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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 | |