Low-code ETL for structured and unstructured data. Generates Python code you can deploy anywhere.
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Updated
Jul 27, 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 Llama3.1 and OpenAI Models via the Groq API.
Demo LLM (RAG pipeline) web app running locally using docker-compose. LLM and embedding models are consumed as services from OpenAI.
AI-driven prompt generation and evaluation system, designed to optimize the use of Language Models (LLMs) in various industries. The project consists of both frontend and backend components, facilitating prompt generation, automatic evaluation data generation, and prompt testing.
This is a an Advanced RAG system, where I tried to make it functioning in regular PC with a CPU using all free resources, using APIs and tools to make it happen.
This is a production-ready applications using RAG-based Language Model.
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