Building a University Knowledge Base Agent with AWS Bedrock and Streamlit using RAG

In the realm of educational technology, creating intelligent knowledge base agents can significantly enhance student and faculty experiences. By leveraging AWS Bedrock for LLM capabilities and Streamlit for a user-friendly interface, we can build a powerful Retrieval-Augmented Generation (RAG) agent to assist with university-related queries.

What is RAG?

Retrieval-Augmented Generation (RAG) enhances LLM responses by dynamically retrieving relevant information from external knowledge bases. This approach ensures accurate and up-to-date information, making it ideal for a university knowledge base where data spans across courses, schedules, faculty profiles, and research materials.

Architecture Overview

1. AWS Bedrock LLM: Provides the foundation model for natural language understanding and generation.
2. Document Store (e.g., Amazon OpenSearch): Stores structured and unstructured university data.
3. Retriever Component: Fetches relevant documents from the knowledge base.
4. Streamlit UI: A simple and interactive front-end for user interaction.

Step-by-Step Implementation

1. Data Ingestion and Preprocessing

• Collect university data (e.g., course syllabi, research papers, faculty profiles).
• Index data in Amazon OpenSearch for efficient retrieval.
• Perform data cleaning and semantic embedding for better relevance scoring.

2. AWS Bedrock Integration

• Utilize foundation models like Anthropic’s Claude or Amazon Titan via AWS Bedrock.
• Set up API endpoints for LLM inference.

3. Retrieval Mechanism

• Implement a retriever using similarity search algorithms (e.g., BM25 or dense embeddings).
• Fetch the top relevant documents based on user queries.

4. Streamlit Front-End

import streamlit as st
import requests

def query_bedrock(prompt):
    response = requests.post("AWS_BEDROCK_API_ENDPOINT", json={"input": prompt})
    return response.json()["output"]

st.title("University Knowledge Base Agent")
query = st.text_input("Ask your question:")

if query:
    result = query_bedrock(query)
    st.write("Response:", result)

5. RAG Pipeline Integration

• Combine retrieved documents with the user query.
• Use the LLM to generate contextually enriched responses.

Benefits of the Approach

• Real-time Information Access: Quickly retrieve data on courses, faculty, and schedules.
• Enhanced Accuracy: LLM-powered generation with verified data sources.
• User-Friendly Interface: Streamlit provides an intuitive UI for students and staff.

Conclusion

By integrating AWS Bedrock with Streamlit and leveraging RAG, universities can offer an intelligent and efficient knowledge base agent. This approach not only enhances the learning experience but also streamlines administrative tasks and research support.