text: Skip to main contentğŸ¦œï¸�ğŸ”— LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKUse casesQA and Chat over DocumentsAnalyzing structured dataExtractionInteracting with APIsChatbotsSummarizationCode UnderstandingAgent simulationsAgentsAutonomous (long-running) agentsMulti-modalUse casesAnalyzing structured dataOn this pageAnalyzing structured dataLots of data and information is stored in tabular data, whether it be csvs, excel sheets, or SQL tables.
  This page covers all resources available in LangChain for working with data in this format.Document loadingâ€‹If you have text data stored in a tabular format, you may want to load the data into a Document and then index it as you would
  other text/unstructured data. For this, you should use a document loader like the CSVLoader
  and then you should create an index over that data, and query it that way.Queryingâ€‹If you have more numeric tabular data, or have a large amount of data and don't want to index it, you should get started
  by looking at various chains and agents we have for dealing with this data.Chainsâ€‹If you are just getting started, and you have relatively small/simple tabular data, you should get started with chains.
  Chains are a sequence of predetermined steps, so they are good to get started with as they give you more control and let you
  understand what is happening better.SQL Database ChainAgentsâ€‹Agents are more complex, and involve multiple queries to the LLM to understand what to do.
  The downside of agents are that you have less control. The upside is that they are more powerful,