120 lines
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5 KiB
Markdown
120 lines
No EOL
5 KiB
Markdown
# Edge AI
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## Edge AI & On-Device Memory
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Cognee is bringing AI memory to the edge with **cognee-RS**, our Rust-based SDK designed for resource-constrained devices. Run the full memory pipeline (ingestion, semantic organization, retrieval) directly on-device, sub-100ms recall and data stay local.
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## The Edge AI Opportunity
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Picture this: Your smart glasses capture a conversation during a run, instantly recall your to-do list, and feed you directions - all offline, with zero data uploaded. Or your smart-home hub analyzes your evening routine, suggests energy optimizations for better sleep, and monitors wellness patterns without sending a single byte to the cloud.
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This is the future and the promise of edge AI memory.
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## cognee-RS: Rust-Powered Memory for Devices
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cognee-RS is our experimental Rust SDK. It is a port of cognee's proven memory architecture to edge devices like phones, smartwatches, glasses, and smart-home hubs.
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It combines:
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* A lean retrieval engine optimized for constrained resources
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* Support for on-device LLMs
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* Seamless hybrid switching to cloud when needed
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* Full multimodal support (text, images, audio)
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### Core Capabilities
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**Fully Offline Operation**
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Run with Phi-4-class LLMs and local embeddings—no internet required for queries or retrieval. Toggle to hosted models with a single config flag when you have connectivity and need more power.
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**High Accuracy**
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We're targeting 90%+ answer accuracy, matching our Python SDK. The local semantic layer ensures retrieval fidelity even with smaller models. Graph-aware retrieval boosts accuracy 15-25% through structural cues.
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**Hybrid Execution**
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Route tasks intelligently: local for embeddings, cloud for heavy entity extraction, or split dynamically based on connectivity, battery, and latency requirements.
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**Multimodal Fusion**
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Handles text, images, audio, and sensor data. Real-time fusion from device inputs (mic + camera) creates holistic context that a cloud-only approach can't match.
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**Resource Orchestration**
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Dynamic scheduling caps memory and CPU usage. Heavy processing doesn't interrupt core device functions—retrieval stays prioritized while batch ingestion happens during idle time.
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## Use Cases: Where Edge Memory Excels
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### Personal Voice Assistants
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Smart earbuds and wearables that remember your conversations, preferences, and context—without uploading your private discussions to the cloud.
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> "What did Sarah say about the project deadline during our walk yesterday?"
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Local conversation memory enables instant recall. Sync only opt-in summaries, never raw audio.
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### Smart Home & Wellness
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Baby monitors, vital-sign wearables, and home hubs that analyze patterns locally—complying with GDPR and HIPAA by design.
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* Sleep pattern analysis without cloud dependency
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* Anomaly detection that works during internet outages
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* Behavioral insights that stay on your network
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Your health data stays yours.
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### Robotics & Autonomous Systems
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Drones, robots, and autonomous vehicles need real-time memory access for navigation and decision-making—especially in dead zones.
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```
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Robot enters new environment
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│
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▼
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cognee-RS builds local context map
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│
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▼
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Real-time retrieval: "Have I seen this obstacle type before?"
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│
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▼
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Decision without connectivity delay
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```
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No connectivity? No problem. Local context drives decisions.
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### Industrial IoT
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Factory-floor sensors, offline kiosks, and field equipment often operate in network-constrained environments.
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Edge AI enables:
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* 24/7 local reasoning without persistent connection
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* Anomaly detection at the source
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* Bandwidth savings—only critical events sync to cloud
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* Continued operation during network outages
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## Trade-Offs and Mitigations
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Edge isn't effortless. Smaller models have tighter context windows. Devices have limited compute and battery budgets. Complex reasoning may exceed local capabilities.
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cognee-RS addresses these constraints:
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| Challenge | Mitigation |
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| ---------------------- | ---------------------------------------- |
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| Limited context window | Graph-aware retrieval for precision |
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| Complex reasoning | Hybrid execution—offload when needed |
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| Battery constraints | Dynamic scheduling, idle-time processing |
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| Storage limits | Semantic compression, smart eviction |
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| Model size | Support for Phi-4 class, upgradeable |
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cognee-RS is currently experimental. Early conversations with partners are giving promising results.
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## The Vision: Memory Everywhere
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The future isn't cloud-only AI. It's AI that runs where you are: on your phone, your glasses, your watch, your car. AI that remembers your context without uploading your life to someone else's servers.
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cognee-RS is how we get there: the same semantic memory layer that powers enterprise deployments, compiled to run on the devices in your pocket.
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Privacy-first. Real-time. Offline-capable. Memory-enabled.
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***
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---
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> To find navigation and other pages in this documentation, fetch the llms.txt file at: https://docs.cognee.ai/llms.txt |