Agent language switching

2026-08-09 11:38:03 +08:00
parent 774490b47f
commit e22da1879d
3 changed files with 101 additions and 6 deletions
@@ -7216,7 +7216,8 @@ RNA-seq KEGG/fgsea 会优先使用物种级 KEGG gene set；没有物种级缓
                                session_id: this.sessionId,
                                job_id: window.parentJobInfo ? window.parentJobInfo.job_id : 'default_job',
                                prefix: normalizedPrefix(window.parentParams ? window.parentParams.prefix : ''),
-                                model: 'kimi'
+                                model: 'kimi',
+                                language: window.__CROPAI_LANG__ || 'zh'
                            })
                        });

@@ -9548,7 +9549,8 @@ compare: [[flesh, peel]]`;
                                include_previous: true,
                                mode: 'comprehensive',
                                use_rag_support: Boolean(options.useRagSupport),
-                                refresh_rag: Boolean(options.refreshRag)
+                                refresh_rag: Boolean(options.refreshRag),
+                                language: currentLang.value
                            })
                        });
                        const data = await res.json();
@@ -22548,7 +22550,8 @@ compare: [[flesh, peel]]`;
                            model: selectedModel.value,
                            session_id: currentConversationId.value || sessionId.value,
                            job_id: jobInfo.job_id,
-                            history: agentSession.messages.slice(-10)
+                            history: agentSession.messages.slice(-10),
+                            language: currentLang.value
                        });

                        if (data.success && data.intent) {
@@ -22647,6 +22650,7 @@ compare: [[flesh, peel]]`;
                                            job_id: jobInfo.job_id,  // 传递当前 job_id 用于查询已完成阶段
                                            prefix: normalizedPrefix(params.prefix || ''),
                                            history: agentSession.messages.slice(-10),
+                                            language: currentLang.value,
                                            intent: data.intent  // 传递 chat 返回的 intent，避免重复调用 IntentAgentV2
                                        })
                                    }, 180000, '多智能体规划');
@@ -56,6 +56,8 @@ GF_MAS_ROOT = Path(os.environ.get("GF_MAS_ROOT", "/home/xzq/gene-family-mas-v2")
 if GF_MAS_ROOT.exists() and str(GF_MAS_ROOT) not in sys.path:
    sys.path.insert(0, str(GF_MAS_ROOT))
 PLANTAI_RAG_ROOT = Path(os.environ.get("PLANTAI_RAG_ROOT", "/home/xzq/PlantAI-rag")).expanduser()
+
+DEFAULT_AGENT_LANG = "zh"
 if PLANTAI_RAG_ROOT.exists() and str(PLANTAI_RAG_ROOT) not in sys.path:
    sys.path.insert(0, str(PLANTAI_RAG_ROOT))

@@ -16427,6 +16429,7 @@ class AgentChatRequest(BaseModel):
    session_id: str = ""
    job_id: str = ""  # 当前 job_id，用于查询已完成 stages
    history: list = []
+    language: str = DEFAULT_AGENT_LANG  # "zh" or "en"


 methodology_sandbox_registry_cache: Dict[str, str] = {}
@@ -17139,6 +17142,11 @@ async def _run_agent_chat(
    executes analysis stages here.
    """
    session_id = request.session_id or f"session_{int(time.time())}"
+    # Inject language instruction for non-Chinese languages
+    if request.language and request.language != DEFAULT_AGENT_LANG:
+        lang_map = {"en": "Please respond in English."}
+        lang_instruction = lang_map.get(request.language, f"Please respond in {request.language}.")
+        request.message = f"{lang_instruction}\n\n{request.message}"
    completed_stages: list[str] = []
    if request.job_id:
        final_dir = PROJECT_ROOT / "workspace" / request.job_id / "final"
@@ -17444,6 +17452,7 @@ async def _run_agent_chat(
            "history": list(request.history or []),
            "session_id": session_id,
            "job_id": request.job_id,
+            "language": request.language,
        }
        if router_goal_ledger is not None:
            payload["goal_ledger"] = router_goal_ledger
@@ -18204,6 +18213,7 @@ class AgentExecuteRequest(BaseModel):
    model: str = "kimi"
    history: list = []
    auto_execute: bool = True  # 是否自动执行分析
+    language: str = DEFAULT_AGENT_LANG  # "zh" or "en"

 class AgentStatusResponse(BaseModel):
    """Agent状态响应"""
@@ -18223,12 +18233,20 @@ async def agent_execute(request: AgentExecuteRequest, background_tasks: Backgrou
    与/agent/chat不同，此接口会实际执行分析流程
    """
    task_id = f"agent_task_{int(time.time())}_{uuid.uuid4().hex[:8]}"
-    
+
+    # Inject language instruction for non-Chinese languages
+    message = request.message
+    lang = getattr(request, "language", DEFAULT_AGENT_LANG)
+    if lang and lang != DEFAULT_AGENT_LANG:
+        lang_map = {"en": "Please respond in English."}
+        lang_instruction = lang_map.get(lang, f"Please respond in {lang}.")
+        message = f"{lang_instruction}\n\n{request.message}"
+
    # 初始化任务状态
    agent_tasks[task_id] = {
        "task_id": task_id,
        "status": "pending",
-        "message": request.message,
+        "message": message,
        "job_id": request.job_id,
        "prefix": request.prefix,
        "current_stage": "",
@@ -18267,7 +18285,7 @@ async def agent_execute(request: AgentExecuteRequest, background_tasks: Backgrou
                # 提交任务到Celery队列
                celery_task = execute_agent_task.delay(
                    task_id=task_id,
-                    message=request.message,
+                    message=message,
                    session_id=request.session_id or task_id,
                    job_id=request.job_id,  # 传入选中的 job_id
                    history=request.history if request.history else [],
@@ -18494,6 +18512,7 @@ class AgentPlanRequest(BaseModel):
    prefix: str = ""
    history: list = []
    intent: dict = {}  # 前端可传入已有的intent，避免重复调用IntentAgentV2
+    language: str = DEFAULT_AGENT_LANG  # "zh" or "en"


 SUBFAMILY_PREDICTION_PLAN_STAGES = [
@@ -20485,6 +20504,11 @@ async def agent_plan(request: AgentPlanRequest):
    """
    try:
        session_id = request.session_id or f"session_{int(time.time())}"
+        # Inject language instruction for non-Chinese languages
+        if request.language and request.language != DEFAULT_AGENT_LANG:
+            lang_map = {"en": "Please respond in English."}
+            lang_instruction = lang_map.get(request.language, f"Please respond in {request.language}.")
+            request.message = f"{lang_instruction}\n\n{request.message}"
        methodology_plan, methodology_intent, methodology_params = _load_methodology_plan_for_agent_request(request)
        if methodology_plan == {}:
            return {
@@ -23746,6 +23770,7 @@ class GenerateInterpretationRequest(BaseModel):
    strict_identity: bool = True
    use_rag_support: bool = False
    refresh_rag: bool = False
+    language: str = DEFAULT_AGENT_LANG  # "zh" or "en"

@app.post("/api/agent/interpret")
 async def generate_interpretation(request: GenerateInterpretationRequest):
@@ -0,0 +1,66 @@
+# Plan: Agent 跟随页面语言切换
+
+## Context
+
+[页面已有中英文切换](apps/web/vue3/index_ml.html#L1250)（`currentLang` + `switchLang()`），但内嵌的 Agent 对话始终使用默认中文回复。需要在语言切换后将语言参数传递给后端，让 Agent 用对应语言回应。
+
+## 受影响的 Agent 入口
+
+**前端 fetch 调用（[index_ml.html](apps/web/vue3/index_ml.html)）：**
+
+| 调用点 | 行号 | 端点 | 用途 |
+|--------|------|------|------|
+| RAG Agent `sendMessage` | ~7211 | `/api/agent/execute` | RAG Agent 组件对话 |
+| `requestAgentChatWithProgress` | ~22179 | `/api/agent/chat/stream` | 主 Agent 流式对话 |
+| `showInterpretationModal` | ~9533 | `/api/agent/interpret` | 深度解读报告 |
+
+**后端 Request 模型（[main.py](apps/web/vue3/main.py)）：**
+
+| 模型 | 行号 | 对应端点 |
+|------|------|----------|
+| `AgentChatRequest` | 16424 | `/api/agent/chat`, `/api/agent/chat/stream` |
+| `AgentExecuteRequest` | 18198 | `/api/agent/execute` |
+| `GenerateInterpretationRequest` | 23733 | `/api/agent/interpret` |
+
+（`AgentPlanRequest` 18489 和 `AgentRunRequest` 22814 也可顺带加上，但影响较小。）
+
+## 实现方案
+
+### 前端（1 个文件）
+
+在每个 Agent fetch payload 中加一行：
+```js
+language: currentLang.value  // "zh" 或 "en"
+```
+
+位置：
+1. `sendMessage()` body ~7215
+2. `requestAgentChatWithProgress()` 的 payload ~22546
+3. `showInterpretationModal()` body ~9535
+
+### 后端（1 个文件）
+
+**Step 1** — 三个 Request 模型各加一个字段：
+```python
+language: str = "zh"  # "zh" or "en"
+```
+
+**Step 2** — 在核心处理函数中注入语言指令。当 `language == "en"` 时，在用户消息前拼接指令：
+
+- `_run_agent_chat()` (~17131)：在传给 RouterAgent 的 `payload["query"]` 前拼接英文指令
+- `agent_execute()` (~18218)：在 `request.message` 存入 task 之前拼接，或存入 task state 供后续 LLM 调用使用
+- `generate_interpretation()` (~23751)：在构造 LLM 提示词时根据 `request.language` 追加语言指令
+
+## 修改文件
+
+| 文件 | 改动数 |
+|------|--------|
+| [index_ml.html](apps/web/vue3/index_ml.html) | ~3 行新增 |
+| [main.py](apps/web/vue3/main.py) | ~3 个 model 字段 + ~3 处语言注入逻辑 |
+
+## 验证
+
+1. 启动服务，页面切换到英文
+2. 在主 Agent 输入框发送消息，确认 Agent 用英文回复
+3. 切回中文，发送消息，确认恢复中文回复
+4. 测试 RAG Agent 组件和深度解读同样生效