RAG Assistant Agent

Elevating Your RAG Experience with Intelligent Query Guidance and Expert Suggestions!

Application Description

One of the difficulties of adopting RAG to a mass audience is lack of understanding of the underline NLP techniques required to produce good queries. With this tool, there is an AI agent that looks at the query and the results to help the user make better queries in the future. For example, If the user never used RAG before, they may ask a vague question. The agent will pick up on this and inform the user. In addition, it will provide suggestion of how to query for better results. This tool is general enough to be easy to adapt with already established RAG pipelines, in addition it is agnostic to data meaning it could be adopted to many fields.

Table of Contents

Table

• Application Description
• Table of Contents
• Application Deployed
• Project Demo
• Cover
• Technology Stack
• Features
• How to use the app
• Collaborators
• Hackathon Link
• License

Application Deployed:

https://rag-assistant.streamlit.app/

Video Demo

View the Demo App

Cover

Technology Stack

Technology	Description
Python	Programming Language
Vectra API	Search as a Service
GPT3.5	Open AI LLM
Deployment	Streamlit

Features

1. Query Generation

2. Open Sourced

How to use the app

Step #1 - Clone the project

$ git clone https://github.com/faranbutt/Rag_Assistant_Agent

Step #2

• Place your keys inside .env file

VECTARA_API_KEY=
VECTARA_CUSTOMER_ID=
VECTARA_CORPUS_ID=
YOUTUBE_DATA_API_KEY=
OPENAI_API_KEY=

Step #3

• Run

python3 query_data.py

Collaborators

Name	Link
Isayah Culbertson	https://github.com/isayahc
Faran Taimoor Butt	https://www.linkedin.com/in/faranbutt/

Hackathon Link

Hackathon Submission

License