Add Zilliz example (#2288)

Add Zilliz example

docs/modules/indexes/vectorstores/examples/zilliz.ipynb

@@ -0,0 +1,112 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "683953b3",
+   "metadata": {},
+   "source": [
+    "# Zilliz\n",
+    "\n",
+    "This notebook shows how to use functionality related to the Zilliz Cloud managed vector database.\n",
+    "\n",
+    "To run, you should have a Zilliz Cloud instance up and running: https://zilliz.com/cloud"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "aac9563e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.embeddings.openai import OpenAIEmbeddings\n",
+    "from langchain.text_splitter import CharacterTextSplitter\n",
+    "from langchain.vectorstores import Milvus\n",
+    "from langchain.document_loaders import TextLoader"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "19a71422",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# replace \n",
+    "ZILLIZ_CLOUD_HOSTNAME = \"\" # example: \"in01-17f69c292d4a50a.aws-us-west-2.vectordb.zillizcloud.com\"\n",
+    "ZILLIZ_CLOUD_PORT = \"\" #example: \"19532\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a3c3999a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.document_loaders import TextLoader\n",
+    "loader = TextLoader('../../../state_of_the_union.txt')\n",
+    "documents = loader.load()\n",
+    "text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)\n",
+    "docs = text_splitter.split_documents(documents)\n",
+    "\n",
+    "embeddings = OpenAIEmbeddings()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "dcf88bdf",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vector_db = Milvus.from_documents(\n",
+    "    docs,\n",
+    "    embeddings,\n",
+    "    connection_args={\"host\": ZILLIZ_CLOUD_HOSTNAME, \"port\": ZILLIZ_CLOUD_PORT},\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a8c513ab",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "docs = vector_db.similarity_search(query)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fc516993",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "docs[0]"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}