fix: auto-fix ruff linting errors and remove unsupported upload-artifact

handler_base/clients/embeddings.py

@@ -1,6 +1,7 @@
 """
 Embeddings service client (Infinity/BGE).
 """
+
 import logging
 from typing import Optional
 
@@ -15,23 +16,23 @@ logger = logging.getLogger(__name__)
 class EmbeddingsClient:
     """
     Client for the embeddings service (Infinity with BGE models).
-    
+
     Usage:
         client = EmbeddingsClient()
         embeddings = await client.embed(["Hello world"])
     """
-    
+
     def __init__(self, settings: Optional[EmbeddingsSettings] = None):
         self.settings = settings or EmbeddingsSettings()
         self._client = httpx.AsyncClient(
             base_url=self.settings.embeddings_url,
             timeout=self.settings.http_timeout,
         )
-    
+
     async def close(self) -> None:
         """Close the HTTP client."""
         await self._client.aclose()
-    
+
     async def embed(
         self,
         texts: list[str],
@@ -39,49 +40,49 @@ class EmbeddingsClient:
     ) -> list[list[float]]:
         """
         Generate embeddings for a list of texts.
-        
+
         Args:
             texts: List of texts to embed
             model: Model name (defaults to settings)
-            
+
         Returns:
             List of embedding vectors
         """
         model = model or self.settings.embeddings_model
-        
+
         with create_span("embeddings.embed") as span:
             if span:
                 span.set_attribute("embeddings.model", model)
                 span.set_attribute("embeddings.batch_size", len(texts))
-            
+
             response = await self._client.post(
                 "/embeddings",
                 json={"input": texts, "model": model},
             )
             response.raise_for_status()
-            
+
             result = response.json()
             embeddings = [d["embedding"] for d in result.get("data", [])]
-            
+
             if span:
                 span.set_attribute("embeddings.dimensions", len(embeddings[0]) if embeddings else 0)
-            
+
             return embeddings
-    
+
     async def embed_single(self, text: str, model: Optional[str] = None) -> list[float]:
         """
         Generate embedding for a single text.
-        
+
         Args:
             text: Text to embed
             model: Model name (defaults to settings)
-            
+
         Returns:
             Embedding vector
         """
         embeddings = await self.embed([text], model)
         return embeddings[0] if embeddings else []
-    
+
     async def health(self) -> bool:
         """Check if the embeddings service is healthy."""
         try: