Low-code ETL for structured and unstructured data. Generates Python code you can deploy anywhere.
-
Updated
Jul 3, 2024 - TypeScript
Low-code ETL for structured and unstructured data. Generates Python code you can deploy anywhere.
Build a RAG preprocessing pipeline
RAG-nificent is a state-of-the-art framework leveraging Retrieval-Augmented Generation (RAG) to provide instant answers and references from a curated directory of PDFs containing information on any given topic. Supports Llama3 and OpenAI Models via the Groq API.
Advanced RAG Pipelines
Demo LLM (RAG pipeline) web app running locally using docker-compose. LLM and embedding models are consumed as services from OpenAI.
This is a production-ready applications using RAG-based Language Model.
Add a description, image, and links to the rag-pipeline topic page so that developers can more easily learn about it.
To associate your repository with the rag-pipeline topic, visit your repo's landing page and select "manage topics."