diff --git a/conf/llm_factories.json b/conf/llm_factories.json
index d3b2dcc1c..3c84bd03d 100644
--- a/conf/llm_factories.json
+++ b/conf/llm_factories.json
@@ -1194,6 +1194,12 @@
                     "tags": "TEXT EMBEDDING",
                     "max_tokens": 8196,
                     "model_type": "embedding"
+                },
+                {
+                    "llm_name": "jina-embeddings-v4",
+                    "tags": "TEXT EMBEDDING",
+                    "max_tokens": 32768,
+                    "model_type": "embedding"
                 }
             ]
         },
diff --git a/rag/llm/embedding_model.py b/rag/llm/embedding_model.py
index 7f2f9ee7d..b8d2f158c 100644
--- a/rag/llm/embedding_model.py
+++ b/rag/llm/embedding_model.py
@@ -357,13 +357,14 @@ class JinaEmbed(Base):
         self.headers = {"Content-Type": "application/json", "Authorization": f"Bearer {key}"}
         self.model_name = model_name
 
+    # Jina v2/v3/v4 never reaches here
     def encode(self, texts: list):
         texts = [truncate(t, 8196) for t in texts]
         batch_size = 16
         ress = []
         token_count = 0
         for i in range(0, len(texts), batch_size):
-            data = {"model": self.model_name, "input": texts[i : i + batch_size], "encoding_type": "float"}
+            data = {"model": self.model_name, "input": texts[i : i + batch_size]}
             response = requests.post(self.base_url, headers=self.headers, json=data)
             try:
                 res = response.json()
@@ -403,11 +404,28 @@ class JinaMultiVecEmbed(Base):
                     img_b64s = base64.b64encode(text).decode('utf8')
                 input.append({"image": img_b64s})  # base64 encoded image
         for i in range(0, len(texts), batch_size):
-            data = {"model": self.model_name, "task": task, "truncate": True, "return_multivector": True, "input": input[i : i + batch_size]}
+            data = {"model": self.model_name, "input": input[i : i + batch_size]}
+            if "v4" in self.model_name:
+                data["return_multivector"] = True
+            
+            if "v3" in self.model_name or "v4" in self.model_name:
+                data['task'] = task
+                data['truncate'] = True
+
             response = requests.post(self.base_url, headers=self.headers, json=data)
             try:
                 res = response.json()
-                ress.extend([d["embeddings"] for d in res["data"]])
+                for d in res['data']:
+                    if data.get("return_multivector", False): # v4
+                        token_embs = np.asarray(d['embeddings'], dtype=np.float32)
+                        chunk_emb = token_embs.mean(axis=0)
+                    
+                    else:
+                        # v2/v3
+                        chunk_emb = np.asarray(d['embedding'], dtype=np.float32)
+
+                    ress.append(chunk_emb)
+
                 token_count += self.total_token_count(res)
             except Exception as _e:
                 log_exception(_e, response)