fix: 修复 Agent 流式对话无响应和工具 schema 兼容性问题

- 在 `run_stream()` LLM 调用前 yield `think` 事件，前端即时显示"思考中..." - 修复 tool schema 规范化逻辑：`{"function":{...}}` 格式缺少 `type` 字段导致 LLM API 拒绝 - 启动时从数据库加载自定义工具（`load_tools_from_db`），解决重启后工具丢失 - 前端 SSE 添加 60s 超时保护，任何事件类型均触发 `receivedFirstEvent` - 流式失败自动降级到非流式 POST - 添加 `scripts/seed_coding_agent.py` 和 `scripts/test_coding_agent.py` Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
2026-05-02 00:38:41 +08:00
parent 342f3fcb16
commit 7aba0f9bc5
10 changed files with 662 additions and 36 deletions
--- a/backend/app/services/llm_service.py
+++ b/backend/app/services/llm_service.py
@@ -21,6 +21,20 @@ _DSML_NO_ARG_TOOLS = {
 }


+def _chat_messages(
+    user_content: str,
+    system_prompt: Optional[str] = None,
+) -> List[Dict[str, str]]:
+    """构建 chat.completions messages：可选 system + user。"""
+    msgs: List[Dict[str, str]] = []
+    if system_prompt is not None:
+        sp = system_prompt.strip()
+        if sp:
+            msgs.append({"role": "system", "content": sp})
+    msgs.append({"role": "user", "content": user_content})
+    return msgs
+
+
 def _resolve_request_timeout(kwargs: Dict[str, Any]) -> float:
    """
    统一解析 LLM 请求超时时间（秒）：
@@ -415,6 +429,7 @@ class LLMService:
        max_tokens: Optional[int] = None,
        api_key: Optional[str] = None,
        base_url: Optional[str] = None,
+        system_prompt: Optional[str] = None,
        **kwargs
    ) -> str:
        """
@@ -470,9 +485,7 @@ class LLMService:
                try:
                    response = await client.chat.completions.create(
                        model=model,
-                        messages=[
-                            {"role": "user", "content": prompt}
-                        ],
+                        messages=_chat_messages(prompt, system_prompt),
                        temperature=temperature,
                        max_tokens=max_tokens,
                        timeout=request_timeout,
@@ -507,6 +520,7 @@ class LLMService:
        max_tokens: Optional[int] = None,
        api_key: Optional[str] = None,
        base_url: Optional[str] = None,
+        system_prompt: Optional[str] = None,
        **kwargs
    ) -> str:
        """
@@ -566,9 +580,7 @@ class LLMService:
                try:
                    response = await client.chat.completions.create(
                        model=model,
-                        messages=[
-                            {"role": "user", "content": prompt}
-                        ],
+                        messages=_chat_messages(prompt, system_prompt),
                        temperature=temperature,
                        max_tokens=max_tokens,
                        timeout=request_timeout,
@@ -602,6 +614,7 @@ class LLMService:
        model: Optional[str] = None,
        temperature: float = 0.7,
        max_tokens: Optional[int] = None,
+        system_prompt: Optional[str] = None,
        **kwargs
    ) -> str:
        """
@@ -627,6 +640,7 @@ class LLMService:
                model=model,
                temperature=temperature,
                max_tokens=max_tokens,
+                system_prompt=system_prompt,
                **kwargs
            )
        elif provider == "deepseek":
@@ -638,6 +652,7 @@ class LLMService:
                model=model,
                temperature=temperature,
                max_tokens=max_tokens,
+                system_prompt=system_prompt,
                **kwargs
            )
        else:
@@ -658,6 +673,7 @@ class LLMService:
        on_tool_executed: Optional[Callable[[str], Awaitable[None]]] = None,
        request_timeout: Optional[float] = None,
        extra_body: Optional[Dict[str, Any]] = None,
+        system_prompt: Optional[str] = None,
    ) -> str:
        """
        调用OpenAI API，支持工具调用
@@ -693,7 +709,7 @@ class LLMService:
                    raise ValueError("OpenAI API Key未配置")
            client = self.openai_client
        
-        messages = [{"role": "user", "content": prompt}]
+        messages = _chat_messages(prompt, system_prompt)
        
        request_timeout = _resolve_request_timeout(
            {"request_timeout": request_timeout} if request_timeout is not None else {}
@@ -716,10 +732,11 @@ class LLMService:
                    openai_tools = []
                    for tool in tools:
                        if isinstance(tool, dict):
-                            if "type" in tool and tool["type"] == "function":
+                            if tool.get("type") == "function":
                                openai_tools.append(tool)
                            elif "function" in tool:
-                                openai_tools.append(tool)
+                                # Has function but missing type
+                                openai_tools.append({"type": "function", "function": tool["function"]})
                            else:
                                # 假设是function格式，包装一下
                                openai_tools.append({
@@ -884,6 +901,7 @@ class LLMService:
        on_tool_executed: Optional[Callable[[str], Awaitable[None]]] = None,
        request_timeout: Optional[float] = None,
        extra_body: Optional[Dict[str, Any]] = None,
+        system_prompt: Optional[str] = None,
    ) -> str:
        """
        调用DeepSeek API，支持工具调用（DeepSeek兼容OpenAI API格式）
@@ -903,6 +921,7 @@ class LLMService:
            on_tool_executed=on_tool_executed,
            request_timeout=request_timeout,
            extra_body=extra_body,
+            system_prompt=system_prompt,
        )
    
    async def call_llm_with_tools(
@@ -917,6 +936,7 @@ class LLMService:
        tool_choice: Optional[str] = None,
        on_tool_executed: Optional[Callable[[str], Awaitable[None]]] = None,
        request_timeout: Optional[float] = None,
+        system_prompt: Optional[str] = None,
        **kwargs
    ) -> str:
        """
@@ -947,6 +967,7 @@ class LLMService:
                tool_choice=tool_choice,
                on_tool_executed=on_tool_executed,
                request_timeout=request_timeout,
+                system_prompt=system_prompt,
                **kwargs
            )
        elif provider == "deepseek":
@@ -962,6 +983,7 @@ class LLMService:
                tool_choice=tool_choice,
                on_tool_executed=on_tool_executed,
                request_timeout=request_timeout,
+                system_prompt=system_prompt,
                **kwargs
            )
        else:
--- a/backend/app/services/workflow_engine.py
+++ b/backend/app/services/workflow_engine.py
@@ -1434,9 +1434,13 @@ class WorkflowEngine:
                logger.debug(f"[rjb] LLM节点数据: node_id={node_id}, node_data keys={list(node_data.keys())}, api_key={'已配置' if node_data.get('api_key') else '未配置'}")
                prompt = node_data.get('prompt', '')
                
-                # 如果prompt为空，使用默认提示词
+                # 如果 prompt 为空：不要用 {input} 展开整包 input_data。
+                # 预览/执行侧常带 user_id、memory、conversation_history 等大对象，模型易照抄成 ```json 回复。
                if not prompt:
-                    prompt = "请处理以下输入数据：\n{input}"
+                    prompt = (
+                        "请根据用户当前问题用自然语言回答；需要时可用工具。"
+                        "不要向用户复述或输出完整的 input_data / API 请求 JSON。"
+                    )
                
                # 格式化prompt，替换变量
                try:
@@ -1804,6 +1808,15 @@ class WorkflowEngine:
                
                # 记录实际发送给LLM的prompt
                logger.info(f"[rjb] 准备调用LLM: node_id={node_id}, provider={provider}, model={model}, prompt前200字符='{prompt[:200] if len(prompt) > 200 else prompt}'")
+
+                _raw_sys = node_data.get("system_prompt")
+                llm_system_prompt: Optional[str] = None
+                if isinstance(_raw_sys, str) and _raw_sys.strip():
+                    llm_system_prompt = _raw_sys.strip()
+                elif _raw_sys is not None and not isinstance(_raw_sys, (dict, list)):
+                    _ts = str(_raw_sys).strip()
+                    if _ts:
+                        llm_system_prompt = _ts
                
                # 检查是否启用工具调用
                enable_tools = node_data.get('enable_tools', False)
@@ -1872,6 +1885,7 @@ class WorkflowEngine:
                            execution_logger=self.logger,
                            tool_choice=_tool_choice,
                            on_tool_executed=self._on_tool_executed_budget,
+                            system_prompt=llm_system_prompt,
                            **_merged_tool_kw,
                        )
                        result = self._enrich_llm_json_user_profile(result, input_data)
@@ -1882,6 +1896,7 @@ class WorkflowEngine:
                            model=model,
                            temperature=temperature,
                            max_tokens=max_tokens,
+                            system_prompt=llm_system_prompt,
                            **llm_extra_kw,
                        )
                        result = self._enrich_llm_json_user_profile(result, input_data)