歉，蚂蚁阿福服务暂时不可用。"
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except requests.exceptions.Timeout:
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logger.bind(tag=TAG).error(f"蚂蚁阿福 Bridge 超时 ({self.timeout}s)")
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self.should_idle = True
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yield "抱歉，回答超时了。"
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except Exception as e:
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logger.bind(tag=TAG).error(f"AntafLLM 异常: {e}")
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self.should_idle = True
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yield "抱歉，发生了错误。"
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