This project is a search engine that uses machine learning models and Elasticsearch to provide advanced document retrieval. You can use Webiks-Hebrew-RAGbot-Demo to demonstrate the engine's document retrieval abilities
Document representation and validation Document embedding and indexing in Elasticsearch Advanced search using machine learning model Integration with LLM (Large Language Model) client for query answering
- Clone the repository:
git clone https://github.com/NNLP-IL/Webiks-Hebrew-RAGbot.git
cd Webiks-Hebrew-RAGbot
- Create a virtual environment and activate it:
python -m venv venv
source venv/bin/activate
On Windows use \venv\\Scripts\\activate\
- Install the required dependencies:
pip install -r requirements.txt
Set the following environment variables:
MODEL_LOCATION: Path to the model directory ES_EMBEDDING_INDEX_LENGTH: Size of any index in elasticsearch EMBEDDING_INDEX: The name of the index we will embed docs into