mirror of
https://github.com/hwchase17/langchain
synced 2024-10-31 15:20:26 +00:00
301 lines
7.9 KiB
Plaintext
301 lines
7.9 KiB
Plaintext
{
|
|
"cells": [
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "c81da886",
|
|
"metadata": {},
|
|
"source": [
|
|
"# Pandas Dataframe Agent\n",
|
|
"\n",
|
|
"This notebook shows how to use agents to interact with a pandas dataframe. It is mostly optimized for question answering.\n",
|
|
"\n",
|
|
"**NOTE: this agent calls the Python agent under the hood, which executes LLM generated Python code - this can be bad if the LLM generated Python code is harmful. Use cautiously.**"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 1,
|
|
"id": "0cdd9bf5",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"from langchain.agents import create_pandas_dataframe_agent\n",
|
|
"from langchain.chat_models import ChatOpenAI\n",
|
|
"from langchain.agents.agent_types import AgentType"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 2,
|
|
"id": "051ebe84",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"from langchain.llms import OpenAI\n",
|
|
"import pandas as pd\n",
|
|
"\n",
|
|
"df = pd.read_csv(\"titanic.csv\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "a62858e2",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Using ZERO_SHOT_REACT_DESCRIPTION\n",
|
|
"\n",
|
|
"This shows how to initialize the agent using the ZERO_SHOT_REACT_DESCRIPTION agent type. Note that this is an alternative to the above."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 3,
|
|
"id": "4185ff46",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"agent = create_pandas_dataframe_agent(OpenAI(temperature=0), df, verbose=True)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "7233ab56",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Using OpenAI Functions\n",
|
|
"\n",
|
|
"This shows how to initialize the agent using the OPENAI_FUNCTIONS agent type. Note that this is an alternative to the above."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 4,
|
|
"id": "a8ea710e",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"agent = create_pandas_dataframe_agent(\n",
|
|
" ChatOpenAI(temperature=0, model=\"gpt-3.5-turbo-0613\"),\n",
|
|
" df,\n",
|
|
" verbose=True,\n",
|
|
" agent_type=AgentType.OPENAI_FUNCTIONS,\n",
|
|
")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 5,
|
|
"id": "a9207a2e",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"\n",
|
|
"\n",
|
|
"\u001b[1m> Entering new chain...\u001b[0m\n",
|
|
"\u001b[32;1m\u001b[1;3m\n",
|
|
"Invoking: `python_repl_ast` with `df.shape[0]`\n",
|
|
"\n",
|
|
"\n",
|
|
"\u001b[0m\u001b[36;1m\u001b[1;3m891\u001b[0m\u001b[32;1m\u001b[1;3mThere 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": 5,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"agent.run(\"how many rows are there?\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 5,
|
|
"id": "bd43617c",
|
|
"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": 5,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"agent.run(\"how many people have more than 3 siblings\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 6,
|
|
"id": "94e64b58",
|
|
"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: The square root of the average age is 5.449689683556195.\u001b[0m\n",
|
|
"\n",
|
|
"\u001b[1m> Finished chain.\u001b[0m\n"
|
|
]
|
|
},
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"'The square root of the average age is 5.449689683556195.'"
|
|
]
|
|
},
|
|
"execution_count": 6,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"agent.run(\"whats the square root of the average age?\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "c4bc0584",
|
|
"metadata": {},
|
|
"source": [
|
|
"### Multi DataFrame Example\n",
|
|
"\n",
|
|
"This next part shows how the agent can interact with multiple dataframes passed in as a list."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 7,
|
|
"id": "42a15bd9",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"df1 = df.copy()\n",
|
|
"df1[\"Age\"] = df1[\"Age\"].fillna(df1[\"Age\"].mean())"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 8,
|
|
"id": "eba13b4d",
|
|
"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": 8,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"agent = create_pandas_dataframe_agent(OpenAI(temperature=0), [df, df1], verbose=True)\n",
|
|
"agent.run(\"how many rows in the age column are different?\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "60d08a56",
|
|
"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
|
|
}
|