Jordan Ramos
916f86725d
Core agent improvements: - RSO (Relevance Scoring & Observation) system: interaction_logger, memory_scorer, signal_detector - Memory access logging (memory_access_log table) for relevance scoring; high-signal turn detection - Rich conversation storage for notable turns; compact_conversation truncates long user messages - Task-type classifier (query/action/analysis/creative) for observation tagging - Nested sub-agent visibility: deep delegations now register against the main agent's manager Child safety (Gabriel profile): - child_safety.py: filtering, audit logging, prompt constants for restricted sessions - .kiro/specs/child-safety-profile: requirements, design, tasks specs - GABRIEL_BOT_PROPOSAL.md: initial proposal doc - Reduced context window (10 msgs) and tutor-mode identity for restricted users Telegram adapter: - Polling watchdog: auto-restarts updater if polling drops unexpectedly - get_me() with exponential-backoff retry on NetworkError at startup - Correct stop() ordering: signal watchdog before cancelling tasks Email / Gmail: - send_email: supports file attachments (attachments list param) - get_email: surfaces attachment metadata in response Scheduled tasks / weather: - Remove OpenWeatherMap API calls from morning-weather task; use wttr.in exclusively - New scheduled tasks and scheduler state persistence Discord: - adapters/discord/__init__.py scaffold - discord-plugin: MCP plugin for Claude Code Discord integration (server.ts, skills, config) Infrastructure: - n8n workflow exports (garvis_webhook, content_pipeline variants) - memory_workspace: context, homelab-repo-updates, weekly observation summaries, error logs - UCS C240 migration plan doc - requirements.txt: new deps - .claude/settings.json, fix_hooks.py: hook/permission tuning
110 lines
3.5 KiB
Python
110 lines
3.5 KiB
Python
"""
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User Signal Detector — heuristic classifier for follow-up messages.
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Classifies the user's next message (after Garvis responded) into one of:
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"positive" — explicit praise / satisfaction
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"negative" — explicit dissatisfaction / error report
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"correction" — user corrects or rephrases Garvis
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"refinement" — user extends the prior request
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"neutral" — new topic or unclassifiable
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This is intentionally heuristic. Aggregate patterns matter more than
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any individual classification. The analysis layer must account for noise.
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No project imports — safe to use anywhere without circular-dep risk.
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"""
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from typing import Optional
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# ---------------------------------------------------------------------------
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# Keyword tables (extend here without touching classify_signal logic)
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# ---------------------------------------------------------------------------
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_POSITIVE_WORDS = frozenset({
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"perfect", "great", "excellent", "exactly", "thanks", "thank",
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"awesome", "good", "nice", "nailed", "correct",
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"yes", "yep", "sure", "right", "wonderful",
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"brilliant", "fantastic", "helpful", "appreciate",
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})
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_NEGATIVE_WORDS = frozenset({
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"no", "nope", "wrong", "incorrect", "bad", "terrible", "awful",
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"failed", "broken", "error", "mistake", "off",
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})
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_CORRECTION_WORDS = frozenset({
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"actually", "wait", "sorry", "clarify",
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})
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_CORRECTION_PHRASES = frozenset({
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"i meant", "i mean", "what i meant", "not that",
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"let me clarify", "to clarify", "scratch that",
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"hold on", "my bad", "that's not", "not what i",
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"try again", "you missed",
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})
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_REFINEMENT_PHRASES = frozenset({
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"can you also", "what about", "and also", "additionally",
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"could you also", "one more", "another thing", "on top of that",
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"while you're at it", "in addition",
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"can you add", "please add", "add to that",
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})
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# Time threshold under which a quick reply skews toward correction.
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_REPHRASE_THRESHOLD_S: float = 30.0
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def classify_signal(
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follow_up_text: str,
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time_delta_seconds: Optional[float] = None,
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) -> str:
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"""Classify a follow-up message as a user feedback signal.
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Args:
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follow_up_text: The user's next message after Garvis responded.
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time_delta_seconds: Seconds elapsed since the previous response.
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If provided and < 30, rapid replies without positive signals
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skew toward "correction".
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Returns:
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One of: "positive", "negative", "correction", "refinement", "neutral"
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"""
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if not follow_up_text or not follow_up_text.strip():
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return "neutral"
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text_lower = follow_up_text.lower().strip()
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words = set(text_lower.split())
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# --- Explicit positive ---
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if words & _POSITIVE_WORDS:
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return "positive"
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# --- Multi-word correction phrases (check before single words) ---
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for phrase in _CORRECTION_PHRASES:
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if phrase in text_lower:
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return "correction"
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# --- Single-word correction signals ---
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if words & _CORRECTION_WORDS:
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return "correction"
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# --- Explicit negative ---
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if words & _NEGATIVE_WORDS:
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return "negative"
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# --- Refinement patterns ---
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for phrase in _REFINEMENT_PHRASES:
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if phrase in text_lower:
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return "refinement"
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# --- Rapid rephrase heuristic ---
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# If the user responds very quickly and no other signal matched,
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# treat it as a soft correction (likely dissatisfied with the answer).
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if (
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time_delta_seconds is not None
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and time_delta_seconds < _REPHRASE_THRESHOLD_S
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):
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return "correction"
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return "neutral"
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