Build conversational AI search with Amazon OpenSearch Service

This article provides a comprehensive guide to building a conversational AI search application using Amazon OpenSearch Service and Amazon Bedrock. The solution demonstrates how to create a Retrieval Augmented Generation (RAG) pipeline with conversational capabilities.

• Uses OpenSearch Service agents and tools for creating conversational search
• Integrates Amazon Titan Text Embedding and Anthropic Claude V1 models
• Creates an embedding pipeline for semantic search with vector indexing
• Implements a sample use case with cricket player batting statistics
• Enables multi-turn conversations with context preservation

Key technical steps include establishing model connections, creating ingestion pipelines, deploying language models, and registering agents with specific tools for vector database and response generation.