From 1bb3a4ccba0808b9c4c96d177887acb444b8c949 Mon Sep 17 00:00:00 2001
From: "shanhaikang.shk" <shanhaikang.shk@oceanbase.com>
Date: Mon, 6 Jan 2025 10:52:37 +0800
Subject: [PATCH] update example notebook

Signed-off-by: shanhaikang.shk <shanhaikang.shk@oceanbase.com>
---
 docs/vectorstores.ipynb | 24 +++++++++++-------------
 1 file changed, 11 insertions(+), 13 deletions(-)

diff --git a/docs/vectorstores.ipynb b/docs/vectorstores.ipynb
index 460d186..bb2db82 100644
--- a/docs/vectorstores.ipynb
+++ b/docs/vectorstores.ipynb
@@ -31,9 +31,11 @@
    ]
   },
   {
-   "cell_type": "raw",
+   "cell_type": "code",
+   "execution_count": null,
    "id": "a7b92118",
    "metadata": {},
+   "outputs": [],
    "source": [
     "%docker run --name=ob433 -e MODE=mini -e OB_SERVER_IP=127.0.0.1 -p 2881:2881 -d quay.io/oceanbase/oceanbase-ce:4.3.3.1-101000012024102216"
    ]
@@ -47,13 +49,11 @@
    ]
   },
   {
-   "cell_type": "raw",
+   "cell_type": "code",
+   "execution_count": null,
    "id": "64e28aa6",
-   "metadata": {
-    "vscode": {
-     "languageId": "raw"
-    }
-   },
+   "metadata": {},
+   "outputs": [],
    "source": [
     "%pip install -qU \"langchain-oceanbase\""
    ]
@@ -118,7 +118,9 @@
    "outputs": [],
    "source": [
     "import os\n",
+    "\n",
     "from langchain_community.embeddings import DashScopeEmbeddings\n",
+    "\n",
     "from langchain_oceanbase.vectorstores import OceanbaseVectorStore\n",
     "\n",
     "DASHSCOPE_API = os.environ.get(\"DASHSCOPE_API_KEY\", \"\")\n",
@@ -276,9 +278,7 @@
     }
    ],
    "source": [
-    "results = vector_store.similarity_search(query=\"thud\",k=1,filter={\"source\":\"https://another-example.com\"})\n",
-    "for doc in results:\n",
-    "    print(f\"* {doc.page_content} [{doc.metadata}]\")"
+    "results = vector_store.similarity_search(query=\"thud\",k=1,filter={\"source\":\"https://another-example.com\"})"
    ]
   },
   {
@@ -304,9 +304,7 @@
     }
    ],
    "source": [
-    "results = vector_store.similarity_search_with_score(query=\"thud\",k=1,filter={\"source\":\"https://example.com\"})\n",
-    "for doc, score in results:\n",
-    "    print(f\"* [SIM={score:3f}] {doc.page_content} [{doc.metadata}]\")"
+    "results = vector_store.similarity_search_with_score(query=\"thud\",k=1,filter={\"source\":\"https://example.com\"})"
    ]
   },
   {