cherry-pick ce28f30c

lightrag/api/lightrag_server.py

@@ -511,7 +511,9 @@ def create_app(args):
 
         return optimized_azure_openai_model_complete
 
-    def create_optimized_gemini_llm_func(config_cache: LLMConfigCache, args):
+    def create_optimized_gemini_llm_func(
+        config_cache: LLMConfigCache, args, llm_timeout: int
+    ):
         """Create optimized Gemini LLM function with cached configuration"""
 
         async def optimized_gemini_model_complete(
@@ -526,6 +528,8 @@ def create_app(args):
             if history_messages is None:
                 history_messages = []
 
+            # Use pre-processed configuration to avoid repeated parsing
+            kwargs["timeout"] = llm_timeout
             if (
                 config_cache.gemini_llm_options is not None
                 and "generation_config" not in kwargs
@@ -567,7 +571,7 @@ def create_app(args):
                     config_cache, args, llm_timeout
                 )
             elif binding == "gemini":
-                return create_optimized_gemini_llm_func(config_cache, args)
+                return create_optimized_gemini_llm_func(config_cache, args, llm_timeout)
             else:  # openai and compatible
                 # Use optimized function with pre-processed configuration
                 return create_optimized_openai_llm_func(config_cache, args, llm_timeout)
@@ -601,7 +605,7 @@ def create_app(args):
         Uses lazy imports for all bindings and avoids repeated configuration parsing.
         """
 
-        async def optimized_embedding_function(texts):
+        async def optimized_embedding_function(texts, embedding_dim=None):
             try:
                 if binding == "lollms":
                     from lightrag.llm.lollms import lollms_embed
@@ -640,13 +644,13 @@ def create_app(args):
                     from lightrag.llm.jina import jina_embed
 
                     return await jina_embed(
-                        texts, base_url=host, api_key=api_key
+                        texts, embedding_dim=embedding_dim, base_url=host, api_key=api_key
                     )
                 else:  # openai and compatible
                     from lightrag.llm.openai import openai_embed
 
                     return await openai_embed(
-                        texts, model=model, base_url=host, api_key=api_key
+                        texts, model=model, base_url=host, api_key=api_key, embedding_dim=embedding_dim
                     )
             except ImportError as e:
                 raise Exception(f"Failed to import {binding} embedding: {e}")
@@ -687,7 +691,7 @@ def create_app(args):
 
     # Create embedding function with optimized configuration
     import inspect
-    
+
     # Create the optimized embedding function
     optimized_embedding_func = create_optimized_embedding_function(
         config_cache=config_cache,
@@ -697,27 +701,33 @@ def create_app(args):
         api_key=args.embedding_binding_api_key,
         args=args,  # Pass args object for fallback option generation
     )
-    
+
     # Check environment variable for sending dimensions
     embedding_send_dim = os.getenv("EMBEDDING_SEND_DIM", "false").lower() == "true"
-    
+
     # Check if the function signature has embedding_dim parameter
     # Note: Since optimized_embedding_func is an async function, inspect its signature
     sig = inspect.signature(optimized_embedding_func)
-    has_embedding_dim_param = 'embedding_dim' in sig.parameters
-    
-    # Determine send_dimensions value
-    # Only send dimensions if both conditions are met:
-    # 1. EMBEDDING_SEND_DIM environment variable is true
-    # 2. The function has embedding_dim parameter
-    send_dimensions = embedding_send_dim and has_embedding_dim_param
-    
+    has_embedding_dim_param = "embedding_dim" in sig.parameters
+
+    # Determine send_dimensions value based on binding type
+    # Jina REQUIRES dimension parameter (forced to True)
+    # OpenAI and others: controlled by EMBEDDING_SEND_DIM environment variable
+    if args.embedding_binding == "jina":
+        # Jina API requires dimension parameter - always send it
+        send_dimensions = has_embedding_dim_param
+        dimension_control = "forced (Jina API requirement)"
+    else:
+        # For OpenAI and other bindings, respect EMBEDDING_SEND_DIM setting
+        send_dimensions = embedding_send_dim and has_embedding_dim_param
+        dimension_control = f"env_var={embedding_send_dim}"
+
     logger.info(
         f"Embedding configuration: send_dimensions={send_dimensions} "
-        f"(env_var={embedding_send_dim}, has_param={has_embedding_dim_param}, "
+        f"({dimension_control}, has_param={has_embedding_dim_param}, "
         f"binding={args.embedding_binding})"
     )
-    
+
     # Create EmbeddingFunc with send_dimensions attribute
     embedding_func = EmbeddingFunc(
         embedding_dim=args.embedding_dim,