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| # Querying Tabular Data | |
| > [Conceptual Guide](https://docs.langchain.com/docs/use-cases/qa-tabular) | |
| Lots 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 Loading | |
| 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](../modules/indexes/document_loaders/examples/csv.ipynb) | |
| and then you should [create an index](../modules/indexes.rst) over that data, and [query it that way](../modules/chains/index_examples/vector_db_qa.ipynb). | |
| ## Querying | |
| 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. | |
| ### Chains | |
| 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 Chain](../modules/chains/examples/sqlite.ipynb) | |
| ### Agents | |
| 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, | |
| which allows you to use them on larger databases and more complex schemas. | |
| - [SQL Agent](../modules/agents/toolkits/examples/sql_database.ipynb) | |
| - [Pandas Agent](../modules/agents/toolkits/examples/pandas.ipynb) | |
| - [CSV Agent](../modules/agents/toolkits/examples/csv.ipynb) | |