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/.

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

• LLMs are a phenomenal piece of technology for knowledge generation and reasoning.
• A big limitation of LLMs is context size (e.g. OpenAI's davinci model for GPT-3 has a limit of 4096 tokens. Large, but not infinite).
• The ability to feed "knowledge" to LLMs is restricted to this limited prompt size and model weights.
• Thought: What if LLMs can have access to potentially a much larger database of knowledge without retraining/finetuning?

That's where the GPT Index comes in. GPT Index is a simple, flexible interface between your external data and LLMs. It resolves the following pain points:

• Provides simple data structures to resolve prompt size limitations.
• Offers data connectors to your external data sources.
• Offers you a comprehensive toolset trading off cost and performance.

At the core of GPT Index is a data structure. Instead of relying on world knowledge encoded in the model weights, a GPT Index data structure does the following:

• Uses a pre-trained LLM primarily for reasoning/summarization instead of prior knowledge.
• Takes as input a large corpus of text data and build a structured index over it (using an LLM or heuristics).
• Allow users to query the index in order to synthesize an answer to the question - this requires both traversal of the index as well as a synthesis of the answer.

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 tree index do the following:

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

To save to disk and load from disk, do

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

To query,

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

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 do pip install -r requirements.txt.