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Interface between LLMs and your data

Project description

🗂️ LlamaIndex 🦙

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LlamaIndex (GPT Index) is a data framework for your LLM application.


LlamaIndex.TS (Typescript/Javascript):





🚀 Overview

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. They are pre-trained on large amounts of publicly available data.
  • How do we best augment LLMs with our own private data?

We need a comprehensive toolkit to help perform this data augmentation for LLMs.

Proposed Solution

That's where LlamaIndex comes in. LlamaIndex is a "data framework" to help you build LLM apps. It provides the following tools:

  • Offers data connectors to ingest your existing data sources and data formats (APIs, PDFs, docs, SQL, etc.)
  • Provides ways to structure your data (indices, graphs) so that this data can be easily used with LLMs.
  • Provides an advanced retrieval/query interface over your data: Feed in any LLM input prompt, get back retrieved context and knowledge-augmented output.
  • Allows easy integrations with your outer application framework (e.g. with LangChain, Flask, Docker, ChatGPT, anything else).

LlamaIndex provides tools for both beginner users and advanced users. Our high-level API allows beginner users to use LlamaIndex to ingest and query their data in 5 lines of code. Our lower-level APIs allow advanced users to customize and extend any module (data connectors, indices, retrievers, query engines, reranking modules), to fit their needs.

💡 Contributing

Interested in contributing? See our Contribution Guide for more details.

📄 Documentation

Full documentation can be found here:

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

from llama_index import VectorStoreIndex, SimpleDirectoryReader
documents = SimpleDirectoryReader('data').load_data()
index = VectorStoreIndex.from_documents(documents)

To query:

query_engine = index.as_query_engine()

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


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 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:

author = {Liu, Jerry},
doi = {10.5281/zenodo.1234},
month = {11},
title = {{LlamaIndex}},
url = {},
year = {2022}

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