personal memory agent
Add discovery mode context injection and aggregate_speculative_facets()
When no facets are defined, the facet classifier now receives explicit discovery-mode
context instructing it to name observed contexts with specific, descriptive labels.
Previously it ran with zero facet context, producing unconstrained speculative output.
Also adds aggregate_speculative_facets() to think/facets.py, which scans per-segment
agents/facets.json files across days, counts facet name frequency, and returns the
top 8 by count with sample activities. Used by onboarding agents to suggest journal
organization to the user.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
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think/facets.py
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think/facets.py
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from typing import Any, Optional
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from think.entities import get_identity_names
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from think.utils import day_path, get_journal, iter_segments
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from think.utils import DATE_RE, day_path, get_journal, iter_segments
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def _get_principal_display_name() -> str | None:
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active.update(load_segment_facets(day, seg_key, stream=stream_name))
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return active
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def aggregate_speculative_facets(days: list[str] | None = None) -> list[dict]:
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"""Aggregate speculative facet outputs from segment classifiers across days.
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Scans per-segment agents/facets.json files produced by the facets classifier
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and counts facet name frequency. Useful during onboarding to suggest journal
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organization to the user.
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Args:
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days: Optional list of days in YYYYMMDD format. If None, scans all days.
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Returns:
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List of dicts with keys:
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- "facet": facet name (str)
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- "count": number of segments where this facet appeared (int)
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- "sample_activities": up to 3 activity descriptions for this facet (list[str])
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Sorted by count descending, capped at 8 entries.
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"""
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journal_path = Path(get_journal())
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if days is not None:
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scan_days = days
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else:
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scan_days = []
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if journal_path.exists():
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for entry in sorted(journal_path.iterdir()):
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if entry.is_dir() and DATE_RE.fullmatch(entry.name):
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scan_days.append(entry.name)
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facet_counts: dict[str, int] = {}
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facet_activities: dict[str, list[str]] = {}
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for day in scan_days:
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for _stream, _seg_key, seg_path in iter_segments(day):
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facets_file = seg_path / "agents" / "facets.json"
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if not facets_file.exists():
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continue
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try:
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content = facets_file.read_text().strip()
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if not content:
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continue
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data = json.loads(content)
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if not isinstance(data, list):
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continue
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for item in data:
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if not isinstance(item, dict):
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continue
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facet_name = item.get("facet")
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if not facet_name:
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continue
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facet_counts[facet_name] = facet_counts.get(facet_name, 0) + 1
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activity = item.get("activity", "")
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if activity:
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samples = facet_activities.setdefault(facet_name, [])
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if len(samples) < 3:
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samples.append(activity)
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except (json.JSONDecodeError, OSError):
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continue
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result = [
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{
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"facet": facet_name,
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"count": count,
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"sample_activities": facet_activities.get(facet_name, []),
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}
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for facet_name, count in sorted(
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facet_counts.items(), key=lambda x: x[1], reverse=True
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)
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]
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return result[:8]
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def set_facet_muted(facet: str, muted: bool) -> None:
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think/muse.py
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think/muse.py
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summary = facet_summaries()
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if summary and summary != "No facets found.":
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extra_parts.append(summary)
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else:
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extra_parts.append(
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"No facets are defined yet. You are in discovery mode. "
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"Name the contexts you observe based on what is actually happening "
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"in this segment \u2014 use specific, descriptive names that reflect the "
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'actual activity (e.g., "engineering-work" not "work", '
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'"investor-calls" not "meetings"). These names will be used to '
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"suggest journal organization to the user."
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)
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except Exception:
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pass # Ignore if facets can't be loaded
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