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A python framework to implement RAG using Vector-store DB

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RAG-Framework

A Python framework to implement Retrieval-Augmented Generation (RAG) using a Vector-store Database. This framework utilizes the Vector-Store repository to store and retrieve embeddings for effective RAG implementation.

Structure

  • main.py - The main RAG pipeline framework, processing input and orchestrating the RAG flow.
  • llm.py - A module that interfaces with the Google Gemini API, simulating a Large Language Model (LLM) for generating responses.
  • get_embedding.py - A script for generating embeddings from .txt files using the SentenceTransformer model.
  • comm-Vectorstore.py - A communication module that uploads embeddings to the Vector Store for storage and retrieval.

Usage

  1. Clone the Vector-Store repository and set it up according to the instructions.
  2. Configure the main.py and get_embedding.py to use your Vector-store Database.
  3. Run main.py to execute the RAG pipeline with any input text.

Requirements

  • Python 3.x
  • Sentence-Transformers - For generating text embeddings
  • nltk - For sentence tokenization
  • Google Gemini API credentials for llm.py (or any compatible LLM API for inference)

Installation

pip install -r requirements.txt

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