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_config.json

@@ -0,0 +1,27 @@
+{
+  "ai": {
+    "enabled": false,
+    "openrouter_key_file": "openrouter_key.txt",
+    "models": {
+      "cheap": "meta-llama/llama-3.3-70b-instruct",
+      "smart": "meta-llama/llama-3.3-70b-instruct"
+    },
+    "parallel_workers": 10,
+    "timeout_seconds": 60,
+    "note": "Using only Llama 70B for cost efficiency"
+  },
+  "cache": {
+    "enabled": true,
+    "ai_cache_dir": "data/filter_cache",
+    "filterset_cache_ttl_hours": 24
+  },
+  "pipeline": {
+    "default_stages": ["categorizer", "moderator", "filter", "ranker"],
+    "batch_size": 50,
+    "enable_parallel": true
+  },
+  "output": {
+    "filtered_dir": "data/filtered",
+    "save_rejected": false
+  }
+}