{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "8c361804",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(234005, 7)\n",
      "['Unnamed: 0', 'date', 'time', 'ticket_number', 'article', 'Quantity', 'unit_price']\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df = pd.read_csv(\"Bakery sales.csv\")\n",
    "print(df.shape)\n",
    "print(df.columns.tolist())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ef2b6978",
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain_community.utilities import SQLDatabase\n",
    "from sqlalchemy import create_engine\n",
    "\n",
    "engine = create_engine(\"sqlite:///bakery.db\")\n",
    "df.to_sql(\"Bakery sales\", engine, index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "6076c047",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sqlite\n",
      "['Bakery sales']\n"
     ]
    }
   ],
   "source": [
    "db = SQLDatabase(engine=engine)\n",
    "print(db.dialect)\n",
    "print(db.get_usable_table_names())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "a1e5c72a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import getpass\n",
    "import os\n",
    "\n",
    "if not os.environ.get(\"GROQ_API_KEY\"):\n",
    "  os.environ[\"GROQ_API_KEY\"] = getpass.getpass(\"Enter API key for Groq: \")\n",
    "\n",
    "from langchain.chat_models import init_chat_model\n",
    "\n",
    "llm = init_chat_model(\"openai/gpt-oss-120b\", model_provider=\"groq\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "5fd9869d",
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain_community.agent_toolkits import create_sql_agent\n",
    "\n",
    "agent_executor = create_sql_agent(llm, db=db, agent_type=\"openai-tools\", verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "f8e16129",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\u001b[1m> Entering new SQL Agent Executor chain...\u001b[0m\n",
      "\u001b[32;1m\u001b[1;3m\n",
      "Invoking: `sql_db_list_tables` with `{'tool_input': ''}`\n",
      "\n",
      "\n",
      "\u001b[0m\u001b[38;5;200m\u001b[1;3mBakery sales\u001b[0m\u001b[32;1m\u001b[1;3m\n",
      "Invoking: `sql_db_schema` with `{'table_names': 'Bakery sales'}`\n",
      "\n",
      "\n",
      "\u001b[0m\u001b[33;1m\u001b[1;3m\n",
      "CREATE TABLE \"Bakery sales\" (\n",
      "\t\"Unnamed: 0\" BIGINT, \n",
      "\tdate TEXT, \n",
      "\ttime TEXT, \n",
      "\tticket_number FLOAT, \n",
      "\tarticle TEXT, \n",
      "\t\"Quantity\" FLOAT, \n",
      "\tunit_price TEXT\n",
      ")\n",
      "\n",
      "/*\n",
      "3 rows from Bakery sales table:\n",
      "Unnamed: 0\tdate\ttime\tticket_number\tarticle\tQuantity\tunit_price\n",
      "0\t2021-01-02\t08:38\t150040.0\tBAGUETTE\t1.0\t0,90 €\n",
      "1\t2021-01-02\t08:38\t150040.0\tPAIN AU CHOCOLAT\t3.0\t1,20 €\n",
      "4\t2021-01-02\t09:14\t150041.0\tPAIN AU CHOCOLAT\t2.0\t1,20 €\n",
      "*/\u001b[0m\u001b[32;1m\u001b[1;3m\n",
      "Invoking: `sql_db_query_checker` with `{'query': 'SELECT article, SUM(Quantity) AS total_quantity FROM \"Bakery sales\" GROUP BY article ORDER BY total_quantity DESC LIMIT 1;'}`\n",
      "\n",
      "\n",
      "\u001b[0m\u001b[36;1m\u001b[1;3mSELECT article, SUM(Quantity) AS total_quantity FROM \"Bakery sales\" GROUP BY article ORDER BY total_quantity DESC LIMIT 1;\u001b[0m\u001b[32;1m\u001b[1;3m\n",
      "Invoking: `sql_db_query` with `{'query': 'SELECT article, SUM(Quantity) AS total_quantity FROM \"Bakery sales\" GROUP BY article ORDER BY total_quantity DESC LIMIT 1;'}`\n",
      "\n",
      "\n",
      "\u001b[0m\u001b[36;1m\u001b[1;3m[('TRADITIONAL BAGUETTE', 117463.0)]\u001b[0m\u001b[32;1m\u001b[1;3mThe article with the highest total quantity sold is **“TRADITIONAL BAGUETTE”**, with a total of **117,463 units** sold.\u001b[0m\n",
      "\n",
      "\u001b[1m> Finished chain.\u001b[0m\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'input': 'which article sold the most?',\n",
       " 'output': 'The article with the highest total quantity sold is **“TRADITIONAL BAGUETTE”**, with a total of **117,463 units** sold.'}"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "agent_executor.invoke({\"input\": \"which article sold the most?\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "749e2e92",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.float64(1.538377385098609)"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from langchain_core.prompts import ChatPromptTemplate\n",
    "from langchain_experimental.tools import PythonAstREPLTool\n",
    "\n",
    "df = pd.read_csv(\"Bakery sales.csv\")\n",
    "tool = PythonAstREPLTool(locals={\"df\": df})\n",
    "tool.invoke(\"df['Quantity'].mean()\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "b864785b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'query': {'description': 'code snippet to run', 'title': 'Query', 'type': 'string'}}\n"
     ]
    }
   ],
   "source": [
    "print(tool.args)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "95b91ef1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\n",
      "  \"description\": \"Python inputs.\",\n",
      "  \"properties\": {\n",
      "    \"query\": {\n",
      "      \"description\": \"code snippet to run\",\n",
      "      \"title\": \"Query\",\n",
      "      \"type\": \"string\"\n",
      "    }\n",
      "  },\n",
      "  \"required\": [\n",
      "    \"query\"\n",
      "  ],\n",
      "  \"title\": \"PythonInputs\",\n",
      "  \"type\": \"object\"\n",
      "}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\jayan\\AppData\\Local\\Temp\\ipykernel_15760\\15192852.py:2: PydanticDeprecatedSince20: The `schema_json` method is deprecated; use `model_json_schema` and json.dumps instead. Deprecated in Pydantic V2.0 to be removed in V3.0. See Pydantic V2 Migration Guide at https://errors.pydantic.dev/2.11/migration/\n",
      "  print(schema.schema_json(indent=2))\n"
     ]
    }
   ],
   "source": [
    "schema = tool.get_input_schema()\n",
    "print(schema.schema_json(indent=2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "df4ffd34",
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain_groq import ChatGroq\n",
    "from langchain_core.prompts.chat import ChatPromptTemplate\n",
    "from langgraph.prebuilt import create_react_agent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "cc59c702",
   "metadata": {},
   "outputs": [],
   "source": [
    "system_prompt = ChatPromptTemplate.from_messages([\n",
    "    (\"system\", \"\"\"You may ONLY respond with either (a) a direct final answer OR (b) a tool call. Nothing else.\n",
    "     You have access to PythonAstREPLTool,use it to answer the user's questions.\"\"\"),\n",
    "    (\"user\", \"{messages}\")\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "b9b8906b",
   "metadata": {},
   "outputs": [],
   "source": [
    "pandas_agent = create_react_agent(model=llm,tools=[tool],prompt=system_prompt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "e4e2e09e",
   "metadata": {},
   "outputs": [
    {
     "ename": "BadRequestError",
     "evalue": "Error code: 400 - {'error': {'message': \"Tool call validation failed: tool call validation failed: parameters for tool python_repl_ast did not match schema: errors: [missing properties: 'query']\", 'type': 'invalid_request_error', 'code': 'tool_use_failed', 'failed_generation': '{\"name\": \"python_repl_ast\", \"arguments\": {\\n  \"code\": \"df.head()\"\\n}}'}}",
     "output_type": "error",
     "traceback": [
      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
      "\u001b[31mBadRequestError\u001b[39m                           Traceback (most recent call last)",
      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[100]\u001b[39m\u001b[32m, line 1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m \u001b[43mpandas_agent\u001b[49m\u001b[43m.\u001b[49m\u001b[43minvoke\u001b[49m\u001b[43m(\u001b[49m\u001b[43m{\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mmessages\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mUse the tool if needed.I have a dataframe \u001b[39;49m\u001b[33;43m'\u001b[39;49m\u001b[33;43mdf\u001b[39;49m\u001b[33;43m'\u001b[39;49m\u001b[33;43m. what is the mean of \u001b[39;49m\u001b[33;43m'\u001b[39;49m\u001b[33;43munit_price\u001b[39;49m\u001b[33;43m'\u001b[39;49m\u001b[33;43m column? \u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m}\u001b[49m\u001b[43m)\u001b[49m\n",
      "\u001b[36mFile \u001b[39m\u001b[32md:\\Code Assistant\\venv\\Lib\\site-packages\\langgraph\\pregel\\main.py:3026\u001b[39m, in \u001b[36mPregel.invoke\u001b[39m\u001b[34m(self, input, config, context, stream_mode, print_mode, output_keys, interrupt_before, interrupt_after, durability, **kwargs)\u001b[39m\n\u001b[32m   3023\u001b[39m chunks: \u001b[38;5;28mlist\u001b[39m[\u001b[38;5;28mdict\u001b[39m[\u001b[38;5;28mstr\u001b[39m, Any] | Any] = []\n\u001b[32m   3024\u001b[39m interrupts: \u001b[38;5;28mlist\u001b[39m[Interrupt] = []\n\u001b[32m-> \u001b[39m\u001b[32m3026\u001b[39m \u001b[43m\u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mchunk\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mstream\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m   3027\u001b[39m \u001b[43m    \u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[32m   3028\u001b[39m \u001b[43m    \u001b[49m\u001b[43mconfig\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   3029\u001b[39m \u001b[43m    \u001b[49m\u001b[43mcontext\u001b[49m\u001b[43m=\u001b[49m\u001b[43mcontext\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   3030\u001b[39m \u001b[43m    \u001b[49m\u001b[43mstream_mode\u001b[49m\u001b[43m=\u001b[49m\u001b[43m[\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mupdates\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mvalues\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\n\u001b[32m   3031\u001b[39m \u001b[43m    \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mstream_mode\u001b[49m\u001b[43m \u001b[49m\u001b[43m==\u001b[49m\u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mvalues\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\n\u001b[32m   3032\u001b[39m \u001b[43m    \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mstream_mode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   3033\u001b[39m \u001b[43m    \u001b[49m\u001b[43mprint_mode\u001b[49m\u001b[43m=\u001b[49m\u001b[43mprint_mode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   3034\u001b[39m \u001b[43m    \u001b[49m\u001b[43moutput_keys\u001b[49m\u001b[43m=\u001b[49m\u001b[43moutput_keys\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   3035\u001b[39m \u001b[43m    \u001b[49m\u001b[43minterrupt_before\u001b[49m\u001b[43m=\u001b[49m\u001b[43minterrupt_before\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   3036\u001b[39m \u001b[43m    \u001b[49m\u001b[43minterrupt_after\u001b[49m\u001b[43m=\u001b[49m\u001b[43minterrupt_after\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   3037\u001b[39m \u001b[43m    \u001b[49m\u001b[43mdurability\u001b[49m\u001b[43m=\u001b[49m\u001b[43mdurability\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   3038\u001b[39m \u001b[43m    \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   3039\u001b[39m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\u001b[43m:\u001b[49m\n\u001b[32m   3040\u001b[39m \u001b[43m    \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mstream_mode\u001b[49m\u001b[43m \u001b[49m\u001b[43m==\u001b[49m\u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mvalues\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\n\u001b[32m   3041\u001b[39m \u001b[43m        \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43mlen\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mchunk\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[43m==\u001b[49m\u001b[43m \u001b[49m\u001b[32;43m2\u001b[39;49m\u001b[43m:\u001b[49m\n",
      "\u001b[36mFile \u001b[39m\u001b[32md:\\Code Assistant\\venv\\Lib\\site-packages\\langgraph\\pregel\\main.py:2647\u001b[39m, in \u001b[36mPregel.stream\u001b[39m\u001b[34m(self, input, config, context, stream_mode, print_mode, output_keys, interrupt_before, interrupt_after, durability, subgraphs, debug, **kwargs)\u001b[39m\n\u001b[32m   2645\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m task \u001b[38;5;129;01min\u001b[39;00m loop.match_cached_writes():\n\u001b[32m   2646\u001b[39m     loop.output_writes(task.id, task.writes, cached=\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[32m-> \u001b[39m\u001b[32m2647\u001b[39m \u001b[43m\u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43m_\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mrunner\u001b[49m\u001b[43m.\u001b[49m\u001b[43mtick\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m   2648\u001b[39m \u001b[43m    \u001b[49m\u001b[43m[\u001b[49m\u001b[43mt\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mt\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mloop\u001b[49m\u001b[43m.\u001b[49m\u001b[43mtasks\u001b[49m\u001b[43m.\u001b[49m\u001b[43mvalues\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[38;5;129;43;01mnot\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mt\u001b[49m\u001b[43m.\u001b[49m\u001b[43mwrites\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   2649\u001b[39m \u001b[43m    \u001b[49m\u001b[43mtimeout\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mstep_timeout\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   2650\u001b[39m \u001b[43m    \u001b[49m\u001b[43mget_waiter\u001b[49m\u001b[43m=\u001b[49m\u001b[43mget_waiter\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   2651\u001b[39m \u001b[43m    \u001b[49m\u001b[43mschedule_task\u001b[49m\u001b[43m=\u001b[49m\u001b[43mloop\u001b[49m\u001b[43m.\u001b[49m\u001b[43maccept_push\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   2652\u001b[39m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\u001b[43m:\u001b[49m\n\u001b[32m   2653\u001b[39m \u001b[43m    \u001b[49m\u001b[38;5;66;43;03m# emit output\u001b[39;49;00m\n\u001b[32m   2654\u001b[39m \u001b[43m    \u001b[49m\u001b[38;5;28;43;01myield from\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43m_output\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m   2655\u001b[39m \u001b[43m        \u001b[49m\u001b[43mstream_mode\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mprint_mode\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msubgraphs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstream\u001b[49m\u001b[43m.\u001b[49m\u001b[43mget\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mqueue\u001b[49m\u001b[43m.\u001b[49m\u001b[43mEmpty\u001b[49m\n\u001b[32m   2656\u001b[39m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m   2657\u001b[39m loop.after_tick()\n",
      "\u001b[36mFile \u001b[39m\u001b[32md:\\Code Assistant\\venv\\Lib\\site-packages\\langgraph\\pregel\\_runner.py:162\u001b[39m, in \u001b[36mPregelRunner.tick\u001b[39m\u001b[34m(self, tasks, reraise, timeout, retry_policy, get_waiter, schedule_task)\u001b[39m\n\u001b[32m    160\u001b[39m t = tasks[\u001b[32m0\u001b[39m]\n\u001b[32m    161\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m162\u001b[39m     \u001b[43mrun_with_retry\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m    163\u001b[39m \u001b[43m        \u001b[49m\u001b[43mt\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    164\u001b[39m \u001b[43m        \u001b[49m\u001b[43mretry_policy\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    165\u001b[39m \u001b[43m        \u001b[49m\u001b[43mconfigurable\u001b[49m\u001b[43m=\u001b[49m\u001b[43m{\u001b[49m\n\u001b[32m    166\u001b[39m \u001b[43m            \u001b[49m\u001b[43mCONFIG_KEY_CALL\u001b[49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mpartial\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m    167\u001b[39m \u001b[43m                \u001b[49m\u001b[43m_call\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    168\u001b[39m \u001b[43m                \u001b[49m\u001b[43mweakref\u001b[49m\u001b[43m.\u001b[49m\u001b[43mref\u001b[49m\u001b[43m(\u001b[49m\u001b[43mt\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    169\u001b[39m \u001b[43m                \u001b[49m\u001b[43mretry_policy\u001b[49m\u001b[43m=\u001b[49m\u001b[43mretry_policy\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    170\u001b[39m \u001b[43m                \u001b[49m\u001b[43mfutures\u001b[49m\u001b[43m=\u001b[49m\u001b[43mweakref\u001b[49m\u001b[43m.\u001b[49m\u001b[43mref\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfutures\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    171\u001b[39m \u001b[43m                \u001b[49m\u001b[43mschedule_task\u001b[49m\u001b[43m=\u001b[49m\u001b[43mschedule_task\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    172\u001b[39m \u001b[43m                \u001b[49m\u001b[43msubmit\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43msubmit\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    173\u001b[39m \u001b[43m            \u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    174\u001b[39m \u001b[43m        \u001b[49m\u001b[43m}\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    175\u001b[39m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m    176\u001b[39m     \u001b[38;5;28mself\u001b[39m.commit(t, \u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[32m    177\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m exc:\n",
      "\u001b[36mFile \u001b[39m\u001b[32md:\\Code Assistant\\venv\\Lib\\site-packages\\langgraph\\pregel\\_retry.py:42\u001b[39m, in \u001b[36mrun_with_retry\u001b[39m\u001b[34m(task, retry_policy, configurable)\u001b[39m\n\u001b[32m     40\u001b[39m     task.writes.clear()\n\u001b[32m     41\u001b[39m     \u001b[38;5;66;03m# run the task\u001b[39;00m\n\u001b[32m---> \u001b[39m\u001b[32m42\u001b[39m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mtask\u001b[49m\u001b[43m.\u001b[49m\u001b[43mproc\u001b[49m\u001b[43m.\u001b[49m\u001b[43minvoke\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtask\u001b[49m\u001b[43m.\u001b[49m\u001b[43minput\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mconfig\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m     43\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m ParentCommand \u001b[38;5;28;01mas\u001b[39;00m exc:\n\u001b[32m     44\u001b[39m     ns: \u001b[38;5;28mstr\u001b[39m = config[CONF][CONFIG_KEY_CHECKPOINT_NS]\n",
      "\u001b[36mFile \u001b[39m\u001b[32md:\\Code Assistant\\venv\\Lib\\site-packages\\langgraph\\_internal\\_runnable.py:657\u001b[39m, in \u001b[36mRunnableSeq.invoke\u001b[39m\u001b[34m(self, input, config, **kwargs)\u001b[39m\n\u001b[32m    655\u001b[39m     \u001b[38;5;66;03m# run in context\u001b[39;00m\n\u001b[32m    656\u001b[39m     \u001b[38;5;28;01mwith\u001b[39;00m set_config_context(config, run) \u001b[38;5;28;01mas\u001b[39;00m context:\n\u001b[32m--> \u001b[39m\u001b[32m657\u001b[39m         \u001b[38;5;28minput\u001b[39m = \u001b[43mcontext\u001b[49m\u001b[43m.\u001b[49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\u001b[43mstep\u001b[49m\u001b[43m.\u001b[49m\u001b[43minvoke\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mconfig\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m    658\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m    659\u001b[39m     \u001b[38;5;28minput\u001b[39m = step.invoke(\u001b[38;5;28minput\u001b[39m, config)\n",
      "\u001b[36mFile \u001b[39m\u001b[32md:\\Code Assistant\\venv\\Lib\\site-packages\\langgraph\\_internal\\_runnable.py:394\u001b[39m, in \u001b[36mRunnableCallable.invoke\u001b[39m\u001b[34m(self, input, config, **kwargs)\u001b[39m\n\u001b[32m    392\u001b[39m     \u001b[38;5;66;03m# run in context\u001b[39;00m\n\u001b[32m    393\u001b[39m     \u001b[38;5;28;01mwith\u001b[39;00m set_config_context(child_config, run) \u001b[38;5;28;01mas\u001b[39;00m context:\n\u001b[32m--> \u001b[39m\u001b[32m394\u001b[39m         ret = \u001b[43mcontext\u001b[49m\u001b[43m.\u001b[49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mfunc\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m    395\u001b[39m \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[32m    396\u001b[39m     run_manager.on_chain_error(e)\n",
      "\u001b[36mFile \u001b[39m\u001b[32md:\\Code Assistant\\venv\\Lib\\site-packages\\langgraph\\prebuilt\\chat_agent_executor.py:627\u001b[39m, in \u001b[36mcreate_react_agent.<locals>.call_model\u001b[39m\u001b[34m(state, runtime, config)\u001b[39m\n\u001b[32m    625\u001b[39m     response = cast(AIMessage, dynamic_model.invoke(model_input, config))  \u001b[38;5;66;03m# type: ignore[arg-type]\u001b[39;00m\n\u001b[32m    626\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m627\u001b[39m     response = cast(AIMessage, \u001b[43mstatic_model\u001b[49m\u001b[43m.\u001b[49m\u001b[43minvoke\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodel_input\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mconfig\u001b[49m\u001b[43m)\u001b[49m)  \u001b[38;5;66;03m# type: ignore[union-attr]\u001b[39;00m\n\u001b[32m    629\u001b[39m \u001b[38;5;66;03m# add agent name to the AIMessage\u001b[39;00m\n\u001b[32m    630\u001b[39m response.name = name\n",
      "\u001b[36mFile \u001b[39m\u001b[32md:\\Code Assistant\\venv\\Lib\\site-packages\\langchain_core\\runnables\\base.py:3245\u001b[39m, in \u001b[36mRunnableSequence.invoke\u001b[39m\u001b[34m(self, input, config, **kwargs)\u001b[39m\n\u001b[32m   3243\u001b[39m                 input_ = context.run(step.invoke, input_, config, **kwargs)\n\u001b[32m   3244\u001b[39m             \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m-> \u001b[39m\u001b[32m3245\u001b[39m                 input_ = \u001b[43mcontext\u001b[49m\u001b[43m.\u001b[49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\u001b[43mstep\u001b[49m\u001b[43m.\u001b[49m\u001b[43minvoke\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minput_\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mconfig\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m   3246\u001b[39m \u001b[38;5;66;03m# finish the root run\u001b[39;00m\n\u001b[32m   3247\u001b[39m \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[36mFile \u001b[39m\u001b[32md:\\Code Assistant\\venv\\Lib\\site-packages\\langchain_core\\runnables\\base.py:5710\u001b[39m, in \u001b[36mRunnableBindingBase.invoke\u001b[39m\u001b[34m(self, input, config, **kwargs)\u001b[39m\n\u001b[32m   5703\u001b[39m \u001b[38;5;129m@override\u001b[39m\n\u001b[32m   5704\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34minvoke\u001b[39m(\n\u001b[32m   5705\u001b[39m     \u001b[38;5;28mself\u001b[39m,\n\u001b[32m   (...)\u001b[39m\u001b[32m   5708\u001b[39m     **kwargs: Optional[Any],\n\u001b[32m   5709\u001b[39m ) -> Output:\n\u001b[32m-> \u001b[39m\u001b[32m5710\u001b[39m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mbound\u001b[49m\u001b[43m.\u001b[49m\u001b[43minvoke\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m   5711\u001b[39m \u001b[43m        \u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[32m   5712\u001b[39m \u001b[43m        \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_merge_configs\u001b[49m\u001b[43m(\u001b[49m\u001b[43mconfig\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   5713\u001b[39m \u001b[43m        \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43m{\u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\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[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m}\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   5714\u001b[39m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n",
      "\u001b[36mFile \u001b[39m\u001b[32md:\\Code Assistant\\venv\\Lib\\site-packages\\langchain_core\\language_models\\chat_models.py:395\u001b[39m, in \u001b[36mBaseChatModel.invoke\u001b[39m\u001b[34m(self, input, config, stop, **kwargs)\u001b[39m\n\u001b[32m    383\u001b[39m \u001b[38;5;129m@override\u001b[39m\n\u001b[32m    384\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34minvoke\u001b[39m(\n\u001b[32m    385\u001b[39m     \u001b[38;5;28mself\u001b[39m,\n\u001b[32m   (...)\u001b[39m\u001b[32m    390\u001b[39m     **kwargs: Any,\n\u001b[32m    391\u001b[39m ) -> BaseMessage:\n\u001b[32m    392\u001b[39m     config = ensure_config(config)\n\u001b[32m    393\u001b[39m     \u001b[38;5;28;01mreturn\u001b[39;00m cast(\n\u001b[32m    394\u001b[39m         \u001b[33m\"\u001b[39m\u001b[33mChatGeneration\u001b[39m\u001b[33m\"\u001b[39m,\n\u001b[32m--> \u001b[39m\u001b[32m395\u001b[39m         \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mgenerate_prompt\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m    396\u001b[39m \u001b[43m            \u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_convert_input\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    397\u001b[39m \u001b[43m            \u001b[49m\u001b[43mstop\u001b[49m\u001b[43m=\u001b[49m\u001b[43mstop\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    398\u001b[39m \u001b[43m            \u001b[49m\u001b[43mcallbacks\u001b[49m\u001b[43m=\u001b[49m\u001b[43mconfig\u001b[49m\u001b[43m.\u001b[49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mcallbacks\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    399\u001b[39m \u001b[43m            \u001b[49m\u001b[43mtags\u001b[49m\u001b[43m=\u001b[49m\u001b[43mconfig\u001b[49m\u001b[43m.\u001b[49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mtags\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    400\u001b[39m \u001b[43m            \u001b[49m\u001b[43mmetadata\u001b[49m\u001b[43m=\u001b[49m\u001b[43mconfig\u001b[49m\u001b[43m.\u001b[49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mmetadata\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    401\u001b[39m \u001b[43m            \u001b[49m\u001b[43mrun_name\u001b[49m\u001b[43m=\u001b[49m\u001b[43mconfig\u001b[49m\u001b[43m.\u001b[49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mrun_name\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    402\u001b[39m \u001b[43m            \u001b[49m\u001b[43mrun_id\u001b[49m\u001b[43m=\u001b[49m\u001b[43mconfig\u001b[49m\u001b[43m.\u001b[49m\u001b[43mpop\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mrun_id\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    403\u001b[39m \u001b[43m            \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    404\u001b[39m \u001b[43m        \u001b[49m\u001b[43m)\u001b[49m.generations[\u001b[32m0\u001b[39m][\u001b[32m0\u001b[39m],\n\u001b[32m    405\u001b[39m     ).message\n",
      "\u001b[36mFile \u001b[39m\u001b[32md:\\Code Assistant\\venv\\Lib\\site-packages\\langchain_core\\language_models\\chat_models.py:1023\u001b[39m, in \u001b[36mBaseChatModel.generate_prompt\u001b[39m\u001b[34m(self, prompts, stop, callbacks, **kwargs)\u001b[39m\n\u001b[32m   1014\u001b[39m \u001b[38;5;129m@override\u001b[39m\n\u001b[32m   1015\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mgenerate_prompt\u001b[39m(\n\u001b[32m   1016\u001b[39m     \u001b[38;5;28mself\u001b[39m,\n\u001b[32m   (...)\u001b[39m\u001b[32m   1020\u001b[39m     **kwargs: Any,\n\u001b[32m   1021\u001b[39m ) -> LLMResult:\n\u001b[32m   1022\u001b[39m     prompt_messages = [p.to_messages() \u001b[38;5;28;01mfor\u001b[39;00m p \u001b[38;5;129;01min\u001b[39;00m prompts]\n\u001b[32m-> \u001b[39m\u001b[32m1023\u001b[39m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mgenerate\u001b[49m\u001b[43m(\u001b[49m\u001b[43mprompt_messages\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstop\u001b[49m\u001b[43m=\u001b[49m\u001b[43mstop\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcallbacks\u001b[49m\u001b[43m=\u001b[49m\u001b[43mcallbacks\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "\u001b[36mFile \u001b[39m\u001b[32md:\\Code Assistant\\venv\\Lib\\site-packages\\langchain_core\\language_models\\chat_models.py:840\u001b[39m, in \u001b[36mBaseChatModel.generate\u001b[39m\u001b[34m(self, messages, stop, callbacks, tags, metadata, run_name, run_id, **kwargs)\u001b[39m\n\u001b[32m    837\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m i, m \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(input_messages):\n\u001b[32m    838\u001b[39m     \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m    839\u001b[39m         results.append(\n\u001b[32m--> \u001b[39m\u001b[32m840\u001b[39m             \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_generate_with_cache\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m    841\u001b[39m \u001b[43m                \u001b[49m\u001b[43mm\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    842\u001b[39m \u001b[43m                \u001b[49m\u001b[43mstop\u001b[49m\u001b[43m=\u001b[49m\u001b[43mstop\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    843\u001b[39m \u001b[43m                \u001b[49m\u001b[43mrun_manager\u001b[49m\u001b[43m=\u001b[49m\u001b[43mrun_managers\u001b[49m\u001b[43m[\u001b[49m\u001b[43mi\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[43mrun_managers\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[32m    844\u001b[39m \u001b[43m                \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    845\u001b[39m \u001b[43m            \u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m    846\u001b[39m         )\n\u001b[32m    847\u001b[39m     \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[32m    848\u001b[39m         \u001b[38;5;28;01mif\u001b[39;00m run_managers:\n",
      "\u001b[36mFile \u001b[39m\u001b[32md:\\Code Assistant\\venv\\Lib\\site-packages\\langchain_core\\language_models\\chat_models.py:1089\u001b[39m, in \u001b[36mBaseChatModel._generate_with_cache\u001b[39m\u001b[34m(self, messages, stop, run_manager, **kwargs)\u001b[39m\n\u001b[32m   1087\u001b[39m     result = generate_from_stream(\u001b[38;5;28miter\u001b[39m(chunks))\n\u001b[32m   1088\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m inspect.signature(\u001b[38;5;28mself\u001b[39m._generate).parameters.get(\u001b[33m\"\u001b[39m\u001b[33mrun_manager\u001b[39m\u001b[33m\"\u001b[39m):\n\u001b[32m-> \u001b[39m\u001b[32m1089\u001b[39m     result = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_generate\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m   1090\u001b[39m \u001b[43m        \u001b[49m\u001b[43mmessages\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstop\u001b[49m\u001b[43m=\u001b[49m\u001b[43mstop\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrun_manager\u001b[49m\u001b[43m=\u001b[49m\u001b[43mrun_manager\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\n\u001b[32m   1091\u001b[39m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m   1092\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m   1093\u001b[39m     result = \u001b[38;5;28mself\u001b[39m._generate(messages, stop=stop, **kwargs)\n",
      "\u001b[36mFile \u001b[39m\u001b[32md:\\Code Assistant\\venv\\Lib\\site-packages\\langchain_groq\\chat_models.py:533\u001b[39m, in \u001b[36mChatGroq._generate\u001b[39m\u001b[34m(self, messages, stop, run_manager, **kwargs)\u001b[39m\n\u001b[32m    528\u001b[39m message_dicts, params = \u001b[38;5;28mself\u001b[39m._create_message_dicts(messages, stop)\n\u001b[32m    529\u001b[39m params = {\n\u001b[32m    530\u001b[39m     **params,\n\u001b[32m    531\u001b[39m     **kwargs,\n\u001b[32m    532\u001b[39m }\n\u001b[32m--> \u001b[39m\u001b[32m533\u001b[39m response = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mclient\u001b[49m\u001b[43m.\u001b[49m\u001b[43mcreate\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmessages\u001b[49m\u001b[43m=\u001b[49m\u001b[43mmessage_dicts\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mparams\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m    534\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m._create_chat_result(response, params)\n",
      "\u001b[36mFile \u001b[39m\u001b[32md:\\Code Assistant\\venv\\Lib\\site-packages\\groq\\resources\\chat\\completions.py:448\u001b[39m, in \u001b[36mCompletions.create\u001b[39m\u001b[34m(self, messages, model, compound_custom, documents, exclude_domains, frequency_penalty, function_call, functions, include_domains, include_reasoning, logit_bias, logprobs, max_completion_tokens, max_tokens, metadata, n, parallel_tool_calls, presence_penalty, reasoning_effort, reasoning_format, response_format, search_settings, seed, service_tier, stop, store, stream, temperature, tool_choice, tools, top_logprobs, top_p, user, extra_headers, extra_query, extra_body, timeout)\u001b[39m\n\u001b[32m    238\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mcreate\u001b[39m(\n\u001b[32m    239\u001b[39m     \u001b[38;5;28mself\u001b[39m,\n\u001b[32m    240\u001b[39m     *,\n\u001b[32m   (...)\u001b[39m\u001b[32m    295\u001b[39m     timeout: \u001b[38;5;28mfloat\u001b[39m | httpx.Timeout | \u001b[38;5;28;01mNone\u001b[39;00m | NotGiven = NOT_GIVEN,\n\u001b[32m    296\u001b[39m ) -> ChatCompletion | Stream[ChatCompletionChunk]:\n\u001b[32m    297\u001b[39m \u001b[38;5;250m    \u001b[39m\u001b[33;03m\"\"\"\u001b[39;00m\n\u001b[32m    298\u001b[39m \u001b[33;03m    Creates a model response for the given chat conversation.\u001b[39;00m\n\u001b[32m    299\u001b[39m \n\u001b[32m   (...)\u001b[39m\u001b[32m    446\u001b[39m \u001b[33;03m      timeout: Override the client-level default timeout for this request, in seconds\u001b[39;00m\n\u001b[32m    447\u001b[39m \u001b[33;03m    \"\"\"\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m448\u001b[39m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_post\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m    449\u001b[39m \u001b[43m        \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43m/openai/v1/chat/completions\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[32m    450\u001b[39m \u001b[43m        \u001b[49m\u001b[43mbody\u001b[49m\u001b[43m=\u001b[49m\u001b[43mmaybe_transform\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m    451\u001b[39m \u001b[43m            \u001b[49m\u001b[43m{\u001b[49m\n\u001b[32m    452\u001b[39m \u001b[43m                \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mmessages\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mmessages\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    453\u001b[39m \u001b[43m                \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mmodel\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    454\u001b[39m \u001b[43m                \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mcompound_custom\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mcompound_custom\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    455\u001b[39m \u001b[43m                \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mdocuments\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mdocuments\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    456\u001b[39m \u001b[43m                \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mexclude_domains\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mexclude_domains\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    457\u001b[39m \u001b[43m                \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mfrequency_penalty\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mfrequency_penalty\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    458\u001b[39m \u001b[43m                \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mfunction_call\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mfunction_call\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    459\u001b[39m \u001b[43m                \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mfunctions\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mfunctions\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    460\u001b[39m \u001b[43m                \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43minclude_domains\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43minclude_domains\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    461\u001b[39m \u001b[43m                \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43minclude_reasoning\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43minclude_reasoning\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    462\u001b[39m \u001b[43m                \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mlogit_bias\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mlogit_bias\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    463\u001b[39m \u001b[43m                \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mlogprobs\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mlogprobs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    464\u001b[39m \u001b[43m                \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mmax_completion_tokens\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mmax_completion_tokens\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    465\u001b[39m \u001b[43m                \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mmax_tokens\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mmax_tokens\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    466\u001b[39m \u001b[43m                \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mmetadata\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mmetadata\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    467\u001b[39m \u001b[43m                \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mn\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mn\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    468\u001b[39m \u001b[43m                \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mparallel_tool_calls\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mparallel_tool_calls\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    469\u001b[39m \u001b[43m                \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mpresence_penalty\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mpresence_penalty\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    470\u001b[39m \u001b[43m                \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mreasoning_effort\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mreasoning_effort\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    471\u001b[39m \u001b[43m                \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mreasoning_format\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mreasoning_format\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    472\u001b[39m \u001b[43m                \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mresponse_format\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mresponse_format\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    473\u001b[39m \u001b[43m                \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43msearch_settings\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43msearch_settings\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    474\u001b[39m \u001b[43m                \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mseed\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mseed\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    475\u001b[39m \u001b[43m                \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mservice_tier\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mservice_tier\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    476\u001b[39m \u001b[43m                \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mstop\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mstop\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    477\u001b[39m \u001b[43m                \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mstore\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mstore\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    478\u001b[39m \u001b[43m                \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mstream\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mstream\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    479\u001b[39m \u001b[43m                \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mtemperature\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mtemperature\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    480\u001b[39m \u001b[43m                \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mtool_choice\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mtool_choice\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    481\u001b[39m \u001b[43m                \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mtools\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mtools\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    482\u001b[39m \u001b[43m                \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mtop_logprobs\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mtop_logprobs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    483\u001b[39m \u001b[43m                \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mtop_p\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mtop_p\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    484\u001b[39m \u001b[43m                \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43muser\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43muser\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    485\u001b[39m \u001b[43m            \u001b[49m\u001b[43m}\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    486\u001b[39m \u001b[43m            \u001b[49m\u001b[43mcompletion_create_params\u001b[49m\u001b[43m.\u001b[49m\u001b[43mCompletionCreateParams\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    487\u001b[39m \u001b[43m        \u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    488\u001b[39m \u001b[43m        \u001b[49m\u001b[43moptions\u001b[49m\u001b[43m=\u001b[49m\u001b[43mmake_request_options\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m    489\u001b[39m \u001b[43m            \u001b[49m\u001b[43mextra_headers\u001b[49m\u001b[43m=\u001b[49m\u001b[43mextra_headers\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mextra_query\u001b[49m\u001b[43m=\u001b[49m\u001b[43mextra_query\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mextra_body\u001b[49m\u001b[43m=\u001b[49m\u001b[43mextra_body\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtimeout\u001b[49m\u001b[43m=\u001b[49m\u001b[43mtimeout\u001b[49m\n\u001b[32m    490\u001b[39m \u001b[43m        \u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    491\u001b[39m \u001b[43m        \u001b[49m\u001b[43mcast_to\u001b[49m\u001b[43m=\u001b[49m\u001b[43mChatCompletion\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    492\u001b[39m \u001b[43m        \u001b[49m\u001b[43mstream\u001b[49m\u001b[43m=\u001b[49m\u001b[43mstream\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[32m    493\u001b[39m \u001b[43m        \u001b[49m\u001b[43mstream_cls\u001b[49m\u001b[43m=\u001b[49m\u001b[43mStream\u001b[49m\u001b[43m[\u001b[49m\u001b[43mChatCompletionChunk\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    494\u001b[39m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n",
      "\u001b[36mFile \u001b[39m\u001b[32md:\\Code Assistant\\venv\\Lib\\site-packages\\groq\\_base_client.py:1242\u001b[39m, in \u001b[36mSyncAPIClient.post\u001b[39m\u001b[34m(self, path, cast_to, body, options, files, stream, stream_cls)\u001b[39m\n\u001b[32m   1228\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mpost\u001b[39m(\n\u001b[32m   1229\u001b[39m     \u001b[38;5;28mself\u001b[39m,\n\u001b[32m   1230\u001b[39m     path: \u001b[38;5;28mstr\u001b[39m,\n\u001b[32m   (...)\u001b[39m\u001b[32m   1237\u001b[39m     stream_cls: \u001b[38;5;28mtype\u001b[39m[_StreamT] | \u001b[38;5;28;01mNone\u001b[39;00m = \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[32m   1238\u001b[39m ) -> ResponseT | _StreamT:\n\u001b[32m   1239\u001b[39m     opts = FinalRequestOptions.construct(\n\u001b[32m   1240\u001b[39m         method=\u001b[33m\"\u001b[39m\u001b[33mpost\u001b[39m\u001b[33m\"\u001b[39m, url=path, json_data=body, files=to_httpx_files(files), **options\n\u001b[32m   1241\u001b[39m     )\n\u001b[32m-> \u001b[39m\u001b[32m1242\u001b[39m     \u001b[38;5;28;01mreturn\u001b[39;00m cast(ResponseT, \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mrequest\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcast_to\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mopts\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstream\u001b[49m\u001b[43m=\u001b[49m\u001b[43mstream\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstream_cls\u001b[49m\u001b[43m=\u001b[49m\u001b[43mstream_cls\u001b[49m\u001b[43m)\u001b[49m)\n",
      "\u001b[36mFile \u001b[39m\u001b[32md:\\Code Assistant\\venv\\Lib\\site-packages\\groq\\_base_client.py:1044\u001b[39m, in \u001b[36mSyncAPIClient.request\u001b[39m\u001b[34m(self, cast_to, options, stream, stream_cls)\u001b[39m\n\u001b[32m   1041\u001b[39m             err.response.read()\n\u001b[32m   1043\u001b[39m         log.debug(\u001b[33m\"\u001b[39m\u001b[33mRe-raising status error\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m-> \u001b[39m\u001b[32m1044\u001b[39m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;28mself\u001b[39m._make_status_error_from_response(err.response) \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[32m   1046\u001b[39m     \u001b[38;5;28;01mbreak\u001b[39;00m\n\u001b[32m   1048\u001b[39m \u001b[38;5;28;01massert\u001b[39;00m response \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m, \u001b[33m\"\u001b[39m\u001b[33mcould not resolve response (should never happen)\u001b[39m\u001b[33m\"\u001b[39m\n",
      "\u001b[31mBadRequestError\u001b[39m: Error code: 400 - {'error': {'message': \"Tool call validation failed: tool call validation failed: parameters for tool python_repl_ast did not match schema: errors: [missing properties: 'query']\", 'type': 'invalid_request_error', 'code': 'tool_use_failed', 'failed_generation': '{\"name\": \"python_repl_ast\", \"arguments\": {\\n  \"code\": \"df.head()\"\\n}}'}}",
      "During task with name 'agent' and id '80623eec-fc64-8bb7-7dcd-00778b630ef8'"
     ]
    }
   ],
   "source": [
    "pandas_agent.invoke({\"messages\": \"Use the tool if needed.I have a dataframe 'df'. what is the mean of 'unit_price' column? \"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "aa91bbfe",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
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   "name": "python",
   "nbconvert_exporter": "python",
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