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Handles context window constraints, result ranking, deduplication of overlapping chunks, and optional reranking to maximize relevance while staying within token budgets. 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Maintains version metadata (timestamp, document count, embedding model used) and supports selective rollback of specific documents or entire snapshots without rebuilding embeddings from scratch.","intents":["I want to revert to a previous version of my knowledge base if new documents introduce errors","I need to track which embedding model was used for each document version","I want to maintain multiple knowledge base versions for A/B testing or gradual rollouts"],"best_for":["production RAG systems requiring reliability and auditability","teams managing knowledge bases with frequent updates","applications needing version control for embedded documents"],"limitations":["versioning requires persistent storage of vector snapshots; storage costs scale with version count","rollback is metadata-based; actual vector store rollback depends on backend capabilities","no built-in conflict resolution for concurrent updates to the same documents"],"requires":["Node.js 14+","persistent storage for version metadata","vector store supporting snapshot or point-in-time recovery"],"input_types":["document updates","version tags or timestamps","rollback targets"],"output_types":["version history with metadata","rollback confirmations","version comparison reports"],"categories":["automation-workflow","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-kb-labs-mind-engine__cap_8","uri":"capability://memory.knowledge.multi.language.embedding.support","name":"multi-language embedding support","description":"Handles embedding and retrieval across documents in multiple languages using language-aware embedding models and optional translation strategies. Automatically detects document language, selects appropriate embedding models, and enables cross-language semantic search through multilingual embedding spaces or translation-based approaches.","intents":["I want to build a RAG system that works with documents in multiple languages","I need to search across documents in different languages and find semantically similar content","I want to support user queries in one language against documents in another"],"best_for":["global applications serving users in multiple languages","knowledge bases with multilingual content","international teams requiring cross-language semantic search"],"limitations":["multilingual embedding models have lower quality than language-specific models","language detection is imperfect for code-mixed or transliterated text","cross-language search quality degrades for low-resource languages"],"requires":["Node.js 14+","multilingual embedding model (e.g., multilingual-e5, mBERT)","optional: language detection library"],"input_types":["documents in multiple languages","queries in any supported language","language hints or metadata"],"output_types":["language-tagged embeddings","cross-language search results","language detection confidence scores"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-kb-labs-mind-engine__cap_9","uri":"capability://data.processing.analysis.embedding.model.evaluation.and.benchmarking","name":"embedding model evaluation and benchmarking","description":"Provides tools to evaluate embedding model quality on custom datasets through metrics like retrieval precision, recall, NDCG, and MRR. 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