Overview Our vector database enables AI-powered search and similarity matching for marketplace assets while maintaining decentralization through SUI blockchain and Walrus storage. Key features include:
Token incentives for vector data contributions
Decentralized storage with Byzantine fault tolerance
Built-in marketplace integration
Verifiable search results
Key Benefits
For AI Developers
• Build token-incentivized AI apps
• Access verified vector datasets
• Pay only for storage used
• Verifiable search results
For Data Contributors
• Earn tokens for vector data
• Track usage and revenue
• Automated reward distribution
• Built-in data marketplace
Architecture Getting Started Quick Start from kyne.vector import VectorDB from kyne.wallet import KyneWallet # Initialize with wallet for rewards db = VectorDB( wallet=KyneWallet.from_private_key("0x..."), config={ "reward_share": 0.1, # Share for data contributors "min_stake": 100 # Min SUI tokens to contribute } ) # Create collection with token incentives collection = await db.create_collection( name="embeddings", dimension=768, rewards_config={ "base_reward": 10, # SUI per contribution "usage_share": 0.05 # 5% of usage fees } ) # Add vectors and earn tokens result = await collection.add( vectors=embeddings, metadata=metadata, stake_amount=100 # Stake SUI tokens ) print(f"Earned rewards: {result.rewards} SUI") Vector Management Adding Vectors
Prepare Data # Stake tokens and add vectors await collection.add_vectors( vectors=embeddings, stake_amount=1000, # SUI tokens metadata={ "source": "training_data_v1", "quality_score": 0.95 } )
Configure Storage # Set storage parameters await collection.configure_storage( min_nodes=7, # 2f+1 for fault tolerance epochs=12, # Storage duration redundancy=2 # Red Stuff encoding level )
Monitor Rewards # Track earnings stats = await collection.get_contributor_stats() print(f"Total rewards: {stats.total_rewards}") print(f"Active stake: {stats.staked_amount}")
Search Operations
Basic Search # Search with token payment results = await collection.search( vector=query, top_k=10, payment_amount=1 # SUI per query )
Batch Operations # Bulk search with rewards async for batch in collection.batch_search( vectors=queries, reward_config={ "per_query": 0.1, # SUI tokens "bulk_bonus": 0.2 # 20% bonus } ): process_results(batch) Token Economics Reward System
Contribution Rewards # Configure rewards await collection.set_rewards({ "base_rate": 10, # SUI per vector "quality_bonus": 0.2, # 20% for high quality "stake_bonus": 0.1 # 10% for staking })
Usage Fees # Set usage pricing await collection.set_usage_fees({ "query_fee": 1, # SUI per query "bulk_discount": 0.2, # 20% for bulk "min_stake": 100 # Required stake }) Staking Mechanics # Configure staking parameters await collection.configure_staking({ "min_stake": 1000, # Minimum SUI tokens "lock_period": 30, # Epochs "reward_share": 0.7, # 70% to stakers "slashing_rate": 0.1 # 10% for violations }) # Monitor staking returns stake_info = await collection.get_stake_info() print(f"APY: {stake_info.apy}%") print(f"Total staked: {stake_info.total_staked}") Data Protection
Byzantine fault tolerant storage
Red Stuff encoding for redundancy
Storage proofs on SUI blockchain
Stake slashing for violations
Token-gated access
Stake-based permissions
Verifiable query results
Automated reward distribution
Query Optimization
Efficient vector indexing
Parallel search operations
Result caching
Load balancing
Storage Efficiency
Red Stuff encoding
Optimal chunk sizes
Proof verification
Node health monitoring
Economic Efficiency
Dynamic pricing
Reward optimization
Stake management
Fee distribution
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