diff --git a/lightrag/api/lightrag_server.py b/lightrag/api/lightrag_server.py
index a8a14c66..16e59d21 100644
--- a/lightrag/api/lightrag_server.py
+++ b/lightrag/api/lightrag_server.py
@@ -780,6 +780,7 @@ def create_app(args):
                     )
                     return await actual_func(
                         texts,
+                        model=model,
                         embedding_dim=embedding_dim,
                         base_url=host,
                         api_key=api_key,
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",