PoC-n-Rag is a proof-of-concept project leveraging Retrieval-Augmented Generation (RAG) techniques. The aim is to combine powerful retrieval mechanisms with generative AI models to produce highly relevant, context-aware responses by pulling data from various sources and generating coherent answers based on that data. This approach is particularly useful for applications in customer support, knowledge management, and any domain that requires generating precise answers from a large dataset.
Our project focuses on testing and refining a RAG-based architecture that can accurately retrieve relevant documents and generate responses for complex user queries, enhancing user experience and efficiency in information retrieval.
Developers
| 
[Valentin Peron] |
Manager
 [Léandre Ramos] |
|---|
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To configure the environment for running this project, you can load your .env file:
export $(grep -v '^#' .env | xargs)To install the required packages, run the following command:
pip install -r requirements.txt