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Crafting QA Tool with Reading Abilities Using RAG and Text-to-Speech
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Crafting QA Tool with Reading Abilities Using RAG and Text-to-Speech

Last Updated on May 15, 2024 by Editorial Team

Author(s): Cornellius Yudha Wijaya

Originally published on Towards AI.

Develop your QA Chat Tool with the latest advancements in AI research.
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We have been in a year where LLM has been used massively by many companies. From simple search engines to extensive chatbots, LLM has facilitated all business needs.

One of the tools that are often required by the business is the Question-Answering (QA) tool. It's an AI-powered tool that can quickly answer user input questions.

In this article, we will develop the QA-LLM-powered tools with RAG and text-to-speech(TTS) capability. How can we do that? Let's get into it.

All the source code can be accessed via this repository.

For this project, we will follow the structure below.

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The project would follow these steps:

Deploy the Open-Source Weaviate Vector Database with Docker.Read the Insurance Handbook PDF file and use the public-hosted embedding model from HuggingFace to embed the data.Store the embedding into the Weaviate Vector Store (Knowledge Base).Develop an RAG system with HuggingFace's public-hosted embedding and generative model.Use the ElevenLabs Text-to-Speech Model to transform the RAG output into audio.Create the front end with Streamlit.

In general, there are 6 steps we would follow to create the QA tool with RAG and TTS. Let's start now.

Before we start, we will prepare a few Python files that contain all the requirements so our application can… Read the full blog for free on Medium.

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