mirror of
https://github.com/hwchase17/langchain
synced 2024-11-10 01:10:59 +00:00
b048236c1a
Note: There are no changes in the file names! - The group name on the main navbar changed: `Agent toolkits` -> `Agents & Toolkits`. Examples here are the mix of the Agent and Toolkit examples because Agents and Toolkits in examples are always used together. - Titles changed: removed "Agent" and "Toolkit" suffixes. The reason is the same. - Formatting: mostly cleaning the header structure, so it could be better on the right-side navbar. Main navbar is looking much cleaner now.
419 lines
16 KiB
Plaintext
419 lines
16 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Spark Dataframe\n",
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"\n",
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"This notebook shows how to use agents to interact with a `Spark DataFrame` and `Spark Connect`. It is mostly optimized for question answering.\n",
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"\n",
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"**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.**"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"import os\n",
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"\n",
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"os.environ[\"OPENAI_API_KEY\"] = \"...input your openai api key here...\""
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## `Spark DataFrame` example"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"23/05/15 20:33:10 WARN Utils: Your hostname, Mikes-Mac-mini.local resolves to a loopback address: 127.0.0.1; using 192.168.68.115 instead (on interface en1)\n",
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"23/05/15 20:33:10 WARN Utils: Set SPARK_LOCAL_IP if you need to bind to another address\n",
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"Setting default log level to \"WARN\".\n",
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"To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use setLogLevel(newLevel).\n",
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"23/05/15 20:33:10 WARN NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable\n"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"+-----------+--------+------+--------------------+------+----+-----+-----+----------------+-------+-----+--------+\n",
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"|PassengerId|Survived|Pclass| Name| Sex| Age|SibSp|Parch| Ticket| Fare|Cabin|Embarked|\n",
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"+-----------+--------+------+--------------------+------+----+-----+-----+----------------+-------+-----+--------+\n",
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"| 1| 0| 3|Braund, Mr. Owen ...| male|22.0| 1| 0| A/5 21171| 7.25| null| S|\n",
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"| 2| 1| 1|Cumings, Mrs. Joh...|female|38.0| 1| 0| PC 17599|71.2833| C85| C|\n",
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"| 3| 1| 3|Heikkinen, Miss. ...|female|26.0| 0| 0|STON/O2. 3101282| 7.925| null| S|\n",
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"| 4| 1| 1|Futrelle, Mrs. Ja...|female|35.0| 1| 0| 113803| 53.1| C123| S|\n",
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"| 5| 0| 3|Allen, Mr. Willia...| male|35.0| 0| 0| 373450| 8.05| null| S|\n",
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"| 6| 0| 3| Moran, Mr. James| male|null| 0| 0| 330877| 8.4583| null| Q|\n",
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"| 7| 0| 1|McCarthy, Mr. Tim...| male|54.0| 0| 0| 17463|51.8625| E46| S|\n",
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"| 8| 0| 3|Palsson, Master. ...| male| 2.0| 3| 1| 349909| 21.075| null| S|\n",
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"| 9| 1| 3|Johnson, Mrs. Osc...|female|27.0| 0| 2| 347742|11.1333| null| S|\n",
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"| 10| 1| 2|Nasser, Mrs. Nich...|female|14.0| 1| 0| 237736|30.0708| null| C|\n",
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"| 11| 1| 3|Sandstrom, Miss. ...|female| 4.0| 1| 1| PP 9549| 16.7| G6| S|\n",
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"| 12| 1| 1|Bonnell, Miss. El...|female|58.0| 0| 0| 113783| 26.55| C103| S|\n",
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"| 13| 0| 3|Saundercock, Mr. ...| male|20.0| 0| 0| A/5. 2151| 8.05| null| S|\n",
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"| 14| 0| 3|Andersson, Mr. An...| male|39.0| 1| 5| 347082| 31.275| null| S|\n",
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"| 15| 0| 3|Vestrom, Miss. Hu...|female|14.0| 0| 0| 350406| 7.8542| null| S|\n",
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"| 16| 1| 2|Hewlett, Mrs. (Ma...|female|55.0| 0| 0| 248706| 16.0| null| S|\n",
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"| 17| 0| 3|Rice, Master. Eugene| male| 2.0| 4| 1| 382652| 29.125| null| Q|\n",
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"| 18| 1| 2|Williams, Mr. Cha...| male|null| 0| 0| 244373| 13.0| null| S|\n",
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"| 19| 0| 3|Vander Planke, Mr...|female|31.0| 1| 0| 345763| 18.0| null| S|\n",
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"| 20| 1| 3|Masselmani, Mrs. ...|female|null| 0| 0| 2649| 7.225| null| C|\n",
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"+-----------+--------+------+--------------------+------+----+-----+-----+----------------+-------+-----+--------+\n",
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"only showing top 20 rows\n",
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"\n"
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]
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}
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],
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"source": [
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"from langchain.llms import OpenAI\n",
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"from pyspark.sql import SparkSession\n",
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"from langchain.agents import create_spark_dataframe_agent\n",
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"\n",
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"spark = SparkSession.builder.getOrCreate()\n",
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"csv_file_path = \"titanic.csv\"\n",
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"df = spark.read.csv(csv_file_path, header=True, inferSchema=True)\n",
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"df.show()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [],
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"source": [
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"agent = create_spark_dataframe_agent(llm=OpenAI(temperature=0), df=df, verbose=True)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"\n",
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"\n",
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"\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
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"\u001b[32;1m\u001b[1;3mThought: I need to find out how many rows are in the dataframe\n",
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"Action: python_repl_ast\n",
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"Action Input: df.count()\u001b[0m\n",
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"Observation: \u001b[36;1m\u001b[1;3m891\u001b[0m\n",
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"Thought:\u001b[32;1m\u001b[1;3m I now know the final answer\n",
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"Final Answer: There are 891 rows in the dataframe.\u001b[0m\n",
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"\n",
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"\u001b[1m> Finished chain.\u001b[0m\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"'There are 891 rows in the dataframe.'"
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]
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},
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"execution_count": 4,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"agent.run(\"how many rows are there?\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"\n",
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"\n",
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"\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
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"\u001b[32;1m\u001b[1;3mThought: I need to find out how many people have more than 3 siblings\n",
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"Action: python_repl_ast\n",
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"Action Input: df.filter(df.SibSp > 3).count()\u001b[0m\n",
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"Observation: \u001b[36;1m\u001b[1;3m30\u001b[0m\n",
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"Thought:\u001b[32;1m\u001b[1;3m I now know the final answer\n",
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"Final Answer: 30 people have more than 3 siblings.\u001b[0m\n",
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"\n",
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"\u001b[1m> Finished chain.\u001b[0m\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"'30 people have more than 3 siblings.'"
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]
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},
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"execution_count": 5,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"agent.run(\"how many people have more than 3 siblings\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"\n",
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"\n",
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"\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
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"\u001b[32;1m\u001b[1;3mThought: I need to get the average age first\n",
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"Action: python_repl_ast\n",
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"Action Input: df.agg({\"Age\": \"mean\"}).collect()[0][0]\u001b[0m\n",
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"Observation: \u001b[36;1m\u001b[1;3m29.69911764705882\u001b[0m\n",
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"Thought:\u001b[32;1m\u001b[1;3m I now have the average age, I need to get the square root\n",
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"Action: python_repl_ast\n",
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"Action Input: math.sqrt(29.69911764705882)\u001b[0m\n",
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"Observation: \u001b[36;1m\u001b[1;3mname 'math' is not defined\u001b[0m\n",
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"Thought:\u001b[32;1m\u001b[1;3m I need to import math first\n",
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"Action: python_repl_ast\n",
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"Action Input: import math\u001b[0m\n",
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"Observation: \u001b[36;1m\u001b[1;3m\u001b[0m\n",
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"Thought:\u001b[32;1m\u001b[1;3m I now have the math library imported, I can get the square root\n",
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"Action: python_repl_ast\n",
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"Action Input: math.sqrt(29.69911764705882)\u001b[0m\n",
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"Observation: \u001b[36;1m\u001b[1;3m5.449689683556195\u001b[0m\n",
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"Thought:\u001b[32;1m\u001b[1;3m I now know the final answer\n",
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"Final Answer: 5.449689683556195\u001b[0m\n",
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"\n",
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"\u001b[1m> Finished chain.\u001b[0m\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"'5.449689683556195'"
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]
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},
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"execution_count": 6,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"agent.run(\"whats the square root of the average age?\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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"outputs": [],
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"source": [
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"spark.stop()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## `Spark Connect` example"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# in apache-spark root directory. (tested here with \"spark-3.4.0-bin-hadoop3 and later\")\n",
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"# To launch Spark with support for Spark Connect sessions, run the start-connect-server.sh script.\n",
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"!./sbin/start-connect-server.sh --packages org.apache.spark:spark-connect_2.12:3.4.0"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"23/05/08 10:06:09 WARN Utils: Service 'SparkUI' could not bind on port 4040. Attempting port 4041.\n"
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]
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}
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],
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"source": [
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"from pyspark.sql import SparkSession\n",
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"\n",
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"# Now that the Spark server is running, we can connect to it remotely using Spark Connect. We do this by\n",
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"# creating a remote Spark session on the client where our application runs. Before we can do that, we need\n",
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"# to make sure to stop the existing regular Spark session because it cannot coexist with the remote\n",
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"# Spark Connect session we are about to create.\n",
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"SparkSession.builder.master(\"local[*]\").getOrCreate().stop()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"metadata": {},
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"outputs": [],
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"source": [
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"# The command we used above to launch the server configured Spark to run as localhost:15002.\n",
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"# So now we can create a remote Spark session on the client using the following command.\n",
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"spark = SparkSession.builder.remote(\"sc://localhost:15002\").getOrCreate()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 12,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"+-----------+--------+------+--------------------+------+----+-----+-----+----------------+-------+-----+--------+\n",
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"|PassengerId|Survived|Pclass| Name| Sex| Age|SibSp|Parch| Ticket| Fare|Cabin|Embarked|\n",
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"+-----------+--------+------+--------------------+------+----+-----+-----+----------------+-------+-----+--------+\n",
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"| 1| 0| 3|Braund, Mr. Owen ...| male|22.0| 1| 0| A/5 21171| 7.25| null| S|\n",
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"| 2| 1| 1|Cumings, Mrs. Joh...|female|38.0| 1| 0| PC 17599|71.2833| C85| C|\n",
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"| 3| 1| 3|Heikkinen, Miss. ...|female|26.0| 0| 0|STON/O2. 3101282| 7.925| null| S|\n",
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"| 4| 1| 1|Futrelle, Mrs. Ja...|female|35.0| 1| 0| 113803| 53.1| C123| S|\n",
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"| 5| 0| 3|Allen, Mr. Willia...| male|35.0| 0| 0| 373450| 8.05| null| S|\n",
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"| 6| 0| 3| Moran, Mr. James| male|null| 0| 0| 330877| 8.4583| null| Q|\n",
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"| 7| 0| 1|McCarthy, Mr. Tim...| male|54.0| 0| 0| 17463|51.8625| E46| S|\n",
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"| 8| 0| 3|Palsson, Master. ...| male| 2.0| 3| 1| 349909| 21.075| null| S|\n",
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"| 9| 1| 3|Johnson, Mrs. Osc...|female|27.0| 0| 2| 347742|11.1333| null| S|\n",
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"| 10| 1| 2|Nasser, Mrs. Nich...|female|14.0| 1| 0| 237736|30.0708| null| C|\n",
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"| 11| 1| 3|Sandstrom, Miss. ...|female| 4.0| 1| 1| PP 9549| 16.7| G6| S|\n",
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"| 12| 1| 1|Bonnell, Miss. El...|female|58.0| 0| 0| 113783| 26.55| C103| S|\n",
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"| 13| 0| 3|Saundercock, Mr. ...| male|20.0| 0| 0| A/5. 2151| 8.05| null| S|\n",
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"| 14| 0| 3|Andersson, Mr. An...| male|39.0| 1| 5| 347082| 31.275| null| S|\n",
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"| 15| 0| 3|Vestrom, Miss. Hu...|female|14.0| 0| 0| 350406| 7.8542| null| S|\n",
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"| 16| 1| 2|Hewlett, Mrs. (Ma...|female|55.0| 0| 0| 248706| 16.0| null| S|\n",
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"| 17| 0| 3|Rice, Master. Eugene| male| 2.0| 4| 1| 382652| 29.125| null| Q|\n",
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"| 18| 1| 2|Williams, Mr. Cha...| male|null| 0| 0| 244373| 13.0| null| S|\n",
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"| 19| 0| 3|Vander Planke, Mr...|female|31.0| 1| 0| 345763| 18.0| null| S|\n",
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"| 20| 1| 3|Masselmani, Mrs. ...|female|null| 0| 0| 2649| 7.225| null| C|\n",
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"+-----------+--------+------+--------------------+------+----+-----+-----+----------------+-------+-----+--------+\n",
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"only showing top 20 rows\n",
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"\n"
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]
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}
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],
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"source": [
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"csv_file_path = \"titanic.csv\"\n",
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"df = spark.read.csv(csv_file_path, header=True, inferSchema=True)\n",
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"df.show()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 15,
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain.agents import create_spark_dataframe_agent\n",
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"from langchain.llms import OpenAI\n",
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"import os\n",
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"\n",
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"os.environ[\"OPENAI_API_KEY\"] = \"...input your openai api key here...\"\n",
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"\n",
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"agent = create_spark_dataframe_agent(llm=OpenAI(temperature=0), df=df, verbose=True)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 16,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"\n",
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"\n",
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"\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
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"\u001b[32;1m\u001b[1;3m\n",
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"Thought: I need to find the row with the highest fare\n",
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"Action: python_repl_ast\n",
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"Action Input: df.sort(df.Fare.desc()).first()\u001b[0m\n",
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"Observation: \u001b[36;1m\u001b[1;3mRow(PassengerId=259, Survived=1, Pclass=1, Name='Ward, Miss. Anna', Sex='female', Age=35.0, SibSp=0, Parch=0, Ticket='PC 17755', Fare=512.3292, Cabin=None, Embarked='C')\u001b[0m\n",
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"Thought:\u001b[32;1m\u001b[1;3m I now know the name of the person who bought the most expensive ticket\n",
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"Final Answer: Miss. Anna Ward\u001b[0m\n",
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"\n",
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"\u001b[1m> Finished chain.\u001b[0m\n"
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]
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},
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{
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"data": {
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"text/plain": [
|
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"'Miss. Anna Ward'"
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]
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},
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"execution_count": 16,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"agent.run(\n",
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" \"\"\"\n",
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"who bought the most expensive ticket?\n",
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"You can find all supported function types in https://spark.apache.org/docs/latest/api/python/reference/pyspark.sql/dataframe.html\n",
|
|
"\"\"\"\n",
|
|
")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 17,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"spark.stop()"
|
|
]
|
|
}
|
|
],
|
|
"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": "3.10.12"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 4
|
|
}
|