docs/OLD_README.md

Resonate

Current Phase: Sprint 1

Project Overview

Resonate is a Retrieval-augmented generation (RAG) powered Large Language Model application that helps you chat with your meetings to answer questions and generate insights.

Objectives

• User should be able to upload an audio/video meeting file along with a meeting Topic
• There can be multiple meeting topics. With each topic having a series of meetings.
• Use would then be able to choose a topic and chat with the meeting just and ask any question

Initial Sketches

RAG Inference

• The user would select the meeting Topic and ask a question.
• Pinecone would retrieve relevant information and would feed the LLm with custom prompt, context, and the user query.
• We also plan to add a Semantic Router to route queries according to the user input.
• The LLm would then generate the result and answer the question.

Data Store

• The below diagram shows how we plan to store data using Pinecone which is a popular Vector DB.
• User would upload meetings in audio/video format.
• We would use AWS Transcribe to diarize and transcribe the audio file into timestamp, speaker, text (this is simplified)
• We would embed the text data into vectors that would be uploaded to Pinecone serverless.

Research

• We would try multiple Vector embeddings and also fine-tune LLM Models using Microsoft DeepSpeed on the custom dataset and compare the performance of these models.

Proposed UI

• Below is the sketch of proposed UI.