Interface between LLMs and your data
Project description
🗂️ LlamaIndex 🦙
LlamaIndex (GPT Index) is a data framework for your LLM application.
PyPI:
- LlamaIndex: https://pypi.tw.martin98.com/project/llama-index/.
- GPT Index (duplicate): https://pypi.tw.martin98.com/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 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, 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 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:
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:
query_engine = index.as_query_engine()
query_engine.query("<question_text>?")
By default, data is stored in-memory.
To persist to disk (under ./storage):
index.storage_context.persist()
To reload from disk:
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}
}
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
Filter files by name, interpreter, ABI, and platform.
If you're not sure about the file name format, learn more about wheel file names.
Copy a direct link to the current filters
File details
Details for the file llama_index-0.8.41.tar.gz.
File metadata
- Download URL: llama_index-0.8.41.tar.gz
- Upload date:
- Size: 561.8 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/4.0.2 CPython/3.9.18
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
9b8e074fe101a9448cc31724aa9c14061f7496d6de61642831b069964a13a212
|
|
| MD5 |
7ba4166b735f3df6a14a6dec227372f4
|
|
| BLAKE2b-256 |
cb3ed5bb20e6272cd9e78732c55b438957b45794dd7a536526709dc718231b77
|
File details
Details for the file llama_index-0.8.41-py3-none-any.whl.
File metadata
- Download URL: llama_index-0.8.41-py3-none-any.whl
- Upload date:
- Size: 868.1 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/4.0.2 CPython/3.9.18
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
f8ad2655f53285b61005c5bfd3a90ecc05b107babc66cb7949cf6e9d01766f52
|
|
| MD5 |
d730cb670d1afbf521f6116e2e621189
|
|
| BLAKE2b-256 |
950b91a514d589e6088320583f86b2e7b78ce695f03ae3eaf0f3cbcebad3ee2a
|
