feat: cockpit agent 切原生 function calling
- llm_bridge 新增 llm_call_msgs_native 支持 tools 参数+解析 tool_calls - agent_loop_v2._call_llm 优先走 native function calling，失败回退文本 - run loop 处理原生 tool_calls（role=tool 回填） - _init_components 把 config.tools 注册进 ToolRegistry（修复 registry 空导致 tools_description 为空、schema 为空的 bug）
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ymq
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@ -126,13 +126,59 @@ class AgentExecutor:
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if self.config.compression.enabled:
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await self._maybe_compress()
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# 5b. LLM 调用
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raw = await self._call_llm()
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# 5b. LLM 调用（native function calling，返回 dict）
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resp = await self._call_llm()
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# 5c. 解析 LLM 输出
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# 5c. 原生 function calling：优先处理 tool_calls
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native_calls = (resp.get("tool_calls") or []) if isinstance(resp, dict) else []
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if native_calls:
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# 回填 assistant 消息（含 tool_calls，OpenAI 原生格式要求）
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self._msgs.append({
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"role": "assistant",
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"content": resp.get("content") or None,
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"tool_calls": native_calls,
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})
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for tc in native_calls:
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fn = tc.get("function", {}) if isinstance(tc, dict) else {}
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tool_name = fn.get("name", "")
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try:
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tool_params = json.loads(fn.get("arguments") or "{}")
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except Exception:
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tool_params = {}
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# 需要确认的工具
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if self._needs_confirmation(tool_name):
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yield json.dumps({
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"type": "confirm", "tool": tool_name, "params": tool_params,
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}, ensure_ascii=False) + "\n"
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await self._save_turn(user_input, f"[需确认] {tool_name}")
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return
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yield json.dumps({
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"type": "tool_call", "tool": tool_name, "params": tool_params,
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}, ensure_ascii=False) + "\n"
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result = await self._execute_tool(tool_name, tool_params)
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if not result.startswith("未知工具") and not result.startswith("ERROR"):
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self._tool_call_count += 1
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yield json.dumps({
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"type": "tool_result", "tool": tool_name, "result": result[:500],
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}, ensure_ascii=False) + "\n"
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# 原生回填：role=tool + tool_call_id
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self._msgs.append({
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"role": "tool",
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"tool_call_id": tc.get("id", "") if isinstance(tc, dict) else "",
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"content": str(result),
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})
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continue
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# 5d. 文本解析（回退路径，纯文本模型）
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raw = resp.get("content", "") if isinstance(resp, dict) else str(resp)
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actions = self._parse_actions(raw)
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# 5d. 分发处理
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# 5e. 分发处理
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for act in actions:
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action_type = act.get("action", "")
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@ -232,6 +278,12 @@ class AgentExecutor:
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try:
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from pipeline_core.tool_registry import get_tool_registry
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self._tool_registry = get_tool_registry()
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# 把 config.tools 注册进 registry（registry 是全局单例，可能为空）
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if self._tool_registry and self.config.tools:
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existing = set(self._tool_registry.get_tool_names())
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for t in self.config.tools:
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if t.name not in existing:
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self._tool_registry.register(t)
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except ImportError:
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self._tool_registry = None
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@ -315,22 +367,40 @@ class AgentExecutor:
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# LLM 调用
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# ═══════════════════════════════════════════════════════
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async def _call_llm(self) -> str:
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"""调用 LLM，返回原始文本"""
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from pipeline_service.llm_bridge import llm_call_msgs
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async def _call_llm(self) -> dict:
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"""调用 LLM，返回 {"content": str, "tool_calls": [...]}。
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优先使用 OpenAI native function calling。若 registry 不可用或调用失败，
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回退到纯文本调用（content 为原始文本，tool_calls 为空）。
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"""
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from pipeline_service.llm_bridge import llm_call_msgs_native
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# 使用 OpenAI native function calling（如果模型支持）
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use_native = False
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tools_schema = None
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if self._tool_registry and use_native:
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tools_schema = self._tool_registry.to_openai_schema()
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if self._tool_registry:
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try:
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tools_schema = self._tool_registry.to_openai_schema()
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except Exception as e:
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logger.error(f"to_openai_schema failed: {e}")
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# 简单消息调用（兼容所有模型）
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return await llm_call_msgs(
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if tools_schema:
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try:
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return await llm_call_msgs_native(
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self._msgs,
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tools=tools_schema,
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model=self.model_name,
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temperature=self.config.temperature,
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)
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except Exception as e:
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logger.error(f"native function calling failed, fallback to text: {e}")
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# 回退：纯文本调用（content 为原始文本）
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from pipeline_service.llm_bridge import llm_call_msgs
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content = await llm_call_msgs(
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self._msgs,
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model=self.model_name,
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temperature=self.config.temperature,
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)
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return {"content": content or "", "tool_calls": []}
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# ═══════════════════════════════════════════════════════
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# 解析 LLM 输出
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@ -160,3 +160,47 @@ async def llm_call_msgs(messages: list, model: str = None, temperature: float =
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raise ValueError(f"LLM API error {resp.status}: {text[:300]}")
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data = await resp.json()
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return data["choices"][0]["message"]["content"]
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async def llm_call_msgs_native(messages: list, tools: list = None, model: str = None, temperature: float = 0.7) -> dict:
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"""Native function calling. 传入 tools JSON schema，返回 message dict。
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Returns:
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{"content": str, "tool_calls": [{"id","type","function":{"name","arguments"}}]}
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当模型返回 tool_calls 时，content 通常为空字符串。
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"""
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import aiohttp
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cfg = await _get_model_config(model)
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if cfg.get("api_key") and cfg.get("api_base"):
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api_base = cfg["api_base"]
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api_key = cfg["api_key"]
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model_id = cfg.get("model_id") or model or "default"
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else:
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api_base = os.environ.get("LLM_API_BASE", "https://api.openai.com/v1")
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api_key = os.environ.get("LLM_API_KEY", "")
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model_id = model or os.environ.get("LLM_MODEL", "gpt-4o-mini")
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if not api_key:
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raise ValueError("No LLM API configured")
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headers = {"Authorization": f"Bearer {api_key}", "Content-Type": "application/json"}
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payload = {"model": model_id, "messages": messages, "temperature": temperature}
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if tools:
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payload["tools"] = tools
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payload["tool_choice"] = "auto"
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url = api_base.rstrip("/") + "/chat/completions"
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async with aiohttp.ClientSession() as session:
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async with session.post(url, headers=headers, json=payload,
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timeout=aiohttp.ClientTimeout(total=180)) as resp:
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if resp.status != 200:
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text = await resp.text()
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raise ValueError(f"LLM API error {resp.status}: {text[:300]}")
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data = await resp.json()
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msg = data["choices"][0]["message"]
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return {
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"content": msg.get("content") or "",
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"tool_calls": msg.get("tool_calls") or [],
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}
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