langchain/docs/modules/agents/toolkits/examples/csv.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "7094e328",
   "metadata": {},
   "source": [
    "# CSV Agent\n",
    "\n",
    "This notebook shows how to use agents to interact with a csv. It is mostly optimized for question answering.\n",
    "\n",
    "**NOTE: this agent calls the Pandas DataFrame agent under the hood, which in turn calls the Python agent, which executes LLM generated Python code - this can be bad if the LLM generated Python code is harmful. Use cautiously.**\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "827982c7",
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain.agents import create_csv_agent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "caae0bec",
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain.llms import OpenAI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "16c4dc59",
   "metadata": {},
   "outputs": [],
   "source": [
    "agent = create_csv_agent(OpenAI(temperature=0), 'titanic.csv', verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "46b9489d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
      "\u001b[32;1m\u001b[1;3mThought: I need to count the number of rows\n",
      "Action: python_repl_ast\n",
      "Action Input: len(df)\u001b[0m\n",
      "Observation: \u001b[36;1m\u001b[1;3m891\u001b[0m\n",
      "Thought:\u001b[32;1m\u001b[1;3m I now know the final answer\n",
      "Final Answer: There are 891 rows in the dataframe.\u001b[0m\n",
      "\n",
      "\u001b[1m> Finished chain.\u001b[0m\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'There are 891 rows in the dataframe.'"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "agent.run(\"how many rows are there?\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a96309be",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
      "\u001b[32;1m\u001b[1;3mThought: I need to count the number of people with more than 3 siblings\n",
      "Action: python_repl_ast\n",
      "Action Input: df[df['SibSp'] > 3].shape[0]\u001b[0m\n",
      "Observation: \u001b[36;1m\u001b[1;3m30\u001b[0m\n",
      "Thought:\u001b[32;1m\u001b[1;3m I now know the final answer\n",
      "Final Answer: 30 people have more than 3 siblings.\u001b[0m\n",
      "\n",
      "\u001b[1m> Finished chain.\u001b[0m\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'30 people have more than 3 siblings.'"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "agent.run(\"how many people have more than 3 sibligngs\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "964a09f7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
      "\u001b[32;1m\u001b[1;3mThought: I need to calculate the average age first\n",
      "Action: python_repl_ast\n",
      "Action Input: df['Age'].mean()\u001b[0m\n",
      "Observation: \u001b[36;1m\u001b[1;3m29.69911764705882\u001b[0m\n",
      "Thought:\u001b[32;1m\u001b[1;3m I can now calculate the square root\n",
      "Action: python_repl_ast\n",
      "Action Input: math.sqrt(df['Age'].mean())\u001b[0m\n",
      "Observation: \u001b[36;1m\u001b[1;3mname 'math' is not defined\u001b[0m\n",
      "Thought:\u001b[32;1m\u001b[1;3m I need to import the math library\n",
      "Action: python_repl_ast\n",
      "Action Input: import math\u001b[0m\n",
      "Observation: \u001b[36;1m\u001b[1;3mNone\u001b[0m\n",
      "Thought:\u001b[32;1m\u001b[1;3m I can now calculate the square root\n",
      "Action: python_repl_ast\n",
      "Action Input: math.sqrt(df['Age'].mean())\u001b[0m\n",
      "Observation: \u001b[36;1m\u001b[1;3m5.449689683556195\u001b[0m\n",
      "Thought:\u001b[32;1m\u001b[1;3m I now know the final answer\n",
      "Final Answer: 5.449689683556195\u001b[0m\n",
      "\n",
      "\u001b[1m> Finished chain.\u001b[0m\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'5.449689683556195'"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "agent.run(\"whats the square root of the average age?\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "551de2be",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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