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RAG++ Architecture

RAG++ is a trajectory-aware retrieval-augmented generation system that maintains a unified knowledge fabric across conversations, ideas, code, and motion data. It powers the CognitiveTwin — a personalized AI that learns user-specific reasoning patterns.

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**Version**: 3.0 (Post-CognitiveTwin V3 + Idea Vault Integration) **Last Updated**: January 2025 RAG++ is a trajectory-aware retrieval-augmented generation system that maintains a unified knowledge fabric across conversations, ideas, code, and motion data. It powers the CognitiveTwin — a personalized AI that learns user-specific reasoning patterns. Every piece of knowledge (conversation, idea, claim, code) flows into this table: | File | Purpose | |------|---------| | `chatgpt.py` | ChatGPT conversation ingestion | | `claude.py` | Claude conversation ingestion | | `unified.py` | Unified ingestion orchestrator | | `embedder.py` | Embedding generation | | `trajectory.py` | Trajectory coordinate computation | | `primitive_enricher.py` | V3 primitive scoring (stall, exec, domain) | | `vision.py` | Image/vision content handling | | `media.py` | Audio/video handling | | File | Purpose | |------|---------| | `query.py` | `MemoryRetriever`, `SearchQuery` | | `intent.py` | `QueryIntentAnalyzer`, directive detection | | `quality.py` | `QualityReranker`, lifecycle scoring | | `priors.py` | Prior bundle generation | | `idea_retriever.py` | Idea-specific retrieval |

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