{"info":{"author":null,"author_email":"Jerry Liu <jerry@llamaindex.ai>","bugtrack_url":null,"classifiers":["Topic :: Scientific/Engineering :: Artificial Intelligence","Topic :: Software Development :: Libraries :: Application Frameworks","Topic :: Software Development :: Libraries :: Python Modules"],"description":"# 🗂️ LlamaIndex 🦙\n\n[![PyPI - Downloads](https://img.shields.io/pypi/dm/llama-index)](https://pypi.org/project/llama-index/)\n[![Build](https://github.com/run-llama/llama_index/actions/workflows/build_package.yml/badge.svg)](https://github.com/run-llama/llama_index/actions/workflows/build_package.yml)\n[![GitHub contributors](https://img.shields.io/github/contributors/jerryjliu/llama_index)](https://github.com/jerryjliu/llama_index/graphs/contributors)\n[![Discord](https://img.shields.io/discord/1059199217496772688)](https://discord.gg/dGcwcsnxhU)\n[![Twitter](https://img.shields.io/twitter/follow/llama_index)](https://x.com/llama_index)\n[![Reddit](https://img.shields.io/reddit/subreddit-subscribers/LlamaIndex?style=plastic&logo=reddit&label=r%2FLlamaIndex&labelColor=white)](https://www.reddit.com/r/LlamaIndex/)\n[![Ask AI](https://img.shields.io/badge/Phorm-Ask_AI-%23F2777A.svg?&logo=data:image/svg+xml;base64,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)](https://www.phorm.ai/query?projectId=c5863b56-6703-4a5d-87b6-7e6031bf16b6)\n\nLlamaIndex 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.\n\n> ### 📚 **Documentation:**\n>\n> - [LlamaParse](https://developers.llamaindex.ai/python/cloud/llamaparse/?utm_medium=li_github&utm_source=github&utm_campaign=2026--)\n> - [LlamaIndex OSS](https://developers.llamaindex.ai/python/framework/?utm_medium=li_github&utm_source=github&utm_campaign=2026--)\n> - [LlamaAgents](https://developers.llamaindex.ai/python/llamaagents/overview/?utm_medium=li_github&utm_source=github&utm_campaign=2026--)\n\nBuilding 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\nPython:\n\n1. **Starter**: [`llama-index`](https://pypi.org/project/llama-index/). A starter Python package that includes core LlamaIndex as well as a selection of integrations.\n\n2. **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/)\n   that are required for your application. There are over 300 LlamaIndex integration\n   packages that work seamlessly with core, allowing you to build with your preferred\n   LLM, embedding, and vector store providers.\n\nThe LlamaIndex Python library is namespaced such that import statements which\ninclude `core` imply that the core package is being used. In contrast, those\nstatements without `core` imply that an integration package is being used.\n\n```python\n# typical pattern\nfrom llama_index.core.xxx import ClassABC  # core submodule xxx\nfrom llama_index.xxx.yyy import (\n    SubclassABC,\n)  # integration yyy for submodule xxx\n\n# concrete example\nfrom llama_index.core.llms import LLM\nfrom llama_index.llms.openai import OpenAI\n```\n\n### LlamaParse (document agent platform)\n\n**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.\n\n- **[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.\n- **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--)\n- **Extract** — Structured data extraction from documents. [Docs](https://developers.llamaindex.ai/python/cloud/llamaextract/?utm_medium=li_github&utm_source=github&utm_campaign=2026--)\n- **Index** — Ingest, index, and RAG pipelines. [Docs](https://developers.llamaindex.ai/python/cloud/llamacloud/?utm_medium=li_github&utm_source=github&utm_campaign=2026--)\n- **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--)\n- **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--)\n\n### Important Links\n\n[Documentation](https://developers.llamaindex.ai/python/framework/?utm_medium=li_github&utm_source=github&utm_campaign=2026--)\n\n[X (formerly Twitter)](https://x.com/llama_index)\n\n[LinkedIn](https://www.linkedin.com/company/llamaindex/)\n\n[Reddit](https://www.reddit.com/r/LlamaIndex/)\n\n[Discord](https://discord.gg/dGcwcsnxhU)\n\n## 🚀 Overview\n\n**NOTE**: This README is not updated as frequently as the documentation. Please check out the documentation above for the latest updates!\n\n### Context\n\n- LLMs are a phenomenal piece of technology for knowledge generation and reasoning. They are pre-trained on large amounts of publicly available data.\n- How do we best augment LLMs with our own private data?\n\nWe need a comprehensive toolkit to help perform this data augmentation for LLMs.\n\n### Proposed Solution\n\nThat's where **LlamaIndex** comes in. LlamaIndex is a \"data framework\" to help you build LLM apps. It provides the following tools:\n\n- Offers **data connectors** to ingest your existing data sources and data formats (APIs, PDFs, docs, SQL, etc.).\n- Provides ways to **structure your data** (indices, graphs) so that this data can be easily used with LLMs.\n- Provides an **advanced retrieval/query interface over your data**: Feed in any LLM input prompt, get back retrieved context and knowledge-augmented output.\n- Allows easy integrations with your outer application framework (e.g. with LangChain, Flask, Docker, ChatGPT, or anything else).\n\nLlamaIndex 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\n5 lines of code. Our lower-level APIs allow advanced users to customize and extend any module (data connectors, indices, retrievers, query engines, reranking modules),\nto fit their needs.\n\n## 💡 Contributing\n\nInterested in contributing? Contributions to LlamaIndex core as well as contributing\nintegrations that build on the core are both accepted and highly encouraged! See our [Contribution Guide](CONTRIBUTING.md) for more details.\n\nNew integrations should meaningfully integrate with existing LlamaIndex framework components. At the discretion of LlamaIndex maintainers, some integrations may be declined.\n\n## 📄 Documentation\n\nFull documentation can be found [here](https://developers.llamaindex.ai/python/framework/?utm_medium=li_github&utm_source=github&utm_campaign=2026--)\n\nPlease check it out for the most up-to-date tutorials, how-to guides, references, and other resources!\n\n## 💻 Example Usage\n\n```sh\n# custom selection of integrations to work with core\npip install llama-index-core\npip install llama-index-llms-openai\npip install llama-index-llms-ollama\npip install llama-index-embeddings-huggingface\n```\n\nExamples are in the `docs/examples` folder. Indices are in the `indices` folder (see list of indices below).\n\nTo build a simple vector store index using OpenAI:\n\n```python\nimport os\n\nos.environ[\"OPENAI_API_KEY\"] = \"YOUR_OPENAI_API_KEY\"\n\nfrom llama_index.core import VectorStoreIndex, SimpleDirectoryReader\n\ndocuments = SimpleDirectoryReader(\"YOUR_DATA_DIRECTORY\").load_data()\nindex = VectorStoreIndex.from_documents(documents)\n```\n\nTo build a simple vector store index using non-OpenAI LLMs, e.g. LLMs hosted through Ollama:\n\n```python\nfrom llama_index.core import Settings, VectorStoreIndex, SimpleDirectoryReader\nfrom llama_index.embeddings.huggingface import HuggingFaceEmbedding\nfrom llama_index.llms.ollama import Ollama\nfrom transformers import AutoTokenizer\n\n# set the LLM\nSettings.llm = Ollama(\n    model=\"llama-3.1:latest\",\n    request_timeout=360.0,\n)\n\n# set tokenizer to match LLM\nSettings.tokenizer = AutoTokenizer.from_pretrained(\n    \"meta-llama/Llama-3.1-8B-Instruct\"\n)\n\n# set the embed model\nSettings.embed_model = HuggingFaceEmbedding(\n    model_name=\"BAAI/bge-small-en-v1.5\"\n)\n\ndocuments = SimpleDirectoryReader(\"YOUR_DATA_DIRECTORY\").load_data()\nindex = VectorStoreIndex.from_documents(\n    documents,\n)\n```\n\nTo query:\n\n```python\nquery_engine = index.as_query_engine()\nquery_engine.query(\"YOUR_QUESTION\")\n```\n\nBy default, data is stored in-memory.\nTo persist to disk (under `./storage`):\n\n```python\nindex.storage_context.persist()\n```\n\nTo reload from disk:\n\n```python\nfrom llama_index.core import StorageContext, load_index_from_storage\n\n# rebuild storage context\nstorage_context = StorageContext.from_defaults(persist_dir=\"./storage\")\n# load index\nindex = load_index_from_storage(storage_context)\n```\n\n## A note on Verification of Build Assets\n\nBy 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.\n\nTo 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.\n\nTo verify this, you can run the following script (pointing to your installed package):\n\n```bash\n#!/bin/bash\nSTATIC_DIR=\"venv/lib/python3.13/site-packages/llama_index/core/_static\"\nREPO=\"run-llama/llama_index\"\n\nfind \"$STATIC_DIR\" -type f | while read -r file; do\n    echo \"Verifying: $file\"\n    gh attestation verify \"$file\" -R \"$REPO\" || echo \"Failed to verify: $file\"\ndone\n```\n\n## 📖 Citation\n\nReference to cite if you use LlamaIndex in a paper:\n\n```\n@software{Liu_LlamaIndex_2022,\nauthor = {Liu, Jerry},\ndoi = {10.5281/zenodo.1234},\nmonth = {11},\ntitle = {{LlamaIndex}},\nurl = {https://github.com/jerryjliu/llama_index},\nyear = 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