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https://github.com/seanoliver/llama_index
https://github.com/seanoliver/llama_index
Last synced: about 14 hours ago
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- Host: GitHub
- URL: https://github.com/seanoliver/llama_index
- Owner: seanoliver
- License: mit
- Created: 2023-09-19T23:31:56.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2023-09-19T23:35:19.000Z (over 1 year ago)
- Last Synced: 2025-01-10T21:04:26.616Z (14 days ago)
- Language: Python
- Size: 60.2 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Citation: CITATION.cff
Awesome Lists containing this project
README
# 🗂️ LlamaIndex 🦙
[](https://pypi.org/project/llama-index/)
[](https://github.com/jerryjliu/llama_index/graphs/contributors)
[](https://discord.gg/dGcwcsnxhU)
LlamaIndex (GPT Index) is a data framework for your LLM application.
PyPI:
- LlamaIndex: https://pypi.org/project/llama-index/.
- GPT Index (duplicate): https://pypi.org/project/gpt-index/.
LlamaIndex.TS (Typescript/Javascript): https://github.com/run-llama/LlamaIndexTS.
Documentation: https://gpt-index.readthedocs.io/.
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 phenomenonal 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, 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? See our [Contribution Guide](CONTRIBUTING.md) for more details.
## 📄 Documentation
Full documentation can be found here: https://gpt-index.readthedocs.io/en/latest/.
Please check it out for the most up-to-date tutorials, how-to guides, references, and other resources!
## 💻 Example Usage
```
pip install llama-index
```
Examples are in the `examples` folder. Indices are in the `indices` folder (see list of indices below).
To build a simple vector store index:
```python
import os
os.environ["OPENAI_API_KEY"] = 'YOUR_OPENAI_API_KEY'
from llama_index import VectorStoreIndex, SimpleDirectoryReader
documents = SimpleDirectoryReader('data').load_data()
index = VectorStoreIndex.from_documents(documents)
```
To query:
```python
query_engine = index.as_query_engine()
query_engine.query("?")
```
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 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)
```
## 🔧 Dependencies
The main third-party package requirements are `tiktoken`, `openai`, and `langchain`.
All requirements should be contained within the `setup.py` file. To run the package locally without building the wheel, simply run `pip install -r requirements.txt`.
## 📖 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}
}
```