docs: add milvus multitenancy doc (#16177)

- **Description:** add milvus multitenancy doc, it is an example for
this [pr](https://github.com/langchain-ai/langchain/pull/15740) .
  - **Issue:** No,
  - **Dependencies:** No,
  - **Twitter handle:** No

Signed-off-by: ChengZi <chen.zhang@zilliz.com>

docs/docs/integrations/vectorstores/milvus.ipynb

@@ -201,6 +201,120 @@
    "source": [
     "After retreival you can go on querying it as usual."
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "### Per-User Retrieval\n",
+    "\n",
+    "When building a retrieval app, you often have to build it with multiple users in mind. This means that you may be storing data not just for one user, but for many different users, and they should not be able to see eachother’s data.\n",
+    "\n",
+    "Milvus recommends using [partition_key](https://milvus.io/docs/multi_tenancy.md#Partition-key-based-multi-tenancy) to implement multi-tenancy, here is an example."
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "outputs": [],
+   "source": [
+    "from langchain_core.documents import Document\n",
+    "\n",
+    "docs = [\n",
+    "    Document(page_content=\"i worked at kensho\", metadata={\"namespace\": \"harrison\"}),\n",
+    "    Document(page_content=\"i worked at facebook\", metadata={\"namespace\": \"ankush\"}),\n",
+    "]\n",
+    "vectorstore = Milvus.from_documents(\n",
+    "    docs,\n",
+    "    embeddings,\n",
+    "    connection_args={\"host\": \"127.0.0.1\", \"port\": \"19530\"},\n",
+    "    drop_old=True,\n",
+    "    partition_key_field=\"namespace\",  # Use the \"namespace\" field as the partition key\n",
+    ")"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "To conduct a search using the partition key, you should include either of the following in the boolean expression of the search request:\n",
+    "\n",
+    "`search_kwargs={\"expr\": '<partition_key> == \"xxxx\"'}`\n",
+    "\n",
+    "`search_kwargs={\"expr\": '<partition_key> == in [\"xxx\", \"xxx\"]'}`\n",
+    "\n",
+    "Do replace `<partition_key>` with the name of the field that is designated as the partition key.\n",
+    "\n",
+    "Milvus changes to a partition based on the specified partition key, filters entities according to the partition key, and searches among the filtered entities.\n"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "[Document(page_content='i worked at facebook', metadata={'namespace': 'ankush'})]"
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# This will only get documents for Ankush\n",
+    "vectorstore.as_retriever(\n",
+    "    search_kwargs={\"expr\": 'namespace == \"ankush\"'}\n",
+    ").get_relevant_documents(\"where did i work?\")"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "[Document(page_content='i worked at kensho', metadata={'namespace': 'harrison'})]"
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# This will only get documents for Harrison\n",
+    "vectorstore.as_retriever(\n",
+    "    search_kwargs={\"expr\": 'namespace == \"harrison\"'}\n",
+    ").get_relevant_documents(\"where did i work?\")"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
   }
  ],
  "metadata": {

docs/docs/use_cases/question_answering/per_user.ipynb

@@ -298,6 +298,18 @@
     "    config={\"configurable\": {\"search_kwargs\": {\"namespace\": \"ankush\"}}},\n",
     ")"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "For more vectorstore implementations for multi-user, please refer to specific pages, such as [Milvus](/docs/integrations/vectorstores/milvus)."
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   }
   }
  ],
  "metadata": {