mirror of
https://github.com/hwchase17/langchain.git
synced 2025-11-04 10:10:09 +00:00
7652d2abb0
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>
252 lines
6.7 KiB
Plaintext
252 lines
6.7 KiB
Plaintext
{
|
|
"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'"
|
|
]
|
|
},
|
|
"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": [],
|
|
"source": []
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"display_name": "Python 3 (ipykernel)",
|
|
"language": "python",
|
|
"name": "python3"
|
|
},
|
|
"language_info": {
|
|
"codemirror_mode": {
|
|
"name": "ipython",
|
|
"version": 3
|
|
},
|
|
"file_extension": ".py",
|
|
"mimetype": "text/x-python",
|
|
"name": "python",
|
|
"nbconvert_exporter": "python",
|
|
"pygments_lexer": "ipython3",
|
|
"version": "3.9.1"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 5
|
|
}
|