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36 lines
1.8 KiB
Markdown
36 lines
1.8 KiB
Markdown
# Querying Tabular Data
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.. note::
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`Conceptual Guide <https://docs.langchain.com/docs/use-cases/qa-tabular>`_
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Lots of data and information is stored in tabular data, whether it be csvs, excel sheets, or SQL tables.
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This page covers all resources available in LangChain for working with data in this format.
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## Document Loading
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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
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other text/unstructured data. For this, you should use a document loader like the [CSVLoader](../modules/indexes/document_loaders/examples/csv.ipynb)
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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).
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## Querying
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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
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by looking at various chains and agents we have for dealing with this data.
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### Chains
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If you are just getting started, and you have relatively small/simple tabular data, you should get started with chains.
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Chains are a sequence of predetermined steps, so they are good to get started with as they give you more control and let you
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understand what is happening better.
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- [SQL Database Chain](../modules/chains/examples/sqlite.ipynb)
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### Agents
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Agents are more complex, and involve multiple queries to the LLM to understand what to do.
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The downside of agents are that you have less control. The upside is that they are more powerful,
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which allows you to use them on larger databases and more complex schemas.
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- [SQL Agent](../modules/agents/toolkits/examples/sql_database.ipynb)
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- [Pandas Agent](../modules/agents/toolkits/examples/pandas.ipynb)
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- [CSV Agent](../modules/agents/toolkits/examples/csv.ipynb)
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