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Ability to use static files as indexes. In particular, this means one can share indexes across processes. Annoy also decouples creating indexes from loading them, so one can pass around indexes as files and map them into memory quickly. Annoy is designed to work efficiently with high-dimensional data and is currently used by Spotify for their music recommendation engine.5. Hierarchical Navigable Small World (HNSW) Graphs:The Hierarchical Navigable Small World (HNSW) algorithm is an approximate nearest neighbor search method used for vector search in high-dimensional spaces. It constructs a hierarchical graph where each node represents a data point, and edges connect nearby points. The graph has multiple layers, with each layer representing a different level of granularity. The algorithm allows for efficient nearest neighbor searches by navigating the graph's layers. HNSW is known for its high search speed and accuracy.6. ScaNN (Scalable Nearest Neighbors)The ScaNN (Scalable Nearest Neighbors) algorithm is an approximate nearest neighbor search method developed by Google Research. It is designed to efficiently search for nearest neighbors in large-scale, high-dimensional datasets. ScaNN achieves high search accuracy and speed by combining several techniques, including quantization, vector decomposition, and graph-based search.7. Hybrid, as the name suggests, is some form of a combination of the above implementations.There’s many more algorithms currently being researched and developed. This field is progressing rapidly given the rise of foundation models and importance of vector search in this context.Using vector databasesThere are a number of options when it comes to vector databases. Each of them has its unique advantages. Depending on the nature of your application, and whether you’re trying to build the infrastructure from scratch, one of these may be a right option for you.Vector database options include:Vertex matching engine by GooglePinecone, a fully managed vector databaseWeaviate, an open-source vector search engineRedis as a vector databaseQdrant, a vector search engineMilvus, a vector database built for scalable similarity searchChroma, an open-source embeddings storeElastic, an open source and hosted databaseUtilizing Labelbox Catalog for vector search applicationsLabelbox Catalog is designed with vector search capabilities to help you better organize, enrich and make useful applications with unstructured data. With Catalog, teams can easily upload text snippets, conversations or PDF documents within the UI or via the Python SDK. Labelbox will automatically generate embeddings with the uploaded data and allow you to start capitalizing on the power of vector search.Natural language searchNatural language search is powered by vector embeddings. A vector

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Embracing gen AI means providing delightful AI-driven experiences to your users. As you take this journey, databases provide the foundation for building enterprise gen AI apps that are accurate, relevant and grounded in enterprise truth. At Google Cloud, we’re focused on helping you in three key ways.First, helping developers build intelligent apps with operational data. Second, leveraging gen AI — and specifically our Gemini models — to simplify every stage of the database journey, including migrations, fleet management, troubleshooting, and performance optimization. And third, modernizing your databases so you can take advantage of gen AI. Let’s see how all three come to life.Build enterprise gen AI apps with operational dataDevelopers play a key role in the success of gen AI in the enterprise. According to the Bureau of Labor Statistics, there are 10x more software developers compared to data scientists — an order of magnitude more technical practitioners who can innovate with AI. That’s why we’re excited about the innovations we've brought to developers over the past year: vector search, LangChain integration, and foundation models are supported across all our databases. We’ve also added powerful new features all in a single database, Spanner - the always-on, globally consistent, and virtually unlimited scale database. These features include graph processing, advanced full-text search, and vector search — so you can build a new class of smart, contextually relevant applications that rely on interconnected data and semantic search.We’ve also enhanced the gen AI capabilities in AlloyDB, the database that combines the best of Google with 100% PostgreSQL compatibility. Today, we’re announcing that the ScaNN vector index in AlloyDB is now generally available. This is the same technology that powers Google’s most popular services including Google Search and YouTube, infusing 12 years of research and innovation into AlloyDB. The ScaNN index is the first PostgreSQL-compatible index that can scale to support more than one billion vectors while maintaining state-of-the-art query performance — enabling high performance workloads for every enterprise.We know that many organizations need the flexibility to deploy and run applications in various environments, and that’s why last year we launched AlloyDB Omni, a

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