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://docs.llamaindex.ai/en/stable/.

Twitter: https://twitter.com/llama_index.

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

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

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.

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.

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

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!

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 using OpenAI:

import os

os.environ["OPENAI_API_KEY"] = "YOUR_OPENAI_API_KEY"

from llama_index import VectorStoreIndex, SimpleDirectoryReader

documents = SimpleDirectoryReader("YOUR_DATA_DIRECTORY").load_data()
index = VectorStoreIndex.from_documents(documents)

To build a simple vector store index using non-OpenAI LLMs, e.g. Llama 2 hosted on Replicate, where you can easily create a free trial API token:

import os

os.environ["REPLICATE_API_TOKEN"] = "YOUR_REPLICATE_API_TOKEN"

from llama_index.llms import Replicate

llama2_7b_chat = "meta/llama-2-7b-chat:8e6975e5ed6174911a6ff3d60540dfd4844201974602551e10e9e87ab143d81e"
llm = Replicate(
    model=llama2_7b_chat,
    temperature=0.01,
    additional_kwargs={"top_p": 1, "max_new_tokens": 300},
)

# set tokenizer to match LLM
from llama_index import set_global_tokenizer
from transformers import AutoTokenizer

set_global_tokenizer(
    AutoTokenizer.from_pretrained("NousResearch/Llama-2-7b-chat-hf").encode
)

from llama_index.embeddings import HuggingFaceEmbedding
from llama_index import ServiceContext

embed_model = HuggingFaceEmbedding(model_name="BAAI/bge-small-en-v1.5")
service_context = ServiceContext.from_defaults(
    llm=llm, embed_model=embed_model
)

from llama_index import VectorStoreIndex, SimpleDirectoryReader

documents = SimpleDirectoryReader("YOUR_DATA_DIRECTORY").load_data()
index = VectorStoreIndex.from_documents(
    documents, service_context=service_context
)

To query:

query_engine = index.as_query_engine()
query_engine.query("YOUR_QUESTION")

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)

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 poetry
poetry install --with dev

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}
}