Mosaic AI Vector Search
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Enterprise Vector Search with Databricks Mosaic AI
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Automatic syncing between Delta Lake tables and vector indexes ensures real-time data consistency without manual pipeline management or ETL processes.
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Fully managed serverless compute eliminates infrastructure management, provides automatic scaling, and delivers predictable performance with zero operational overhead.
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Advanced indexing algorithms and optimized query execution deliver up to 5x faster search performance compared to traditional vector databases.
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Combines vector similarity search with BM25 keyword search and advanced reranking for superior retrieval accuracy and high recall rates.
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Storage-optimized endpoints provide up to 7x cost reduction compared to traditional vector databases while maintaining high performance for batch workloads.
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Native integration with Unity Catalog provides enterprise-grade governance, fine-grained access control, data lineage, and audit logging for vector indexes.
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Purpose-built for Retrieval Augmented Generation workflows with built-in chunking, embedding generation, and seamless LLM integration.
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Choose between compute-optimized endpoints for real-time queries or storage-optimized endpoints for cost-efficient batch processing workloads.
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Built-in reranking algorithms improve search relevance by re-scoring initial results using sophisticated machine learning models.
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First-class integration with LangChain framework enables rapid development of RAG applications with minimal code and best-practice patterns.
Demos, docs and playgrounds
Helpful resources
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Official product documentation covering architecture, features, and getting started with Databricks Vector Search.
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Step-by-step guide for creating Delta Sync indexes with automatic embedding generation and managed synchronization.
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Python SDK documentation for programmatic index creation, querying, and management with code examples.
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Complete tutorial on building RAG applications with Vector Search, including chunking strategies and embedding models.
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Best practices for optimizing query performance, batch size tuning, and choosing between endpoint types.
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Guide to implementing hybrid search combining vector similarity and BM25 keyword search with reranking.
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Understanding how Vector Search inherits Unity Catalog permissions, lineage tracking, and audit capabilities.
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Official LangChain vector store integration with usage examples and best practices for RAG applications.
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Complete REST API documentation for endpoints, indexes, and query operations with request/response schemas.
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Supported embedding models including BGE, E5, and custom model deployment options for vector generation.
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Detailed configuration for Delta Sync indexes with automatic embedding pipeline and incremental updates.
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Pricing details for compute-optimized vs storage-optimized endpoints with cost estimation tools.
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Official GitHub repository with Vector Search examples, RAG implementations, and reference architectures.
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Building agentic applications with Vector Search integration for retrieval-augmented agent systems.
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Complete guide to querying indexes with filters, hybrid search, num_results tuning, and reranking options.
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Common issues, error messages, debugging techniques, and performance troubleshooting for Vector Search.
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Best practices for document chunking, overlap strategies, and chunk size optimization for Vector Search.
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Product announcement blog with architecture overview, performance benchmarks, and use case examples.
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Security architecture, encryption, access control, and compliance features for Vector Search deployments.
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Tools and frameworks for evaluating RAG application performance, retrieval quality, and answer relevance.
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Discover Mosaic AI Vector Search, a scalable, secure, and serverless solution for embedding-based data retrieval. Key benefits include reliability, governance with Unity Catalog, ease of use via simple APIs, and Databricks’ trusted security practices. Learn about embeddings, optimized vector databases, and their applications in RAG systems, recommendations, and image/video recognition.
