🗂️ ️GPT Index

GPT Index is a project consisting of a set of data structures that are created using LLMs and can be traversed using LLMs in order to answer queries.

PyPi: https://pypi.org/project/gpt-index/.

Documentation: https://gpt-index.readthedocs.io/en/latest/.

Twitter: https://twitter.com/gpt_index.

Discord: https://discord.gg/dGcwcsnxhU.

🚀 Overview

NOTE: This README is not updated as frequently as the documentation. Please check out the documentation above for the latest updates!

Context

• LLMs are a phenomenonal piece of technology for knowledge generation and reasoning.
• A big limitation of LLMs is context size (e.g. Davinci's limit is 4096 tokens. Large, but not infinite).
• The ability to feed "knowledge" to LLMs is restricted to this limited prompt size and model weights.

Proposed Solution

At its core, GPT Index contains a toolkit of index data structures designed to easily connect LLM's with your external data. GPT Index helps to provide the following advantages:

• Remove concerns over prompt size limitations.
• Abstract common usage patterns to reduce boilerplate code in your LLM app.
• Provide data connectors to your common data sources (Google Docs, Slack, etc.).
• Provide cost transparency + tools that reduce cost while increasing performance.

Each data structure offers distinct use cases and a variety of customizable parameters. These indices can then be queried in a general purpose manner, in order to achieve any task that you would typically achieve with an LLM:

• Question-Answering
• Summarization
• Text Generation (Stories, TODO's, emails, etc.)
• and more!

💡 Contributing

Interesting in contributing? See our Contribution Guide for more details.

📄 Documentation

Full documentation can be found here: https://gpt-index.readthedocs.io/en/latest/.

Please check it out for the most up-to-date tutorials, how-to guides, references, and other resources!

💻 Example Usage

pip install gpt-index

Examples are in the examples folder. Indices are in the indices folder (see list of indices below).

To build a simple vector store index:

from gpt_index import GPTSimpleVectorIndex, SimpleDirectoryReader
documents = SimpleDirectoryReader('data').load_data()
index = GPTSimpleVectorIndex(documents)

To save to and load from disk:

# save to disk
index.save_to_disk('index.json')
# load from disk
index = GPTSimpleVectorIndex.load_from_disk('index.json')

To query:

index.query("<question_text>?", child_branch_factor=1)

🔧 Dependencies

The main third-party package requirements are tiktoken, openai, and langchain.

All requirements should be contained within the setup.py file. To run the package locally without building the wheel, simply run pip install -r requirements.txt.