This Elasticsearch-based knowledge graph implementation for MCP replaces the previous JSON file-based approach with a scalable, performant solution. It provides distributed storage for entities and relations, advanced search capabilities with fuzzy matching and relevancy ranking, and complete CRUD operations. The system tracks access patterns to prioritize recently viewed and important entities, simulating memory-like behavior where important, recent, and frequently accessed information rises to the top. Built with TypeScript and the Elasticsearch client, it includes tools for migration, management, and maintenance through a comprehensive admin CLI.
아직 리뷰가 없습니다. 첫 번째 리뷰를 작성해 보세요!
대화에 참여하려면 로그인하세요
Create entities with optional observations and reviewInterval.
Update existing entities.
Delete entities (with optional cascade).
Add observations as separate entities with their own freshness.
Confirm entity is still accurate, extend review interval.
Search with progressive freshness filtering.
Get specific entities by name with freshness metadata.
Get recently accessed entities.
Create relationships between entities.
Remove relationships.
AI-powered entity retrieval with tentative answers.
AI-powered file content inspection.
List memory zones (with AI relevance scoring).
Manage memory zones.
Manage memory zones.
Transfer entities between zones.
Transfer entities between zones.
Merge zones with conflict resolution.
Get entity/relation counts for a zone.
Boost entity relevance score.
Get current UTC time.
