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Content Summarizer Using Amazon Bedrock – Detailed Technical Report

Content Summarizer Using Amazon Bedrock – Detailed Technical Report

This project involved building a complete document summarization application using the Amazon Bedrock Converse API and Streamlit. The goal was to design a system that can accept PDF documents, process the content using a large language model, and produce concise, accurate summaries according to user instructions. The assignment demonstrates practical integration of Amazon Bedrock into a real-world application, combining frontend, backend, and model orchestration in a single workflow.

Problem Statement

Long-form documents such as reports, academic papers, research articles, case histories, and transcripts often require considerable time to read and understand. Manual summarization is inefficient and impractical, especially when dealing with repetitive or high-volume workloads.
Organizations and individuals need a fast and reliable way to extract essential information from lengthy documents without reading them in full.

Proposed Solution

The solution is a PDF Content Summarizer Application built with the following components:
  • Amazon Bedrock (Claude 3.7 Sonnet model)
  • Bedrock Converse API with document-chat capability
  • Python (boto3)
  • Streamlit for user interface
Users can upload a PDF file, specify the type of summary they need, and receive a well-structured response generated by Amazon Bedrock. The model understands document context and produces summaries that are both coherent and relevant to the user’s instructions.

Target Users

This solution benefits several user groups:
  • Students who need quick summaries of long study materials
  • Customer support teams analyzing case histories
  • Analysts reviewing complex reports
  • Professionals handling lengthy, unstructured documents
  • Anyone who needs fast, accurate summarization without manual reading

Technical Implementation

AWS Services Used:

Amazon Bedrock Converse API
Used for sending the document and user prompt to Claude, and receiving the summarized output. It supports structured document-chat input.
Claude 3.7 Sonnet Model
Chosen due to its strong reasoning capabilities and ability to interpret long, unstructured PDFs. It generates high-quality natural language summaries.
boto3
AWS SDK for Python used to interact with the Bedrock runtime.
Streamlit
Provides the user interface where users upload PDFs, enter prompts, and view summaries.

Workflow

  1. The user uploads a PDF file through the Streamlit UI.
  2. The PDF file is read as byte data.
  3. The backend prepares a Bedrock message containing:
    • Document bytes
    • A user prompt describing the summary type
  4. This message is sent to the Bedrock Converse API.
  5. Amazon Bedrock processes the content using Claude.
  6. The model generates a contextual summary.
  7. Streamlit displays the summarized output to the user.

System Architecture Diagram:

The UI includes a file upload component, a text input box for instructions, and an output panel showing the generated summary.
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import streamlit as st
import summarization_lib as glib

st.set_page_config(page_title="Content Summarizer", layout="wide")
st.title("Content Summarizer")

left_col, right_col = st.columns([1, 1])

with left_col:
input_text = st.text_area("How would you like the document summarized?")

uploaded_file = st.file_uploader("Upload your PDF file", type=["pdf"])

summarize_button = st.button("Summarize", type="primary")

with right_col:
st.subheader("Summary Output")

if summarize_button:
if uploaded_file is None:
st.error("Please upload a PDF file before summarizing.")
elif not input_text.strip():
st.error("Please enter how you want the document summarized.")
else:
with st.spinner("Running..."):
response_content = glib.get_summary(input_text, uploaded_file)
st.write(response_content)
This project provided a comprehensive understanding of how to build functional Generative AI applications using Amazon Bedrock. The Content Summarizer application demonstrated the practical use of large language models for document processing, user interaction, and workflow automation.
The workshop offered insights into foundational concepts such as inference parameters, embeddings, streaming responses, and tool use. The summarization project, in particular, showcased how AI can be integrated into real user workflows to solve meaningful problems.
Streaming api's
Future work could include multi-document summarization, topic extraction, semantic search, and integrating guardrails for safer outputs.
# ai-agents# amazon-a2i# amazon-bedrock# amazon-cloudfront
Any opinions in this article are those of the individual author and may not reflect the opinions of AWS.
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