metadata

title: README
emoji: 🦙
colorFrom: yellow
colorTo: purple
sdk: static
pinned: false

🗂️ 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

💻 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)