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docs: update gpt index references to LlamaIndex (#1856)

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The GPT Index project is transitioning to the new project name,
LlamaIndex.

I've updated a few files referencing the old project name and repository
URL to the current ones.

From the [LlamaIndex repo](https://github.com/jerryjliu/llama_index):
> NOTE: We are rebranding GPT Index as LlamaIndex! We will carry out
this transition gradually.
>
> 2/25/2023: By default, our docs/notebooks/instructions now reference
"LlamaIndex" instead of "GPT Index".
>
> 2/19/2023: By default, our docs/notebooks/instructions now use the
llama-index package. However the gpt-index package still exists as a
duplicate!
>
> 2/16/2023: We have a duplicate llama-index pip package. Simply replace
all imports of gpt_index with llama_index if you choose to pip install
llama-index.

I'm not associated with LlamaIndex in any way. I just noticed the
discrepancy when studying the lanchain documentation.
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6
docs/gallery.rst
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@ -158,14 +158,14 @@ Open Source
--- ---
.. link-button:: https://github.com/jerryjliu/gpt_index .. link-button:: https://github.com/jerryjliu/llama_index
:type: url :type: url
:text: GPT Index :text: LlamaIndex
:classes: stretched-link btn-lg :classes: stretched-link btn-lg
+++ +++
GPT Index is a project consisting of a set of data structures that are created using GPT-3 and can be traversed using GPT-3 in order to answer queries. LlamaIndex (formerly GPT Index) is a project consisting of a set of data structures that are created using GPT-3 and can be traversed using GPT-3 in order to answer queries.
--- ---

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docs/modules/indexes.rst
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@ -4,7 +4,7 @@ Indexes
Indexes refer to ways to structure documents so that LLMs can best interact with them. 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. 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). 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>`_. For more complicated index structures, it is worth checking out `LlamaIndex <https://gpt-index.readthedocs.io/en/latest/index.html>`_.
The following sections of documentation are provided: The following sections of documentation are provided:

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docs/use_cases/combine_docs.md
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@ -76,7 +76,7 @@ Examples of vector database companies include [Pinecone](https://www.pinecone.io
Although this is perhaps the most common way of document retrieval, people are starting to think about alternative Although this is perhaps the most common way of document retrieval, people are starting to think about alternative
data structures and indexing techniques specifically for working with language models. For a leading example of this, data structures and indexing techniques specifically for working with language models. For a leading example of this,
check out [GPT Index](https://github.com/jerryjliu/gpt_index) - a collection of data structures created by and optimized check out [LlamaIndex](https://github.com/jerryjliu/llama_index) - a collection of data structures created by and optimized
for language models. for language models.
## Augmenting ## Augmenting