🧠 Building a Conversational Chatbot with AWS Bedrock (Amazon Titan)

Overview

Large Language Models don’t magically “remember” conversations. In real-world systems, conversation state must be explicitly managed.

In this project, we build a deterministic, production-style conversational chatbot using:

• AWS Bedrock
• Amazon Titan Text (amazon.titan-text-express-v1)
• Python (boto3)

This project demonstrates how teams can safely integrate foundation models into enterprise workflows without giving up control, observability, or reproducibility.

🔷 What Is AWS Bedrock?

Amazon Bedrock is a fully managed service that provides access to multiple foundation models (FMs) via a single API — without requiring you to manage infrastructure.

With Bedrock, you can:

• Invoke models securely using IAM
• Choose models from different providers
• Keep data within AWS (no model training on your prompts by default)
• Integrate generative AI directly into existing AWS architectures

🔑 Key Bedrock Characteristics

• Serverless (no infrastructure management)
• Model-agnostic API
• Enterprise-grade security
• Pay-per-use pricing
• Native AWS integration

💼 Business Use Cases of AWS Bedrock

AWS Bedrock is designed for real business workloads, not just demos.

Common Enterprise Use Cases

• Internal chatbots & AI copilots
• Document summarization & analysis
• Automated reporting & insight generation
• Semantic search over internal data
• AI-assisted debugging & data quality analysis
• Analytics narrative generation
• Customer support automation

Why Companies Choose Bedrock

• Data never leaves AWS
• IAM-controlled access
• Works seamlessly with S3, Lambda, Glue, Databricks, Redshift
• No lock-in to a single model provider

🔶 Amazon Titan Text (amazon.titan-text-express-v1)

Amazon Titan Text Express is a fast, cost-efficient text generation model built by AWS.

Key Characteristics

• Optimized for low-latency text generation
• Ideal for chatbots, summarization, and explanations
• Deterministic behavior when temperature is low
• Fully managed and secured by AWS

When to Use Titan Text Express

• Conversational assistants
• Structured responses
• Enterprise-safe workloads
• Cost-sensitive applications

⚠️ Titan does not manage conversation state — which is why explicit memory handling (as shown in this project) is essential.

🏗 Architecture Overview

Important: The entire conversation history is sent on every request.

🧠 Core Design Decisions

1️⃣ Explicit Conversation Memory

Amazon Titan does not track sessions.

We:

• Store user and assistant messages
• Append them to a history list
• Inject the full history into each prompt

This makes the system:

• Predictable
• Auditable
• Easy to debug

2️⃣ Role-Based Prompt Formatting

Conversation is formatted as:

User: ... Assistant: ...

This significantly improves response quality and consistency.

3️⃣ Stop Sequences

We configure:

"stopSequences": ["User:"]

This prevents the model from hallucinating the next user message.

4️⃣ Deterministic Generation

Low temperature

Explicit assistant cue

Token limits

How to Run the Project

Clone the Github repo AWS Bedrock Chatbot(Titan) Prerequisites

1. Python 3.9+
2. AWS credentials configured

Install Dependencies
pip install boto3

Run the Chatbot
python chatbot.py


Type exit to quit

You can also experiment with other models available on the AWS Bedrock service page.

On the AWS Bedrock service page Click on Model Catalog . Here you have access to other model providers like Meta, Anthropic, Mistral AI etc. You can search for different models from different or same providers.

Click on a Model, read it’s documentation to understand how to use it in your project and copy the model’s ID