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LARA, the Local Arabic Retrieval Augmented Generation Assistant, transforms Arabic data processing with RAG, localized Text2Speech, and Text Generation using Gemma CPP multilingual models, enhancing accessibility and creation for Arabic-speaking communities.

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LARA

Installation Guide

  1. Clone the repo
git clone https://github.com/Ro2yaLabs/LARA.git
  1. Create Python Environment
conda create --name lara python==3.12.2
  1. Activate the environment
conda activate lara
  1. install the requirments
pip install -r requirements.txt
  1. Rename .env.example to .env and add the secret keys of

    • openai
    • elevenlabs
  2. Run the following docker command

#CHANGE M:\Projects\ro2ya\LARA\store => to the location in your hard disk
docker run -p 8000:8000 -e CHROMA_SERVER_AUTH_CREDENTIALS_PROVIDER="chromadb.auth.token.TokenConfigServerAuthCredentialsProvider" -e CHROMA_SERVER_AUTH_PROVIDER="chromadb.auth.token.TokenAuthServerProvider" -e CHROMA_SERVER_AUTH_CREDENTIALS="zed@12345678" -e CHROMA_SERVER_AUTH_TOKEN_TRANSPORT_HEADER="X_CHROMA_TOKEN" -v /home/mahmoud/Ro2ya/LARA/store:/chroma/chroma -d chromadb/chroma
  1. Download mpv for streaming and put it in program files
https://sourceforge.net/projects/mpv-player-windows/
  1. Start the app
uvicorn main:app --port 8000
  1. APIs documentation at
https://www.postman.com/satellite-geoscientist-95548148/workspace/lara/collection/33188959-17e24558-26fe-4408-9681-29fa9b17ceb7?action=share&creator=33188959
  1. You can test the app from test_main.pynb script

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LARA, the Local Arabic Retrieval Augmented Generation Assistant, transforms Arabic data processing with RAG, localized Text2Speech, and Text Generation using Gemma CPP multilingual models, enhancing accessibility and creation for Arabic-speaking communities.

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