LlamaIndex (GPT Index) is a data framework for your LLM application. Building with LlamaIndex typically involves working with LlamaIndex core and a chosen set of integrations (or plugins). There are two ways to start building with LlamaIndex in Python:
-
Starter:
llama-index(https://pypi.org/project/llama-index/). A starter Python package that includes core LlamaIndex as well as a selection of integrations. -
Customized:
llama-index-core(https://pypi.org/project/llama-index-core/). Install core LlamaIndex and add your chosen LlamaIndex integration packages on LlamaHub that are required for your application. There are over 300 LlamaIndex integration packages that work seamlessly with core, allowing you to build with your preferred LLM, embedding, and vector store providers.
The LlamaIndex Python library is namespaced such that import statements which
include core imply that the core package is being used. In contrast, those
statements without core imply that an integration package is being used.
# typical pattern
from llama_index.core.xxx import ClassABC # core submodule xxx
from llama_index.xxx.yyy import (
SubclassABC,
) # integration yyy for submodule xxx
# concrete example
from llama_index.core.llms import LLM
from llama_index.llms.openai import OpenAILlamaIndex.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? Contributions to LlamaIndex core as well as contributing integrations that build on the core are both accepted and highly encouraged! See our Contribution Guide for more details.
Full documentation can be found here: https://docs.llamaindex.ai/en/latest/.
Please check it out for the most up-to-date tutorials, how-to guides, references, and other resources!
# custom selection of integrations to work with core
pip install llama-index-core
pip install llama-index-llms-openai
pip install llama-index-llms-replicate
pip install llama-index-embeddings-huggingfaceExamples are in the docs/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.core 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.core import Settings, VectorStoreIndex, SimpleDirectoryReader
from llama_index.embeddings.huggingface import HuggingFaceEmbedding
from llama_index.llms.replicate import Replicate
from transformers import AutoTokenizer
# set the LLM
llama2_7b_chat = "meta/llama-2-7b-chat:8e6975e5ed6174911a6ff3d60540dfd4844201974602551e10e9e87ab143d81e"
Settings.llm = Replicate(
model=llama2_7b_chat,
temperature=0.01,
additional_kwargs={"top_p": 1, "max_new_tokens": 300},
)
# set tokenizer to match LLM
Settings.tokenizer = AutoTokenizer.from_pretrained(
"NousResearch/Llama-2-7b-chat-hf"
)
# set the embed model
Settings.embed_model = HuggingFaceEmbedding(
model_name="BAAI/bge-small-en-v1.5"
)
documents = SimpleDirectoryReader("YOUR_DATA_DIRECTORY").load_data()
index = VectorStoreIndex.from_documents(
documents,
)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.core 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)We use poetry as the package manager for all Python packages. As a result, the
dependencies of each Python package can be found by referencing the pyproject.toml
file in each of the package's folders.
cd <desired-package-folder>
pip install poetry
poetry install --with devReference 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}
}