fix: use /embeddings endpoint for embedding models in connectivity test

text-embedding-* and other embedding models must call /embeddings not
/chat/completions. Added _is_embedding_model() heuristic that checks model
name keywords to route to the correct endpoint automatically.

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>

webapp/api/llm_profiles.py

@@ -23,16 +23,29 @@ router = APIRouter(prefix="/api/llm-profiles", tags=["llm-profiles"])
 logger = logging.getLogger("webapp.api.llm_profiles")
 
 
+# 常见 embedding 模型名称关键词，用于自动判断走 /embeddings 端点
+_EMBEDDING_MODEL_KEYWORDS = (
+    "embedding", "embed", "text-search", "text-similarity",
+    "code-search", "ada-002",
+)
+
+
+def _is_embedding_model(model: str) -> bool:
+    """Heuristic: return True if the model name looks like an embedding model."""
+    return any(kw in model.lower() for kw in _EMBEDDING_MODEL_KEYWORDS)
+
+
 def _do_connectivity_test(
     model: str,
     base_url: str,
     api_key: str,
     timeout_seconds: int,
 ) -> ProfileTestResponse:
-    """Send a minimal chat completion request and return the test result.
+    """Send a minimal request and return the connectivity test result.
 
-    Tries max_completion_tokens first (required by newer OpenAI models like gpt-5.x),
+    - Embedding models → POST /embeddings with a short text
-    then falls back to max_tokens for older models / compatible APIs.
+    - Chat models → POST /chat/completions, tries max_completion_tokens first
+      (required by newer models like gpt-5.x), falls back to max_tokens.
     """
     client = OpenAI(
         api_key=api_key,
@@ -40,7 +53,18 @@ def _do_connectivity_test(
         timeout=float(timeout_seconds),
     )
     t0 = time.monotonic()
-    # Try newer parameter first, fall back to legacy max_tokens on failure
+
+    if _is_embedding_model(model):
+        # Embedding 模型走 /embeddings 端点
+        try:
+            client.embeddings.create(model=model, input="test")
+            latency_ms = int((time.monotonic() - t0) * 1000)
+            return ProfileTestResponse(ok=True, message="连接成功（embedding）", latency_ms=latency_ms)
+        except Exception as exc:  # noqa: BLE001
+            latency_ms = int((time.monotonic() - t0) * 1000)
+            return ProfileTestResponse(ok=False, message=str(exc), latency_ms=latency_ms)
+
+    # Chat 模型：先用 max_completion_tokens，失败时 fallback 到 max_tokens
     for kwargs in [{"max_completion_tokens": 1}, {"max_tokens": 1}]:
         try:
             client.chat.completions.create(
@@ -52,11 +76,12 @@ def _do_connectivity_test(
             return ProfileTestResponse(ok=True, message="连接成功", latency_ms=latency_ms)
         except Exception as exc:  # noqa: BLE001
             err_str = str(exc)
-            # Only retry if the error is specifically about the token parameter name
+            # 仅当错误明确提示参数名称问题时才重试
             if "max_tokens" in err_str and "max_completion_tokens" in err_str and kwargs.get("max_completion_tokens"):
                 continue
             latency_ms = int((time.monotonic() - t0) * 1000)
             return ProfileTestResponse(ok=False, message=err_str, latency_ms=latency_ms)
+
     latency_ms = int((time.monotonic() - t0) * 1000)
     return ProfileTestResponse(ok=False, message="连接测试失败", latency_ms=latency_ms)