diff --git a/pilot/vector_store/milvus_store.py b/pilot/vector_store/milvus_store.py
index ee304fe25..582289d80 100644
--- a/pilot/vector_store/milvus_store.py
+++ b/pilot/vector_store/milvus_store.py
@@ -1,4 +1,6 @@
 from __future__ import annotations
+
+import json
 import logging
 import os
 from typing import Any, Iterable, List, Optional, Tuple
@@ -30,7 +32,7 @@ class MilvusStore(VectorStoreBase):
         self.secure = ctx.get("MILVUS_SECURE", os.getenv("MILVUS_SECURE"))
         self.collection_name = ctx.get("vector_store_name", None)
         self.embedding = ctx.get("embeddings", None)
-        self.fields = []
+        self.fields = ["metadata"]
         self.alias = "default"
 
         # use HNSW by default.
@@ -55,6 +57,7 @@ class MilvusStore(VectorStoreBase):
         self.primary_field = "pk_id"
         self.vector_field = "vector"
         self.text_field = "content"
+        self.metadata_field = "metadata"
 
         if (self.username is None) != (self.password is None):
             raise ValueError(
@@ -127,6 +130,7 @@ class MilvusStore(VectorStoreBase):
         primary_field = self.primary_field
         vector_field = self.vector_field
         text_field = self.text_field
+        metadata_field = self.metadata_field
         # self.text_field = text_field
         collection_name = vector_name
         fields = []
@@ -141,6 +145,8 @@ class MilvusStore(VectorStoreBase):
         )
         # vector field
         fields.append(FieldSchema(vector_field, DataType.FLOAT_VECTOR, dim=dim))
+
+        fields.append(FieldSchema(metadata_field, DataType.VARCHAR, max_length=65535))
         schema = CollectionSchema(fields)
         # Create the collection
         collection = Collection(collection_name, schema)
@@ -233,11 +239,11 @@ class MilvusStore(VectorStoreBase):
                 self.embedding.embed_query(x) for x in texts
             ]
         # Collect the metadata into the insert dict.
+        # self.fields.extend(metadatas[0].keys())
         if len(self.fields) > 2 and metadatas is not None:
             for d in metadatas:
-                for key, value in d.items():
-                    if key in self.fields:
-                        insert_dict.setdefault(key, []).append(value)
+                # for key, value in d.items():
+                insert_dict.setdefault("metadata", []).append(json.dumps(d))
         # Convert dict to list of lists for insertion
         insert_list = [insert_dict[x] for x in self.fields]
         # Insert into the collection.
@@ -261,7 +267,7 @@ class MilvusStore(VectorStoreBase):
         doc_ids = [str(doc_id) for doc_id in doc_ids]
         return doc_ids
 
-    def similar_search(self, text, topk) -> None:
+    def similar_search(self, text, topk):
         from pymilvus import Collection, DataType
 
         """similar_search in vector database."""
@@ -276,7 +282,16 @@ class MilvusStore(VectorStoreBase):
             if x.dtype == DataType.FLOAT_VECTOR or x.dtype == DataType.BINARY_VECTOR:
                 self.vector_field = x.name
         _, docs_and_scores = self._search(text, topk)
-        return [doc for doc, _, _ in docs_and_scores]
+        from langchain.schema import Document
+
+        return [
+            Document(
+                metadata=json.loads(doc.metadata.get("metadata", "")),
+                page_content=doc.page_content,
+            )
+            for doc, _, _ in docs_and_scores
+        ]
+        # return [doc for doc, _, _ in docs_and_scores]
 
     def _search(
         self,