Knowledge Base Manager

Overview

Provides a structured methodology for selecting, designing, and governing knowledge bases. Covers architecture decisions (document-based vs entity-based vs hybrid), content curation, quality metrics, versioning strategies, and maintenance governance. Use when choosing a KB architecture, establishing curation workflows, or building governance processes for organizational knowledge.

When NOT to use: Static documentation suffices, fewer than 50 FAQ items cover all questions, or no maintenance resources are available. For implementing retrieval pipelines (chunking, embeddings, vector stores), use the rag-implementer skill. For implementing knowledge graphs (ontology, entity extraction, graph databases), use the knowledge-graph-builder skill.

Quick Reference

Aspect	Options	Key Considerations
Architecture	Document-based (RAG), Entity-based (Graph), Hybrid	Match to query patterns; start simple, add complexity when needed
Document-based	Vector DB (Pinecone, Weaviate, pgvector)	Best for docs, FAQs, manuals; semantic search; easy to add content
Entity-based	Graph DB (Neo4j, ArangoDB)	Best for org charts, catalogs, networks; relationship traversal
Hybrid	Both + linking layer	Enterprise, medical, legal; combined queries; highest complexity
When to skip KB	Static docs, <50 FAQ items	No maintenance resources, information never changes
Implementation	6 phases	Audit, Curation, Storage, Quality, Versioning, Governance
Accuracy target	>90% on test questions	Create 100+ test questions with known correct answers
Coverage target	>80% questions answerable	Validate against real user queries continuously
Freshness target	<30 days average age	Automated freshness monitoring + scheduled updates
Consistency target	>95% conflict-free	Deduplication + single source of truth
Query latency	<100ms median	Caching and optimization for common access patterns
Storage tech	pgvector, Pinecone, Weaviate, Chroma	pgvector for existing Postgres; Pinecone for managed scale
Index types	HNSW, IVFFlat	HNSW for recall; IVFFlat for frequently rebuilt indexes
Ingestion pipeline	Load, clean, chunk, embed, store	Chunk at semantic boundaries; 512 tokens max; 10-15% overlap
Deduplication	Content hashing, semantic similarity	Hash for exact dupes; cosine similarity >0.95 for semantic dupes
Quality testing	Recall@K, MRR, accuracy sampling	100+ test questions; measure recall@10 >0.8 and MRR >0.7
Drift detection	Embedding distribution monitoring	Track mean shift; alert when >0.1 threshold
Versioning	Snapshot, Event-sourced, Git-style	Snapshot for simple; event-sourced for audit; git-style for teams
Maintenance	Daily, Weekly, Monthly, Quarterly	Establish schedule from day 1; monitor errors and user feedback

Common Mistakes

Mistake	Correct Pattern
Ingesting raw data without curation or normalization	Curate, clean, and deduplicate before ingesting; quality over quantity
Skipping version control for KB content	Implement versioning from day one with rollback and audit trail
Building a KB without validating against user questions	Start with user research and test against real queries for >90% accuracy
Choosing hybrid architecture when document-based suffices	Match architecture to actual query patterns; start simple, add complexity when needed
Launching without freshness monitoring or update schedules	Set up automated freshness checks and scheduled content reviews
No provenance tracking on knowledge entries	Always track source URL, timestamp, author, and confidence score
Duplicate information across sources	Establish single source of truth; merge similar entries with conflict resolution rules
Perfectionism delaying launch	Launch at 80% coverage and iterate based on real usage data

Delegation

Audit existing knowledge sources and classify content types: Use Explore agent to inventory documents, assess quality, and identify gaps
Implement end-to-end KB pipeline with storage and retrieval: Use Task agent to deploy database, configure search, and run quality checks
Design KB architecture and governance model: Use Plan agent to select between document-based, entity-based, or hybrid approaches

For implementing document retrieval pipelines (chunking, embeddings, vector stores, hybrid search), use the rag-implementer skill. For implementing knowledge graphs (ontology design, entity extraction, graph databases), use the knowledge-graph-builder skill.

References