vectoriz 0.0.3

Python library for creating vectorized data from text or files.

RAG-vector-creator

Overview

This project implements a RAG (Retrieval-Augmented Generation) system for creating and managing vector embeddings from documents using FAISS and NumPy libraries. It efficiently transforms text data into high-dimensional vector representations that enable semantic search capabilities, similarity matching, and context-aware document retrieval for enhanced question answering applications.

Features

• Document ingestion and preprocessing
• Vector embedding generation using state-of-the-art models
• Efficient storage and retrieval of embeddings
• Integration with LLM-based generation systems

Installation

pip install -r requirements.txt
python app.py

Build lib

To build the lib run the commands:

python setup.py sdist bdist_wheel

To test the install run:

pip install .

License

MIT