feat: Add token tracking support to openai_embed function

- Add optional token_tracker parameter to openai_embed()
- Track prompt_tokens and total_tokens for embedding API calls
- Enables monitoring of embedding token usage alongside LLM calls
- Maintains backward compatibility with existing code

lightrag/llm/openai.py

@@ -579,6 +579,7 @@ async def openai_embed(
     base_url: str | None = None,
     api_key: str | None = None,
     client_configs: dict[str, Any] | None = None,
+    token_tracker: Any | None = None,
 ) -> np.ndarray:
     """Generate embeddings for a list of texts using OpenAI's API.
 
@@ -590,6 +591,7 @@ async def openai_embed(
         client_configs: Additional configuration options for the AsyncOpenAI client.
             These will override any default configurations but will be overridden by
             explicit parameters (api_key, base_url).
+        token_tracker: Optional token usage tracker for monitoring API usage.
 
     Returns:
         A numpy array of embeddings, one per input text.
@@ -608,6 +610,14 @@ async def openai_embed(
         response = await openai_async_client.embeddings.create(
             model=model, input=texts, encoding_format="base64"
         )
+        
+        if token_tracker and hasattr(response, "usage"):
+            token_counts = {
+                "prompt_tokens": getattr(response.usage, "prompt_tokens", 0),
+                "total_tokens": getattr(response.usage, "total_tokens", 0),
+            }
+            token_tracker.add_usage(token_counts)
+        
         return np.array(
             [
                 np.array(dp.embedding, dtype=np.float32)