🗂️ LlamaIndex (OSS Framework) 🦙
[!NOTE] The current focus of LlamaIndex is to build the best AI-powered engine for document parsing and extraction. LlamaParse is our enterprise platform for agentic OCR, parsing, extraction, indexing and more. LiteParse represents our efforts to build the best free, fast, cheap text parser in the market. ParseBench and ExtractBench represent our commitment towards open benchmarking for parsing and extraction.
The company itself has undergone an evolution since when this OSS framework first launched 3 years ago in 2023. Since the early days, the framework has consisted of a broad set of orchestration tools enabling developers to build various RAG and agent applications.
While we still have the OSS framework available as an open toolkit that you're welcome to use, our primary focus has shifted towards LlamaParse, along with liteparse and our benchmarking efforts. We have a strong belief that agents are the new consumers of documents, and they fundamentally need the right tools to unlock context from the world's hardest documents accurately/cheaply at scale. Whether you're an AI startup processing documents or an enterprise looking to automate document workflows, come talk to us.
LlamaIndex OSS (by LlamaIndex) is an open-source framework to build agentic applications. 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:
Starter:
llama-index. A starter Python package that includes core LlamaIndex as well as a selection of integrations.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.
- Sign up for LlamaParse — Create an account and get your API key.
- Parse — Agentic OCR and document parsing (130+ formats). Docs
- Extract — Structured data extraction from documents. Docs
- Index — Ingest, index, and RAG pipelines. Docs
- Split — Split large documents into subcategories. Docs
- Agents — Build end-to-end document agents with
Workflowsand Agent Builder. Docs
Important Links
🚀 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
# 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