{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "0ed6aab1",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "# SQLite example\n",
    "\n",
    "This example showcases hooking up an LLM to answer questions over a database."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b2f66479",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "This uses the example Chinook database.\n",
    "To set it up follow the instructions on https://database.guide/2-sample-databases-sqlite/, placing the `.db` file in a notebooks folder at the root of this repository."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d0e27d88",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "from langchain import OpenAI, SQLDatabase, SQLDatabaseChain"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "72ede462",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "db = SQLDatabase.from_uri(\"sqlite:///../../../../notebooks/Chinook.db\")\n",
    "llm = OpenAI(temperature=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a8fc8f23",
   "metadata": {},
   "outputs": [],
   "source": [
    "db_chain = SQLDatabaseChain(llm=llm, database=db, verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "15ff81df",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\u001b[1m> Entering new SQLDatabaseChain chain...\u001b[0m\n",
      "How many employees are there? \n",
      "SQLQuery:\u001b[32;1m\u001b[1;3m SELECT COUNT(*) FROM Employee;\u001b[0m\n",
      "SQLResult: \u001b[33;1m\u001b[1;3m[(9,)]\u001b[0m\n",
      "Answer:\u001b[32;1m\u001b[1;3m There are 9 employees.\u001b[0m\n",
      "\u001b[1m> Finished chain.\u001b[0m\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "' There are 9 employees.'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "db_chain.run(\"How many employees are there?\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aad2cba6",
   "metadata": {},
   "source": [
    "## Customize Prompt\n",
    "You can also customize the prompt that is used. Here is an example prompting it to understand that foobar is the same as the Employee table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "8ca7bafb",
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain.prompts.prompt import PromptTemplate\n",
    "\n",
    "_DEFAULT_TEMPLATE = \"\"\"Given an input question, first create a syntactically correct {dialect} query to run, then look at the results of the query and return the answer.\n",
    "Use the following format:\n",
    "\n",
    "Question: \"Question here\"\n",
    "SQLQuery: \"SQL Query to run\"\n",
    "SQLResult: \"Result of the SQLQuery\"\n",
    "Answer: \"Final answer here\"\n",
    "\n",
    "Only use the following tables:\n",
    "\n",
    "{table_info}\n",
    "\n",
    "If someone asks for the table foobar, they really mean the employee table.\n",
    "\n",
    "Question: {input}\"\"\"\n",
    "PROMPT = PromptTemplate(\n",
    "    input_variables=[\"input\", \"table_info\", \"dialect\"], template=_DEFAULT_TEMPLATE\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ec47a2bf",
   "metadata": {},
   "outputs": [],
   "source": [
    "db_chain = SQLDatabaseChain(llm=llm, database=db, prompt=PROMPT, verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ebb0674e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\u001b[1m> Entering new SQLDatabaseChain chain...\u001b[0m\n",
      "How many employees are there in the foobar table? \n",
      "SQLQuery:\u001b[32;1m\u001b[1;3m SELECT COUNT(*) FROM Employee;\u001b[0m\n",
      "SQLResult: \u001b[33;1m\u001b[1;3m[(9,)]\u001b[0m\n",
      "Answer:\u001b[32;1m\u001b[1;3m There are 9 employees in the foobar table.\u001b[0m\n",
      "\u001b[1m> Finished chain.\u001b[0m\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "' There are 9 employees in the foobar table.'"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "db_chain.run(\"How many employees are there in the foobar table?\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b408f800",
   "metadata": {},
   "source": [
    "## Choosing how to limit the number of rows returned\n",
    "If you are querying for several rows of a table you can select the maximum number of results you want to get by using the 'top_k' parameter (default is 10). This is useful for avoiding query results that exceed the prompt max length or consume tokens unnecessarily."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "6adaa799",
   "metadata": {},
   "outputs": [],
   "source": [
    "db_chain = SQLDatabaseChain(llm=llm, database=db, verbose=True, top_k=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "edfc8a8e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\u001b[1m> Entering new SQLDatabaseChain chain...\u001b[0m\n",
      "What are some example tracks by composer Johann Sebastian Bach? \n",
      "SQLQuery:\u001b[32;1m\u001b[1;3m SELECT Name FROM Track WHERE Composer = 'Johann Sebastian Bach' LIMIT 3;\u001b[0m\n",
      "SQLResult: \u001b[33;1m\u001b[1;3m[('Concerto for 2 Violins in D Minor, BWV 1043: I. Vivace',), ('Aria Mit 30 Veränderungen, BWV 988 \"Goldberg Variations\": Aria',), ('Suite for Solo Cello No. 1 in G Major, BWV 1007: I. Prélude',)]\u001b[0m\n",
      "Answer:\u001b[32;1m\u001b[1;3m Examples of tracks by Johann Sebastian Bach include 'Concerto for 2 Violins in D Minor, BWV 1043: I. Vivace', 'Aria Mit 30 Veränderungen, BWV 988 \"Goldberg Variations\": Aria', and 'Suite for Solo Cello No. 1 in G Major, BWV 1007: I. Prélude'.\u001b[0m\n",
      "\u001b[1m> Finished chain.\u001b[0m\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "' Examples of tracks by Johann Sebastian Bach include \\'Concerto for 2 Violins in D Minor, BWV 1043: I. Vivace\\', \\'Aria Mit 30 Veränderungen, BWV 988 \"Goldberg Variations\": Aria\\', and \\'Suite for Solo Cello No. 1 in G Major, BWV 1007: I. Prélude\\'.'"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "db_chain.run(\"What are some example tracks by composer Johann Sebastian Bach?\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c12ae15a",
   "metadata": {},
   "source": [
    "## SQLDatabaseSequentialChain\n",
    "\n",
    "Chain for querying SQL database that is a sequential chain.\n",
    "\n",
    "The chain is as follows:\n",
    "\n",
    "    1. Based on the query, determine which tables to use.\n",
    "    2. Based on those tables, call the normal SQL database chain.\n",
    "\n",
    "This is useful in cases where the number of tables in the database is large."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e59a4740",
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain.chains import SQLDatabaseSequentialChain"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "58bb49b6",
   "metadata": {},
   "outputs": [],
   "source": [
    "chain = SQLDatabaseSequentialChain.from_llm(llm, db, verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "95017b1a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\u001b[1m> Entering new SQLDatabaseSequentialChain chain...\u001b[0m\n",
      "Table names to use:\n",
      "\u001b[33;1m\u001b[1;3m['Customer', 'Employee']\u001b[0m\n",
      "\n",
      "\u001b[1m> Entering new SQLDatabaseChain chain...\u001b[0m\n",
      "How many employees are also customers? \n",
      "SQLQuery:\u001b[32;1m\u001b[1;3m SELECT COUNT(*) FROM Customer c INNER JOIN Employee e ON c.SupportRepId = e.EmployeeId;\u001b[0m\n",
      "SQLResult: \u001b[33;1m\u001b[1;3m[(59,)]\u001b[0m\n",
      "Answer:\u001b[32;1m\u001b[1;3m There are 59 employees who are also customers.\u001b[0m\n",
      "\u001b[1m> Finished chain.\u001b[0m\n",
      "\n",
      "\u001b[1m> Finished chain.\u001b[0m\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "' There are 59 employees who are also customers.'"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chain.run(\"How many employees are also customers?\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b2998b03",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.10.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}