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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 inPython:

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 onLlamaHubthat are required for your application. There are over 300 LlamaIndex integrationpackages that work seamlessly with core, allowing you to build with your preferredLLM, embedding, and vector store providers.

The LlamaIndex Python library is namespaced such that import statements whichincludecore imply that the core package is being used. In contrast, thosestatements withoutcore imply that an integration package is being used.

# typical patternfromllama_index.core.xxximportClassABC# core submodule xxxfromllama_index.xxx.yyyimport (SubclassABC,)# integration yyy for submodule xxx# concrete examplefromllama_index.core.llmsimportLLMfromllama_index.llms.openaiimportOpenAI

LlamaIndex.TS(Typescript/Javascript)

Documentation

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

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 whereLlamaIndex comes in. LlamaIndex is a "data framework" to help you build LLM apps. It provides the following tools:

• Offersdata connectors to ingest your existing data sources and data formats (APIs, PDFs, docs, SQL, etc.).
• Provides ways tostructure your data (indices, graphs) so that this data can be easily used with LLMs.
• Provides anadvanced 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 in5 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 contributingintegrations that build on the core are both accepted and highly encouraged! See ourContribution 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.

Full documentation can be foundhere

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 corepip install llama-index-corepip install llama-index-llms-openaipip install llama-index-llms-replicatepip install llama-index-embeddings-huggingface

Examples are in thedocs/examples folder. Indices are in theindices folder (see list of indices below).

To build a simple vector store index using OpenAI:

importosos.environ["OPENAI_API_KEY"]="YOUR_OPENAI_API_KEY"fromllama_index.coreimportVectorStoreIndex,SimpleDirectoryReaderdocuments=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 onReplicate, where you can easily create a free trial API token:

importosos.environ["REPLICATE_API_TOKEN"]="YOUR_REPLICATE_API_TOKEN"fromllama_index.coreimportSettings,VectorStoreIndex,SimpleDirectoryReaderfromllama_index.embeddings.huggingfaceimportHuggingFaceEmbeddingfromllama_index.llms.replicateimportReplicatefromtransformersimportAutoTokenizer# set the LLMllama2_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 LLMSettings.tokenizer=AutoTokenizer.from_pretrained("NousResearch/Llama-2-7b-chat-hf")# set the embed modelSettings.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:

fromllama_index.coreimportStorageContext,load_index_from_storage# rebuild storage contextstorage_context=StorageContext.from_defaults(persist_dir="./storage")# load indexindex=load_index_from_storage(storage_context)

We use poetry as the package manager for all Python packages. As a result, thedependencies of each Python package can be found by referencing thepyproject.tomlfile in each of the package's folders.

cd<desired-package-folder>pip install poetrypoetry 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}}