Add step by step notebooks for drias

sandbox/talk_to_data/20250306 - CQA - Drias.ipynb

@@ -0,0 +1,82 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Import the function in main.py"
+   ]
+  },
+  {
+   "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"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create a human query"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "query = \"Comment vont évoluer les températures à marseille ?\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Call the function ask vanna, it gives an output of a the sql query and the dataframe of the result (tuple)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sql_query, df, fig = ask_vanna(query)\n",
+    "print(df.head())\n",
+    "fig.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "climateqa",
+   "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": "3.11.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

sandbox/talk_to_data/20250306 - CQA - Step_by_step_vanna.ipynb

@@ -0,0 +1,218 @@
+{
+ "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": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "climateqa",
+   "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": "3.11.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}