Building a Knowledge Base Frontend with AWS Bedrock and RAG Using Streamlit

Introduction

In the age of AI-driven applications, organizations seek efficient ways to manage and retrieve knowledge from vast datasets. One powerful approach is using Retrieval-Augmented Generation (RAG), which combines information retrieval with generative AI models. AWS Bedrock, Amazon’s cloud-based AI service, allows seamless integration of powerful foundation models with retrieval mechanisms. In this blog, we explore how to build a knowledge base frontend using AWS Bedrock and Streamlit while leveraging RAG for intelligent information retrieval.

What is AWS Bedrock?

AWS Bedrock is a fully managed service that provides access to foundational AI models from leading providers, such as Anthropic, Cohere, and Amazon’s own Titan models. With AWS Bedrock, developers can build AI-powered applications without needing to manage the underlying infrastructure.

Key Features of AWS Bedrock:

• Access to multiple foundation models (FMs)
• API-based inference and customization
• Integration with AWS services (S3, DynamoDB, etc.)
• No need for GPU management or fine-tuning

Understanding Retrieval-Augmented Generation (RAG)

RAG is an advanced technique that enhances large language models (LLMs) by integrating external knowledge sources. Instead of relying solely on a pre-trained model, RAG retrieves relevant documents from a database before generating responses. This improves accuracy, reduces hallucinations, and allows real-time knowledge updates.

How RAG Works:

1. Query Processing: The user submits a query.
2. Retrieval Component: The system searches a document store for relevant context.
3. Augmentation: Retrieved documents are provided as input to the AI model.
4. Generation: The model generates a response using both the query and retrieved information.

Why Use Streamlit for the Frontend?

Streamlit is a lightweight Python framework for building interactive web applications. It is ideal for AI-powered dashboards and knowledge base interfaces because:

• It requires minimal coding for UI development.
• It supports fast prototyping and deployment.
• It integrates well with APIs, databases, and AI models.

Building the Knowledge Base Frontend

1. Prerequisites

To build a knowledge base frontend, you need:

• An AWS account with Bedrock enabled
• Python installed with Streamlit and Boto3
• A dataset (e.g., stored in Amazon S3 or DynamoDB)

2. Setting Up AWS Bedrock API

To access AWS Bedrock models, install boto3 and configure credentials:

import boto3

# Initialize AWS Bedrock Client
bedrock = boto3.client("bedrock-runtime", region_name="us-east-1")

def generate_response(prompt):
    response = bedrock.invoke_model(
        modelId="amazon.titan-text-express",
        body={"inputText": prompt}
    )
    return response["outputText"]

3. Implementing RAG for Knowledge Retrieval

For document retrieval, you can use Amazon OpenSearch or FAISS for vector search:

from sentence_transformers import SentenceTransformer
import faiss
import numpy as np

# Load embedding model
model = SentenceTransformer("all-MiniLM-L6-v2")

# Indexing documents (Assume pre-stored documents)
documents = ["AWS Bedrock is a cloud AI service", "RAG improves LLM accuracy", "Streamlit simplifies frontend development"]
doc_embeddings = model.encode(documents)
index = faiss.IndexFlatL2(dimension=384)
index.add(np.array(doc_embeddings))

def retrieve_documents(query):
    query_embedding = model.encode([query])
    D, I = index.search(query_embedding, k=2)
    return [documents[i] for i in I[0]]

4. Creating the Streamlit Frontend

Now, let’s integrate everything into a simple Streamlit UI:

import streamlit as st

st.title("AI-Powered Knowledge Base with AWS Bedrock and RAG")

query = st.text_input("Enter your query:")
if query:
    retrieved_docs = retrieve_documents(query)
    context = " ".join(retrieved_docs)
    response = generate_response(f"Context: {context}\nQuery: {query}")
    
    st.subheader("Retrieved Context")
    st.write(context)
    
    st.subheader("AI Response")
    st.write(response)

Conclusion

By combining AWS Bedrock, RAG, and Streamlit, we can create a powerful knowledge base application capable of retrieving and generating relevant responses efficiently. This setup allows businesses to build AI-driven knowledge assistants for customer support, research, and internal documentation.