🗂️ LlamaIndex 🦙 (GPT 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.

LlamaIndex (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:

• LlamaIndex: https://pypi.org/project/llama-index/.
• GPT Index (duplicate): 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.

LlamaHub (community library of data loaders): https://llamahub.ai

🚀 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, LlamaIndex contains a toolkit of index data structures designed to easily connect LLM's with your external data. LlamaIndex 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 llama-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:

import os
os.environ["OPENAI_API_KEY"] = 'YOUR_OPENAI_API_KEY'

from llama_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>?")

🔧 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.

📖 Citation

Reference to cite if you use LlamaIndex in a paper:

@software{Liu_LlamaIndex_2022,
author = {Liu, Jerry},
doi = {10.5281/zenodo.1234},
month = {11},
title = {{LlamaIndex}},
url = {https://github.com/jerryjliu/gpt_index},year = {2022}
}