Knowledge Base Manager

What this skill does

# Knowledge Base Manager

Build and maintain high-quality knowledge bases for AI systems and human consumption.

## Core Principle

**Knowledge Base = Structured Information + Quality Curation + Accessibility**

A knowledge base is not just a data dump—it's curated, validated, versioned information designed to answer questions and enable reasoning.

---

## When to Use Knowledge Bases

### Use Knowledge Bases When:

- ✅ Need to answer factual questions consistently
- ✅ Information changes frequently and needs version control
- ✅ Multiple sources need to be unified and reconciled
- ✅ Provenance and citation tracking is critical
- ✅ Building AI systems that need grounded, verifiable information
- ✅ Organizational knowledge needs to be preserved and searchable
- ✅ Complex domain with interconnected concepts

### Don't Use Knowledge Bases When:

- ❌ Static documentation is sufficient (use docs + search)
- ❌ No one will maintain/update it (knowledge rot guaranteed)
- ❌ Simple FAQ covers all questions (<50 items)
- ❌ Information doesn't change (static site faster/cheaper)
- ❌ Team lacks resources for curation

---

## Knowledge Base Types: Decision Framework

### 1. Document-Based Knowledge Base (RAG)

**What it is:** Collection of documents, chunked and embedded for semantic search

**Best for:**

- Technical documentation
- Support articles, FAQs
- Policy documents
- Research papers
- Blog content
- User manuals

**Strengths:**

- Easy to add new documents
- Preserves full context
- Natural for text-heavy content

**Weaknesses:**

- Hard to query relationships ("Who works where?")
- Duplicate information across documents
- Difficult to keep facts consistent

**Use:** `rag-implementer` skill + `vector-database-mcp`

---

### 2. Entity-Based Knowledge Base (Knowledge Graph)

**What it is:** Network of entities (people, places, things) connected by relationships

**Best for:**

- Organizational charts
- Product catalogs with relationships
- Social networks
- Recommendation systems
- Fraud detection
- Supply chain tracking

**Strengths:**

- Excellent for "how are X and Y related?" queries
- Consistent facts (one source of truth)
- Powerful traversal ("friends of friends")

**Weaknesses:**

- Upfront modeling required (ontology design)
- Harder to add unstructured information
- Learning curve for graph queries

**Use:** `knowledge-graph-builder` skill + `graph-database-mcp`

---

### 3. Hybrid Knowledge Base (RAG + Graph)

**What it is:** Documents for unstructured knowledge + Graph for structured entities/relationships

**Best for:**

- Enterprise knowledge management
- Research with citations and relationships
- Medical systems (documents + patient/drug relationships)
- Legal systems (cases + precedents + entities)
- E-commerce (products + specs + relationships)

**Strengths:**

- Best of both worlds
- Flexible for different knowledge types
- Rich querying capabilities

**Weaknesses:**

- Most complex to build and maintain
- Requires expertise in both RAG and graphs
- Higher infrastructure costs

**Use:** Both `rag-implementer` + `knowledge-graph-builder` skills

---

## Decision Tree: Which KB Type?

```
What kind of knowledge do you have?

├─ Mostly unstructured text (docs, articles, content)?
│  └─ Document-Based KB (RAG)
│     Use: rag-implementer skill
│
├─ Mostly structured entities with relationships?
│  └─ Entity-Based KB (Graph)
│     Use: knowledge-graph-builder skill
│
└─ Mix of both?
   └─ Hybrid KB (RAG + Graph)
      Use: Both skills + This skill for integration
```

---

## 6-Phase Knowledge Base Implementation

### Phase 1: Knowledge Audit & Architecture

**Goal**: Understand what knowledge exists and how to structure it

**Actions**:

1. **Inventory existing knowledge sources**
   - Internal: databases, documents, wikis, Slack, emails
   - External: public data, APIs, third-party sources
   - Tribal: SME interviews, recorded conversations

2. **Classify knowledge types**
   - **Factual**: Verifiable facts ("Product X costs $50")
   - **Procedural**: How-to knowledge ("How to deploy")
   - **Conceptual**: Definitions and explanations
   - **Relationship**: Connections between entities

3. **Choose KB architecture**
   - Document-based? Entity-based? Hybrid?
   - Decision: Use framework above

4. **Define knowledge schema**
   - For documents: metadata fields (source, date, author, category)
   - For entities: ontology (entity types, relationship types, properties)

**Validation**:

- [ ] All knowledge sources inventoried and prioritized
- [ ] KB architecture chosen and justified
- [ ] Schema defined and validated with users
- [ ] Success metrics established

---

### Phase 2: Knowledge Curation & Ingestion

**Goal**: Transform raw information into high-quality knowledge

**Actions**:

1. **Extract knowledge from sources**
   - Automated: scraping, API ingestion, file parsing
   - Manual: expert input, annotation, validation

2. **Clean and normalize**
   - Remove duplicates
   - Standardize formats
   - Fix inconsistencies
   - Enrich with metadata

3. **Structure knowledge**
   - For documents: chunk intelligently (semantic boundaries)
   - For entities: extract entities, relationships, properties

4. **Add provenance**
   - Source URL or reference
   - Last updated timestamp
   - Author/contributor
   - Confidence score (if applicable)

**Curation Best Practices**:

- **Single Source of Truth**: One canonical answer per question
- **Deduplication**: Merge similar knowledge entries
- **Conflict Resolution**: When sources disagree, establish priority rules
- **Metadata Richness**: More metadata = better filtering and search

**Validation**:

- [ ] Knowledge extracted and structured
- [ ] Quality metrics above threshold (accuracy >95%)
- [ ] Provenance tracked for all entries
- [ ] Sample queries return relevant results

---

### Phase 3: Storage & Retrieval Setup

**Goal**: Implement technical infrastructure for knowledge access

**Architecture Patterns**:

**For Document-Based KB:**

```typescript
// Vector database for semantic search
interface DocumentKB {
  store: 'Pinecone' | 'Weaviate' | 'pgvector'
  chunks: {
    content: string
    embedding: number[]
    metadata: {
      source: string
      title: string
      updated_at: string
      category: string
    }
  }[]
}
```

**For Entity-Based KB:**

```typescript
// Graph database for relationship queries
interface EntityKB {
  store: 'Neo4j' | 'ArangoDB'
  nodes: {
    id: string
    type: 'Person' | 'Organization' | 'Product' | 'Concept'
    properties: Record<string, any>
  }[]
  relationships: {
    from: string
    to: string
    type: string
    properties: Record<string, any>
  }[]
}
```

**For Hybrid KB:**

```typescript
// Both vector DB + graph DB
interface HybridKB {
  vectorDB: DocumentKB
  graphDB: EntityKB
  linker: {
    // Links documents to entities mentioned in them
    linkDocumentToEntities(docId: string): string[]
    // Links entities to documents that mention them
    linkEntityToDocuments(entityId: string): string[]
  }
}
```

**Actions**:

1. **Choose database(s)**
   - Document: Pinecone, Weaviate, pgvector
   - Entity: Neo4j, ArangoDB
   - Hybrid: Both + linking layer

2. **Implement search/query layer**
   - Vector similarity search (for documents)
   - Graph traversal (for entities)
   - Hybrid queries (combining both)

3. **Add caching and optimization**
   - Cache frequent queries
   - Optimize for common access patterns

**Validation**:

- [ ] Database deployed and accessible
- [ ] Search/query functionality working
- [ ] Performance meets requirements (<100ms for most queries)

---

### Phase 4: Quality Control & Validation

**Goal**: Ensure knowledge base accuracy and reliability

**Quality Metrics**:

1. **Accuracy**: % of correct answers to test questions
2. **Coverage**: % of user questions answerable
3. **Freshness**: Average age of knowledge
4. **Consistency**: % of conflicts/contradictions
5. **Source Quality**: % from authoritative sources