Indexes
==========================

Indexes refer to ways to structure documents so that LLMs can best interact with them.
This module contains utility functions for working with documents, different types of indexes, and then examples for using those indexes in chains.
LangChain provides common indices for working with data (most prominently support for vector databases).
For more complicated index structures, it is worth checking out `GPTIndex <https://gpt-index.readthedocs.io/en/latest/index.html>`_.

The following sections of documentation are provided:

- `Getting Started <./indexes/getting_started.html>`_: An overview of all the functionality LangChain provides for working with indexes.

- `Key Concepts <./indexes/key_concepts.html>`_: A conceptual guide going over the various concepts related to indexes and the tools needed to create them.

- `How-To Guides <./indexes/how_to_guides.html>`_: A collection of how-to guides. These highlight how to use all the relevant tools, the different types of vector databases, and how to use indexes in chains.


.. toctree::
   :maxdepth: 1
   :name: LLMs
   :hidden:

   ./indexes/getting_started.ipynb
   ./indexes/key_concepts.md
   ./indexes/how_to_guides.rst
