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  {
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   "id": "683953b3",
   "metadata": {},
   "source": [
    "# MyScale\n",
    "\n",
    ">[MyScale](https://docs.myscale.com/en/overview/) is a cloud-based database optimized for AI applications and solutions, built on the open-source [ClickHouse](https://github.com/ClickHouse/ClickHouse). \n",
    "\n",
    "This notebook shows how to use functionality related to the `MyScale` vector database."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "43ead5d5-2c1f-4dce-a69a-cb00e4f9d6f0",
   "metadata": {},
   "source": [
    "## Setting up envrionments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7dccc580-8270-4714-ad61-f79783dd6eea",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "!pip install clickhouse-connect"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "15a1d477-9cdb-4d82-b019-96951ecb2b72",
   "metadata": {},
   "source": [
    "We want to use OpenAIEmbeddings so we have to get the OpenAI API Key."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "91003ea5-0c8c-436c-a5de-aaeaeef2f458",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import getpass\n",
    "\n",
    "os.environ[\"OPENAI_API_KEY\"] = getpass.getpass(\"OpenAI API Key:\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "a9d16fa3",
   "metadata": {},
   "source": [
    "There are two ways to set up parameters for myscale index.\n",
    "\n",
    "1. Environment Variables\n",
    "\n",
    "    Before you run the app, please set the environment variable with `export`:\n",
    "    `export MYSCALE_HOST='<your-endpoints-url>' MYSCALE_PORT=<your-endpoints-port> MYSCALE_USERNAME=<your-username> MYSCALE_PASSWORD=<your-password> ...`\n",
    "\n",
    "    You can easily find your account, password and other info on our SaaS. For details please refer to [this document](https://docs.myscale.com/en/cluster-management/)\n",
    "\n",
    "    Every attributes under `MyScaleSettings` can be set with prefix `MYSCALE_` and is case insensitive.\n",
    "\n",
    "2. Create `MyScaleSettings` object with parameters\n",
    "\n",
    "\n",
    "    ```python\n",
    "    from langchain.vectorstores import MyScale, MyScaleSettings\n",
    "    config = MyScaleSetting(host=\"<your-backend-url>\", port=8443, ...)\n",
    "    index = MyScale(embedding_function, config)\n",
    "    index.add_documents(...)\n",
    "    ```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "aac9563e",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from langchain.embeddings.openai import OpenAIEmbeddings\n",
    "from langchain.text_splitter import CharacterTextSplitter\n",
    "from langchain.vectorstores import MyScale\n",
    "from langchain.document_loaders import TextLoader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a3c3999a",
   "metadata": {
    "tags": []
   },
   "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",
    "embeddings = OpenAIEmbeddings()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6e104aee",
   "metadata": {},
   "outputs": [],
   "source": [
    "for d in docs:\n",
    "    d.metadata = {\"some\": \"metadata\"}\n",
    "docsearch = MyScale.from_documents(docs, embeddings)\n",
    "\n",
    "query = \"What did the president say about Ketanji Brown Jackson\"\n",
    "docs = docsearch.similarity_search(query)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9c608226",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(docs[0].page_content)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "e3a8b105",
   "metadata": {},
   "source": [
    "## Get connection info and data schema"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "69996818",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(str(docsearch))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "f59360c0",
   "metadata": {},
   "source": [
    "## Filtering\n",
    "\n",
    "You can have direct access to myscale SQL where statement. You can write `WHERE` clause following standard SQL.\n",
    "\n",
    "**NOTE**: Please be aware of SQL injection, this interface must not be directly called by end-user.\n",
    "\n",
    "If you custimized your `column_map` under your setting, you search with filter like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "232055f6",
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain.vectorstores import MyScale, MyScaleSettings\n",
    "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",
    "embeddings = OpenAIEmbeddings()\n",
    "\n",
    "for i, d in enumerate(docs):\n",
    "    d.metadata = {\"doc_id\": i}\n",
    "\n",
    "docsearch = MyScale.from_documents(docs, embeddings)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "8d867b05",
   "metadata": {},
   "source": [
    "### Similarity search with score"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "9ec25cc5",
   "metadata": {},
   "source": [
    "The returned distance score is cosine distance. Therefore, a lower score is better."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ddbcee77",
   "metadata": {},
   "outputs": [],
   "source": [
    "meta = docsearch.metadata_column\n",
    "output = docsearch.similarity_search_with_relevance_scores(\n",
    "    \"What did the president say about Ketanji Brown Jackson?\",\n",
    "    k=4,\n",
    "    where_str=f\"{meta}.doc_id<10\",\n",
    ")\n",
    "for d, dist in output:\n",
    "    print(dist, d.metadata, d.page_content[:20] + \"...\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "a359ed74",
   "metadata": {},
   "source": [
    "## Deleting your data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fb6a9d36",
   "metadata": {},
   "outputs": [],
   "source": [
    "docsearch.drop()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "48dbd8e0",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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