metarag 0.1.7

MetaRAG: A multi-LLM ensemble Retrieval-Augmented Generation framework with cosine similarity ranking.

MetaRAG is a Python framework for multi-model Retrieval-Augmented Generation. It queries multiple LLMs in parallel, scores the responses based on cosine similarity with the context, and aggregates the top responses for a more accurate and comprehensive answer.

Features

• 🔍 Multi-LLM querying using Groq's LLMs (LLaMA3, Gemma, etc.)
• 🤝 Cosine similarity scoring of responses
• 🧠 Top-k response aggregation
• 📄 Works with PDFs and plain text
• ⚡ Fast execution with thread pooling

Installation

pip install metarag

Pre-requisites

Load your GROQ_API_KEY to the project from https://console.groq.com/keys and follow the sample example shown below.

Example Usage (Local PC)

import metarag
from dotenv import load_dotenv
load_dotenv()  
def test_metarag_functionality():
    
    document_paths = ["VectorDB_Paper.pdf"]  

    rag_instance = metarag.MetaRAG(document_paths=document_paths)

    query = "What is the methodology in 5 lines like a professor and extract 5 keywords?"

    result = rag_instance.run(query)

    print(f"Aggregated Response: {result}")

if __name__ == "__main__":
    test_metarag_functionality()

On Google Colab

import metarag
import os

os.environ["GROQ_API_KEY"]="groq_api_key"

    
document_paths = ["/content/VectorDB_Paper.pdf"]  

rag_instance = metarag.MetaRAG(document_paths=document_paths)

query = "What is the methodology in 5 lines like a professor and extract 5 keywords?"

result = rag_instance.run(query)

print(f"Aggregated Response: {result}")

Requirements

Python 3.8+

License

MIT License - see LICENSE file for details.

Author

Nisharg Nargund
Founder @OpenRAG