diff --git a/lightrag/api/lightrag_server.py b/lightrag/api/lightrag_server.py
index 930358a7..16e59d21 100644
--- a/lightrag/api/lightrag_server.py
+++ b/lightrag/api/lightrag_server.py
@@ -713,7 +713,6 @@ def create_app(args):
         )
 
         # Step 3: Create optimized embedding function (calls underlying function directly)
-        # Note: When model is None, each binding will use its own default model
         async def optimized_embedding_function(texts, embedding_dim=None):
             try:
                 if binding == "lollms":
@@ -725,9 +724,9 @@ def create_app(args):
                         if isinstance(lollms_embed, EmbeddingFunc)
                         else lollms_embed
                     )
-                    # lollms embed_model is not used (server uses configured vectorizer)
-                    # Only pass base_url and api_key
-                    return await actual_func(texts, base_url=host, api_key=api_key)
+                    return await actual_func(
+                        texts, embed_model=model, host=host, api_key=api_key
+                    )
                 elif binding == "ollama":
                     from lightrag.llm.ollama import ollama_embed
 
@@ -746,16 +745,13 @@ def create_app(args):
 
                         ollama_options = OllamaEmbeddingOptions.options_dict(args)
 
-                    # Pass embed_model only if provided, let function use its default (bge-m3:latest)
-                    kwargs = {
-                        "texts": texts,
-                        "host": host,
-                        "api_key": api_key,
-                        "options": ollama_options,
-                    }
-                    if model:
-                        kwargs["embed_model"] = model
-                    return await actual_func(**kwargs)
+                    return await actual_func(
+                        texts,
+                        embed_model=model,
+                        host=host,
+                        api_key=api_key,
+                        options=ollama_options,
+                    )
                 elif binding == "azure_openai":
                     from lightrag.llm.azure_openai import azure_openai_embed
 
@@ -764,11 +760,7 @@ def create_app(args):
                         if isinstance(azure_openai_embed, EmbeddingFunc)
                         else azure_openai_embed
                     )
-                    # Pass model only if provided, let function use its default otherwise
-                    kwargs = {"texts": texts, "api_key": api_key}
-                    if model:
-                        kwargs["model"] = model
-                    return await actual_func(**kwargs)
+                    return await actual_func(texts, model=model, api_key=api_key)
                 elif binding == "aws_bedrock":
                     from lightrag.llm.bedrock import bedrock_embed
 
@@ -777,11 +769,7 @@ def create_app(args):
                         if isinstance(bedrock_embed, EmbeddingFunc)
                         else bedrock_embed
                     )
-                    # Pass model only if provided, let function use its default otherwise
-                    kwargs = {"texts": texts}
-                    if model:
-                        kwargs["model"] = model
-                    return await actual_func(**kwargs)
+                    return await actual_func(texts, model=model)
                 elif binding == "jina":
                     from lightrag.llm.jina import jina_embed
 
@@ -790,16 +778,13 @@ def create_app(args):
                         if isinstance(jina_embed, EmbeddingFunc)
                         else jina_embed
                     )
-                    # Pass model only if provided, let function use its default (jina-embeddings-v4)
-                    kwargs = {
-                        "texts": texts,
-                        "embedding_dim": embedding_dim,
-                        "base_url": host,
-                        "api_key": api_key,
-                    }
-                    if model:
-                        kwargs["model"] = model
-                    return await actual_func(**kwargs)
+                    return await actual_func(
+                        texts,
+                        model=model,
+                        embedding_dim=embedding_dim,
+                        base_url=host,
+                        api_key=api_key,
+                    )
                 elif binding == "gemini":
                     from lightrag.llm.gemini import gemini_embed
 
@@ -817,19 +802,14 @@ def create_app(args):
 
                         gemini_options = GeminiEmbeddingOptions.options_dict(args)
 
-                    # Pass model only if provided, let function use its default (gemini-embedding-001)
-                    kwargs = {
-                        "texts": texts,
-                        "base_url": host,
-                        "api_key": api_key,
-                        "embedding_dim": embedding_dim,
-                        "task_type": gemini_options.get(
-                            "task_type", "RETRIEVAL_DOCUMENT"
-                        ),
-                    }
-                    if model:
-                        kwargs["model"] = model
-                    return await actual_func(**kwargs)
+                    return await actual_func(
+                        texts,
+                        model=model,
+                        base_url=host,
+                        api_key=api_key,
+                        embedding_dim=embedding_dim,
+                        task_type=gemini_options.get("task_type", "RETRIEVAL_DOCUMENT"),
+                    )
                 else:  # openai and compatible
                     from lightrag.llm.openai import openai_embed
 
@@ -838,16 +818,13 @@ def create_app(args):
                         if isinstance(openai_embed, EmbeddingFunc)
                         else openai_embed
                     )
-                    # Pass model only if provided, let function use its default (text-embedding-3-small)
-                    kwargs = {
-                        "texts": texts,
-                        "base_url": host,
-                        "api_key": api_key,
-                        "embedding_dim": embedding_dim,
-                    }
-                    if model:
-                        kwargs["model"] = model
-                    return await actual_func(**kwargs)
+                    return await actual_func(
+                        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}")
 
diff --git a/lightrag/llm/jina.py b/lightrag/llm/jina.py
index f61faadd..41251f4a 100644
--- a/lightrag/llm/jina.py
+++ b/lightrag/llm/jina.py
@@ -69,6 +69,7 @@ async def fetch_data(url, headers, data):
 )
 async def jina_embed(
     texts: list[str],
+    model: str = "jina-embeddings-v4",
     embedding_dim: int = 2048,
     late_chunking: bool = False,
     base_url: str = None,
@@ -78,6 +79,8 @@ async def jina_embed(
 
     Args:
         texts: List of texts to embed.
+        model: The Jina embedding model to use (default: jina-embeddings-v4).
+            Supported models: jina-embeddings-v3, jina-embeddings-v4, etc.
         embedding_dim: The embedding dimensions (default: 2048 for jina-embeddings-v4).
             **IMPORTANT**: This parameter is automatically injected by the EmbeddingFunc wrapper.
             Do NOT manually pass this parameter when calling the function directly.
@@ -107,7 +110,7 @@ async def jina_embed(
         "Authorization": f"Bearer {os.environ['JINA_API_KEY']}",
     }
     data = {
-        "model": "jina-embeddings-v4",
+        "model": model,
         "task": "text-matching",
         "dimensions": embedding_dim,
         "embedding_type": "base64",