kami
4c40a183cf
- Add events table (migration #4): stores normalized (action, object, ts) triples extracted from facts, indexed for recurrence detection. - Add proposed_routines table: stores inferred recurring patterns with proposed/accepted/dismissed status and optional linked reminder. - Add pattern package: Extractor normalizes fact text into (action, object) pairs with TTS normalization; Detector groups events to find recurring patterns and proposes routines. - Add internal/ttsnorm: text normalization pipeline for Russian/English (lowercase, punctuation strip, number normalization, stopword removal). - Add chat seed file for LLM phraser.
86 lines
3.0 KiB
Go
86 lines
3.0 KiB
Go
// Package pattern extracts normalized events from facts and detects recurring
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// patterns to propose as routines (recurring reminders).
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//
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// The pipeline: fact write → extractor (action+object) → detector (intervals) →
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// proposed routine → human confirms → recurring reminder.
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package pattern
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import (
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"strings"
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"time"
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)
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// Event is a normalized, derived observation — the output of the extractor
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// and the input to the pattern detector.
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type Event struct {
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FactID int64
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Action string // normalized action, e.g. "refill"
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Object string // normalized object, e.g. "cat_water_fountain"
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Ts time.Time
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}
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// actionLexicon maps observed value words to canonical actions. The key is the
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// canonical form; the values are observed inflections/alternatives (lowercase).
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// Pure additive — unrecognized values silently produce no event (false
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// negative), which is harmless.
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var actionLexicon = map[string][]string{
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"refill": {"refilled", "refill", "fills", "filling", "заправил", "налил", "долил", "пополнил"},
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"feed": {"fed", "feed", "feeds", "feeding", "покормил", "кормил", "покормить"},
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"change": {"changed", "change", "changes", "changing", "поменял", "сменил", "заменил"},
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"clean": {"cleaned", "clean", "cleans", "cleaning", "почистил", "убрал", "убирал", "помыл", "мыл"},
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"take": {"took", "take", "takes", "taking", "принял", "выпил", "пил", "съел", "ел"},
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"walk": {"walked", "walk", "walks", "walking", "гулял", "выгулял", "прогулка"},
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"water": {"watered", "water", "waters", "watering", "полил", "поливал"},
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}
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// invertLexicon builds a fast map from any variant → canonical action.
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var variantToAction map[string]string
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func init() {
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variantToAction = make(map[string]string)
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for canonical, variants := range actionLexicon {
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for _, v := range variants {
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variantToAction[v] = canonical
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}
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}
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}
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// Extract attempts to normalize a fact (key + value) into an Event.
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// Returns nil when the fact doesn't describe a recognizable action —
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// structured JSON values, empty values, and unrecognized actions all
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// produce no event (false negative by design).
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//
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// Rules:
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// - If value starts with '{' or '[' (JSON), skip — it's structured data,
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// not an action statement.
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// - The value (trimmed, lowered) is looked up in the action lexicon.
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// - The key (trimmed, lowered) becomes the object.
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// - Both action and object must be non-empty.
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func Extract(factID int64, key, value string, ts time.Time) *Event {
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v := strings.TrimSpace(value)
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if v == "" {
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return nil
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}
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// Skip structured JSON values — measurements, config, etc.
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if v[0] == '{' || v[0] == '[' {
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return nil
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}
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action, ok := variantToAction[strings.ToLower(v)]
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if !ok {
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return nil
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}
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object := strings.TrimSpace(strings.ToLower(key))
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if object == "" {
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return nil
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}
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return &Event{
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FactID: factID,
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Action: action,
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Object: object,
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Ts: ts,
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}
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}
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