metarag 0.1.3

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

Example Usage

from metarag import MetaRAG

rag = MetaRAG(["VectorDB_Paper.pdf"])
result = rag.query("Explain the abstract in simple terms")
print(result["aggregated_response"])

Requirements

Python 3.8+

License

MIT License - see LICENSE file for details.

Author

Nisharg Nargund
Founder @OpenRAG