03_langchain_sql_agents_Huggingface.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Langchain Agents\n",
    "\n",
    "A simple notebook that uses the Langchain agents to query the SQLite database \"sql_lite_database.db\".\n",
    "\n",
    "In this notebook I use the Hugging Face Hub instead of OpenAI. The Hugging Face Hub is a platform with over 120k models, datasets and demo apps. They are all open source and publicly available."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\z004j58u\\repos\\sonstiges\\langchain-sql-agent\\.venv\\lib\\site-packages\\huggingface_hub\\utils\\_deprecation.py:131: FutureWarning: 'InferenceApi' (from 'huggingface_hub.inference_api') is deprecated and will be removed from version '1.0'. `InferenceApi` client is deprecated in favor of the more feature-complete `InferenceClient`. Check out this guide to learn how to convert your script to use it: https://huggingface.co/docs/huggingface_hub/guides/inference#legacy-inferenceapi-client.\n",
      "  warnings.warn(warning_message, FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "Error raised by inference API: Model tiiuae/falcon-40b time out",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[6], line 34\u001b[0m\n\u001b[0;32m     32\u001b[0m \u001b[38;5;66;03m# define the user's question\u001b[39;00m\n\u001b[0;32m     33\u001b[0m question \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mHow many customers do we have in our database?\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m---> 34\u001b[0m \u001b[43magent_executor\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43minvoke\u001b[49m\u001b[43m(\u001b[49m\u001b[43mquestion\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32mc:\\Users\\z004j58u\\repos\\sonstiges\\langchain-sql-agent\\.venv\\lib\\site-packages\\langchain\\chains\\base.py:162\u001b[0m, in \u001b[0;36mChain.invoke\u001b[1;34m(self, input, config, **kwargs)\u001b[0m\n\u001b[0;32m    160\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mBaseException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m    161\u001b[0m     run_manager\u001b[38;5;241m.\u001b[39mon_chain_error(e)\n\u001b[1;32m--> 162\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m e\n\u001b[0;32m    163\u001b[0m run_manager\u001b[38;5;241m.\u001b[39mon_chain_end(outputs)\n\u001b[0;32m    164\u001b[0m final_outputs: Dict[\u001b[38;5;28mstr\u001b[39m, Any] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mprep_outputs(\n\u001b[0;32m    165\u001b[0m     inputs, outputs, return_only_outputs\n\u001b[0;32m    166\u001b[0m )\n",
      "File \u001b[1;32mc:\\Users\\z004j58u\\repos\\sonstiges\\langchain-sql-agent\\.venv\\lib\\site-packages\\langchain\\chains\\base.py:156\u001b[0m, in \u001b[0;36mChain.invoke\u001b[1;34m(self, input, config, **kwargs)\u001b[0m\n\u001b[0;32m    149\u001b[0m run_manager \u001b[38;5;241m=\u001b[39m callback_manager\u001b[38;5;241m.\u001b[39mon_chain_start(\n\u001b[0;32m    150\u001b[0m     dumpd(\u001b[38;5;28mself\u001b[39m),\n\u001b[0;32m    151\u001b[0m     inputs,\n\u001b[0;32m    152\u001b[0m     name\u001b[38;5;241m=\u001b[39mrun_name,\n\u001b[0;32m    153\u001b[0m )\n\u001b[0;32m    154\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m    155\u001b[0m     outputs \u001b[38;5;241m=\u001b[39m (\n\u001b[1;32m--> 156\u001b[0m         \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call\u001b[49m\u001b[43m(\u001b[49m\u001b[43minputs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrun_manager\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrun_manager\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    157\u001b[0m         \u001b[38;5;28;01mif\u001b[39;00m new_arg_supported\n\u001b[0;32m    158\u001b[0m         \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_call(inputs)\n\u001b[0;32m    159\u001b[0m     )\n\u001b[0;32m    160\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mBaseException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m    161\u001b[0m     run_manager\u001b[38;5;241m.\u001b[39mon_chain_error(e)\n",
      "File \u001b[1;32mc:\\Users\\z004j58u\\repos\\sonstiges\\langchain-sql-agent\\.venv\\lib\\site-packages\\langchain\\agents\\agent.py:1329\u001b[0m, in \u001b[0;36mAgentExecutor._call\u001b[1;34m(self, inputs, run_manager)\u001b[0m\n\u001b[0;32m   1327\u001b[0m \u001b[38;5;66;03m# We now enter the agent loop (until it returns something).\u001b[39;00m\n\u001b[0;32m   1328\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_should_continue(iterations, time_elapsed):\n\u001b[1;32m-> 1329\u001b[0m     next_step_output \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_take_next_step\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m   1330\u001b[0m \u001b[43m        \u001b[49m\u001b[43mname_to_tool_map\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1331\u001b[0m \u001b[43m        \u001b[49m\u001b[43mcolor_mapping\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1332\u001b[0m \u001b[43m        \u001b[49m\u001b[43minputs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1333\u001b[0m \u001b[43m        \u001b[49m\u001b[43mintermediate_steps\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1334\u001b[0m \u001b[43m        \u001b[49m\u001b[43mrun_manager\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrun_manager\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1335\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1336\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(next_step_output, AgentFinish):\n\u001b[0;32m   1337\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_return(\n\u001b[0;32m   1338\u001b[0m             next_step_output, intermediate_steps, run_manager\u001b[38;5;241m=\u001b[39mrun_manager\n\u001b[0;32m   1339\u001b[0m         )\n",
      "File \u001b[1;32mc:\\Users\\z004j58u\\repos\\sonstiges\\langchain-sql-agent\\.venv\\lib\\site-packages\\langchain\\agents\\agent.py:1055\u001b[0m, in \u001b[0;36mAgentExecutor._take_next_step\u001b[1;34m(self, name_to_tool_map, color_mapping, inputs, intermediate_steps, run_manager)\u001b[0m\n\u001b[0;32m   1046\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_take_next_step\u001b[39m(\n\u001b[0;32m   1047\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[0;32m   1048\u001b[0m     name_to_tool_map: Dict[\u001b[38;5;28mstr\u001b[39m, BaseTool],\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m   1052\u001b[0m     run_manager: Optional[CallbackManagerForChainRun] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[0;32m   1053\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Union[AgentFinish, List[Tuple[AgentAction, \u001b[38;5;28mstr\u001b[39m]]]:\n\u001b[0;32m   1054\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_consume_next_step(\n\u001b[1;32m-> 1055\u001b[0m         [\n\u001b[0;32m   1056\u001b[0m             a\n\u001b[0;32m   1057\u001b[0m             \u001b[38;5;28;01mfor\u001b[39;00m a \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_iter_next_step(\n\u001b[0;32m   1058\u001b[0m                 name_to_tool_map,\n\u001b[0;32m   1059\u001b[0m                 color_mapping,\n\u001b[0;32m   1060\u001b[0m                 inputs,\n\u001b[0;32m   1061\u001b[0m                 intermediate_steps,\n\u001b[0;32m   1062\u001b[0m                 run_manager,\n\u001b[0;32m   1063\u001b[0m             )\n\u001b[0;32m   1064\u001b[0m         ]\n\u001b[0;32m   1065\u001b[0m     )\n",
      "File \u001b[1;32mc:\\Users\\z004j58u\\repos\\sonstiges\\langchain-sql-agent\\.venv\\lib\\site-packages\\langchain\\agents\\agent.py:1055\u001b[0m, in \u001b[0;36m<listcomp>\u001b[1;34m(.0)\u001b[0m\n\u001b[0;32m   1046\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_take_next_step\u001b[39m(\n\u001b[0;32m   1047\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[0;32m   1048\u001b[0m     name_to_tool_map: Dict[\u001b[38;5;28mstr\u001b[39m, BaseTool],\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m   1052\u001b[0m     run_manager: Optional[CallbackManagerForChainRun] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[0;32m   1053\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Union[AgentFinish, List[Tuple[AgentAction, \u001b[38;5;28mstr\u001b[39m]]]:\n\u001b[0;32m   1054\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_consume_next_step(\n\u001b[1;32m-> 1055\u001b[0m         [\n\u001b[0;32m   1056\u001b[0m             a\n\u001b[0;32m   1057\u001b[0m             \u001b[38;5;28;01mfor\u001b[39;00m a \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_iter_next_step(\n\u001b[0;32m   1058\u001b[0m                 name_to_tool_map,\n\u001b[0;32m   1059\u001b[0m                 color_mapping,\n\u001b[0;32m   1060\u001b[0m                 inputs,\n\u001b[0;32m   1061\u001b[0m                 intermediate_steps,\n\u001b[0;32m   1062\u001b[0m                 run_manager,\n\u001b[0;32m   1063\u001b[0m             )\n\u001b[0;32m   1064\u001b[0m         ]\n\u001b[0;32m   1065\u001b[0m     )\n",
      "File \u001b[1;32mc:\\Users\\z004j58u\\repos\\sonstiges\\langchain-sql-agent\\.venv\\lib\\site-packages\\langchain\\agents\\agent.py:1083\u001b[0m, in \u001b[0;36mAgentExecutor._iter_next_step\u001b[1;34m(self, name_to_tool_map, color_mapping, inputs, intermediate_steps, run_manager)\u001b[0m\n\u001b[0;32m   1080\u001b[0m     intermediate_steps \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_prepare_intermediate_steps(intermediate_steps)\n\u001b[0;32m   1082\u001b[0m     \u001b[38;5;66;03m# Call the LLM to see what to do.\u001b[39;00m\n\u001b[1;32m-> 1083\u001b[0m     output \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39magent\u001b[38;5;241m.\u001b[39mplan(\n\u001b[0;32m   1084\u001b[0m         intermediate_steps,\n\u001b[0;32m   1085\u001b[0m         callbacks\u001b[38;5;241m=\u001b[39mrun_manager\u001b[38;5;241m.\u001b[39mget_child() \u001b[38;5;28;01mif\u001b[39;00m run_manager \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[0;32m   1086\u001b[0m         \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39minputs,\n\u001b[0;32m   1087\u001b[0m     )\n\u001b[0;32m   1088\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m OutputParserException \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m   1089\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandle_parsing_errors, \u001b[38;5;28mbool\u001b[39m):\n",
      "File \u001b[1;32mc:\\Users\\z004j58u\\repos\\sonstiges\\langchain-sql-agent\\.venv\\lib\\site-packages\\langchain\\agents\\agent.py:652\u001b[0m, in \u001b[0;36mAgent.plan\u001b[1;34m(self, intermediate_steps, callbacks, **kwargs)\u001b[0m\n\u001b[0;32m    640\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Given input, decided what to do.\u001b[39;00m\n\u001b[0;32m    641\u001b[0m \n\u001b[0;32m    642\u001b[0m \u001b[38;5;124;03mArgs:\u001b[39;00m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    649\u001b[0m \u001b[38;5;124;03m    Action specifying what tool to use.\u001b[39;00m\n\u001b[0;32m    650\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m    651\u001b[0m full_inputs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_full_inputs(intermediate_steps, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m--> 652\u001b[0m full_output \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mllm_chain\u001b[38;5;241m.\u001b[39mpredict(callbacks\u001b[38;5;241m=\u001b[39mcallbacks, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mfull_inputs)\n\u001b[0;32m    653\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moutput_parser\u001b[38;5;241m.\u001b[39mparse(full_output)\n",
      "File \u001b[1;32mc:\\Users\\z004j58u\\repos\\sonstiges\\langchain-sql-agent\\.venv\\lib\\site-packages\\langchain\\chains\\llm.py:293\u001b[0m, in \u001b[0;36mLLMChain.predict\u001b[1;34m(self, callbacks, **kwargs)\u001b[0m\n\u001b[0;32m    278\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mpredict\u001b[39m(\u001b[38;5;28mself\u001b[39m, callbacks: Callbacks \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs: Any) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28mstr\u001b[39m:\n\u001b[0;32m    279\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"Format prompt with kwargs and pass to LLM.\u001b[39;00m\n\u001b[0;32m    280\u001b[0m \n\u001b[0;32m    281\u001b[0m \u001b[38;5;124;03m    Args:\u001b[39;00m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    291\u001b[0m \u001b[38;5;124;03m            completion = llm.predict(adjective=\"funny\")\u001b[39;00m\n\u001b[0;32m    292\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[1;32m--> 293\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcallbacks\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcallbacks\u001b[49m\u001b[43m)\u001b[49m[\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moutput_key]\n",
      "File \u001b[1;32mc:\\Users\\z004j58u\\repos\\sonstiges\\langchain-sql-agent\\.venv\\lib\\site-packages\\langchain_core\\_api\\deprecation.py:142\u001b[0m, in \u001b[0;36mdeprecated.<locals>.deprecate.<locals>.warning_emitting_wrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    140\u001b[0m     warned \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[0;32m    141\u001b[0m     emit_warning()\n\u001b[1;32m--> 142\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m wrapped(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
      "File \u001b[1;32mc:\\Users\\z004j58u\\repos\\sonstiges\\langchain-sql-agent\\.venv\\lib\\site-packages\\langchain\\chains\\base.py:363\u001b[0m, in \u001b[0;36mChain.__call__\u001b[1;34m(self, inputs, return_only_outputs, callbacks, tags, metadata, run_name, include_run_info)\u001b[0m\n\u001b[0;32m    331\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Execute the chain.\u001b[39;00m\n\u001b[0;32m    332\u001b[0m \n\u001b[0;32m    333\u001b[0m \u001b[38;5;124;03mArgs:\u001b[39;00m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    354\u001b[0m \u001b[38;5;124;03m        `Chain.output_keys`.\u001b[39;00m\n\u001b[0;32m    355\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m    356\u001b[0m config \u001b[38;5;241m=\u001b[39m {\n\u001b[0;32m    357\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcallbacks\u001b[39m\u001b[38;5;124m\"\u001b[39m: callbacks,\n\u001b[0;32m    358\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtags\u001b[39m\u001b[38;5;124m\"\u001b[39m: tags,\n\u001b[0;32m    359\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmetadata\u001b[39m\u001b[38;5;124m\"\u001b[39m: metadata,\n\u001b[0;32m    360\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrun_name\u001b[39m\u001b[38;5;124m\"\u001b[39m: run_name,\n\u001b[0;32m    361\u001b[0m }\n\u001b[1;32m--> 363\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43minvoke\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    364\u001b[0m \u001b[43m    \u001b[49m\u001b[43minputs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    365\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcast\u001b[49m\u001b[43m(\u001b[49m\u001b[43mRunnableConfig\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m{\u001b[49m\u001b[43mk\u001b[49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mv\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mk\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mv\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mconfig\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mitems\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mv\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mis\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mnot\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m}\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    366\u001b[0m \u001b[43m    \u001b[49m\u001b[43mreturn_only_outputs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mreturn_only_outputs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    367\u001b[0m \u001b[43m    \u001b[49m\u001b[43minclude_run_info\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minclude_run_info\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    368\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32mc:\\Users\\z004j58u\\repos\\sonstiges\\langchain-sql-agent\\.venv\\lib\\site-packages\\langchain\\chains\\base.py:162\u001b[0m, in \u001b[0;36mChain.invoke\u001b[1;34m(self, input, config, **kwargs)\u001b[0m\n\u001b[0;32m    160\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mBaseException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m    161\u001b[0m     run_manager\u001b[38;5;241m.\u001b[39mon_chain_error(e)\n\u001b[1;32m--> 162\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m e\n\u001b[0;32m    163\u001b[0m run_manager\u001b[38;5;241m.\u001b[39mon_chain_end(outputs)\n\u001b[0;32m    164\u001b[0m final_outputs: Dict[\u001b[38;5;28mstr\u001b[39m, Any] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mprep_outputs(\n\u001b[0;32m    165\u001b[0m     inputs, outputs, return_only_outputs\n\u001b[0;32m    166\u001b[0m )\n",
      "File \u001b[1;32mc:\\Users\\z004j58u\\repos\\sonstiges\\langchain-sql-agent\\.venv\\lib\\site-packages\\langchain\\chains\\base.py:156\u001b[0m, in \u001b[0;36mChain.invoke\u001b[1;34m(self, input, config, **kwargs)\u001b[0m\n\u001b[0;32m    149\u001b[0m run_manager \u001b[38;5;241m=\u001b[39m callback_manager\u001b[38;5;241m.\u001b[39mon_chain_start(\n\u001b[0;32m    150\u001b[0m     dumpd(\u001b[38;5;28mself\u001b[39m),\n\u001b[0;32m    151\u001b[0m     inputs,\n\u001b[0;32m    152\u001b[0m     name\u001b[38;5;241m=\u001b[39mrun_name,\n\u001b[0;32m    153\u001b[0m )\n\u001b[0;32m    154\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m    155\u001b[0m     outputs \u001b[38;5;241m=\u001b[39m (\n\u001b[1;32m--> 156\u001b[0m         \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call\u001b[49m\u001b[43m(\u001b[49m\u001b[43minputs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrun_manager\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrun_manager\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    157\u001b[0m         \u001b[38;5;28;01mif\u001b[39;00m new_arg_supported\n\u001b[0;32m    158\u001b[0m         \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_call(inputs)\n\u001b[0;32m    159\u001b[0m     )\n\u001b[0;32m    160\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mBaseException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m    161\u001b[0m     run_manager\u001b[38;5;241m.\u001b[39mon_chain_error(e)\n",
      "File \u001b[1;32mc:\\Users\\z004j58u\\repos\\sonstiges\\langchain-sql-agent\\.venv\\lib\\site-packages\\langchain\\chains\\llm.py:103\u001b[0m, in \u001b[0;36mLLMChain._call\u001b[1;34m(self, inputs, run_manager)\u001b[0m\n\u001b[0;32m     98\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_call\u001b[39m(\n\u001b[0;32m     99\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[0;32m    100\u001b[0m     inputs: Dict[\u001b[38;5;28mstr\u001b[39m, Any],\n\u001b[0;32m    101\u001b[0m     run_manager: Optional[CallbackManagerForChainRun] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[0;32m    102\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Dict[\u001b[38;5;28mstr\u001b[39m, \u001b[38;5;28mstr\u001b[39m]:\n\u001b[1;32m--> 103\u001b[0m     response \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgenerate\u001b[49m\u001b[43m(\u001b[49m\u001b[43m[\u001b[49m\u001b[43minputs\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrun_manager\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrun_manager\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    104\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcreate_outputs(response)[\u001b[38;5;241m0\u001b[39m]\n",
      "File \u001b[1;32mc:\\Users\\z004j58u\\repos\\sonstiges\\langchain-sql-agent\\.venv\\lib\\site-packages\\langchain\\chains\\llm.py:115\u001b[0m, in \u001b[0;36mLLMChain.generate\u001b[1;34m(self, input_list, run_manager)\u001b[0m\n\u001b[0;32m    113\u001b[0m callbacks \u001b[38;5;241m=\u001b[39m run_manager\u001b[38;5;241m.\u001b[39mget_child() \u001b[38;5;28;01mif\u001b[39;00m run_manager \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m    114\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mllm, BaseLanguageModel):\n\u001b[1;32m--> 115\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mllm\u001b[38;5;241m.\u001b[39mgenerate_prompt(\n\u001b[0;32m    116\u001b[0m         prompts,\n\u001b[0;32m    117\u001b[0m         stop,\n\u001b[0;32m    118\u001b[0m         callbacks\u001b[38;5;241m=\u001b[39mcallbacks,\n\u001b[0;32m    119\u001b[0m         \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mllm_kwargs,\n\u001b[0;32m    120\u001b[0m     )\n\u001b[0;32m    121\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    122\u001b[0m     results \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mllm\u001b[38;5;241m.\u001b[39mbind(stop\u001b[38;5;241m=\u001b[39mstop, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mllm_kwargs)\u001b[38;5;241m.\u001b[39mbatch(\n\u001b[0;32m    123\u001b[0m         cast(List, prompts), {\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcallbacks\u001b[39m\u001b[38;5;124m\"\u001b[39m: callbacks}\n\u001b[0;32m    124\u001b[0m     )\n",
      "File \u001b[1;32mc:\\Users\\z004j58u\\repos\\sonstiges\\langchain-sql-agent\\.venv\\lib\\site-packages\\langchain_core\\language_models\\llms.py:525\u001b[0m, in \u001b[0;36mBaseLLM.generate_prompt\u001b[1;34m(self, prompts, stop, callbacks, **kwargs)\u001b[0m\n\u001b[0;32m    517\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mgenerate_prompt\u001b[39m(\n\u001b[0;32m    518\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[0;32m    519\u001b[0m     prompts: List[PromptValue],\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    522\u001b[0m     \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs: Any,\n\u001b[0;32m    523\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m LLMResult:\n\u001b[0;32m    524\u001b[0m     prompt_strings \u001b[38;5;241m=\u001b[39m [p\u001b[38;5;241m.\u001b[39mto_string() \u001b[38;5;28;01mfor\u001b[39;00m p \u001b[38;5;129;01min\u001b[39;00m prompts]\n\u001b[1;32m--> 525\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mgenerate(prompt_strings, stop\u001b[38;5;241m=\u001b[39mstop, callbacks\u001b[38;5;241m=\u001b[39mcallbacks, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
      "File \u001b[1;32mc:\\Users\\z004j58u\\repos\\sonstiges\\langchain-sql-agent\\.venv\\lib\\site-packages\\langchain_core\\language_models\\llms.py:698\u001b[0m, in \u001b[0;36mBaseLLM.generate\u001b[1;34m(self, prompts, stop, callbacks, tags, metadata, run_name, **kwargs)\u001b[0m\n\u001b[0;32m    682\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[0;32m    683\u001b[0m             \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mAsked to cache, but no cache found at `langchain.cache`.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    684\u001b[0m         )\n\u001b[0;32m    685\u001b[0m     run_managers \u001b[38;5;241m=\u001b[39m [\n\u001b[0;32m    686\u001b[0m         callback_manager\u001b[38;5;241m.\u001b[39mon_llm_start(\n\u001b[0;32m    687\u001b[0m             dumpd(\u001b[38;5;28mself\u001b[39m),\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    696\u001b[0m         )\n\u001b[0;32m    697\u001b[0m     ]\n\u001b[1;32m--> 698\u001b[0m     output \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_generate_helper(\n\u001b[0;32m    699\u001b[0m         prompts, stop, run_managers, \u001b[38;5;28mbool\u001b[39m(new_arg_supported), \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs\n\u001b[0;32m    700\u001b[0m     )\n\u001b[0;32m    701\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m output\n\u001b[0;32m    702\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(missing_prompts) \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m0\u001b[39m:\n",
      "File \u001b[1;32mc:\\Users\\z004j58u\\repos\\sonstiges\\langchain-sql-agent\\.venv\\lib\\site-packages\\langchain_core\\language_models\\llms.py:562\u001b[0m, in \u001b[0;36mBaseLLM._generate_helper\u001b[1;34m(self, prompts, stop, run_managers, new_arg_supported, **kwargs)\u001b[0m\n\u001b[0;32m    560\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m run_manager \u001b[38;5;129;01min\u001b[39;00m run_managers:\n\u001b[0;32m    561\u001b[0m         run_manager\u001b[38;5;241m.\u001b[39mon_llm_error(e, response\u001b[38;5;241m=\u001b[39mLLMResult(generations\u001b[38;5;241m=\u001b[39m[]))\n\u001b[1;32m--> 562\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m e\n\u001b[0;32m    563\u001b[0m flattened_outputs \u001b[38;5;241m=\u001b[39m output\u001b[38;5;241m.\u001b[39mflatten()\n\u001b[0;32m    564\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m manager, flattened_output \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mzip\u001b[39m(run_managers, flattened_outputs):\n",
      "File \u001b[1;32mc:\\Users\\z004j58u\\repos\\sonstiges\\langchain-sql-agent\\.venv\\lib\\site-packages\\langchain_core\\language_models\\llms.py:549\u001b[0m, in \u001b[0;36mBaseLLM._generate_helper\u001b[1;34m(self, prompts, stop, run_managers, new_arg_supported, **kwargs)\u001b[0m\n\u001b[0;32m    539\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_generate_helper\u001b[39m(\n\u001b[0;32m    540\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[0;32m    541\u001b[0m     prompts: List[\u001b[38;5;28mstr\u001b[39m],\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    545\u001b[0m     \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs: Any,\n\u001b[0;32m    546\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m LLMResult:\n\u001b[0;32m    547\u001b[0m     \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m    548\u001b[0m         output \u001b[38;5;241m=\u001b[39m (\n\u001b[1;32m--> 549\u001b[0m             \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_generate(\n\u001b[0;32m    550\u001b[0m                 prompts,\n\u001b[0;32m    551\u001b[0m                 stop\u001b[38;5;241m=\u001b[39mstop,\n\u001b[0;32m    552\u001b[0m                 \u001b[38;5;66;03m# TODO: support multiple run managers\u001b[39;00m\n\u001b[0;32m    553\u001b[0m                 run_manager\u001b[38;5;241m=\u001b[39mrun_managers[\u001b[38;5;241m0\u001b[39m] \u001b[38;5;28;01mif\u001b[39;00m run_managers \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[0;32m    554\u001b[0m                 \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs,\n\u001b[0;32m    555\u001b[0m             )\n\u001b[0;32m    556\u001b[0m             \u001b[38;5;28;01mif\u001b[39;00m new_arg_supported\n\u001b[0;32m    557\u001b[0m             \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_generate(prompts, stop\u001b[38;5;241m=\u001b[39mstop)\n\u001b[0;32m    558\u001b[0m         )\n\u001b[0;32m    559\u001b[0m     \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mBaseException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m    560\u001b[0m         \u001b[38;5;28;01mfor\u001b[39;00m run_manager \u001b[38;5;129;01min\u001b[39;00m run_managers:\n",
      "File \u001b[1;32mc:\\Users\\z004j58u\\repos\\sonstiges\\langchain-sql-agent\\.venv\\lib\\site-packages\\langchain_core\\language_models\\llms.py:1134\u001b[0m, in \u001b[0;36mLLM._generate\u001b[1;34m(self, prompts, stop, run_manager, **kwargs)\u001b[0m\n\u001b[0;32m   1131\u001b[0m new_arg_supported \u001b[38;5;241m=\u001b[39m inspect\u001b[38;5;241m.\u001b[39msignature(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_call)\u001b[38;5;241m.\u001b[39mparameters\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrun_manager\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m   1132\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m prompt \u001b[38;5;129;01min\u001b[39;00m prompts:\n\u001b[0;32m   1133\u001b[0m     text \u001b[38;5;241m=\u001b[39m (\n\u001b[1;32m-> 1134\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_call(prompt, stop\u001b[38;5;241m=\u001b[39mstop, run_manager\u001b[38;5;241m=\u001b[39mrun_manager, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m   1135\u001b[0m         \u001b[38;5;28;01mif\u001b[39;00m new_arg_supported\n\u001b[0;32m   1136\u001b[0m         \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_call(prompt, stop\u001b[38;5;241m=\u001b[39mstop, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m   1137\u001b[0m     )\n\u001b[0;32m   1138\u001b[0m     generations\u001b[38;5;241m.\u001b[39mappend([Generation(text\u001b[38;5;241m=\u001b[39mtext)])\n\u001b[0;32m   1139\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m LLMResult(generations\u001b[38;5;241m=\u001b[39mgenerations)\n",
      "File \u001b[1;32mc:\\Users\\z004j58u\\repos\\sonstiges\\langchain-sql-agent\\.venv\\lib\\site-packages\\langchain_community\\llms\\huggingface_hub.py:113\u001b[0m, in \u001b[0;36mHuggingFaceHub._call\u001b[1;34m(self, prompt, stop, run_manager, **kwargs)\u001b[0m\n\u001b[0;32m    111\u001b[0m response \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mclient(inputs\u001b[38;5;241m=\u001b[39mprompt, params\u001b[38;5;241m=\u001b[39mparams)\n\u001b[0;32m    112\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124merror\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01min\u001b[39;00m response:\n\u001b[1;32m--> 113\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mError raised by inference API: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mresponse[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124merror\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m    114\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mclient\u001b[38;5;241m.\u001b[39mtask \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtext-generation\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[0;32m    115\u001b[0m     \u001b[38;5;66;03m# Text generation return includes the starter text.\u001b[39;00m\n\u001b[0;32m    116\u001b[0m     text \u001b[38;5;241m=\u001b[39m response[\u001b[38;5;241m0\u001b[39m][\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mgenerated_text\u001b[39m\u001b[38;5;124m\"\u001b[39m][\u001b[38;5;28mlen\u001b[39m(prompt) :]\n",
      "\u001b[1;31mValueError\u001b[0m: Error raised by inference API: Model tiiuae/falcon-40b time out"
     ]
    }
   ],
   "source": [
    "from langchain.utilities import SQLDatabase\n",
    "from langchain.llms import OpenAI\n",
    "from langchain.agents.agent_types import AgentType\n",
    "from langchain.agents.agent_toolkits import SQLDatabaseToolkit \n",
    "from langchain.utilities import SQLDatabase\n",
    "from langchain_community.llms import HuggingFaceHub\n",
    "from langchain.agents import create_sql_agent \n",
    "import os\n",
    "from dotenv import load_dotenv\n",
    "load_dotenv()\n",
    "\n",
    "# define the database we want to use for our test\n",
    "db = SQLDatabase.from_uri('sqlite:///sql_lite_database.db')\n",
    "\n",
    "\n",
    "# choose llm model, in this case the default OpenAI model\n",
    "repo_id = \"tiiuae/falcon-40b\"  # See https://huggingface.co/models?pipeline_tag=text-generation&sort=downloads for some other options\n",
    "llm = HuggingFaceHub(\n",
    "                    repo_id=repo_id, \n",
    "                    model_kwargs={\"temperature\": 0.5, \"max_length\": 2000}\n",
    "                    )\n",
    "\n",
    "# setup agent\n",
    "toolkit = SQLDatabaseToolkit(db=db, llm=llm)\n",
    "agent_executor = create_sql_agent(\n",
    "    llm=llm,\n",
    "    toolkit=toolkit,\n",
    "    verbose=True,\n",
    "    agent_type=AgentType.ZERO_SHOT_REACT_DESCRIPTION,\n",
    ")\n",
    "\n",
    "# define the user's question\n",
    "question = \"How many customers do we have in our database?\"\n",
    "agent_executor.invoke(question)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'agent_executor' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[5], line 3\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;66;03m# define the user's question\u001b[39;00m\n\u001b[0;32m      2\u001b[0m question \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mList all customers from the US\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m----> 3\u001b[0m \u001b[43magent_executor\u001b[49m\u001b[38;5;241m.\u001b[39minvoke(question)\n",
      "\u001b[1;31mNameError\u001b[0m: name 'agent_executor' is not defined"
     ]
    }
   ],
   "source": [
    "# define the user's question\n",
    "question = \"List all customers from the US\"\n",
    "agent_executor.invoke(question)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'agent_executor' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[4], line 3\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;66;03m# define the user's question\u001b[39;00m\n\u001b[0;32m      2\u001b[0m question \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mWhat is the phone number of the customer Albert?\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m----> 3\u001b[0m \u001b[43magent_executor\u001b[49m\u001b[38;5;241m.\u001b[39minvoke(question)\n",
      "\u001b[1;31mNameError\u001b[0m: name 'agent_executor' is not defined"
     ]
    }
   ],
   "source": [
    "# define the user's question\n",
    "question = \"What is the phone number of the customer Albert?\"\n",
    "agent_executor.invoke(question)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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