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36 lines
1.7 KiB
Markdown
36 lines
1.7 KiB
Markdown
---
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title: Real-time vector retrieval for Edge AI in resource-constrained environments
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features:
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- id: 0
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image:
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src: /img/edge-beta-features/native-vector.svg
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alt: Native Vector Search
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title: Native Vector Search for Embedded & Edge AI
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description: Runs as a lightweight, in-process library. No background threads, no services - ideal for mobile, robotic, and embedded environments.
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- id: 1
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image:
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src: /img/edge-beta-features/optimized-for-low-memory.svg
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alt: Low-Memory
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title: Optimized for Low-Memory, Low-Compute Devices
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description: Dramatically Designed for resource-constrained hardware. No idle overhead, no runtime daemons. Fits into tightly scoped edge deployments. memory usage with built-in compression options and offload data to disk.
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- id: 2
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image:
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src: /img/edge-beta-features/local-by-default.svg
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alt: Local by Default
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title: Local by Default, Cloud-Connected When Needed
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description: Retrieval runs fully offline. Sync with Qdrant Cloud only when required - for data transfer, tenant promotion, or coordination at scale.
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- id: 3
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image:
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src: /img/edge-beta-features/hybrid-and-multimodal-search.svg
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alt: Hybrid & Multimodal Search
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title: Hybrid & Multimodal Search On-Device
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description: Supports dense and multimodal vectors with structured filtering. Enables real-time retrieval from text, image, audio, or sensor-derived embeddings.
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- id: 4
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image:
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src: /img/edge-beta-features/multitenancy-built.svg
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alt: Multitenancy Built
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title: Edge-Scale Multitenancy with Native SDKs
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description: Supports payload- and shard-based tenant isolation. Routes queries across uneven edge workloads. Native SDKs in Java (Android), Swift (Apple), and more.
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sitemapExclude: true
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---
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