🗂️ LlamaIndex 🦙

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

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

LlamaIndex.TS (Typescript/Javascript): https://github.com/run-llama/LlamaIndexTS.

Documentation: https://gpt-index.readthedocs.io/.

Twitter: https://twitter.com/llama_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 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?

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: 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: ```python import os os.environ["OPENAIAPIKEY"] = 'YOUROPENAIAPI_KEY'

from llamaindex import VectorStoreIndex, SimpleDirectoryReader documents = SimpleDirectoryReader('data').loaddata() index = VectorStoreIndex.from_documents(documents) ```

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

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

python index.storage_context.persist()

To reload from disk: ```python from llamaindex import StorageContext, loadindexfromstorage

rebuild storage context

storagecontext = StorageContext.fromdefaults(persist_dir='./storage')

load index

index = loadindexfromstorage(storagecontext) ```

🔧 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} }