Update llm library web interface

api/app/routers/llm.py

@@ -10,7 +10,7 @@ from ..db import get_db
 from ..models import LLMModel
 from ..schemas import (
     LLMModelCreate, LLMModelUpdate, LLMModelOut,
-    LLMModelTestResponse
+    LLMModelTestResponse, LLMPreviewRequest, LLMPreviewResponse
 )
 
 router = APIRouter(prefix="/llm", tags=["LLM Models"])
@@ -204,3 +204,66 @@ def chat_with_llm(
 
     except Exception as e:
         raise HTTPException(status_code=500, detail=str(e))
+
+
+@router.post("/{id}/preview", response_model=LLMPreviewResponse)
+def preview_llm_model(
+    id: str,
+    request: LLMPreviewRequest,
+    db: Session = Depends(get_db)
+):
+    """预览 LLM 输出，基于 OpenAI-compatible /chat/completions。"""
+    model = db.query(LLMModel).filter(LLMModel.id == id).first()
+    if not model:
+        raise HTTPException(status_code=404, detail="LLM Model not found")
+
+    user_message = (request.message or "").strip()
+    if not user_message:
+        raise HTTPException(status_code=400, detail="Preview message cannot be empty")
+
+    messages = []
+    if request.system_prompt and request.system_prompt.strip():
+        messages.append({"role": "system", "content": request.system_prompt.strip()})
+    messages.append({"role": "user", "content": user_message})
+
+    payload = {
+        "model": model.model_name or "gpt-3.5-turbo",
+        "messages": messages,
+        "max_tokens": request.max_tokens or 512,
+        "temperature": request.temperature if request.temperature is not None else (model.temperature or 0.7),
+    }
+    headers = {"Authorization": f"Bearer {(request.api_key or model.api_key).strip()}"}
+
+    start_time = time.time()
+    try:
+        with httpx.Client(timeout=60.0) as client:
+            response = client.post(
+                f"{model.base_url.rstrip('/')}/chat/completions",
+                json=payload,
+                headers=headers
+            )
+    except Exception as exc:
+        raise HTTPException(status_code=502, detail=f"LLM request failed: {exc}") from exc
+
+    if response.status_code != 200:
+        detail = response.text
+        try:
+            detail_json = response.json()
+            detail = detail_json.get("error", {}).get("message") or detail_json.get("detail") or detail
+        except Exception:
+            pass
+        raise HTTPException(status_code=502, detail=f"LLM vendor error: {detail}")
+
+    result = response.json()
+    reply = ""
+    choices = result.get("choices", [])
+    if choices:
+        reply = choices[0].get("message", {}).get("content", "") or ""
+
+    return LLMPreviewResponse(
+        success=bool(reply),
+        reply=reply,
+        usage=result.get("usage"),
+        latency_ms=int((time.time() - start_time) * 1000),
+        error=None if reply else "No response content",
+    )

api/app/schemas.py

@@ -153,6 +153,22 @@ class LLMModelTestResponse(BaseModel):
     message: Optional[str] = None
 
 
+class LLMPreviewRequest(BaseModel):
+    message: str
+    system_prompt: Optional[str] = None
+    max_tokens: Optional[int] = None
+    temperature: Optional[float] = None
+    api_key: Optional[str] = None
+
+
+class LLMPreviewResponse(BaseModel):
+    success: bool
+    reply: Optional[str] = None
+    usage: Optional[dict] = None
+    latency_ms: Optional[int] = None
+    error: Optional[str] = None
+
+
 # ============ ASR Model ============
 class ASRModelBase(BaseModel):
     name: str

api/tests/test_llm.py

@@ -244,3 +244,55 @@ class TestLLMModelAPI:
         response = client.post("/api/llm", json=data)
         assert response.status_code == 200
         assert response.json()["type"] == "embedding"
+
+    def test_preview_llm_model_success(self, client, sample_llm_model_data, monkeypatch):
+        """Test LLM preview endpoint returns model reply."""
+        from app.routers import llm as llm_router
+
+        create_response = client.post("/api/llm", json=sample_llm_model_data)
+        model_id = create_response.json()["id"]
+
+        class DummyResponse:
+            status_code = 200
+
+            def json(self):
+                return {
+                    "choices": [{"message": {"content": "Preview OK"}}],
+                    "usage": {"prompt_tokens": 10, "completion_tokens": 2, "total_tokens": 12}
+                }
+
+            @property
+            def text(self):
+                return '{"ok":true}'
+
+        class DummyClient:
+            def __init__(self, *args, **kwargs):
+                pass
+
+            def __enter__(self):
+                return self
+
+            def __exit__(self, exc_type, exc, tb):
+                return False
+
+            def post(self, url, json=None, headers=None):
+                assert url.endswith("/chat/completions")
+                assert headers["Authorization"] == f"Bearer {sample_llm_model_data['api_key']}"
+                assert json["messages"][0]["role"] == "user"
+                return DummyResponse()
+
+        monkeypatch.setattr(llm_router.httpx, "Client", DummyClient)
+
+        response = client.post(f"/api/llm/{model_id}/preview", json={"message": "hello"})
+        assert response.status_code == 200
+        data = response.json()
+        assert data["success"] is True
+        assert data["reply"] == "Preview OK"
+
+    def test_preview_llm_model_reject_empty_message(self, client, sample_llm_model_data):
+        """Test LLM preview endpoint validates message."""
+        create_response = client.post("/api/llm", json=sample_llm_model_data)
+        model_id = create_response.json()["id"]
+
+        response = client.post(f"/api/llm/{model_id}/preview", json={"message": "   "})
+        assert response.status_code == 400