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# 🗂️ LlamaIndex 🦙
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[](https://pypi.org/project/llama-index/)
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[](https://github.com/run-llama/llama_index/actions/workflows/build_package.yml)
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[](https://github.com/jerryjliu/llama_index/graphs/contributors)
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[](https://discord.gg/dGcwcsnxhU)
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[](https://x.com/llama_index)
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[](https://www.reddit.com/r/LlamaIndex/)
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[](https://www.phorm.ai/query?projectId=c5863b56-6703-4a5d-87b6-7e6031bf16b6)
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LlamaIndex OSS (by [LlamaIndex](https://llamaindex.ai?utm_medium=li_github&utm_source=github&utm_campaign=2026--)) is an open-source framework to build agentic applications. **[Parse](https://cloud.llamaindex.ai?utm_medium=li_github&utm_source=github&utm_campaign=2026--)** 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](#llamacloud-document-agent-platform) below for signup and product links.
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> ### 📚 **Documentation:**
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>
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> - [LlamaParse](https://developers.llamaindex.ai/python/cloud/llamaparse/?utm_medium=li_github&utm_source=github&utm_campaign=2026--)
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> - [LlamaIndex OSS](https://developers.llamaindex.ai/python/framework/?utm_medium=li_github&utm_source=github&utm_campaign=2026--)
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> - [LlamaAgents](https://developers.llamaindex.ai/python/llamaagents/overview/?utm_medium=li_github&utm_source=github&utm_campaign=2026--)
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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
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Python:
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1. **Starter**: [`llama-index`](https://pypi.org/project/llama-index/). A starter Python package that includes core LlamaIndex as well as a selection of integrations.
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2. **Customized**: [`llama-index-core`](https://pypi.org/project/llama-index-core/). Install core LlamaIndex and add your chosen LlamaIndex integration packages on [LlamaHub](https://llamahub.ai/)
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that are required for your application. There are over 300 LlamaIndex integration
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packages that work seamlessly with core, allowing you to build with your preferred
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LLM, embedding, and vector store providers.
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The LlamaIndex Python library is namespaced such that import statements which
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include `core` imply that the core package is being used. In contrast, those
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statements without `core` imply that an integration package is being used.
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```python
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# typical pattern
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from llama_index.core.xxx import ClassABC # core submodule xxx
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from llama_index.xxx.yyy import (
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SubclassABC,
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) # integration yyy for submodule xxx
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# concrete example
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from llama_index.core.llms import LLM
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from llama_index.llms.openai import OpenAI
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```
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### LlamaParse (document agent platform)
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**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.
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- **[Sign up for LlamaParse](https://cloud.llamaindex.ai?utm_medium=li_github&utm_source=github&utm_campaign=2026--)** — Create an account and get your API key.
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- **Parse** — Agentic OCR and document parsing (130+ formats). [Docs](https://developers.llamaindex.ai/python/cloud/llamaparse/?utm_medium=li_github&utm_source=github&utm_campaign=2026--)
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- **Extract** — Structured data extraction from documents. [Docs](https://developers.llamaindex.ai/python/cloud/llamaextract/?utm_medium=li_github&utm_source=github&utm_campaign=2026--)
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- **Index** — Ingest, index, and RAG pipelines. [Docs](https://developers.llamaindex.ai/python/cloud/llamacloud/?utm_medium=li_github&utm_source=github&utm_campaign=2026--)
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- **Split** — Split large documents into subcategories. [Docs](https://developers.llamaindex.ai/python/cloud/split/getting_started/?utm_medium=li_github&utm_source=github&utm_campaign=2026--)
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- **Agents** — Build end-to-end document agents with `Workflows` and Agent Builder. [Docs](https://developers.llamaindex.ai/python/llamaagents/overview/?utm_medium=li_github&utm_source=github&utm_campaign=2026--)
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### Important Links
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[Documentation](https://developers.llamaindex.ai/python/framework/?utm_medium=li_github&utm_source=github&utm_campaign=2026--)
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[X (formerly Twitter)](https://x.com/llama_index)
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[LinkedIn](https://www.linkedin.com/company/llamaindex/)
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[Reddit](https://www.reddit.com/r/LlamaIndex/)
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[Discord](https://discord.gg/dGcwcsnxhU)
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## 🚀 Overview
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**NOTE**: This README is not updated as frequently as the documentation. Please check out the documentation above for the latest updates!
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### Context
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- LLMs are a phenomenal piece of technology for knowledge generation and reasoning. They are pre-trained on large amounts of publicly available data.
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- How do we best augment LLMs with our own private data?
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We need a comprehensive toolkit to help perform this data augmentation for LLMs.
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### Proposed Solution
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That's where **LlamaIndex** comes in. LlamaIndex is a "data framework" to help you build LLM apps. It provides the following tools:
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- Offers **data connectors** to ingest your existing data sources and data formats (APIs, PDFs, docs, SQL, etc.).
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- Provides ways to **structure your data** (indices, graphs) so that this data can be easily used with LLMs.
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- Provides an **advanced retrieval/query interface over your data**: Feed in any LLM input prompt, get back retrieved context and knowledge-augmented output.
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- Allows easy integrations with your outer application framework (e.g. with LangChain, Flask, Docker, ChatGPT, or anything else).
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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
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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),
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to fit their needs.
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## 💡 Contributing
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Interested in contributing? Contributions to LlamaIndex core as well as contributing
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integrations that build on the core are both accepted and highly encouraged! See our [Contribution Guide](CONTRIBUTING.md) for more details.
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New integrations should meaningfully integrate with existing LlamaIndex framework components. At the discretion of LlamaIndex maintainers, some integrations may be declined.
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## 📄 Documentation
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Full documentation can be found [here](https://developers.llamaindex.ai/python/framework/?utm_medium=li_github&utm_source=github&utm_campaign=2026--)
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Please check it out for the most up-to-date tutorials, how-to guides, references, and other resources!
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## 💻 Example Usage
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```sh
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# custom selection of integrations to work with core
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pip install llama-index-core
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pip install llama-index-llms-openai
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pip install llama-index-llms-ollama
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pip install llama-index-embeddings-huggingface
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```
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Examples are in the `docs/examples` folder. Indices are in the `indices` folder (see list of indices below).
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To build a simple vector store index using OpenAI:
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```python
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import os
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os.environ["OPENAI_API_KEY"] = "YOUR_OPENAI_API_KEY"
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from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
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documents = SimpleDirectoryReader("YOUR_DATA_DIRECTORY").load_data()
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index = VectorStoreIndex.from_documents(documents)
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```
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To build a simple vector store index using non-OpenAI LLMs, e.g. LLMs hosted through Ollama:
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```python
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from llama_index.core import Settings, VectorStoreIndex, SimpleDirectoryReader
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from llama_index.embeddings.huggingface import HuggingFaceEmbedding
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from llama_index.llms.ollama import Ollama
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from transformers import AutoTokenizer
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# set the LLM
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Settings.llm = Ollama(
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model="llama-3.1:latest",
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request_timeout=360.0,
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)
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# set tokenizer to match LLM
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Settings.tokenizer = AutoTokenizer.from_pretrained(
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"meta-llama/Llama-3.1-8B-Instruct"
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)
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# set the embed model
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Settings.embed_model = HuggingFaceEmbedding(
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model_name="BAAI/bge-small-en-v1.5"
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)
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documents = SimpleDirectoryReader("YOUR_DATA_DIRECTORY").load_data()
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index = VectorStoreIndex.from_documents(
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documents,
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)
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```
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To query:
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```python
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query_engine = index.as_query_engine()
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query_engine.query("YOUR_QUESTION")
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```
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By default, data is stored in-memory.
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To persist to disk (under `./storage`):
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```python
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index.storage_context.persist()
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```
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To reload from disk:
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```python
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from llama_index.core import StorageContext, load_index_from_storage
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# rebuild storage context
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storage_context = StorageContext.from_defaults(persist_dir="./storage")
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# load index
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index = load_index_from_storage(storage_context)
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```
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## A note on Verification of Build Assets
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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.
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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.
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To verify this, you can run the following script (pointing to your installed package):
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```bash
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#!/bin/bash
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STATIC_DIR="venv/lib/python3.13/site-packages/llama_index/core/_static"
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REPO="run-llama/llama_index"
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find "$STATIC_DIR" -type f | while read -r file; do
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echo "Verifying: $file"
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gh attestation verify "$file" -R "$REPO" || echo "Failed to verify: $file"
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done
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```
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## 📖 Citation
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Reference to cite if you use LlamaIndex in a paper:
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```
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@software{Liu_LlamaIndex_2022,
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author = {Liu, Jerry},
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doi = {10.5281/zenodo.1234},
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month = {11},
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title = {{LlamaIndex}},
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url = {https://github.com/jerryjliu/llama_index},
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year = {2022}
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}
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```
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