llama_index README

The project's README from github.com/run-llama/llama_index. As one markdown file: README.md.

🗂️ LlamaIndex 🦙

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LlamaIndex OSS (by LlamaIndex) is an open-source framework to build agentic applications. Parse is our enterprise platform for agentic OCR, parsing, extraction, indexing and more. You can use LlamaParse with this framework or on its own; see LlamaParse below for signup and product links.

📚 Documentation:

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.

# 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 OpenAI

LlamaParse (document agent platform)

LlamaParse is its own platform—focused on document agents and agentic OCR. It includes Parse (parsing), LlamaAgents (deployed document agents), Extract (structured extraction), and Index (ingest and RAG). You can use it with the LlamaIndex framework or standalone.

Documentation

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🚀 Overview

NOTE: This README is not updated as frequently as the documentation. Please check out the documentation above for the latest updates!

Context

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:

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

# custom selection of integrations to work with core
pip install llama-index-core
pip install llama-index-llms-openai
pip install llama-index-llms-ollama
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:

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. LLMs hosted through Ollama:

from llama_index.core import Settings, VectorStoreIndex, SimpleDirectoryReader
from llama_index.embeddings.huggingface import HuggingFaceEmbedding
from llama_index.llms.ollama import Ollama
from transformers import AutoTokenizer

# set the LLM
Settings.llm = Ollama(
    model="llama-3.1:latest",
    request_timeout=360.0,
)

# set tokenizer to match LLM
Settings.tokenizer = AutoTokenizer.from_pretrained(
    "meta-llama/Llama-3.1-8B-Instruct"
)

# 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)

A note on Verification of Build Assets

By default, llama-index-core includes a _static folder that contains the nltk and tiktoken cache that is included with the package installation. This ensures that you can easily run llama-index in environments with restrictive disk access permissions at runtime.

To verify that these files are safe and valid, we use the github attest-build-provenance action. This action will verify that the files in the _static folder are the same as the files in the llama-index-core/llama_index/core/_static folder.

To verify this, you can run the following script (pointing to your installed package):

#!/bin/bash
STATIC_DIR="venv/lib/python3.13/site-packages/llama_index/core/_static"
REPO="run-llama/llama_index"

find "$STATIC_DIR" -type f | while read -r file; do
    echo "Verifying: $file"
    gh attestation verify "$file" -R "$REPO" || echo "Failed to verify: $file"
done

📖 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}
}

llama_index (run-llama/llama_index)

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Llama_index
What is this?LlamaIndex is an open-source Python framework for building agentic applications that augment LLMs with your own private data, letting you ingest, index, and query documents with LLM-powered pipelines.
Is it a framework or a standalone app?It is a data framework and library you import into Python code, not a standalone application. There is also a separate commercial platform called LlamaParse for agentic document parsing/OCR that can be used with the framework or on its own.
How do I add it to a project?Install the starter package 'llama-index' for core plus a curated set of integrations, or install 'llama-index-core' plus the specific integration packages from LlamaHub you need (300+ available).
Do I need an LLM provider API key to use it?You need access to an LLM, either via a provider API key like OpenAI or a locally hosted model through something like Ollama; the README shows examples for both.
Is this README kept up to date?The maintainers explicitly warn the README lags behind, so check the full documentation site for current info.
Is this for beginners or advanced developers?Both: a high-level API lets beginners ingest and query data in about 5 lines of code, while a lower-level API lets advanced users customize data connectors, indices, retrievers, and query engines.
Does my data stay local or go anywhere by default?By default, indexed data is stored in-memory on your machine and can be persisted to local disk, with no external storage involved unless you choose a remote vector store integration.
What is LlamaParse?LlamaParse is LlamaIndex's separate enterprise platform for agentic OCR and document parsing, covering 130+ formats, plus structured extraction, indexing, splitting, and building document agents. It can be used with the LlamaIndex framework or entirely standalone.
Can I integrate LlamaIndex with other app frameworks?Yes, it is designed to plug into outer application frameworks like LangChain, Flask, Docker, or ChatGPT-based apps.
How many integrations does LlamaIndex support?Over 300 integration packages on LlamaHub cover different LLM providers, embedding models, and vector stores that work with the core package.
How does LlamaIndex verify the files bundled in its package?It uses GitHub's attest-build-provenance action to verify that the nltk/tiktoken cache files shipped in the static folder match the source repo, and provides a script to check this yourself.
What are similar projects?PageIndex, LightRAG, m_flow, RAG-Anything, ai-system-design, PocketFlow, kotaemon, turbovec
Which topics is it in?llamaindex, application, retrieval, data, retrieval-augmented-generation, multi-agent-systems, langchain, vector-database, ocr, embeddings, document-processing, fine-tuning
Creator
run-llama
Size
486.1 MB
Stars
52.5K
Updated
CommitOct 14Releasev0.14.25Sep 21
Age
3y
Commits · 90 Days