Content Management System Integration
Learn how to integrate uubed encoding into content management systems for efficient content similarity and search.
Overview
Content Management Systems (CMS) can leverage uubed for:
- Content Similarity: Find related articles and pages
- Duplicate Detection: Identify duplicate or near-duplicate content
- Content Search: Semantic search across large content repositories
- Recommendation Systems: Suggest related content to users
Common CMS Integrations
WordPress
<?php
// WordPress plugin integration example
class UubedContentAnalyzer {
public function encode_post_content($post_id) {
$content = get_post_field('post_content', $post_id);
$embedding = $this->extract_embedding($content);
// Use Python subprocess to encode with uubed
$encoded = $this->call_uubed_encode($embedding, 'shq64');
// Store encoded representation as post meta
update_post_meta($post_id, 'uubed_encoding', $encoded);
return $encoded;
}
public function find_similar_posts($post_id, $limit = 5) {
$target_encoding = get_post_meta($post_id, 'uubed_encoding', true);
// Query posts with similar encodings
$similar_posts = $this->similarity_search($target_encoding, $limit);
return $similar_posts;
}
}
?>
Drupal
<?php
// Drupal module implementation
function uubed_content_node_insert($node) {
if ($node->getType() == 'article') {
$content = $node->get('body')->value;
$embedding = uubed_extract_embedding($content);
$encoded = uubed_encode($embedding, 'eq64');
// Store in custom field
$node->set('field_uubed_encoding', $encoded);
$node->save();
}
}
function uubed_content_get_related($node_id, $count = 3) {
$node = Node::load($node_id);
$encoding = $node->get('field_uubed_encoding')->value;
return uubed_similarity_search($encoding, $count);
}
?>
Django CMS
# Django models and views
from django.db import models
from uubed import encode, decode
import numpy as np
class ContentPage(models.Model):
title = models.CharField(max_length=200)
content = models.TextField()
uubed_encoding = models.TextField(blank=True)
def save(self, *args, **kwargs):
if self.content:
# Extract embedding from content
embedding = self.extract_embedding()
self.uubed_encoding = encode(embedding, method="shq64")
super().save(*args, **kwargs)
def extract_embedding(self):
# Use your preferred embedding method (BERT, etc.)
from sentence_transformers import SentenceTransformer
model = SentenceTransformer('all-MiniLM-L6-v2')
return model.encode(self.content)
def find_similar(self, limit=5):
# Find pages with similar encodings
return ContentPage.objects.filter(
# Custom similarity query based on encoded representations
).exclude(id=self.id)[:limit]
Implementation Patterns
Content Pipeline
# Content processing pipeline
class ContentProcessor:
def __init__(self, encoding_method="shq64"):
self.encoding_method = encoding_method
self.embedder = SentenceTransformer('all-MiniLM-L6-v2')
def process_content(self, content_item):
# Extract text content
text = self.extract_text(content_item)
# Generate embedding
embedding = self.embedder.encode(text)
# Encode with uubed
encoded = encode(embedding, method=self.encoding_method)
# Store in content metadata
content_item.metadata['uubed_encoding'] = encoded
return content_item
def batch_process(self, content_list):
# Efficient batch processing
texts = [self.extract_text(item) for item in content_list]
embeddings = self.embedder.encode(texts)
from uubed.streaming import batch_encode
encoded_batch = batch_encode(embeddings, method=self.encoding_method)
for item, encoded in zip(content_list, encoded_batch):
item.metadata['uubed_encoding'] = encoded
return content_list
Similarity Search
# Content similarity service
class ContentSimilarityService:
def __init__(self, content_repository):
self.repository = content_repository
def find_similar_content(self, target_content, similarity_threshold=0.8):
target_encoding = target_content.metadata.get('uubed_encoding')
if not target_encoding:
return []
similar_items = []
for content in self.repository.all():
content_encoding = content.metadata.get('uubed_encoding')
if content_encoding and content != target_content:
similarity = self.calculate_similarity(
target_encoding,
content_encoding
)
if similarity >= similarity_threshold:
similar_items.append((content, similarity))
# Sort by similarity score
similar_items.sort(key=lambda x: x[1], reverse=True)
return similar_items
Use Cases
Content Recommendation
# Recommend related articles
def get_content_recommendations(user_id, current_article_id):
current_article = get_article(current_article_id)
user_preferences = get_user_preferences(user_id)
# Find similar content
similar_articles = find_similar_content(
current_article.uubed_encoding,
limit=10
)
# Filter based on user preferences
recommendations = filter_by_preferences(
similar_articles,
user_preferences
)
return recommendations[:5]
Duplicate Detection
# Detect duplicate or near-duplicate content
def detect_duplicates(content_repository, similarity_threshold=0.95):
duplicates = []
for i, content_a in enumerate(content_repository):
for content_b in content_repository[i+1:]:
similarity = calculate_similarity(
content_a.uubed_encoding,
content_b.uubed_encoding
)
if similarity >= similarity_threshold:
duplicates.append((content_a, content_b, similarity))
return duplicates
Performance Optimization
Caching Strategy
# Implement caching for frequently accessed encodings
from django.core.cache import cache
class CachedEncodingService:
def get_encoding(self, content_id):
cache_key = f"uubed_encoding_{content_id}"
encoding = cache.get(cache_key)
if encoding is None:
content = get_content(content_id)
encoding = content.uubed_encoding
cache.set(cache_key, encoding, timeout=3600)
return encoding
Database Indexing
-- Create indexes for efficient similarity queries
CREATE INDEX idx_content_encoding ON content_pages(uubed_encoding);
CREATE INDEX idx_content_type_encoding ON content_pages(content_type, uubed_encoding);
Best Practices
- Batch Processing: Process content in batches for better performance
- Incremental Updates: Only re-encode content when it changes significantly
- Method Selection: Choose encoding method based on content type and use case
- Caching: Cache frequently accessed encodings to reduce computation
- Monitoring: Track encoding performance and similarity accuracy
Integration Checklist
- Set up content embedding pipeline
- Implement uubed encoding for new content
- Create similarity search functionality
- Add content recommendation features
- Set up duplicate detection
- Implement caching strategy
- Monitor performance metrics