persona.py
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"""
Persona module — user profile that gets injected into Hermes system prompt.
Loaded from ~/.hermes/persona.yaml, auto-updated from behavior data.
"""
from __future__ import annotations
import os
import json
import time
from pathlib import Path
def _load_hermes_env():
termux_home = Path("/data/data/com.termux/files/home")
home = termux_home if termux_home.exists() else Path(os.environ.get("HOME", str(termux_home)))
env_file = home / ".hermes" / ".env"
if not env_file.exists():
return
for raw in env_file.read_text(encoding="utf-8").splitlines():
line = raw.strip()
if not line or line.startswith("#") or "=" not in line:
continue
k, _, v = line.partition("=")
k = k.strip(); v = v.strip().strip('"').strip("'")
if k and not os.environ.get(k):
os.environ[k] = v
_load_hermes_env()
_HERMES_HOME = Path(os.environ.get("HOME", "/data/data/com.termux/files/home")) / ".hermes"
PERSONA_PATH = os.environ.get(
"PERSONA_PATH",
str(_HERMES_HOME / "persona.yaml"),
)
DEFAULT_PERSONA = {
"name": "用户",
"language": "zh-CN",
"style": "concise",
"timezone": "Asia/Shanghai",
"preferences": {
"apps": ["WeChat", "Calendar"],
"work_hours": "9:00-22:00",
"response_style": "简洁直接",
},
"important_contacts": [],
"current_goals": [],
"last_updated": 0,
}
def _load() -> dict:
try:
p = Path(PERSONA_PATH)
if not p.exists():
return dict(DEFAULT_PERSONA)
# Try YAML, fallback to JSON
text = p.read_text(encoding="utf-8")
try:
import yaml
return yaml.safe_load(text) or {}
except ImportError:
import json as _json
return _json.loads(text)
except Exception:
return dict(DEFAULT_PERSONA)
def _save(persona: dict):
p = Path(PERSONA_PATH)
p.parent.mkdir(parents=True, exist_ok=True)
persona["last_updated"] = int(time.time())
try:
import yaml
p.write_text(yaml.dump(persona, allow_unicode=True, default_flow_style=False), encoding="utf-8")
except ImportError:
p.write_text(json.dumps(persona, ensure_ascii=False, indent=2), encoding="utf-8")
def get_persona() -> dict:
return _load()
def update_persona(key: str, value) -> dict:
persona = _load()
# Support dot-notation keys like "preferences.apps"
parts = key.split(".")
target = persona
for part in parts[:-1]:
target = target.setdefault(part, {})
target[parts[-1]] = value
_save(persona)
return persona
def generate_system_prompt_fragment() -> str:
p = _load()
name = p.get("name", "用户")
style = p.get("style", "concise")
prefs = p.get("preferences", {})
goals = p.get("current_goals", [])
contacts = p.get("important_contacts", [])
tz = p.get("timezone", "Asia/Shanghai")
lines = [
f"## 用户信息",
f"- 姓名：{name}",
f"- 语言：{p.get('language', 'zh-CN')}",
f"- 时区：{tz}",
f"- 回复风格：{prefs.get('response_style', style)}",
]
if prefs.get("work_hours"):
lines.append(f"- 工作时间：{prefs['work_hours']}")
if goals:
lines.append(f"- 当前目标：" + "、".join(goals[:3]))
if contacts:
lines.append(f"- 重要联系人：" + "、".join(str(c) for c in contacts[:5]))
return "\n".join(lines)
def infer_from_memory_graph() -> dict:
"""Auto-update persona from behavior data in Memory Graph."""
try:
import sys
_base = str(Path(__file__).parent)
if _base not in sys.path:
sys.path.insert(0, _base)
from memory_graph import get_stats
import sqlite3
db_path = os.environ.get("MEMORY_GRAPH_DB",
str(_HERMES_HOME / "memory_graph.db"))
conn = sqlite3.connect(db_path)
conn.row_factory = sqlite3.Row
# Top contacts by message frequency
top_contacts = conn.execute(
"SELECT p.nickname, p.wxid, COUNT(m.id) as cnt "
"FROM message m JOIN person p ON m.person_id = p.id "
"WHERE p.nickname != '' AND m.talker NOT LIKE '%@chatroom' "
"GROUP BY m.person_id ORDER BY cnt DESC LIMIT 10"
).fetchall()
conn.close()
if top_contacts:
contacts = [{"name": r["nickname"], "wxid": r["wxid"], "msg_count": r["cnt"]}
for r in top_contacts]
persona = _load()
persona["important_contacts"] = contacts
persona["preferences"]["apps"] = ["WeChat"]
_save(persona)
return {"updated_contacts": len(contacts)}
except Exception as e:
return {"error": str(e)}
return {}
if __name__ == "__main__":
# Initialize persona
p = get_persona()
print("Current persona:", json.dumps(p, ensure_ascii=False, indent=2))
print("\nSystem prompt fragment:")
print(generate_system_prompt_fragment())
print("\nInferring from memory graph...")
result = infer_from_memory_graph()
print("Inference result:", result)