Milla Jovovich
068dbd9a7b
The memory system: - Palace structure: Wings (people/projects) → Rooms (topics) → Closets (AAAK compressed) → Drawers (verbatim transcripts) - Halls connect related rooms within a wing - Tunnels cross-reference rooms across wings - AAAK: 30x lossless compression dialect for AI agents - Knowledge graph: temporal entity-relationship triples (SQLite) - Palace graph: room-based navigation with tunnel detection - MCP server: 19 tools — search, graph traversal, agent diary, AAAK auto-teach - Onboarding: guided setup generates wing config + AAAK entity registry - Contradiction detection: catches wrong pronouns, names, ages - Auto-save hooks for Claude Code 96.6% Recall@5 on LongMemEval — highest zero-API score published. 100% with optional Haiku rerank (500/500). Local. Free. No API key required.
41 lines
2.5 KiB
Markdown
41 lines
2.5 KiB
Markdown
# mempalace/ — Core Package
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The Python package that powers MemPalace. All modules, all logic.
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## Modules
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| Module | What it does |
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|--------|-------------|
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| `cli.py` | CLI entry point — routes to mine, search, init, compress, wake-up |
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| `config.py` | Configuration loading — `~/.mempalace/config.json`, env vars, defaults |
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| `normalize.py` | Converts 5 chat formats (Claude Code JSONL, Claude.ai JSON, ChatGPT JSON, Slack JSON, plain text) to standard transcript format |
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| `miner.py` | Project file ingest — scans directories, chunks by paragraph, stores to ChromaDB |
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| `convo_miner.py` | Conversation ingest — chunks by exchange pair (Q+A), detects rooms from content |
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| `searcher.py` | Semantic search via ChromaDB vectors — filters by wing/room, returns verbatim + scores |
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| `layers.py` | 4-layer memory stack: L0 (identity), L1 (critical facts), L2 (room recall), L3 (deep search) |
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| `dialect.py` | AAAK compression — entity codes, emotion markers, 30x lossless ratio |
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| `knowledge_graph.py` | Temporal entity-relationship graph — SQLite, time-filtered queries, fact invalidation |
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| `palace_graph.py` | Room-based navigation graph — BFS traversal, tunnel detection across wings |
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| `mcp_server.py` | MCP server — 19 tools, AAAK auto-teach, Palace Protocol, agent diary |
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| `onboarding.py` | Guided first-run setup — asks about people/projects, generates AAAK bootstrap + wing config |
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| `entity_registry.py` | Entity code registry — maps names to AAAK codes, handles ambiguous names |
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| `entity_detector.py` | Auto-detect people and projects from file content |
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| `general_extractor.py` | Classifies text into 5 memory types (decision, preference, milestone, problem, emotional) |
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| `room_detector_local.py` | Maps folders to room names using 70+ patterns — no API |
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| `spellcheck.py` | Name-aware spellcheck — won't "correct" proper nouns in your entity registry |
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| `split_mega_files.py` | Splits concatenated transcript files into per-session files |
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## Architecture
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```
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User → CLI → miner/convo_miner → ChromaDB (palace)
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↕
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knowledge_graph (SQLite)
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↕
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User → MCP Server → searcher → results
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→ kg_query → entity facts
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→ diary → agent journal
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```
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The palace (ChromaDB) stores verbatim content. The knowledge graph (SQLite) stores structured relationships. The MCP server exposes both to any AI tool.
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