add baidu cloud vectorsearch document (#12928)

**Description:** 
Add BaiduCloud VectorSearch document with implement of BESVectorSearch
in langchain vectorstores

---------

Co-authored-by: wemysschen <root@icoding-cwx.bcc-szzj.baidu.com>

docs/docs/integrations/vectorstores/baiducloud_vector_search.ipynb

@@ -0,0 +1,160 @@
+{
+ "cells": [
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Biadu Cloud ElasticSearch VectorSearch\n",
+    "\n",
+    ">[Baidu Cloud VectorSearch](https://cloud.baidu.com/doc/BES/index.html?from=productToDoc) is a fully managed, enterprise-level distributed search and analysis service which is 100% compatible to open source. Baidu Cloud VectorSearch provides low-cost, high-performance, and reliable retrieval and analysis platform level product services for structured/unstructured data. As a vector database , it supports multiple index types and similarity distance methods. \n",
+    "\n",
+    ">`Baidu Cloud ElasticSearch` provides a privilege management mechanism, for you to  configure the cluster privileges freely, so as to further ensure data security.\n",
+    "\n",
+    "This notebook shows how to use functionality related to the `Baidu Cloud ElasticSearch VectorStore`.\n",
+    "To run, you should have an [Baidu Cloud ElasticSearch](https://cloud.baidu.com/product/bes.html) instance up and running:\n",
+    "\n",
+    "Read the [help document](https://cloud.baidu.com/doc/BES/s/8llyn0hh4 ) to quickly familiarize and configure Baidu Cloud ElasticSearch instance."
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "After the instance is up and running, follow these steps to split documents, get embeddings, connect to the baidu cloud elasticsearch instance, index documents, and perform vector retrieval."
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We need to install the following Python packages first."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#!pip install elasticsearch == 7.11.0"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "First, we want to use `QianfanEmbeddings` so we have to get the Qianfan AK and SK. Details for QianFan is related to [Baidu Qianfan Workshop](https://cloud.baidu.com/product/wenxinworkshop)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import getpass\n",
+    "\n",
+    "os.environ['QIANFAN_AK'] = getpass.getpass(\"Your Qianfan AK:\")\n",
+    "os.environ['QIANFAN_SK'] = getpass.getpass(\"Your Qianfan SK:\")"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Secondly, split documents and get embeddings."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.document_loaders import TextLoader\n",
+    "\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",
+    "from langchain.embeddings import QianfanEmbeddingsEndpoint\n",
+    "embeddings = QianfanEmbeddingsEndpoint()"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Then, create a Baidu ElasticeSearch accessable instance."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create a bes instance and index docs.\n",
+    "from langchain.vectorstores import BESVectorStore\n",
+    "bes = BESVectorStore.from_documents(\n",
+    "    documents=docs, embedding=embeddings, bes_url=\"your bes cluster url\", index_name=\"your vector index\"\n",
+    ")\n",
+    "bes.client.indices.refresh(index=\"your vector index\")"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Finally, Query and retrive data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "query = \"What did the president say about Ketanji Brown Jackson\"\n",
+    "docs = bes.similarity_search(query)\n",
+    "print(docs[0].page_content)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Please feel free to contact <liuboyao@baidu.com> if you encounter any problems during use, and we will do our best to support you."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "name": "python",
+   "version": "3.9.17"
+  },
+  "orig_nbformat": 4,
+  "vscode": {
+   "interpreter": {
+    "hash": "aee8b7b246df8f9039afb4144a1f6fd8d2ca17a180786b69acc140d282b71a49"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}