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my-project-public — gitlawb

#1	"""
#2	Mnemosyne Diagnostics
#3	=====================
#4	PII-safe debug logging for troubleshooting installation and runtime issues.
#5
#6	Logs to ~/.hermes/mnemosyne/logs/diagnose_YYYY-MM-DD_HHMMSS.jsonl
#7	Never includes memory content, user queries, or API keys.
#8	"""
#9
#10	import json
#11	import os
#12	import sys
#13	import platform
#14	from datetime import datetime
#15	from pathlib import Path
#16	from typing import Dict, List
#17
#18	LOG_DIR = Path.home() / ".hermes" / "mnemosyne" / "logs"
#19
#20
#21	def _ensure_log_dir():
#22	LOG_DIR.mkdir(parents=True, exist_ok=True)
#23
#24
#25	def _log_path() -> Path:
#26	_ensure_log_dir()
#27	ts = datetime.now().strftime("%Y-%m-%d_%H%M%S")
#28	return LOG_DIR / f"diagnose_{ts}.jsonl"
#29
#30
#31	def _safe_env(name: str) -> str:
#32	"""Return env var presence indicator, never the value."""
#33	val = os.environ.get(name, "")
#34	return "set" if val else "unset"
#35
#36
#37	def run_diagnostics() -> Dict:
#38	"""
#39	Run full diagnostic scan and write PII-safe log.
#40	Returns summary dict for display.
#41	"""
#42	log_path = _log_path()
#43	entries: List[Dict] = []
#44
#45	def log(category: str, check: str, status: str, detail: str = ""):
#46	entry = {
#47	"ts": datetime.now().isoformat(),
#48	"category": category,
#49	"check": check,
#50	"status": status,
#51	"detail": detail
#52	}
#53	entries.append(entry)
#54	return entry
#55
#56	# --- Python environment ---
#57	log("env", "python_version", sys.version.split()[0])
#58	log("env", "platform", platform.platform())
#59	log("env", "python_executable", sys.executable)
#60
#61	# --- Mnemosyne package ---
#62	try:
#63	import mnemosyne
#64	log("package", "mnemosyne_version", mnemosyne.__version__)
#65	except Exception as e:
#66	log("package", "mnemosyne_version", "ERROR", str(e))
#67
#68	# --- Core dependencies ---
#69	deps = {
#70	"fastembed": "fastembed",
#71	"sqlite_vec": "sqlite_vec",
#72	"numpy": "numpy",
#73	"ctransformers": "ctransformers",
#74	"huggingface_hub": "huggingface_hub",
#75	}
#76	for name, module in deps.items():
#77	try:
#78	mod = __import__(module)
#79	ver = getattr(mod, "__version__", "unknown")
#80	log("deps", name, "OK", f"version={ver}")
#81	except ImportError:
#82	log("deps", name, "MISSING")
#83	except Exception as e:
#84	log("deps", name, "ERROR", str(e))
#85
#86	# --- Mnemosyne core components ---
#87	try:
#88	from mnemosyne.core import embeddings as _embeddings
#89	log("core", "embeddings_available", "YES" if _embeddings.available() else "NO")
#90	log("core", "embeddings_model", _embeddings._DEFAULT_MODEL)
#91	except Exception as e:
#92	log("core", "embeddings", "ERROR", str(e))
#93
#94	try:
#95	from mnemosyne.core.beam import _SQLITE_VEC_AVAILABLE
#96	log("core", "sqlite_vec_available", "YES" if _SQLITE_VEC_AVAILABLE else "NO")
#97	except Exception as e:
#98	log("core", "sqlite_vec", "ERROR", str(e))
#99
#100	# --- Database state ---
#101	try:
#102	from mnemosyne.core.memory import Mnemosyne
#103	mem = Mnemosyne()
#104	stats = mem.get_stats()
#105
#106	# PII-safe: counts and config only, never content
#107	log("db", "legacy_total", str(stats.get("total_memories", 0)))
#108	log("db", "total_sessions", str(stats.get("total_sessions", 0)))
#109
#110	beam = stats.get("beam", {})
#111	wm = beam.get("working_memory", {})
#112	ep = beam.get("episodic_memory", {})
#113
#114	log("db", "working_total", str(wm.get("total", 0)))
#115	log("db", "episodic_total", str(ep.get("total", 0)))
#116	log("db", "episodic_vectors", str(ep.get("vectors", 0)))
#117	log("db", "episodic_vec_type", ep.get("vec_type", "none"))
#118	log("db", "db_path", stats.get("database", "unknown"))
#119	except Exception as e:
#120	log("db", "stats", "ERROR", str(e))
#121
#122	# --- Environment variables (presence only, never values) ---
#123	env_vars = [
#124	"MNEMOSYNE_DATA_DIR",
#125	"MNEMOSYNE_LLM_ENABLED",
#126	"MNEMOSYNE_LLM_BASE_URL",
#127	"MNEMOSYNE_VEC_TYPE",
#128	"MNEMOSYNE_WM_MAX_ITEMS",
#129	"HERMES_HOME",
#130	]
#131	for var in env_vars:
#132	log("env", var, _safe_env(var))
#133
#134	# --- Write log file ---
#135	with open(log_path, "w", encoding="utf-8") as f:
#136	for entry in entries:
#137	f.write(json.dumps(entry) + "\n")
#138
#139	# --- Build summary ---
#140	summary = {
#141	"log_path": str(log_path),
#142	"checks_total": len(entries),
#143	"checks_passed": sum(1 for e in entries if e["status"] in ("OK", "YES", "set")),
#144	"checks_failed": sum(1 for e in entries if e["status"] in ("MISSING", "NO", "ERROR")),
#145	"key_findings": []
#146	}
#147
#148	# Auto-detect common problems
#149	embed_ok = any(e["check"] == "embeddings_available" and e["status"] == "YES" for e in entries)
#150	vec_ok = any(e["check"] == "sqlite_vec_available" and e["status"] == "YES" for e in entries)
#151	ep_vec = next((e for e in entries if e["check"] == "episodic_vectors"), None)
#152
#153	if not embed_ok:
#154	summary["key_findings"].append("fastembed not available — install with: pip install mnemosyne-memory[embeddings]")
#155	if not vec_ok:
#156	summary["key_findings"].append("sqlite-vec not available — install with: pip install sqlite-vec")
#157	if embed_ok and vec_ok and ep_vec and ep_vec["status"] == "0":
#158	summary["key_findings"].append("Both fastembed and sqlite-vec are available but episodic vectors=0 — memories may not have been consolidated yet. Run: hermes mnemosyne sleep")
#159	if embed_ok and vec_ok and ep_vec and int(ep_vec["status"]) > 0:
#160	summary["key_findings"].append("Semantic search is active with " + ep_vec["status"] + " vectors in episodic memory")
#161
#162	return summary
#163
#164
#165	if __name__ == "__main__":
#166	result = run_diagnostics()
#167	print(json.dumps(result, indent=2))
#168

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