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

🧠 Building a Conversational Chatbot with AWS Bedrock (Amazon Titan) ## Overview ## 🔷 What Is AWS Bedrock? ## 🔑 Key Bedrock Characteristics ## 💼 Business Use Cases of AWS Bedrock ## Common Enterprise Use Cases ## Why Companies Choose Bedrock ## 🔶 Amazon Titan Text (amazon.titan-text-express-v1) ## Key Characteristics ## When to Use Titan Text Express ## 🏗 Architecture Overview ## 🧠 Core Design Decisions ## 1️⃣ Explicit Conversation Memory ## 2️⃣ Role-Based Prompt Formatting ## 3️⃣ Stop Sequences 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: This project demonstrates how teams can safely integrate foundation models into enterprise workflows without giving up control, observability, or reproducibility. 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: AWS Bedrock is designed for real business workloads, not just demos. Amazon Titan Text Express is a fast, cost-efficient text generation model built by AWS. ⚠️ Titan does not manage conversation state — which is why explicit memory handling (as shown in this project) is essential. Important: The entire conversation history is sent on every request. Amazon Titan does not track sessions. This makes the system: Conversation is formatted as: User: ... Assistant: ... This significantly improves response quality and consistency. This prevents the model from hallucinating the next user message. 4️⃣ Deterministic Generation Explicit assistant cue How to Run the Project Clone the Github repo AWS Bedrock Chatbot(Titan) Prerequisites 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 Templates let you quickly answer FAQs or store snippets for re-use. Are you sure you want to hide this comment? It will become hidden in your post, but will still be visible via the comment's permalink. Hide child comments as well For further actions, you may consider blocking this person and/or reporting abuse CODE_BLOCK: "stopSequences": ["User:"] Enter fullscreen mode Exit fullscreen mode CODE_BLOCK: "stopSequences": ["User:"] CODE_BLOCK: "stopSequences": ["User:"] CODE_BLOCK: Install Dependencies pip install boto3 Run the Chatbot python chatbot.py Type exit to quit Enter fullscreen mode Exit fullscreen mode CODE_BLOCK: Install Dependencies pip install boto3 Run the Chatbot python chatbot.py Type exit to quit CODE_BLOCK: Install Dependencies pip install boto3 Run the Chatbot python chatbot.py Type exit to quit - AWS Bedrock - Amazon Titan Text (amazon.titan-text-express-v1) - Python (boto3) - 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 - Serverless (no infrastructure management) - Model-agnostic API - Enterprise-grade security - Pay-per-use pricing - Native AWS integration - 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 - Data never leaves AWS - IAM-controlled access - Works seamlessly with S3, Lambda, Glue, Databricks, Redshift - No lock-in to a single model provider - Optimized for low-latency text generation - Ideal for chatbots, summarization, and explanations - Deterministic behavior when temperature is low - Fully managed and secured by AWS - Conversational assistants - Structured responses - Enterprise-safe workloads - Cost-sensitive applications - Store user and assistant messages - Append them to a history list - Inject the full history into each prompt - Predictable - Easy to debug - Python 3.9+ - AWS credentials configured