{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "13afcae7",
   "metadata": {},
   "source": [
    "# MyScale\n",
    "\n",
    ">[MyScale](https://docs.myscale.com/en/) is an integrated vector database. You can access your database in SQL and also from here, LangChain.\n",
    ">`MyScale` can make use of [various data types and functions for filters](https://blog.myscale.com/2023/06/06/why-integrated-database-solution-can-boost-your-llm-apps/#filter-on-anything-without-constraints). It will boost up your LLM app no matter if you are scaling up your data or expand your system to broader application.\n",
    "\n",
    "In the notebook, we'll demo the `SelfQueryRetriever` wrapped around a `MyScale` vector store with some extra pieces we contributed to LangChain. \n",
    "\n",
    "In short, it can be condensed into 4 points:\n",
    "1. Add `contain` comparator to match the list of any if there is more than one element matched\n",
    "2. Add `timestamp` data type for datetime match (ISO-format, or YYYY-MM-DD)\n",
    "3. Add `like` comparator for string pattern search\n",
    "4. Add arbitrary function capability"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "68e75fb9",
   "metadata": {},
   "source": [
    "## Creating a MyScale vector store\n",
    "MyScale has already been integrated to LangChain for a while. So you can follow [this notebook](/docs/integrations/vectorstores/myscale) to create your own vectorstore for a self-query retriever.\n",
    "\n",
    "**Note:** All self-query retrievers requires you to have `lark` installed (`pip install lark`). We use `lark` for grammar definition. Before you proceed to the next step, we also want to remind you that `clickhouse-connect` is also needed to interact with your MyScale backend."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "63a8af5b",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "! pip install lark clickhouse-connect"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "83811610-7df3-4ede-b268-68a6a83ba9e2",
   "metadata": {},
   "source": [
    "In this tutorial we follow other example's setting and use `OpenAIEmbeddings`. Remember to get an OpenAI API Key for valid access to LLMs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dd01b61b-7d32-4a55-85d6-b2d2d4f18840",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import getpass\n",
    "import os\n",
    "\n",
    "os.environ[\"OPENAI_API_KEY\"] = getpass.getpass(\"OpenAI API Key:\")\n",
    "os.environ[\"MYSCALE_HOST\"] = getpass.getpass(\"MyScale URL:\")\n",
    "os.environ[\"MYSCALE_PORT\"] = getpass.getpass(\"MyScale Port:\")\n",
    "os.environ[\"MYSCALE_USERNAME\"] = getpass.getpass(\"MyScale Username:\")\n",
    "os.environ[\"MYSCALE_PASSWORD\"] = getpass.getpass(\"MyScale Password:\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cb4a5787",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from langchain.embeddings.openai import OpenAIEmbeddings\n",
    "from langchain.schema import Document\n",
    "from langchain.vectorstores import MyScale\n",
    "\n",
    "embeddings = OpenAIEmbeddings()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bf7f6fc4",
   "metadata": {},
   "source": [
    "## Create some sample data\n",
    "As you can see, the data we created has some differences compared to other self-query retrievers. We replaced the keyword `year` with `date` which gives you finer control on timestamps. We also changed the type of the keyword `gerne` to a list of strings, where an LLM can use a new `contain` comparator to construct filters. We also provide the `like` comparator and arbitrary function support to filters, which will be introduced in next few cells.\n",
    "\n",
    "Now let's look at the data first."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bcbe04d9",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "docs = [\n",
    "    Document(\n",
    "        page_content=\"A bunch of scientists bring back dinosaurs and mayhem breaks loose\",\n",
    "        metadata={\"date\": \"1993-07-02\", \"rating\": 7.7, \"genre\": [\"science fiction\"]},\n",
    "    ),\n",
    "    Document(\n",
    "        page_content=\"Leo DiCaprio gets lost in a dream within a dream within a dream within a ...\",\n",
    "        metadata={\"date\": \"2010-12-30\", \"director\": \"Christopher Nolan\", \"rating\": 8.2},\n",
    "    ),\n",
    "    Document(\n",
    "        page_content=\"A psychologist / detective gets lost in a series of dreams within dreams within dreams and Inception reused the idea\",\n",
    "        metadata={\"date\": \"2006-04-23\", \"director\": \"Satoshi Kon\", \"rating\": 8.6},\n",
    "    ),\n",
    "    Document(\n",
    "        page_content=\"A bunch of normal-sized women are supremely wholesome and some men pine after them\",\n",
    "        metadata={\"date\": \"2019-08-22\", \"director\": \"Greta Gerwig\", \"rating\": 8.3},\n",
    "    ),\n",
    "    Document(\n",
    "        page_content=\"Toys come alive and have a blast doing so\",\n",
    "        metadata={\"date\": \"1995-02-11\", \"genre\": [\"animated\"]},\n",
    "    ),\n",
    "    Document(\n",
    "        page_content=\"Three men walk into the Zone, three men walk out of the Zone\",\n",
    "        metadata={\n",
    "            \"date\": \"1979-09-10\",\n",
    "            \"director\": \"Andrei Tarkovsky\",\n",
    "            \"genre\": [\"science fiction\", \"adventure\"],\n",
    "            \"rating\": 9.9,\n",
    "        },\n",
    "    ),\n",
    "]\n",
    "vectorstore = MyScale.from_documents(\n",
    "    docs,\n",
    "    embeddings,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5ecaab6d",
   "metadata": {},
   "source": [
    "## Creating our self-querying retriever\n",
    "Just like other retrievers... simple and nice."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "86e34dbf",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from langchain.chains.query_constructor.base import AttributeInfo\n",
    "from langchain.llms import OpenAI\n",
    "from langchain.retrievers.self_query.base import SelfQueryRetriever\n",
    "\n",
    "metadata_field_info = [\n",
    "    AttributeInfo(\n",
    "        name=\"genre\",\n",
    "        description=\"The genres of the movie\",\n",
    "        type=\"list[string]\",\n",
    "    ),\n",
    "    # If you want to include length of a list, just define it as a new column\n",
    "    # This will teach the LLM to use it as a column when constructing filter.\n",
    "    AttributeInfo(\n",
    "        name=\"length(genre)\",\n",
    "        description=\"The length of genres of the movie\",\n",
    "        type=\"integer\",\n",
    "    ),\n",
    "    # Now you can define a column as timestamp. By simply set the type to timestamp.\n",
    "    AttributeInfo(\n",
    "        name=\"date\",\n",
    "        description=\"The date the movie was released\",\n",
    "        type=\"timestamp\",\n",
    "    ),\n",
    "    AttributeInfo(\n",
    "        name=\"director\",\n",
    "        description=\"The name of the movie director\",\n",
    "        type=\"string\",\n",
    "    ),\n",
    "    AttributeInfo(\n",
    "        name=\"rating\", description=\"A 1-10 rating for the movie\", type=\"float\"\n",
    "    ),\n",
    "]\n",
    "document_content_description = \"Brief summary of a movie\"\n",
    "llm = OpenAI(temperature=0)\n",
    "retriever = SelfQueryRetriever.from_llm(\n",
    "    llm, vectorstore, document_content_description, metadata_field_info, verbose=True\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ea9df8d4",
   "metadata": {},
   "source": [
    "## Testing it out with self-query retriever's existing functionalities\n",
    "And now we can try actually using our retriever!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "38a126e9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# This example only specifies a relevant query\n",
    "retriever.get_relevant_documents(\"What are some movies about dinosaurs\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fc3f1e6e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# This example only specifies a filter\n",
    "retriever.get_relevant_documents(\"I want to watch a movie rated higher than 8.5\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b19d4da0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# This example specifies a query and a filter\n",
    "retriever.get_relevant_documents(\"Has Greta Gerwig directed any movies about women\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f900e40e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# This example specifies a composite filter\n",
    "retriever.get_relevant_documents(\n",
    "    \"What's a highly rated (above 8.5) science fiction film?\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "12a51522",
   "metadata": {},
   "outputs": [],
   "source": [
    "# This example specifies a query and composite filter\n",
    "retriever.get_relevant_documents(\n",
    "    \"What's a movie after 1990 but before 2005 that's all about toys, and preferably is animated\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "86371ac8",
   "metadata": {},
   "source": [
    "# Wait a second... what else?\n",
    "\n",
    "Self-query retriever with MyScale can do more! Let's find out."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1d043096",
   "metadata": {},
   "outputs": [],
   "source": [
    "# You can use length(genres) to do anything you want\n",
    "retriever.get_relevant_documents(\"What's a movie that have more than 1 genres?\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d570d33c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fine-grained datetime? You got it already.\n",
    "retriever.get_relevant_documents(\"What's a movie that release after feb 1995?\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fbe0b21b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Don't know what your exact filter should be? Use string pattern match!\n",
    "retriever.get_relevant_documents(\"What's a movie whose name is like Andrei?\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6a514104",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Contain works for lists: so you can match a list with contain comparator!\n",
    "retriever.get_relevant_documents(\n",
    "    \"What's a movie who has genres science fiction and adventure?\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "39bd1de1-b9fe-4a98-89da-58d8a7a6ae51",
   "metadata": {},
   "source": [
    "## Filter k\n",
    "\n",
    "We can also use the self query retriever to specify `k`: the number of documents to fetch.\n",
    "\n",
    "We can do this by passing `enable_limit=True` to the constructor."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bff36b88-b506-4877-9c63-e5a1a8d78e64",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "retriever = SelfQueryRetriever.from_llm(\n",
    "    llm,\n",
    "    vectorstore,\n",
    "    document_content_description,\n",
    "    metadata_field_info,\n",
    "    enable_limit=True,\n",
    "    verbose=True,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2758d229-4f97-499c-819f-888acaf8ee10",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# This example only specifies a relevant query\n",
    "retriever.get_relevant_documents(\"what are two movies about dinosaurs\")"
   ]
  }
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