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Timmy-time-dashboard/src/timmy/thinking/__init__.py

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Python

"""Timmy's thinking engine — public façade.
When the server starts, Timmy begins pondering: reflecting on his existence,
recent swarm activity, scripture, creative ideas, or pure stream of
consciousness. Each thought builds on the previous one, maintaining a
continuous chain of introspection.
Usage::
from timmy.thinking import thinking_engine
# Run one thinking cycle (called by the background loop)
await thinking_engine.think_once()
# Query the thought stream
thoughts = thinking_engine.get_recent_thoughts(limit=10)
chain = thinking_engine.get_thought_chain(thought_id)
"""
import logging
import sqlite3
from datetime import datetime
from pathlib import Path
# Re-export HOT_MEMORY_PATH and SOUL_PATH so existing patch targets continue to work.
# Tests that patch "timmy.thinking.HOT_MEMORY_PATH" or "timmy.thinking.SOUL_PATH"
# should instead patch "timmy.thinking._snapshot.HOT_MEMORY_PATH" etc., but these
# re-exports are kept for any code that reads them from the top-level namespace.
from timmy.memory_system import HOT_MEMORY_PATH, SOUL_PATH # noqa: F401
from timmy.thinking._db import Thought, _get_conn
from timmy.thinking.engine import ThinkingEngine
from timmy.thinking.seeds import (
_META_OBSERVATION_PHRASES,
_SENSITIVE_PATTERNS,
_THINK_TAG_RE,
_THINKING_PROMPT,
SEED_TYPES,
)
logger = logging.getLogger(__name__)
# Module-level singleton
thinking_engine = ThinkingEngine()
__all__ = [
"ThinkingEngine",
"Thought",
"SEED_TYPES",
"thinking_engine",
"search_thoughts",
"_THINKING_PROMPT",
"_SENSITIVE_PATTERNS",
"_META_OBSERVATION_PHRASES",
"_THINK_TAG_RE",
"HOT_MEMORY_PATH",
"SOUL_PATH",
]
# ── Search helpers ─────────────────────────────────────────────────────────
def _query_thoughts(
db_path: Path, query: str, seed_type: str | None, limit: int
) -> list[sqlite3.Row]:
"""Run the thought-search SQL and return matching rows."""
pattern = f"%{query}%"
with _get_conn(db_path) as conn:
if seed_type:
return conn.execute(
"""
SELECT id, content, seed_type, created_at
FROM thoughts
WHERE content LIKE ? AND seed_type = ?
ORDER BY created_at DESC
LIMIT ?
""",
(pattern, seed_type, limit),
).fetchall()
return conn.execute(
"""
SELECT id, content, seed_type, created_at
FROM thoughts
WHERE content LIKE ?
ORDER BY created_at DESC
LIMIT ?
""",
(pattern, limit),
).fetchall()
def _format_thought_rows(rows: list[sqlite3.Row], query: str, seed_type: str | None) -> str:
"""Format thought rows into a human-readable string."""
lines = [f'Found {len(rows)} thought(s) matching "{query}":']
if seed_type:
lines[0] += f' [seed_type="{seed_type}"]'
lines.append("")
for row in rows:
ts = datetime.fromisoformat(row["created_at"])
local_ts = ts.astimezone()
time_str = local_ts.strftime("%Y-%m-%d %I:%M %p").lstrip("0")
seed = row["seed_type"]
content = row["content"].replace("\n", " ") # Flatten newlines for display
lines.append(f"[{time_str}] ({seed}) {content[:150]}")
return "\n".join(lines)
def search_thoughts(query: str, seed_type: str | None = None, limit: int = 10) -> str:
"""Search Timmy's thought history for reflections matching a query.
Use this tool when Timmy needs to recall his previous thoughts on a topic,
reflect on past insights, or build upon earlier reflections. This enables
self-awareness and continuity of thinking across time.
Args:
query: Search term to match against thought content (case-insensitive).
seed_type: Optional filter by thought category (e.g., 'existential',
'swarm', 'sovereignty', 'creative', 'memory', 'observation').
limit: Maximum number of thoughts to return (default 10, max 50).
Returns:
Formatted string with matching thoughts, newest first, including
timestamps and seed types. Returns a helpful message if no matches found.
"""
limit = max(1, min(limit, 50))
try:
rows = _query_thoughts(thinking_engine._db_path, query, seed_type, limit)
if not rows:
if seed_type:
return f'No thoughts found matching "{query}" with seed_type="{seed_type}".'
return f'No thoughts found matching "{query}".'
return _format_thought_rows(rows, query, seed_type)
except Exception as exc:
logger.warning("Thought search failed: %s", exc)
return f"Error searching thoughts: {exc}"