Amazon Web Services Vector Search
AWS has introduced vector search in several of its managed database services, including OpenSearch, Bedrock, MemoryDB, Neptune, and Amazon Q, making it a comprehensive platform for vector search solutions.
About this tool
Amazon Web Services Vector Search
Amazon Web Services (AWS) offers advanced vector search capabilities across its managed database services, notably through Amazon OpenSearch Service, with integrations in Bedrock, MemoryDB, Neptune, and Amazon Q. These services provide comprehensive support for vector search solutions, enabling enterprise-grade semantic and hybrid search applications at scale.
Features
- Multiple Vector Engines: Supports FAISS, NMSLIB, and Lucene for vector search.
- Similarity Search: Offers exact and approximate nearest-neighbor (ANN) search with algorithms like HNSW and IVF.
- Distance Metrics: Supports Cartesian, cosine similarity, Manhattan, and more.
- Hybrid Search: Combines lexical (TF/IDF), vector, and neural search; supports blended ranking and score normalization.
- Neural Search: Semantic queries using text input, with AI connectors to Amazon SageMaker, Bedrock, and OpenAI for vector generation.
- Sparse and Dense Vectors: Supports both sparse and dense embeddings, optimized for different use cases.
- Vector Quantization: Scalar, binary, and product quantization reduce memory usage and cost, with minimal accuracy loss.
- Disk-based Vector Search: Enables large-scale vector search with reduced RAM requirements using compressed vectors in memory.
- Native Chunking: Automatic chunking of long documents into retrievable vector segments.
- Advanced Filtering: k-NN queries support filtering by distance and vector score, not just top-k.
- Parallelization: Hybrid search queries can be processed in parallel for lower latency.
- Aggregation Support: Hybrid queries support aggregations for advanced analytics.
- Multimodal Search: Supports semantic queries with combined text and image inputs (via Bedrock connectors).
- Conversational Search: Enables chat-based search with memory modules and RAG pipelines, integrating with LLMs like Claude, ChatGPT, and DeepSeek.
- AI-Native Pipelines: ML inference processors for enriching data flows with any ML/AI model or service.
- Performance Optimizations: SIMD support for faster exact and ANN queries; support for latest Java versions (JDK21).
- Production-Ready: Scalable, cost-effective, and low-latency search suitable for enterprise and AI-driven applications.
Pricing
No specific pricing details are provided in the source content. For up-to-date pricing information, refer to the AWS OpenSearch Service Pricing page.
Source
Amazon OpenSearch Service vector database capabilities revisited (AWS Big Data Blog)
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