Zero-Vector v3
JSON →A server for Zero-Vector's hybrid vector-graph persona and memory management system, featuring advanced LangGraph workflow capabilities.
Tools · 24
- hybrid_retrieval Performs hybrid vector-graph retrieval combining semantic search with knowledge graph traversal for context-aware memory recall
- persona_memory Manages AI persona memory with entity extraction, relationship mapping, and context-aware storage
- multi_step_reasoning Executes multi-step reasoning chains with approval gates and performance monitoring for sophisticated AI decision-making
- human_approval Handles human-in-the-loop approval workflows for critical AI operations and decision gates
- workflow_management Orchestrates and manages LangGraph workflow execution including state management and agent coordination
- graph_exploration Explores and traverses the knowledge graph to discover entity relationships and patterns
- entity_extraction Automatically extracts entities from text and maps relationships within the knowledge graph
- embedding_service Generates vector embeddings using OpenAI or local transformers for semantic search operations
- performance_cache Manages Redis/PostgreSQL caching layer for optimized performance with 95%+ cache hit rate
- state_management Handles LangGraph state persistence and checkpointing using PostgreSQL for workflow continuity
- hybrid_memory_manager Coordinates hybrid memory retrieval combining vector search and graph traversal for comprehensive context
- feature_flags Manages feature flag system for controlled rollout of new capabilities and A/B testing
- approval_service Provides human-in-the-loop approval processing for critical workflow decision points
- graph_service Manages knowledge graph operations including entity creation, relationship mapping, and traversal queries
- vector_search Performs high-performance vector similarity search with sub-50ms latency for 10,000+ vectors
- knowledge_graph_intelligence Provides automatic entity recognition and relationship mapping for building intelligent knowledge graphs
- multi_agent_orchestration Coordinates 7-node workflow graph with multiple AI agents for complex task execution
- langgraph_compilation Compiles LangGraph workflow graphs with sub-3ms compilation time for 7-node configurations
- memory_integration Integrates hybrid memory retrieval with sub-500ms latency for context-aware AI responses
- system_initialization Handles complete system initialization with 2-3 second startup time for all services
- service_coordination Coordinates v2/v3 system services for seamless multi-server operation and data consistency
- content_access Provides advanced content access capabilities with context-aware retrieval and filtering
- workflow_execution Executes standard AI conversation workflows with sub-2s completion time
- infrastructure_setup Manages PostgreSQL and Redis infrastructure setup for production deployment
Environment variables
ZERO_VECTOR_BASE_URLZERO_VECTOR_API_KEYZERO_VECTOR_V3_BASE_URLZERO_VECTOR_V3_API_KEYPOSTGRES_URLREDIS_URLOPENAI_API_KEY