langchain/docs/modules/agents/toolkits/examples/csv.ipynb
Nicholas Liu 7652d2abb0
Add Multi-CSV/DF support in CSV and DataFrame Toolkits (#5009)
Add Multi-CSV/DF support in CSV and DataFrame Toolkits
* CSV and DataFrame toolkits now accept list of CSVs/DFs
* Add default prompts for many dataframes in `pandas_dataframe` toolkit

Fixes #1958
Potentially fixes #4423

## Testing
* Add single and multi-dataframe integration tests for
`pandas_dataframe` toolkit with permutations of `include_df_in_prompt`
* Add single and multi-CSV integration tests for csv toolkit
---------

Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
2023-05-25 14:23:11 -07:00

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"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": 4,
"id": "16c4dc59",
"metadata": {},
"outputs": [],
"source": [
"agent = create_csv_agent(OpenAI(temperature=0), 'titanic.csv', verbose=True)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"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: df.shape[0]\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.\u001b[0m\n",
"\n",
"\u001b[1m> Finished chain.\u001b[0m\n"
]
},
{
"data": {
"text/plain": [
"'There are 891 rows.'"
]
},
"execution_count": 5,
"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 siblings\")"
]
},
{
"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 now need to calculate the square root of the average age\n",
"Action: python_repl_ast\n",
"Action Input: math.sqrt(df['Age'].mean())\u001b[0m\n",
"Observation: \u001b[36;1m\u001b[1;3mNameError(\"name '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;3m\u001b[0m\n",
"Thought:\u001b[32;1m\u001b[1;3m I now need to calculate the square root of the average age\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'"
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},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"agent.run(\"whats the square root of the average age?\")"
]
},
{
"cell_type": "markdown",
"id": "09539c18",
"metadata": {},
"source": [
"### Multi CSV Example\n",
"\n",
"This next part shows how the agent can interact with multiple csv files passed in as a list."
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "15f11fbd",
"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 compare the age columns in both dataframes\n",
"Action: python_repl_ast\n",
"Action Input: len(df1[df1['Age'] != df2['Age']])\u001b[0m\n",
"Observation: \u001b[36;1m\u001b[1;3m177\u001b[0m\n",
"Thought:\u001b[32;1m\u001b[1;3m I now know the final answer\n",
"Final Answer: 177 rows in the age column are different.\u001b[0m\n",
"\n",
"\u001b[1m> Finished chain.\u001b[0m\n"
]
},
{
"data": {
"text/plain": [
"'177 rows in the age column are different.'"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"agent = create_csv_agent(OpenAI(temperature=0), ['titanic.csv', 'titanic_age_fillna.csv'], verbose=True)\n",
"agent.run(\"how many rows in the age column are different?\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f2909808",
"metadata": {},
"outputs": [],
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}
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