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DLM-RCP Integration

- **Cross-Conversation Understanding**: Query across all 277 conversations simultaneously - **Unified Knowledge Base**: Treat all conversations as one interconnected system - **Dynamic Context Assembly**: Automatically find and assemble relevant messages - **Knowledge Evolution**: Track knowledge building without regression - **High Performance**: Built-in caching and optimization

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This module provides integration between the Dynamic Language Model (DLM) and Ring Contextual Propagation (RCP) systems. The RCP Bridge enables DLM to leverage RCP's cross-conversation understanding capabilities: - **Cross-Conversation Understanding**: Query across all 277 conversations simultaneously - **Unified Knowledge Base**: Treat all conversations as one interconnected system - **Dynamic Context Assembly**: Automatically find and assemble relevant messages - **Knowledge Evolution**: Track knowledge building without regression - **High Performance**: Built-in caching and optimization - `initialize(verbose=True)` - Initialize RCP system (loads all conversations) - `query_with_rcp(query, max_context_messages=50, use_cache=True)` - Query with cross-conversation understanding - `expand_context(response_id, additional_messages=20)` - Expand previous response with more messages - `get_conversation_context(conversation_id, include_cross_conversation=True)` - Get conversation context - `find_similar_messages(message_id, max_similar=20)` - Find similar messages across conversations - `get_system_status()` - Get system status and statistics - `clear_cache()` - Clear query cache - `export_knowledge_state(output_path)` - Export knowledge state for backup - `query` (str) - Original query - `response_id` (str) - Unique response identifier - `assembled_context` (str) - Assembled text context from relevant messages - `raw_context` (DynamicContext) - Full RCP context object - `confidence` (float) - Response confidence score [0, 1] - `source_conversations` (List[str]) - IDs of source conversations - `knowledge_clusters` (List[int]) - Knowledge clusters used - `message_count` (int) - Number of messages in context - `temporal_span` (Tuple[float, float]) - Time range of messages - `knowledge_state_id` (str) - Current knowledge state ID - `knowledge_gain` (Optional[float]) - Knowledge gain from query - `is_expandable` (bool) - Whether context can be expanded - `processing_time` (float) - Query processing time in seconds - `timestamp` (float) - Result timestamp

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