---
title: README
emoji: 🦙
colorFrom: yellow
colorTo: purple
sdk: static
pinned: false
---


# 🗂️ LlamaIndex 🦙

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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 in
Python:

1. **Starter**: [`pip install llama-index`](https://pypi.org/project/llama-index/). A starter Python package that includes core LlamaIndex as well as a selection of integrations.

2. **Customized**: [`pip install 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/)
   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.

### Important Links

- LlamaIndex.TS [(Typescript/Javascript)](https://github.com/run-llama/LlamaIndexTS)

- [Documentation](https://docs.llamaindex.ai/en/stable/)

- [Twitter](https://twitter.com/llama_index)

- [Discord](https://discord.gg/dGcwcsnxhU)

### Ecosystem

- LlamaHub [(community library of data loaders)](https://llamahub.ai)
- LlamaLab [(cutting-edge AGI projects using LlamaIndex)](https://github.com/run-llama/llama-lab)

## 🚀 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.

## 📄 Documentation

Full documentation can be found [here](https://docs.llamaindex.ai/en/latest/)

Please check it out for the most up-to-date tutorials, how-to guides, references, and other resources!

## 💻 Example Usage

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.

```python
# 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
```

To get started, we can install llama-index directly using the starter dependencies (mainly OpenAI):

```sh
pip install llama-index
```

Or we can do a more custom isntallation:

```sh
# 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
pip install llama-index-readers-file
```

To build a simple vector store index using OpenAI:

```python
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 models, we can leverage Ollama and HuggingFace. This assumes you've already installed Ollama and have pulled the model you want to use.

```python
from llama_index.core import Settings, VectorStoreIndex, SimpleDirectoryReader
from llama_index.embeddings.huggingface import HuggingFaceEmbedding
from llama_index.llms.ollama import Ollama

# set the LLM
llama2_7b_chat = "meta/llama-2-7b-chat:8e6975e5ed6174911a6ff3d60540dfd4844201974602551e10e9e87ab143d81e"
Settings.llm = Ollama(
    model="llama3.1:latest",
    temperature=0.1,
    request_timeout=360.0,
)

# set the embed model
Settings.embed_model = HuggingFaceEmbedding(
    model_name="BAAI/bge-small-en-v1.5",
    embed_batch_size=2,
)

documents = SimpleDirectoryReader("YOUR_DATA_DIRECTORY").load_data()
index = VectorStoreIndex.from_documents(
    documents,
)
```

To query:

```python
query_engine = index.as_query_engine()
query_engine.query("YOUR_QUESTION")
```

Or chat:

```python
chat_engine = index.as_chat_engine(chat_mode="condense_plus_context")
chat_engine.chat("YOUR MESSAGE")
```

By default, data is stored in-memory.
To persist to disk (under `./storage`):

```python
index.storage_context.persist()
```

To reload from disk:

```python
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)
```