Skip to main content

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.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

kolzchut_ragbot-1.7.12.tar.gz (13.6 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

kolzchut_ragbot-1.7.12-py3-none-any.whl (11.5 kB view details)

Uploaded Python 3

File details

Details for the file kolzchut_ragbot-1.7.12.tar.gz.

File metadata

  • Download URL: kolzchut_ragbot-1.7.12.tar.gz
  • Upload date:
  • Size: 13.6 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.12.12

File hashes

Hashes for kolzchut_ragbot-1.7.12.tar.gz
Algorithm Hash digest
SHA256 0649700d0a2d45b0c09f0273a2bafb2f60eb8a62fec0489e3c06813b9dbf291e
MD5 e11621f9140f71155b305297cb79beeb
BLAKE2b-256 1dafed0abee20292a66b96d2232a26d6f6f8b36a23924d0beb590325d2d16ffa

See more details on using hashes here.

File details

Details for the file kolzchut_ragbot-1.7.12-py3-none-any.whl.

File metadata

File hashes

Hashes for kolzchut_ragbot-1.7.12-py3-none-any.whl
Algorithm Hash digest
SHA256 1f6743a12207ea8856a496f302486ecf32ed5f03c91f310ec2b111ce4767bc39
MD5 2a3e20ce8537ffd426904806bfdf47de
BLAKE2b-256 8537a34918ff88d0aaef46805505c6c24118568a38a1858911c120e897faf607

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page