langchain/docs/docs/integrations/vectorstores/baiducloud_vector_search.ipynb

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    "# Baidu 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."
   ]
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   "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."
   ]
  },
  {
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   "source": [
    "We need to install the following Python packages first."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%pip install --upgrade --quiet  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 getpass\n",
    "import os\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_community.document_loaders import TextLoader\n",
    "from langchain_text_splitters import CharacterTextSplitter\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_community.embeddings import QianfanEmbeddingsEndpoint\n",
    "\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_community.vectorstores import BESVectorStore\n",
    "\n",
    "bes = BESVectorStore.from_documents(\n",
    "    documents=docs,\n",
    "    embedding=embeddings,\n",
    "    bes_url=\"your bes cluster url\",\n",
    "    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> or <chenweixu01@baidu.com> if you encounter any problems during use, and we will do our best to support you."
   ]
  }
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