LlamaIndex

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:

1. Starter: llama-index. A starter Python package that includes core LlamaIndex as well as a selection of integrations.

2. Customized: 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.

```python

typical pattern

from llamaindex.core.xxx import ClassABC # core submodule xxx from llamaindex.xxx.yyy import ( SubclassABC, ) # integration yyy for submodule xxx

concrete example

from llamaindex.core.llms import LLM from llamaindex.llms.openai import OpenAI ```

Important Links

LlamaIndex.TS (Typescript/Javascript)

Documentation

X (formerly Twitter)

LinkedIn

Reddit

Discord

Ecosystem

• LlamaHub (community library of data loaders)
• LlamaLab (cutting-edge AGI projects using LlamaIndex)

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 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, or 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? 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.

New integrations should meaningfully integrate with existing LlamaIndex framework components. At the discretion of LlamaIndex maintainers, some integrations may be declined.

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

```sh

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-huggingface ```

Examples 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:

```python import os

os.environ["OPENAIAPIKEY"] = "YOUROPENAIAPI_KEY"

from llama_index.core import VectorStoreIndex, SimpleDirectoryReader

documents = SimpleDirectoryReader("YOURDATADIRECTORY").loaddata() index = VectorStoreIndex.fromdocuments(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:

```python import os

os.environ["REPLICATEAPITOKEN"] = "YOURREPLICATEAPI_TOKEN"

from llamaindex.core import Settings, VectorStoreIndex, SimpleDirectoryReader from llamaindex.embeddings.huggingface import HuggingFaceEmbedding from llama_index.llms.replicate import Replicate from transformers import AutoTokenizer

set the LLM

llama27bchat = "meta/llama-2-7b-chat:8e6975e5ed6174911a6ff3d60540dfd4844201974602551e10e9e87ab143d81e" Settings.llm = Replicate( model=llama27bchat, temperature=0.01, additionalkwargs={"topp": 1, "maxnewtokens": 300}, )

set tokenizer to match LLM

Settings.tokenizer = AutoTokenizer.from_pretrained( "NousResearch/Llama-2-7b-chat-hf" )

set the embed model

Settings.embedmodel = HuggingFaceEmbedding( modelname="BAAI/bge-small-en-v1.5" )

documents = SimpleDirectoryReader("YOURDATADIRECTORY").loaddata() index = VectorStoreIndex.fromdocuments( documents, ) ```

To query:

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

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.core import StorageContext, loadindexfromstorage

rebuild storage context

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

load index

index = loadindexfromstorage(storagecontext) ```

Dependencies

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.

bash cd <desired-package-folder> pip install poetry poetry install --with dev

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} }