mirror of
https://github.com/hwchase17/langchain
synced 2024-11-08 07:10:35 +00:00
35 lines
1.8 KiB
Markdown
35 lines
1.8 KiB
Markdown
# 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)
|