text: Skip to main contentğŸ¦œï¸�ğŸ”— LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKIntegrationsCallbacksChat modelsDocument loadersDocument transformersLLMsMemoryRetrieversText embedding modelsAgent toolkitsAmadeus ToolkitAzure Cognitive Services ToolkitCSV AgentDocument ComparisonGitHubGmail ToolkitJiraJSON AgentMultion ToolkitOffice365 ToolkitOpenAPI agentsNatural Language APIsPandas Dataframe AgentPlayWright Browser ToolkitPowerBI Dataset AgentPython AgentSpark Dataframe AgentSpark SQL AgentSQL Database AgentVectorstore AgentXorbits AgentToolsVector storesGrouped by providerIntegrationsAgent toolkitsSpark SQL AgentOn this pageSpark SQL AgentThis notebook shows how to use agents to interact with a Spark SQL. Similar to SQL Database Agent, it is designed to address general inquiries about Spark SQL and facilitate error recovery.NOTE: Note that, as this agent is in active development, all answers might not be correct. Additionally, it is not guaranteed that the agent won't perform DML statements on your Spark cluster given certain questions. Be careful running it on sensitive data!Initializationâ€‹from langchain.agents import create_spark_sql_agentfrom langchain.agents.agent_toolkits import SparkSQLToolkitfrom langchain.chat_models import ChatOpenAIfrom langchain.utilities.spark_sql import SparkSQLfrom pyspark.sql import SparkSessionspark = SparkSession.builder.getOrCreate()schema = "langchain_example"spark.sql(f"CREATE DATABASE IF NOT EXISTS {schema}")spark.sql(f"USE {schema}")csv_file_path = "titanic.csv"table = "titanic"spark.read.csv(csv_file_path, header=True, inferSchema=True).write.saveAsTable(table)spark.table(table).show()    Setting default log level to "WARN".    To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use setLogLevel(newLevel).