GPT Index is a project consisting of a set of data structures designed to make it easier to use large external knowledge bases with LLMs.
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.
NOTE: This README is not updated as frequently as the documentation. Please check out the documentation above for the latest updates!
- 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.
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!
Interesting in contributing? See our Contribution Guide for more details.
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!
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>?")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.
Reference to cite if you use GPT Index in a paper:
@software{Liu_GPT_Index_2022,
author = {Liu, Jerry},
doi = {10.5281/zenodo.1234},
month = {11},
title = {{GPT Index}},
url = {https://github.com/jerryjliu/gpt_index},year = {2022}
}