LocalAI/examples/llamaindex

LocalAI Demonstration with Embeddings

This demonstration shows you how to use embeddings with existing data in LocalAI. We are using the llama-index library to facilitate the embedding and querying processes. The Weaviate client is used as the embedding source.

Getting Started

1. Clone this repository and navigate to this directory

   git clone git@github.com:mudler/LocalAI.git
   cd LocalAI/examples/llamaindex
   

2. pip install LlamaIndex and Weviate's client: pip install llama-index>=0.9.9 weviate-client

3. Run the example: python main.py

Downloading (…)lve/main/config.json: 100%|███████████████████████████| 684/684 [00:00<00:00, 6.01MB/s]
Downloading model.safetensors: 100%|███████████████████████████████| 133M/133M [00:03<00:00, 39.5MB/s]
Downloading (…)okenizer_config.json: 100%|███████████████████████████| 366/366 [00:00<00:00, 2.79MB/s]
Downloading (…)solve/main/vocab.txt: 100%|█████████████████████████| 232k/232k [00:00<00:00, 6.00MB/s]
Downloading (…)/main/tokenizer.json: 100%|█████████████████████████| 711k/711k [00:00<00:00, 18.8MB/s]
Downloading (…)cial_tokens_map.json: 100%|███████████████████████████| 125/125 [00:00<00:00, 1.18MB/s]
LocalAI is a community-driven project that aims to make AI accessible to everyone. It was created by Ettore Di Giacinto and is focused on providing various AI-related features such as text generation with GPTs, text to audio, audio to text, image generation, and more. The project is constantly growing and evolving, with a roadmap for future improvements. Anyone is welcome to contribute, provide feedback, and submit pull requests to help make LocalAI better.