agentdb-advanced-features
Master Advanced AgentDB Vector Database Features
Also available from: DNYoussef
Build sophisticated distributed AI systems with sub-millisecond synchronization and advanced search capabilities. Learn to implement QUIC sync, custom distance metrics, and hybrid vector plus metadata search for production applications.
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Using "agentdb-advanced-features". Set up QUIC synchronization for three nodes
Expected outcome:
- QUIC sync configured with sub-millisecond latency between nodes
- Automatic retry and TLS 1.3 encryption enabled
- Nodes discover and sync patterns automatically
- Compression reduces network traffic by approximately 60 percent
- Event broadcasting notifies all peers of changes
Using "agentdb-advanced-features". Implement hybrid search for research papers
Expected outcome:
- Hybrid search combines vector similarity with metadata filters
- Filters include year greater than or equal to 2023 and category machine learning
- Citation count filter finds highly cited papers
- Results ranked by weighted combination of similarity and metadata score
Using "agentdb-advanced-features". Create database sharding by domain
Expected outcome:
- Three separate database adapters created for different domains
- Shard key extracted from domain prefix automatically
- Default fallback shard handles unknown domains
- Each shard maintains independent connection and query optimization
Security Audit
SafePure documentation skill containing only SKILL.md with educational content about AgentDB features. All static findings are FALSE POSITIVES: markdown backticks flagged as shell execution, mathematical terms (cosine, dot) flagged as weak crypto, and generic network terms flagged as C2 keywords. No executable code exists. The pre-existing skill-report.json correctly assessed this as 'safe' with no security risks.
Risk Factors
🌐 Network access (17)
📁 Filesystem access (1)
⚙️ External commands (52)
Quality Score
What You Can Build
Distributed AI Agent Networks
Build multi-agent systems with synchronized memory across nodes for collaborative AI applications.
Hybrid Vector Search Systems
Create advanced search engines combining semantic similarity with business metadata filtering.
Production Vector Database Deployment
Deploy and manage scalable vector databases with monitoring, pooling, and synchronization.
Try These Prompts
Show me how to set up AgentDB with QUIC sync between two servers at 192.168.1.10 and 192.168.1.11 on port 4433
Create a hybrid search for products that finds similar items vectorially but filters by price range 10 to 100 dollars and category electronics
Implement a custom distance function that weights the first half of vector dimensions two times more than the second half
Design a sharding strategy for AgentDB that separates data by domain including users, products, and orders with automatic routing
Best Practices
- Use connection pooling in production to avoid creating new adapters for each request
- Implement proper error handling with exponential backoff for transient failures
- Monitor query latency and set up alerts for performance degradation
Avoid
- Do not expose QUIC ports directly to the internet without proper firewall rules
- Avoid creating new database adapters for every operation - use pooling instead
- Do not ignore dimension mismatch errors - they indicate data quality issues