61 lines
2.6 KiB
Markdown
61 lines
2.6 KiB
Markdown
# Memory v2 — Design
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## Architecture
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```
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┌─────────────────────────┐
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│ system-prompt.ts │
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│ (inject memory block) │
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└────────┬────────────────┘
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│
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┌─────────▼──────────┐
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│ memory/recall.ts │
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│ (renamed to query) │
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└─────────┬──────────┘
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│
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┌───────────────┼───────────────┐
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│ │ │
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┌────────▼──────┐ ┌─────▼──────┐ ┌──────▼───────┐
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│ BM25Ranker │ │ EmbedCache │ │ CosineRanker │
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│ (stateless) │ │ (LRU map) │ │ (ONNX) │
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└───────────────┘ └────────────┘ └──────────────┘
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```
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## Module Changes
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### `apps/server/src/services/memory/` — new/changed files
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| File | Change |
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|------|--------|
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| `recall.ts` | Replace `rankByRelevance` with hybrid `rankByHybrid(query, entries)` |
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| `embeddings.ts` | **New** — ONNX model loader + `embed(texts: string[]): number[][]` |
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| `bm25.ts` | **New** — BM25 scorer with `score(query, doc): number` |
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| `ranker.ts` | **New** — weighted merge of BM25 + cosine scores |
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| `entries.ts` | Add `serializeForEmbedding(entry): string` helper |
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### Embedding Model
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- Model: `all-MiniLM-L6-v2` (384-dim, ~23MB ONNX)
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- Runtime: `onnxruntime-node` npm package or subprocess via `node:child_process`
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- Cache: `Map<string, { embedding: number[], mtime: number }>` in-memory, cleared on process restart
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- Fallback: BM25-only when model file is missing
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### Agent Tools (new)
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| Tool | Description |
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|------|-------------|
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| `extract_memory(topic, title, content, tags?)` | Persists a memory entry. Topic must be one of project/user/reference |
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| `search_memory(query)` | Returns up to 10 ranked memory entries matching the query. Replaces blind injection |
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### Scoring Formula
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```
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score = (BM25_score * 0.3) + (cosine_similarity * 0.7)
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```
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Both normalized to [0,1] before merging. Entries below threshold (0.15) are excluded.
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## Rollback
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Set `MEMORY_SEARCH=keyword` env var to fall back to the v1 keyword-only path. Default is `hybrid`.
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