🗂️ 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 thegpt-index
package still exists as a duplicate!
2/16/2023: We have a duplicate
llama-index
pip package. Simply replace all imports ofgpt_index
withllama_index
if you choose topip install llama-index
.
LlamaIndex (GPT Index) is a project that provides a central interface to connect your LLM's with external data.
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. They are pre-trained on large amounts of publicly available data.
- How do we best augment LLMs with our own private data?
- One paradigm that has emerged is in-context learning (the other is finetuning), where we insert context into the input prompt. That way, we take advantage of the LLM's reasoning capabilities to generate a response.
To perform LLM's data augmentation in a performant, efficient, and cheap manner, we need to solve two components:
- Data Ingestion
- Data Indexing
Proposed Solution
That's where the LlamaIndex comes in. LlamaIndex is a simple, flexible interface between your external data and LLMs. It provides the following tools in an easy-to-use fashion:
- Offers data connectors to your existing data sources and data formats (API's, PDF's, docs, SQL, etc.)
- Provides indices over your unstructured and structured data for use with LLM's.
These indices help to abstract away common boilerplate and pain points for in-context learning:
- Storing context in an easy-to-access format for prompt insertion.
- Dealing with prompt limitations (e.g. 4096 tokens for Davinci) when context is too big.
- Dealing with text splitting.
- Provides users an interface to query the index (feed in an input prompt) and obtain a knowledge-augmented output.
- Offers you a comprehensive toolset trading off cost and performance.
💡 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.from_documents(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}
}