Observed Signal · Apr 18, 2026 · Technical Release · Source: DEV Community · Impact: 2/5 · Sentiment: Positive
Serverless Chatbot Built with Amazon Bedrock Agent
A developer tutorial demonstrates how to build a serverless conversational AI assistant for a small eco-friendly laundromat using Amazon Bedrock Agent, a Retrieval-Augmented Generation (RAG) knowledge base, and AWS serverless services. The architecture hosts a static frontend on S3 + CloudFront, routes POST /chat to an API Gateway that invokes an AWS Lambda (Flask) backend, and uses Bedrock Agent (amazon.nova-lite-v1) to manage session memory, decide when to consult a KB, and synthesize contextualized responses. The knowledge base stores source documents in S3 and indexes them in OpenSearch Serverless with embeddings (amazon.titan-embed-text-v1). The post includes AWS CLI and boto3 code samples to create knowledge bases, data sources, agents, and to invoke the agent with streaming responses, plus deployment and update steps for Lambda and CloudFront.
Practical, reproducible blueprint showing how to combine Amazon Bedrock Agent, RAG knowledge bases and AWS serverless services; useful to developers but not industry-shifting.
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Key Takeaways & Evidence Grounding
- The project uses Amazon Bedrock Agent with amazon.nova-lite-v1 as the foundation model for chat.
- Knowledge Base RAG is implemented by storing documents in S3 and indexing them in OpenSearch Serverless with embeddings via amazon.titan-embed-text-v1.
- Frontend is a 100% static site on S3 served via CloudFront; backend is AWS Lambda (Flask) exposed through API Gateway POST /chat.
- The tutorial provides AWS CLI commands to create a knowledge base, create a data source, start ingestion jobs, create/associate an agent, and publish an agent alias.
- A boto3 example shows invoking the bedrock-agent-runtime and streaming completion events to assemble the full text response.
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