goabiaryan/llama_index

LlamaIndex (GPT Index) is a project that provides a central interface to connect your LLM's with external data.

🗂️ LlamaIndex 🦙

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/.

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

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

Ecosystem

• LlamaHub (community library of data loaders): https://llamahub.ai
• LlamaLab (cutting-edge AGI projects using LlamaIndex): https://github.com/run-llama/llama-lab

🚀 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 phenomenal 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

Interested 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 GPTVectorStoreIndex, SimpleDirectoryReader
documents = SimpleDirectoryReader('data').load_data()
index = GPTVectorStoreIndex.from_documents(documents)

To query:

query_engine = index.as_query_engine()
query_engine.query("<question_text>?")

By default, data is stored in-memory. To persist to disk (under ./storage):

index.storage_context.persist()

To reload from disk:

from llama_index import StorageContext, load_index_from_storage

# rebuild storage context
storage_context = StorageContext.from_defaults(persist_dir='./storage')
# load index
index = load_index_from_storage(storage_context)

🔧 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/llama_index},
year = {2022}
}