Add filter pipeline core infrastructure (Phase 1)

Implements plugin-based content filtering system with multi-level caching:

Core Components:
- FilterEngine: Main orchestrator for content filtering
- FilterCache: 3-level caching (memory, AI results, filterset results)
- FilterConfig: Configuration loader for filter_config.json & filtersets.json
- FilterResult & AIAnalysisResult: Data models for filter results

Architecture:
- BaseStage: Abstract class for pipeline stages
- BaseFilterPlugin: Abstract class for filter plugins
- Multi-threaded parallel processing support
- Content-hash based AI result caching (cost savings)
- Filterset result caching (fast filterset switching)

Configuration:
- filter_config.json: AI models, caching, parallel workers
- Using only Llama 70B for cost efficiency
- Compatible with existing filtersets.json

Integration:
- apply_filterset() API compatible with user preferences
- process_batch() for batch post processing
- Lazy-loaded stages to avoid import errors when AI disabled

Related to issue #8 (filtering engine implementation)

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>

filter_pipeline/models.py

@@ -0,0 +1,121 @@
+"""
+Filter Pipeline Models
+Data models for filter results and processing status.
+"""
+
+from dataclasses import dataclass, field
+from typing import Dict, List, Optional, Any
+from datetime import datetime
+from enum import Enum
+
+
+class ProcessingStatus(Enum):
+    """Status of content processing"""
+    PENDING = 'pending'
+    PROCESSING = 'processing'
+    COMPLETED = 'completed'
+    FAILED = 'failed'
+    CACHED = 'cached'
+
+
+@dataclass
+class FilterResult:
+    """
+    Result of filtering pipeline for a single post.
+
+    Attributes:
+        post_uuid: Unique identifier for the post
+        passed: Whether post passed the filter
+        score: Relevance/quality score (0.0-1.0)
+        categories: Detected categories/topics
+        tags: Additional tags applied
+        moderation_data: Safety and quality analysis results
+        filterset_name: Name of filterset applied
+        cache_key: Content hash for caching
+        processed_at: Timestamp of processing
+        status: Processing status
+        error: Error message if failed
+    """
+    post_uuid: str
+    passed: bool
+    score: float
+    categories: List[str] = field(default_factory=list)
+    tags: List[str] = field(default_factory=list)
+    moderation_data: Dict[str, Any] = field(default_factory=dict)
+    filterset_name: str = 'no_filter'
+    cache_key: Optional[str] = None
+    processed_at: Optional[datetime] = None
+    status: ProcessingStatus = ProcessingStatus.PENDING
+    error: Optional[str] = None
+
+    # Detailed scoring breakdown
+    score_breakdown: Dict[str, float] = field(default_factory=dict)
+
+    def to_dict(self) -> Dict:
+        """Convert to dictionary for JSON serialization"""
+        return {
+            'post_uuid': self.post_uuid,
+            'passed': self.passed,
+            'score': self.score,
+            'categories': self.categories,
+            'tags': self.tags,
+            'moderation_data': self.moderation_data,
+            'filterset_name': self.filterset_name,
+            'cache_key': self.cache_key,
+            'processed_at': self.processed_at.isoformat() if self.processed_at else None,
+            'status': self.status.value if isinstance(self.status, ProcessingStatus) else self.status,
+            'error': self.error,
+            'score_breakdown': self.score_breakdown
+        }
+
+    @classmethod
+    def from_dict(cls, data: Dict) -> 'FilterResult':
+        """Create from dictionary"""
+        # Handle datetime deserialization
+        if data.get('processed_at') and isinstance(data['processed_at'], str):
+            data['processed_at'] = datetime.fromisoformat(data['processed_at'])
+
+        # Handle enum deserialization
+        if data.get('status') and isinstance(data['status'], str):
+            data['status'] = ProcessingStatus(data['status'])
+
+        return cls(**{k: v for k, v in data.items() if k in cls.__dataclass_fields__})
+
+
+@dataclass
+class AIAnalysisResult:
+    """
+    Result of AI analysis (categorization, moderation, etc).
+    Cached separately from FilterResult for reuse across filtersets.
+    """
+    content_hash: str
+    categories: List[str] = field(default_factory=list)
+    category_scores: Dict[str, float] = field(default_factory=dict)
+    moderation: Dict[str, Any] = field(default_factory=dict)
+    quality_score: float = 0.5
+    sentiment: Optional[str] = None
+    sentiment_score: float = 0.0
+    analyzed_at: Optional[datetime] = None
+    model_used: Optional[str] = None
+
+    def to_dict(self) -> Dict:
+        """Convert to dictionary for JSON serialization"""
+        return {
+            'content_hash': self.content_hash,
+            'categories': self.categories,
+            'category_scores': self.category_scores,
+            'moderation': self.moderation,
+            'quality_score': self.quality_score,
+            'sentiment': self.sentiment,
+            'sentiment_score': self.sentiment_score,
+            'analyzed_at': self.analyzed_at.isoformat() if self.analyzed_at else None,
+            'model_used': self.model_used
+        }
+
+    @classmethod
+    def from_dict(cls, data: Dict) -> 'AIAnalysisResult':
+        """Create from dictionary"""
+        if data.get('analyzed_at') and isinstance(data['analyzed_at'], str):
+            data['analyzed_at'] = datetime.fromisoformat(data['analyzed_at'])
+
+        return cls(**{k: v for k, v in data.items() if k in cls.__dataclass_fields__})