feat: record embedding call usage into ModelUsageTracker

backend/app/infrastructure/embedding/openai_compatible_embedding_provider.py

@@ -3,11 +3,13 @@
 from __future__ import annotations
 
 import os
+import time
 
 import httpx
 
 from app.config.settings import settings
 from app.domain.retrieval import EmbeddingProvider
+from app.shared.model_usage_tracker import get_model_usage_tracker
 # Keep adapter behavior explicit so integration details remain easy to audit.
 
 EMBEDDING_BATCH_SIZE = 8
@@ -45,20 +47,41 @@ class OpenAICompatibleEmbeddingProvider(EmbeddingProvider):
         """Handle request for this module for the Open A I Compatible Embedding Provider instance."""
         if not self.api_key:
             raise ValueError("缺少 EMBEDDING_API_KEY / OPENAI_API_KEY")
-        response = httpx.post(
-            f"{self.base_url}/embeddings",
-            headers={
-                "Authorization": f"Bearer {self.api_key}",
-                "Content-Type": "application/json",
-            },
-            json={"model": self.model, "input": texts},
-            timeout=self.timeout,
-        )
-        self._raise_for_status(response, batch_size=len(texts))
-        data = response.json()
+        start = time.time()
+        try:
+            response = httpx.post(
+                f"{self.base_url}/embeddings",
+                headers={
+                    "Authorization": f"Bearer {self.api_key}",
+                    "Content-Type": "application/json",
+                },
+                json={"model": self.model, "input": texts},
+                timeout=self.timeout,
+            )
+            self._raise_for_status(response, batch_size=len(texts))
+            data = response.json()
+        except Exception as exc:
+            # Record the failed call so the Status page can show it as an error,
+            # then re-raise unchanged so existing callers keep their current behavior.
+            get_model_usage_tracker().record(
+                provider="embedding",
+                model=self.model,
+                success=False,
+                latency_ms=int((time.time() - start) * 1000),
+                error=str(exc),
+            )
+            raise
         vectors = [item["embedding"] for item in sorted(data.get("data", []), key=lambda item: item["index"])]
         if any(len(vector) != self.dimension for vector in vectors):
             raise ValueError(f"embedding 维度不匹配，期望 {self.dimension}")
+        # Record token usage from the OpenAI-compatible response, e.g. {"total_tokens": N}.
+        get_model_usage_tracker().record(
+            provider="embedding",
+            model=self.model,
+            success=True,
+            usage=data.get("usage", {}),
+            latency_ms=int((time.time() - start) * 1000),
+        )
         return vectors
 
     def embed_texts(self, texts: list[str]) -> list[list[float]]: