{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "import os\n",
    "sys.path.append(os.path.dirname(os.path.dirname(os.getcwd())))\n",
    "\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "from climateqa.engine.talk_to_data.main import ask_vanna\n",
    "\n",
    "import sqlite3\n",
    "import os\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from climateqa.engine.talk_to_data.myVanna import MyVanna\n",
    "from climateqa.engine.talk_to_data.utils import loc2coords, detect_location_with_openai, detectTable, nearestNeighbourSQL, detect_relevant_tables, replace_coordonates#,nearestNeighbourPostgres\n",
    "\n",
    "from climateqa.engine.llm import get_llm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Vanna Ask\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from dotenv import load_dotenv\n",
    "\n",
    "load_dotenv()\n",
    "\n",
    "llm = get_llm(provider=\"openai\")\n",
    "\n",
    "OPENAI_API_KEY = os.getenv('OPENAI_API_KEY')\n",
    "PC_API_KEY = os.getenv('VANNA_PINECONE_API_KEY')\n",
    "INDEX_NAME = os.getenv('VANNA_INDEX_NAME')\n",
    "VANNA_MODEL = os.getenv('VANNA_MODEL')\n",
    "\n",
    "ROOT_PATH = os.path.dirname(os.path.dirname(os.getcwd()))\n",
    "\n",
    "#Vanna object\n",
    "vn = MyVanna(config = {\"temperature\": 0, \"api_key\": OPENAI_API_KEY, 'model': VANNA_MODEL, 'pc_api_key': PC_API_KEY, 'index_name': INDEX_NAME, \"top_k\" : 4})\n",
    "\n",
    "db_vanna_path = ROOT_PATH + \"/data/drias/drias.db\"\n",
    "vn.connect_to_sqlite(db_vanna_path)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# User query"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "query = \"Quelle sera la température à Marseille sur les prochaines années ?\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Detect location"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "location = detect_location_with_openai(OPENAI_API_KEY, query)\n",
    "print(location)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Convert location to longitude, latitude coordonate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "coords = loc2coords(location)\n",
    "user_input = query.lower().replace(location.lower(), f\"lat, long : {coords}\")\n",
    "print(user_input)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Find closest coordonates and replace lat,lon\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "relevant_tables = detect_relevant_tables(user_input, llm)            \n",
    "coords_tables = [nearestNeighbourSQL(db_vanna_path, coords, relevant_tables[i]) for i in range(len(relevant_tables))]\n",
    "user_input_with_coords = replace_coordonates(coords, user_input, coords_tables)\n",
    "print(user_input_with_coords)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Ask Vanna with correct coordonates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "user_input_with_coords"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sql_query, result_dataframe, figure = vn.ask(user_input_with_coords, print_results=False, allow_llm_to_see_data=True, auto_train=False)\n",
    "print(result_dataframe.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "result_dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "figure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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  "kernelspec": {
   "display_name": "climateqa",
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   "codemirror_mode": {
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    "version": 3
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