Building Hybrid Geospatial RAG Applications with Elastic and Amazon Bedrock

This technical article details the construction of a hybrid Retrieval Augmented Generation (RAG) system that integrates geospatial data using Elasticsearch, Amazon Bedrock, and LangChain. The authors demonstrate how to combine lexical search, geospatial queries, and vector similarity search within a single platform to enhance generative AI applications. The guide focuses on creating an intelligent real estate assistant capable of providing personalized property recommendations by processing complex user queries involving location and amenities. The architecture utilizes AWS services, including Lambda and Location Service, alongside Elasticsearch’s vector database capabilities to store and query embeddings efficiently. By leveraging Amazon Bedrock for foundation models and entity extraction, the system improves the accuracy and relevance of AI outputs without requiring model retraining. This approach simplifies data management for enterprises by unifying traditional search methods with advanced AI-driven retrieval, enabling scalable and context-aware decision support systems for location-based information retrieval.