Chapter 21: Document and Content Management
Overview
Document and content management systems are natural applications for Vektagraf's combined vector search and graph capabilities. This chapter demonstrates how to build sophisticated content management systems that support semantic search, automatic classification, version control, and fine-grained access control.
Learning Objectives
- Build semantic search systems with embeddings and full-text search
- Implement automatic content classification and tagging
- Design version control and document lifecycle management
- Create secure content access control patterns
- Optimize content discovery and recommendation
Prerequisites
- Completed Chapters 1-6 (Foundations and Core Features)
- Understanding of vector embeddings and similarity search
- Basic knowledge of content management concepts
Core Concepts
Semantic Search Architecture
Vektagraf excels at semantic search by treating document embeddings as first-class object properties:
class Document extends VektaObject {
late String title;
late String content;
late List<double> contentEmbedding; // Semantic vector
late List<double> titleEmbedding; // Title-specific vector
late Map<String, dynamic> metadata;
late List<String> tags;
late String contentType;
}
Content Classification
Automatic classification uses both rule-based and ML-based approaches:
- Vector-Based Classification: Use embeddings to find similar content
- Hierarchical Tagging: Build tag hierarchies with graph relationships
- Content Analysis: Extract entities, topics, and sentiment
- User Behavior: Learn from user interactions and feedback
Practical Examples
Complete Document Management System
Let's build a comprehensive enterprise document management system:
1. Schema Definition
{
"name": "DocumentManagementSystem",
"version": "1.0.0",
"objects": {
"Document": {
"properties": {
"title": {"type": "string", "required": true},
"content": {"type": "text", "searchable": true},
"summary": {"type": "string"},
"contentEmbedding": {
"type": "vector",
"dimensions": 384,
"algorithm": "hnsw",
"distance": "cosine"
},
"titleEmbedding": {
"type": "vector",
"dimensions": 384,
"algorithm": "hnsw",
"distance": "cosine"
},
"metadata": {
"type": "object",
"properties": {
"author": {"type": "string"},
"department": {"type": "string"},
"contentType": {"type": "string"},
"language": {"type": "string", "default": "en"},
"wordCount": {"type": "integer"},
"readingTime": {"type": "integer"},
"confidentialityLevel": {"type": "string", "enum": ["public", "internal", "confidential", "restricted"]}
}
},
"tags": {"type": "array", "items": {"type": "string"}},
"categories": {"type": "array", "items": {"type": "string"}},
"status": {"type": "string", "enum": ["draft", "review", "published", "archived"], "default": "draft"},
"version": {"type": "integer", "default": 1},
"parentDocumentId": {"type": "string"},
"createdAt": {"type": "datetime", "required": true},
"updatedAt": {"type": "datetime", "required": true},
"publishedAt": {"type": "datetime"},
"expiresAt": {"type": "datetime"}
}
},
"DocumentVersion": {
"properties": {
"documentId": {"type": "string", "required": true},
"version": {"type": "integer", "required": true},
"title": {"type": "string", "required": true},
"content": {"type": "text"},
"changes": {"type": "text"},
"changeType": {"type": "string", "enum": ["major", "minor", "patch"]},
"author": {"type": "string", "required": true},
"createdAt": {"type": "datetime", "required": true}
}
},
"Tag": {
"properties": {
"name": {"type": "string", "required": true, "unique": true},
"description": {"type": "string"},
"color": {"type": "string"},
"category": {"type": "string"},
"parentTagId": {"type": "string"},
"usageCount": {"type": "integer", "default": 0},
"createdAt": {"type": "datetime", "required": true}
}
},
"Category": {
"properties": {
"name": {"type": "string", "required": true, "unique": true},
"description": {"type": "string"},
"parentCategoryId": {"type": "string"},
"path": {"type": "string"},
"level": {"type": "integer", "default": 0},
"documentCount": {"type": "integer", "default": 0},
"createdAt": {"type": "datetime", "required": true}
}
},
"ContentAccess": {
"properties": {
"documentId": {"type": "string", "required": true},
"userId": {"type": "string", "required": true},
"accessType": {"type": "string", "enum": ["read", "write", "admin"], "required": true},
"grantedBy": {"type": "string", "required": true},
"grantedAt": {"type": "datetime", "required": true},
"expiresAt": {"type": "datetime"},
"conditions": {"type": "object"}
}
},
"SearchQuery": {
"properties": {
"userId": {"type": "string", "required": true},
"query": {"type": "string", "required": true},
"queryEmbedding": {
"type": "vector",
"dimensions": 384,
"algorithm": "hnsw",
"distance": "cosine"
},
"filters": {"type": "object"},
"results": {"type": "array", "items": {"type": "string"}},
"resultCount": {"type": "integer"},
"executionTime": {"type": "number"},
"timestamp": {"type": "datetime", "required": true}
}
}
},
"relationships": {
"DocumentVersions": {
"from": "Document",
"to": "DocumentVersion",
"type": "one_to_many",
"foreignKey": "documentId"
},
"DocumentAccess": {
"from": "Document",
"to": "ContentAccess",
"type": "one_to_many",
"foreignKey": "documentId"
},
"TagHierarchy": {
"from": "Tag",
"to": "Tag",
"type": "one_to_many",
"foreignKey": "parentTagId"
},
"CategoryHierarchy": {
"from": "Category",
"to": "Category",
"type": "one_to_many",
"foreignKey": "parentCategoryId"
}
}
}
2. Semantic Search Implementation
class SemanticSearchEngine {
final VektaDatabase db;
final EmbeddingService embeddingService;
SemanticSearchEngine(this.db, this.embeddingService);
/// Perform hybrid semantic and full-text search
Future<SearchResults> search(
String query, {
String? userId,
SearchFilters? filters,
int limit = 20,
double semanticWeight = 0.7,
}) async {
// Generate query embedding
final queryEmbedding = await embeddingService.generateEmbedding(query);
// Store search query for analytics
await _storeSearchQuery(query, queryEmbedding, userId, filters);
// Perform semantic search
final semanticResults = await _performSemanticSearch(
queryEmbedding,
filters,
limit * 2,
);
// Perform full-text search
final textResults = await _performFullTextSearch(query, filters, limit * 2);
// Combine and rank results
final combinedResults = await _combineSearchResults(
semanticResults,
textResults,
semanticWeight: semanticWeight,
);
// Apply access control
final accessibleResults = await _filterByAccess(combinedResults, userId);
// Apply business rules and boost factors
final rankedResults = await _applyRankingRules(accessibleResults, query, userId);
return SearchResults(
query: query,
results: rankedResults.take(limit).toList(),
totalCount: rankedResults.length,
executionTime: DateTime.now().millisecondsSinceEpoch,
facets: await _generateFacets(rankedResults),
);
}
/// Semantic search using vector similarity
Future<List<SearchResult>> _performSemanticSearch(
List<double> queryEmbedding,
SearchFilters? filters,
int limit,
) async {
var query = db.documents.vectorSearch(
'contentEmbedding',
queryEmbedding,
limit: limit,
threshold: 0.5,
);
// Apply filters
if (filters != null) {
query = _applyFilters(query, filters);
}
final documents = await query.find();
return documents.map((doc) => SearchResult(
document: doc,
score: _calculateSemanticScore(queryEmbedding, doc.contentEmbedding),
matchType: 'semantic',
highlights: [],
)).toList();
}
/// Full-text search with highlighting
Future<List<SearchResult>> _performFullTextSearch(
String query,
SearchFilters? filters,
int limit,
) async {
var searchQuery = db.documents.search(query, fields: ['title', 'content']);
if (filters != null) {
searchQuery = _applyFilters(searchQuery, filters);
}
final documents = await searchQuery.limit(limit).find();
return documents.map((doc) => SearchResult(
document: doc,
score: _calculateTextScore(query, doc),
matchType: 'full_text',
highlights: _generateHighlights(query, doc),
)).toList();
}
/// Combine semantic and text search results
Future<List<SearchResult>> _combineSearchResults(
List<SearchResult> semanticResults,
List<SearchResult> textResults, {
double semanticWeight = 0.7,
}) async {
final combined = <String, SearchResult>{};
// Add semantic results
for (final result in semanticResults) {
combined[result.document.id] = result.copyWith(
score: result.score * semanticWeight,
);
}
// Add or merge text results
for (final result in textResults) {
final existing = combined[result.document.id];
if (existing != null) {
// Combine scores
combined[result.document.id] = existing.copyWith(
score: existing.score + (result.score * (1 - semanticWeight)),
matchType: 'hybrid',
highlights: [...existing.highlights, ...result.highlights],
);
} else {
combined[result.document.id] = result.copyWith(
score: result.score * (1 - semanticWeight),
);
}
}
// Sort by combined score
final results = combined.values.toList()
..sort((a, b) => b.score.compareTo(a.score));
return results;
}
/// Apply access control filtering
Future<List<SearchResult>> _filterByAccess(
List<SearchResult> results,
String? userId,
) async {
if (userId == null) {
// Return only public documents
return results.where((r) =>
r.document.metadata['confidentialityLevel'] == 'public').toList();
}
final accessibleDocIds = await _getUserAccessibleDocuments(userId);
return results.where((r) =>
accessibleDocIds.contains(r.document.id) ||
r.document.metadata['confidentialityLevel'] == 'public').toList();
}
}
3. Automatic Content Classification
class ContentClassifier {
final VektaDatabase db;
final EmbeddingService embeddingService;
final Map<String, List<double>> _categoryEmbeddings = {};
ContentClassifier(this.db, this.embeddingService);
/// Classify document using multiple approaches
Future<ClassificationResult> classifyDocument(Document document) async {
// Generate content embedding if not exists
if (document.contentEmbedding.isEmpty) {
document.contentEmbedding = await embeddingService.generateEmbedding(
'${document.title}\n${document.content}',
);
}
// Vector-based classification
final vectorCategories = await _classifyByVector(document);
// Rule-based classification
final ruleCategories = await _classifyByRules(document);
// Content analysis classification
final analysisCategories = await _classifyByContentAnalysis(document);
// Combine classifications
final combinedCategories = _combineClassifications([
vectorCategories,
ruleCategories,
analysisCategories,
]);
// Generate tags
final suggestedTags = await _generateTags(document, combinedCategories);
return ClassificationResult(
categories: combinedCategories,
tags: suggestedTags,
confidence: _calculateConfidence(combinedCategories),
method: 'hybrid',
);
}
/// Vector-based classification using similarity
Future<List<CategoryScore>> _classifyByVector(Document document) async {
await _ensureCategoryEmbeddings();
final scores = <CategoryScore>[];
for (final entry in _categoryEmbeddings.entries) {
final similarity = _calculateCosineSimilarity(
document.contentEmbedding,
entry.value,
);
if (similarity > 0.6) {
scores.add(CategoryScore(
category: entry.key,
score: similarity,
method: 'vector',
));
}
}
scores.sort((a, b) => b.score.compareTo(a.score));
return scores.take(5).toList();
}
/// Rule-based classification using keywords and patterns
Future<List<CategoryScore>> _classifyByRules(Document document) async {
final rules = await _getClassificationRules();
final scores = <CategoryScore>[];
final text = '${document.title} ${document.content}'.toLowerCase();
for (final rule in rules) {
double score = 0.0;
int matches = 0;
// Check keywords
for (final keyword in rule.keywords) {
if (text.contains(keyword.toLowerCase())) {
score += keyword.length > 5 ? 0.3 : 0.1; // Longer keywords get higher weight
matches++;
}
}
// Check patterns
for (final pattern in rule.patterns) {
final regex = RegExp(pattern, caseSensitive: false);
final patternMatches = regex.allMatches(text).length;
score += patternMatches * 0.2;
matches += patternMatches;
}
if (score > 0.3) {
scores.add(CategoryScore(
category: rule.category,
score: math.min(score, 1.0),
method: 'rules',
details: {'matches': matches, 'keywords': rule.keywords},
));
}
}
scores.sort((a, b) => b.score.compareTo(a.score));
return scores.take(3).toList();
}
/// Content analysis classification using NLP
Future<List<CategoryScore>> _classifyByContentAnalysis(Document document) async {
final analysis = await _analyzeContent(document.content);
final scores = <CategoryScore>[];
// Entity-based classification
for (final entity in analysis.entities) {
final category = await _mapEntityToCategory(entity);
if (category != null) {
scores.add(CategoryScore(
category: category,
score: entity.confidence * 0.8,
method: 'entity_analysis',
details: {'entity': entity.text, 'type': entity.type},
));
}
}
// Topic-based classification
for (final topic in analysis.topics) {
scores.add(CategoryScore(
category: topic.name,
score: topic.confidence,
method: 'topic_analysis',
details: {'keywords': topic.keywords},
));
}
scores.sort((a, b) => b.score.compareTo(a.score));
return scores.take(3).toList();
}
/// Generate relevant tags for document
Future<List<TagSuggestion>> _generateTags(
Document document,
List<CategoryScore> categories,
) async {
final suggestions = <TagSuggestion>[];
// Extract entities as potential tags
final analysis = await _analyzeContent(document.content);
for (final entity in analysis.entities) {
if (entity.confidence > 0.7) {
suggestions.add(TagSuggestion(
tag: entity.text.toLowerCase(),
confidence: entity.confidence,
source: 'entity_extraction',
));
}
}
// Use category-based tags
for (final category in categories.take(3)) {
final categoryTags = await _getCategoryTags(category.category);
for (final tag in categoryTags) {
suggestions.add(TagSuggestion(
tag: tag,
confidence: category.score * 0.8,
source: 'category_mapping',
));
}
}
// Find similar documents and extract common tags
final similarDocs = await db.documents
.vectorSearch('contentEmbedding', document.contentEmbedding, limit: 10)
.where('id', notEquals: document.id)
.find();
final tagFrequency = <String, int>{};
for (final doc in similarDocs) {
for (final tag in doc.tags) {
tagFrequency[tag] = (tagFrequency[tag] ?? 0) + 1;
}
}
for (final entry in tagFrequency.entries) {
if (entry.value >= 3) { // Tag appears in at least 3 similar documents
suggestions.add(TagSuggestion(
tag: entry.key,
confidence: (entry.value / similarDocs.length) * 0.9,
source: 'similar_documents',
));
}
}
// Remove duplicates and sort by confidence
final uniqueTags = <String, TagSuggestion>{};
for (final suggestion in suggestions) {
final existing = uniqueTags[suggestion.tag];
if (existing == null || suggestion.confidence > existing.confidence) {
uniqueTags[suggestion.tag] = suggestion;
}
}
final result = uniqueTags.values.toList()
..sort((a, b) => b.confidence.compareTo(a.confidence));
return result.take(10).toList();
}
}
4. Version Control and Document Lifecycle
class DocumentVersionManager {
final VektaDatabase db;
DocumentVersionManager(this.db);
/// Create new version of document
Future<DocumentVersion> createVersion(
String documentId,
String newContent, {
String? newTitle,
String? changes,
String changeType = 'minor',
required String author,
}) async {
final document = await db.documents.findById(documentId);
if (document == null) throw Exception('Document not found');
// Create version record for current state
final currentVersion = DocumentVersion()
..documentId = documentId
..version = document.version
..title = document.title
..content = document.content
..changes = changes ?? 'Version ${document.version}'
..changeType = changeType
..author = author
..createdAt = DateTime.now();
await db.documentVersions.save(currentVersion);
// Update document with new content
final oldTitle = document.title;
document.title = newTitle ?? document.title;
document.content = newContent;
document.version = document.version + 1;
document.updatedAt = DateTime.now();
// Regenerate embeddings if content changed significantly
if (_isSignificantChange(document.content, newContent)) {
document.contentEmbedding = await embeddingService.generateEmbedding(newContent);
if (newTitle != null && newTitle != oldTitle) {
document.titleEmbedding = await embeddingService.generateEmbedding(newTitle);
}
// Reclassify document
final classification = await contentClassifier.classifyDocument(document);
document.categories = classification.categories.map((c) => c.category).toList();
document.tags = classification.tags.map((t) => t.tag).toList();
}
await db.documents.save(document);
// Create new version record
final newVersion = DocumentVersion()
..documentId = documentId
..version = document.version
..title = document.title
..content = document.content
..changes = changes ?? 'Updated to version ${document.version}'
..changeType = changeType
..author = author
..createdAt = DateTime.now();
await db.documentVersions.save(newVersion);
return newVersion;
}
/// Get version history for document
Future<List<DocumentVersion>> getVersionHistory(String documentId) async {
return await db.documentVersions
.where('documentId', documentId)
.orderBy('version', descending: true)
.find();
}
/// Restore document to specific version
Future<Document> restoreVersion(
String documentId,
int version,
String restoredBy,
) async {
final targetVersion = await db.documentVersions
.where('documentId', documentId)
.where('version', version)
.findFirst();
if (targetVersion == null) {
throw Exception('Version $version not found');
}
// Create new version with restored content
return await createVersion(
documentId,
targetVersion.content,
newTitle: targetVersion.title,
changes: 'Restored from version $version',
changeType: 'major',
author: restoredBy,
);
}
/// Compare two versions
Future<VersionComparison> compareVersions(
String documentId,
int version1,
int version2,
) async {
final v1 = await db.documentVersions
.where('documentId', documentId)
.where('version', version1)
.findFirst();
final v2 = await db.documentVersions
.where('documentId', documentId)
.where('version', version2)
.findFirst();
if (v1 == null || v2 == null) {
throw Exception('One or both versions not found');
}
return VersionComparison(
version1: v1,
version2: v2,
titleDiff: _generateDiff(v1.title, v2.title),
contentDiff: _generateDiff(v1.content, v2.content),
similarity: _calculateSimilarity(v1.content, v2.content),
);
}
bool _isSignificantChange(String oldContent, String newContent) {
final similarity = _calculateSimilarity(oldContent, newContent);
return similarity < 0.8; // Less than 80% similar
}
}
5. Content Security and Access Control
class ContentSecurityManager {
final VektaDatabase db;
ContentSecurityManager(this.db);
/// Check if user has access to document
Future<bool> hasAccess(
String userId,
String documentId,
String accessType,
) async {
final document = await db.documents.findById(documentId);
if (document == null) return false;
// Check public access
if (document.metadata['confidentialityLevel'] == 'public' &&
accessType == 'read') {
return true;
}
// Check explicit access grants
final access = await db.contentAccess
.where('documentId', documentId)
.where('userId', userId)
.where('accessType', accessType)
.where('expiresAt', greaterThan: DateTime.now())
.findFirst();
if (access != null) {
return _checkAccessConditions(access, userId);
}
// Check role-based access
return await _checkRoleBasedAccess(userId, document, accessType);
}
/// Grant access to document
Future<void> grantAccess(
String documentId,
String userId,
String accessType,
String grantedBy, {
DateTime? expiresAt,
Map<String, dynamic>? conditions,
}) async {
final access = ContentAccess()
..documentId = documentId
..userId = userId
..accessType = accessType
..grantedBy = grantedBy
..grantedAt = DateTime.now()
..expiresAt = expiresAt
..conditions = conditions ?? {};
await db.contentAccess.save(access);
// Log access grant
await _logSecurityEvent('access_granted', {
'documentId': documentId,
'userId': userId,
'accessType': accessType,
'grantedBy': grantedBy,
});
}
/// Revoke access to document
Future<void> revokeAccess(
String documentId,
String userId,
String accessType,
String revokedBy,
) async {
await db.contentAccess
.where('documentId', documentId)
.where('userId', userId)
.where('accessType', accessType)
.delete();
// Log access revocation
await _logSecurityEvent('access_revoked', {
'documentId': documentId,
'userId': userId,
'accessType': accessType,
'revokedBy': revokedBy,
});
}
/// Apply content redaction based on user access level
Future<Document> applyRedaction(Document document, String userId) async {
final userClearanceLevel = await _getUserClearanceLevel(userId);
final docConfidentiality = document.metadata['confidentialityLevel'];
if (_canAccessFullContent(userClearanceLevel, docConfidentiality)) {
return document;
}
// Create redacted copy
final redacted = document.copy();
// Redact sensitive content
redacted.content = await _redactSensitiveContent(
document.content,
userClearanceLevel,
);
// Remove sensitive metadata
redacted.metadata = _filterMetadata(document.metadata, userClearanceLevel);
return redacted;
}
Future<String> _redactSensitiveContent(
String content,
String clearanceLevel,
) async {
// Use pattern matching to identify and redact sensitive information
final patterns = await _getSensitivityPatterns(clearanceLevel);
String redactedContent = content;
for (final pattern in patterns) {
redactedContent = redactedContent.replaceAll(
RegExp(pattern.regex),
pattern.replacement,
);
}
return redactedContent;
}
}
Best Practices
1. Embedding Quality and Maintenance
class EmbeddingQualityManager {
/// Ensure embedding quality through validation and updates
Future<void> maintainEmbeddingQuality() async {
// Find documents with outdated embeddings
final outdatedDocs = await db.documents
.where('updatedAt', greaterThan:
DateTime.now().subtract(Duration(days: 30)))
.where('contentEmbedding', isEmpty: true)
.find();
for (final doc in outdatedDocs) {
// Regenerate embeddings
doc.contentEmbedding = await embeddingService.generateEmbedding(
'${doc.title}\n${doc.content}',
);
doc.titleEmbedding = await embeddingService.generateEmbedding(doc.title);
await db.documents.save(doc);
}
// Update category embeddings
await _updateCategoryEmbeddings();
}
/// Validate embedding consistency
Future<bool> validateEmbeddings() async {
final documents = await db.documents.limit(100).find();
for (final doc in documents) {
// Check embedding dimensions
if (doc.contentEmbedding.length != 384) {
print('Invalid embedding dimensions for document ${doc.id}');
return false;
}
// Check for NaN values
if (doc.contentEmbedding.any((v) => v.isNaN)) {
print('NaN values in embedding for document ${doc.id}');
return false;
}
// Check embedding magnitude
final magnitude = math.sqrt(
doc.contentEmbedding.map((v) => v * v).reduce((a, b) => a + b),
);
if (magnitude < 0.1 || magnitude > 10.0) {
print('Unusual embedding magnitude for document ${doc.id}: $magnitude');
}
}
return true;
}
}
2. Search Performance Optimization
class SearchOptimizer {
final Map<String, SearchResults> _searchCache = {};
final Duration _cacheExpiry = Duration(minutes: 15);
/// Cache frequently used search results
Future<SearchResults> getCachedSearch(
String query,
SearchFilters? filters,
String? userId,
) async {
final cacheKey = _generateCacheKey(query, filters, userId);
if (_searchCache.containsKey(cacheKey)) {
final cached = _searchCache[cacheKey]!;
if (DateTime.now().difference(cached.timestamp) < _cacheExpiry) {
return cached;
}
}
// Perform fresh search
final results = await searchEngine.search(query,
userId: userId, filters: filters);
_searchCache[cacheKey] = results;
return results;
}
/// Optimize search indexes
Future<void> optimizeIndexes() async {
// Rebuild vector indexes for better performance
await db.documents.rebuildVectorIndex('contentEmbedding');
await db.documents.rebuildVectorIndex('titleEmbedding');
// Update full-text search indexes
await db.documents.rebuildTextIndex(['title', 'content']);
// Clean up old search queries
await db.searchQueries
.where('timestamp', lessThan:
DateTime.now().subtract(Duration(days: 30)))
.delete();
}
}
Advanced Topics
Content Recommendation Engine
class ContentRecommendationEngine {
/// Recommend related documents
Future<List<Document>> getRelatedDocuments(
String documentId, {
int limit = 10,
String? userId,
}) async {
final document = await db.documents.findById(documentId);
if (document == null) return [];
// Find similar documents using content embedding
final similar = await db.documents
.vectorSearch('contentEmbedding', document.contentEmbedding, limit: limit * 2)
.where('id', notEquals: documentId)
.find();
// Apply access control
final accessible = <Document>[];
for (final doc in similar) {
if (await securityManager.hasAccess(userId ?? '', doc.id, 'read')) {
accessible.add(doc);
}
}
return accessible.take(limit).toList();
}
/// Recommend documents based on user reading history
Future<List<Document>> getPersonalizedRecommendations(
String userId, {
int limit = 10,
}) async {
// Get user's reading history
final searchHistory = await db.searchQueries
.where('userId', userId)
.orderBy('timestamp', descending: true)
.limit(50)
.find();
if (searchHistory.isEmpty) {
return _getPopularDocuments(limit);
}
// Calculate user interest vector
final interestVector = _calculateUserInterestVector(searchHistory);
// Find documents matching user interests
final recommendations = await db.documents
.vectorSearch('contentEmbedding', interestVector, limit: limit * 2)
.find();
// Filter by access and remove already seen
final seenDocIds = searchHistory
.expand((q) => q.results)
.toSet();
final filtered = <Document>[];
for (final doc in recommendations) {
if (!seenDocIds.contains(doc.id) &&
await securityManager.hasAccess(userId, doc.id, 'read')) {
filtered.add(doc);
}
}
return filtered.take(limit).toList();
}
}
Summary
This chapter demonstrated how to build comprehensive document and content management systems using Vektagraf's semantic search and security capabilities. Key takeaways include:
- Semantic Search: Combine vector similarity with full-text search for better results
- Automatic Classification: Use multiple approaches for accurate content categorization
- Version Control: Implement comprehensive document lifecycle management
- Security: Apply fine-grained access control and content redaction
- Performance: Optimize search with caching and index management
- Recommendations: Provide personalized content discovery
Vektagraf's vector-first approach makes it particularly well-suited for content management, as document embeddings are treated as first-class properties alongside traditional metadata.
Next Steps
Related Resources
- Vector Search Documentation (Chapter 5)
- Security and Access Control (Chapters 8-10)
- Performance Optimization (Chapter 7)
- Multi-Tenant Architecture (Chapter 11)