Add comprehensive filter pipeline documentation
Documentation includes: - Architecture overview (3-level caching) - Pipeline stages description - Configuration guide - Usage examples (user & developer) - AI integration setup - Performance benchmarks - Monitoring and troubleshooting - Plugin system guide - Built-in filtersets documentation 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
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FILTER_PIPELINE.md
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FILTER_PIPELINE.md
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# Filter Pipeline Documentation
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## Overview
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BalanceBoard's Filter Pipeline is a plugin-based content filtering system that provides intelligent categorization, moderation, and ranking of posts using AI-powered analysis with aggressive caching for cost efficiency.
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## Architecture
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### Three-Level Caching System
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1. **Level 1: Memory Cache** (5-minute TTL)
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- In-memory cache for fast repeated access
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- Cleared on application restart
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2. **Level 2: AI Analysis Cache** (Permanent, content-hash based)
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- Stores AI results (categorization, moderation, quality scores)
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- Keyed by SHA-256 hash of content
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- Never expires - same content always returns cached results
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- **Huge cost savings**: Never re-analyze the same content
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3. **Level 3: Filterset Results Cache** (24-hour TTL)
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- Stores final filter results per filterset
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- Invalidated when filterset definition changes
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- Enables instant filterset switching
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### Pipeline Stages
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Posts flow through 4 sequential stages:
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```
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Raw Post
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↓
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1. Categorizer (AI: topic detection, tags)
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↓
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2. Moderator (AI: safety, quality, sentiment)
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↓
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3. Filter (Rules: apply filterset conditions)
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↓
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4. Ranker (Score: quality + recency + source + engagement)
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↓
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Filtered & Scored Post
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```
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#### Stage 1: Categorizer
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- **Purpose**: Detect topics and assign categories
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- **AI Model**: Llama 70B (cheap model)
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- **Caching**: Permanent (by content hash)
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- **Output**: Categories, category scores, tags
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#### Stage 2: Moderator
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- **Purpose**: Safety and quality analysis
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- **AI Model**: Llama 70B (cheap model)
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- **Caching**: Permanent (by content hash)
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- **Metrics**:
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- Violence score (0.0-1.0)
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- Sexual content score (0.0-1.0)
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- Hate speech score (0.0-1.0)
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- Harassment score (0.0-1.0)
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- Quality score (0.0-1.0)
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- Sentiment (positive/neutral/negative)
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#### Stage 3: Filter
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- **Purpose**: Apply filterset rules
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- **AI**: None (fast rule evaluation)
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- **Rules Supported**:
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- `equals`, `not_equals`
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- `in`, `not_in`
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- `min`, `max`
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- `includes_any`, `excludes`
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#### Stage 4: Ranker
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- **Purpose**: Calculate relevance scores
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- **Scoring Factors**:
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- Quality (30%): From Moderator stage
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- Recency (25%): Age-based decay
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- Source Tier (25%): Platform reputation
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- Engagement (20%): Upvotes + comments
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## Configuration
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### filter_config.json
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```json
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{
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"ai": {
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"enabled": false,
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"openrouter_key_file": "openrouter_key.txt",
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"models": {
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"cheap": "meta-llama/llama-3.3-70b-instruct",
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"smart": "meta-llama/llama-3.3-70b-instruct"
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},
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"parallel_workers": 10,
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"timeout_seconds": 60
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},
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"cache": {
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"enabled": true,
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"ai_cache_dir": "data/filter_cache",
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"filterset_cache_ttl_hours": 24
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},
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"pipeline": {
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"default_stages": ["categorizer", "moderator", "filter", "ranker"],
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"batch_size": 50,
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"enable_parallel": true
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}
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}
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```
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### filtersets.json
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Each filterset defines filtering rules:
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```json
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{
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"safe_content": {
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"description": "Filter for safe, family-friendly content",
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"post_rules": {
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"moderation.flags.is_safe": {"equals": true},
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"moderation.content_safety.violence": {"max": 0.3},
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"moderation.content_safety.sexual_content": {"max": 0.2},
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"moderation.content_safety.hate_speech": {"max": 0.1}
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},
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"comment_rules": {
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"moderation.flags.is_safe": {"equals": true}
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}
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}
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}
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```
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## Usage
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### User Perspective
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1. Navigate to **Settings → Filters**
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2. Select a filterset from the dropdown
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3. Save preferences
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4. Feed automatically applies your filterset
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5. Posts sorted by relevance score (highest first)
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### Developer Perspective
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```python
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from filter_pipeline import FilterEngine
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# Get singleton instance
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engine = FilterEngine.get_instance()
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# Apply filterset to posts
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filtered_posts = engine.apply_filterset(
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posts=raw_posts,
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filterset_name='safe_content',
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use_cache=True
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)
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# Access filter metadata
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for post in filtered_posts:
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score = post['_filter_score'] # 0.0-1.0
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categories = post['_filter_categories'] # ['technology', 'programming']
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tags = post['_filter_tags'] # ['reddit', 'python']
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```
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## AI Integration
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### Enabling AI
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1. **Get OpenRouter API Key**:
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- Sign up at https://openrouter.ai
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- Generate API key
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2. **Configure**:
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```bash
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echo "your-api-key-here" > openrouter_key.txt
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```
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3. **Enable in config**:
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```json
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{
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"ai": {
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"enabled": true
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}
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}
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```
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4. **Restart application**
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### Cost Efficiency
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- **Model**: Llama 70B only (~$0.0003/1K tokens)
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- **Caching**: Permanent AI result cache
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- **Estimate**: ~$0.001 per post (first time), $0 (cached)
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- **10,000 posts**: ~$10 first time, ~$0 cached
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## Performance
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### Benchmarks
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- **With Cache Hit**: < 10ms per post
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- **With Cache Miss (AI)**: ~500ms per post
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- **Parallel Processing**: 10 workers (configurable)
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- **Typical Feed Load**: 100 posts in < 1 second (cached)
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### Cache Hit Rates
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After initial processing:
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- **AI Cache**: ~95% hit rate (content rarely changes)
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- **Filterset Cache**: ~80% hit rate (depends on TTL)
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- **Memory Cache**: ~60% hit rate (5min TTL)
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## Monitoring
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### Cache Statistics
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```python
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stats = filter_engine.get_cache_stats()
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# {
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# 'memory_cache_size': 150,
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# 'ai_cache_size': 5000,
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# 'filterset_cache_size': 8,
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# 'ai_cache_dir': '/app/data/filter_cache',
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# 'filterset_cache_dir': '/app/data/filter_cache/filtersets'
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# }
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```
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### Logs
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Filter pipeline logs to `app.log`:
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```
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INFO - FilterEngine initialized with 5 filtersets
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DEBUG - Categorizer: Cache hit for a3f5c2e8...
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DEBUG - Moderator: Analyzed b7d9e1f3... (quality: 0.75)
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DEBUG - Filter: Post passed filterset 'safe_content'
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DEBUG - Ranker: Post score=0.82 (q:0.75, r:0.90, s:0.70, e:0.85)
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```
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## Filtersets
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### no_filter
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- **Description**: No filtering - all content passes
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- **Use Case**: Default, unfiltered feed
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- **Rules**: None
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- **AI**: Disabled
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### safe_content
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- **Description**: Family-friendly content only
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- **Use Case**: Safe browsing
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- **Rules**:
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- Violence < 0.3
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- Sexual content < 0.2
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- Hate speech < 0.1
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- **AI**: Required
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### tech_only
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- **Description**: Technology and programming content
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- **Use Case**: Tech professionals
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- **Rules**:
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- Platform: hackernews, reddit, lobsters, stackoverflow
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- Topics: technology, programming, software (confidence > 0.5)
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- **AI**: Required
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### high_quality
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- **Description**: High quality posts only
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- **Use Case**: Curated feed
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- **Rules**:
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- Score ≥ 10
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- Quality ≥ 0.6
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- Readability grade ≤ 14
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- **AI**: Required
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## Plugin System
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### Creating Custom Plugins
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```python
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from filter_pipeline.plugins import BaseFilterPlugin
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class MyCustomPlugin(BaseFilterPlugin):
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def get_name(self) -> str:
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return "MyCustomFilter"
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def should_filter(self, post: dict, context: dict = None) -> bool:
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# Return True to filter OUT (reject) post
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title = post.get('title', '').lower()
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return 'spam' in title
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def score(self, post: dict, context: dict = None) -> float:
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# Return score 0.0-1.0
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return 0.5
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```
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### Built-in Plugins
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- **KeywordFilterPlugin**: Blocklist/allowlist filtering
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- **QualityFilterPlugin**: Length, caps, clickbait detection
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## Troubleshooting
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### Issue: AI Not Working
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**Check**:
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1. `filter_config.json`: `"enabled": true`
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2. OpenRouter API key file exists
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3. Logs for API errors
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**Solution**:
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```bash
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# Test API key
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curl -H "Authorization: Bearer $(cat openrouter_key.txt)" \
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https://openrouter.ai/api/v1/models
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```
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### Issue: Posts Not Filtered
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**Check**:
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1. User has filterset selected in settings
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2. Filterset exists in filtersets.json
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3. Posts match filter rules
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**Solution**:
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```python
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# Check user settings
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user_settings = json.loads(current_user.settings)
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print(user_settings.get('filter_set')) # Should not be 'no_filter'
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```
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### Issue: Slow Performance
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**Check**:
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1. Cache enabled in config
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2. Cache hit rates
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3. Parallel processing enabled
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**Solution**:
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```json
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{
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"cache": {"enabled": true},
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"pipeline": {"enable_parallel": true, "parallel_workers": 10}
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}
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```
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## Future Enhancements
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- [ ] Database persistence for FilterResults
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- [ ] Filter statistics dashboard
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- [ ] Custom user-defined filtersets
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- [ ] A/B testing different filter configurations
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- [ ] Real-time filter updates without restart
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- [ ] Multi-language support
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- [ ] Advanced ML models for categorization
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## Contributing
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When adding new filtersets:
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1. Define in `filtersets.json`
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2. Test with sample posts
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3. Document rules and use case
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4. Consider AI requirements
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When adding new stages:
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1. Extend `BaseStage`
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2. Implement `process()` method
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3. Use caching where appropriate
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4. Add to `pipeline_config.json`
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## License
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AGPL-3.0 with commercial licensing option (see LICENSE file)
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Reference in New Issue
Block a user