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https://github.com/run-llama/llama_index

LlamaIndex is a data framework for your LLM applications
https://github.com/run-llama/llama_index

agents application data fine-tuning framework llamaindex llm rag vector-database

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LlamaIndex is a data framework for your LLM applications

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README 

        
# 🗂️ LlamaIndex 🦙



[![PyPI - Downloads](https://img.shields.io/pypi/dm/llama-index)](https://pypi.org/project/llama-index/)

[![GitHub contributors](https://img.shields.io/github/contributors/jerryjliu/llama_index)](https://github.com/jerryjliu/llama_index/graphs/contributors)

[![Discord](https://img.shields.io/discord/1059199217496772688)](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://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, 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://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



```

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 using OpenAI:



```python

import os



os.environ["OPENAI_API_KEY"] = "YOUR_OPENAI_API_KEY"



from llama_index 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 LLMs, e.g. Llama 2 hosted on [Replicate](https://replicate.com/), where you can easily create a free trial API token:



```python

import os



os.environ["REPLICATE_API_TOKEN"] = "YOUR_REPLICATE_API_TOKEN"



from llama_index.llms import Replicate



llama2_7b_chat = "meta/llama-2-7b-chat:8e6975e5ed6174911a6ff3d60540dfd4844201974602551e10e9e87ab143d81e"

llm = Replicate(

    model=llama2_7b_chat,

    temperature=0.01,

    additional_kwargs={"top_p": 1, "max_new_tokens": 300},

)



# set tokenizer to match LLM

from llama_index import set_global_tokenizer

from transformers import AutoTokenizer



set_global_tokenizer(

    AutoTokenizer.from_pretrained("NousResearch/Llama-2-7b-chat-hf").encode

)



from llama_index.embeddings import HuggingFaceEmbedding

from llama_index import ServiceContext



embed_model = HuggingFaceEmbedding(model_name="BAAI/bge-small-en-v1.5")

service_context = ServiceContext.from_defaults(

    llm=llm, embed_model=embed_model

)



from llama_index import VectorStoreIndex, SimpleDirectoryReader



documents = SimpleDirectoryReader("YOUR_DATA_DIRECTORY").load_data()

index = VectorStoreIndex.from_documents(

    documents, service_context=service_context

)

```



To query:



```python

query_engine = index.as_query_engine()

query_engine.query("YOUR_QUESTION")

```



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:



```bash

pip install poetry

poetry install --with dev

```



## 📖 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}

}

```