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A search engine using machine learning models and Elasticsearch for advanced document retrieval.

Project description

kolzchut-ragbot

Overview

This project is a search engine that uses machine learning models and Elasticsearch to provide advanced document retrieval. You can use kolzchut-ragbot to demonstrate the engine's document retrieval abilities.

Features

  • 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

Installation

From PyPI

pip install kolzchut-ragbot

From Source

  1. Clone the repository:

    git clone https://github.com/shmuelrob/rag-bot.git
    cd rag-bot
    
  2. Create a virtual environment and activate it:

    python -m venv venv
    source venv/bin/activate  # On Windows use: venv\Scripts\activate
    
  3. Install the required dependencies:

    pip install -r requirements.txt
    

Configuration

Set the following environment variables:

  • ES_EMBEDDING_INDEX: The name of the Elasticsearch index for embeddings.
  • TOKENIZER_LOCATION: The location of the tokenizer model.

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