Merge branch 'HKUDS:main' into main

lightrag/api/lightrag_server.py

@@ -551,10 +551,10 @@ def get_api_key_dependency(api_key: Optional[str]):
 
 def create_app(args):
     # Verify that bindings arer correctly setup
-    if args.llm_binding not in ["lollms", "ollama", "openai"]:
+    if args.llm_binding not in ["lollms", "ollama", "openai", "azure_openai"]:
         raise Exception("llm binding not supported")
 
-    if args.embedding_binding not in ["lollms", "ollama", "openai"]:
+    if args.embedding_binding not in ["lollms", "ollama", "openai", "azure_openai"]:
         raise Exception("embedding binding not supported")
 
     # Add SSL validation

lightrag/lightrag.py

@@ -469,9 +469,8 @@ class LightRAG:
                     error_msg = f"Failed to process document {doc_id}: {str(e)}\n{traceback.format_exc()}"
                     logger.error(error_msg)
                     continue
-
-                finally:
-                    # Ensure all indexes are updated after each document
+                else:
+                    # Only update index when processing succeeds
                     await self._insert_done()
 
     def insert_custom_chunks(self, full_text: str, text_chunks: list[str]):

lightrag/utils.py

@@ -479,9 +479,7 @@ async def handle_cache(hashing_kv, args_hash, prompt, mode="default"):
     quantized = min_val = max_val = None
     if is_embedding_cache_enabled:
         # Use embedding cache
-        embedding_model_func = hashing_kv.global_config[
-            "embedding_func"
-        ].func  # ["func"]
+        embedding_model_func = hashing_kv.global_config["embedding_func"]["func"]
         llm_model_func = hashing_kv.global_config.get("llm_model_func")
 
         current_embedding = await embedding_model_func([prompt])