Add embedding preview

api/app/routers/llm.py

@@ -214,7 +214,7 @@ def preview_llm_model(
     request: LLMPreviewRequest,
     db: Session = Depends(get_db)
 ):
-    """预览 LLM 输出，基于 OpenAI-compatible /chat/completions。"""
+    """预览模型输出，支持 text(chat) 与 embedding 两类模型。"""
     model = db.query(LLMModel).filter(LLMModel.id == id).first()
     if not model:
         raise HTTPException(status_code=404, detail="LLM Model not found")
@@ -223,24 +223,35 @@ def preview_llm_model(
     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),
-    }
+    model_id = model.model_name or "gpt-3.5-turbo"
     headers = {"Authorization": f"Bearer {(request.api_key or model.api_key).strip()}"}
 
     start_time = time.time()
+    endpoint = "/chat/completions"
+    payload = {}
+
+    if model.type == "embedding":
+        endpoint = "/embeddings"
+        payload = {
+            "model": model_id,
+            "input": user_message,
+        }
+    else:
+        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_id,
+            "messages": messages,
+            "max_tokens": request.max_tokens or 512,
+            "temperature": request.temperature if request.temperature is not None else (model.temperature or 0.7),
+        }
+
     try:
         with httpx.Client(timeout=60.0) as client:
             response = client.post(
-                f"{model.base_url.rstrip('/')}/chat/completions",
+                f"{model.base_url.rstrip('/')}{endpoint}",
                 json=payload,
                 headers=headers
             )
@@ -258,9 +269,23 @@ def preview_llm_model(
 
     result = response.json()
     reply = ""
-    choices = result.get("choices", [])
-    if choices:
-        reply = choices[0].get("message", {}).get("content", "") or ""
+    if model.type == "embedding":
+        data_list = result.get("data", [])
+        embedding = []
+        if data_list and isinstance(data_list, list):
+            embedding = data_list[0].get("embedding", []) or []
+        dims = len(embedding) if isinstance(embedding, list) else 0
+        preview_values = []
+        if isinstance(embedding, list):
+            preview_values = embedding[:8]
+        values_text = ", ".join(
+            [f"{float(v):.6f}" if isinstance(v, (float, int)) else str(v) for v in preview_values]
+        )
+        reply = f"Embedding generated successfully. dims={dims}. head=[{values_text}]"
+    else:
+        choices = result.get("choices", [])
+        if choices:
+            reply = choices[0].get("message", {}).get("content", "") or ""
 
     return LLMPreviewResponse(
         success=bool(reply),

api/tests/test_llm.py

@@ -300,3 +300,53 @@ class TestLLMModelAPI:
 
         response = client.post(f"/api/llm/{model_id}/preview", json={"message": "   "})
         assert response.status_code == 400
+
+    def test_preview_embedding_model_success(self, client, monkeypatch):
+        """Test embedding model preview endpoint returns embedding summary."""
+        from app.routers import llm as llm_router
+
+        embedding_model_data = {
+            "id": "preview-emb",
+            "name": "Preview Embedding",
+            "vendor": "OpenAI",
+            "type": "embedding",
+            "base_url": "https://api.openai.com/v1",
+            "api_key": "test-key",
+            "model_name": "text-embedding-3-small"
+        }
+        create_response = client.post("/api/llm", json=embedding_model_data)
+        model_id = create_response.json()["id"]
+
+        class DummyResponse:
+            status_code = 200
+
+            def json(self):
+                return {"data": [{"embedding": [0.1, 0.2, 0.3, 0.4]}], "usage": {"total_tokens": 7}}
+
+            @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("/embeddings")
+                assert json["input"] == "hello embedding"
+                assert headers["Authorization"] == "Bearer test-key"
+                return DummyResponse()
+
+        monkeypatch.setattr(llm_router.httpx, "Client", DummyClient)
+
+        response = client.post(f"/api/llm/{model_id}/preview", json={"message": "hello embedding"})
+        assert response.status_code == 200
+        data = response.json()
+        assert data["success"] is True
+        assert "dims=4" in data["reply"]