09-10, 11:30–12:00 (America/Chicago), Grand G
Explore the cutting-edge optimization of spatial data structures and hybrid search in the Apache Lucene Open Source project, and discover how these advancements are leveraged in Retrieval-Augmented Generation (RAG) for Geospatial Generative AI.
In this talk, we will explore the optimization of spatial data structures for large-scale geospatial search, high-dimensional vector search, and geo-analytics, examining specific implementations within the Apache Lucene Open Source projects. We will begin with an overview of the evolution of essential spatial data structures, such as Quad-trees, KD-trees, and R-Trees, which are crucial for efficiently indexing geospatial data. We will analyze the challenges associated with distributed geospatial search and discuss how optimized algorithms enhance search performance and scalability in distributed environments, referencing ongoing performance benchmarks collected over several years (https://home.apache.org/~mikemccand/geobench.html).
Next, we will introduce and discuss the complexities of high-dimensional vector data and efficient indexing techniques using structures like HNSW graphs, FAISS, and X-Trees. These structures are designed to achieve fast and accurate high-dimensional retrieval, as demonstrated in community-driven benchmark results (https://home.apache.org/~mikemccand/lucenebench/VectorSearch). This discussion will also cover spatial and vector columnar formats for geo-analytics and KNN search, addressing performance, storage demands, and trade-offs relevant to various hybrid search use cases. Optimization techniques, such as dimensionality reduction using principal component analysis (PCA) and compressed columnar formats for reducing data demand, will be introduced and presented through Apache Lucene's Geo Columnar Format (https://github.com/apache/lucene/pull/1017) and dimensional reduction analytic implementations.
Finally, we will integrate these topics and discuss techniques for incorporating geospatial data and analytics into a hybrid search system, enabling retrieval-augmented generation (RAG) in Generative AI applications. We will define hybrid search and RAG, emphasizing their importance in enhancing the context and relevance of geospatial data in generative AI outputs.
The talk will also cover the evolution of these implementations within Apache Lucene and Lucenia projects, pivotal for advancing the hybrid search landscape in the open-source community. We will trace the historical development of geospatial indexing and search capabilities, highlight recent advancements and enhancements in geospatial data structures, indexing techniques, and search algorithms, and preview upcoming features and improvements. This discussion will provide insights into the current state and future trajectory of geospatial data retrieval and analytics.
By examining the advancements in geospatial and hybrid search within the open-source communities, this presentation sets the stage for community involvement and collaboration to significantly impact the future of geospatial applications in Generative AI.
