"""hermes-memory-store — holographic memory plugin using MemoryProvider interface. Registers as a MemoryProvider plugin, giving the agent structured fact storage with entity resolution, trust scoring, and HRR-based compositional retrieval. Original plugin by dusterbloom (PR #2351), adapted to the MemoryProvider ABC. Config in $HERMES_HOME/config.yaml (profile-scoped): plugins: hermes-memory-store: db_path: $HERMES_HOME/memory_store.db # omit to use the default auto_extract: false default_trust: 0.5 min_trust_threshold: 0.3 temporal_decay_half_life: 0 """ from __future__ import annotations import json import logging import re from pathlib import Path from typing import Any, Dict, List from agent.memory_provider import MemoryProvider from tools.registry import tool_error from utils import is_truthy_value from .store import MemoryStore from .retrieval import FactRetriever from hermes_cli.config import cfg_get logger = logging.getLogger(__name__) FACT_STORE_SCHEMA = { "name": "fact_store", "description": ( "Deep structured memory with algebraic reasoning. " "Use alongside the memory tool — memory for always-on context, " "fact_store for deep recall and compositional queries.\n\n" "ACTIONS (simple → powerful):\n" "• add — Store a fact the user would expect you to remember.\n" "• search — Keyword lookup ('editor config', 'deploy process').\n" "• probe — Entity recall: ALL facts about a person/thing.\n" "• related — What connects to an entity? Structural adjacency.\n" "• reason — Compositional: facts connected to MULTIPLE entities simultaneously.\n" "• contradict — Memory hygiene: find facts making conflicting claims.\n" "• update/remove/list — CRUD operations.\n\n" "IMPORTANT: Before answering questions about the user, ALWAYS probe or reason first." ), "parameters": { "type": "object", "properties": { "action": { "type": "string", "enum": ["add", "search", "probe", "related", "reason", "contradict", "update", "remove", "list"], }, "content": {"type": "string", "description": "Fact content (required for 'add')."}, "query": {"type": "string", "description": "Search query (required for 'search')."}, "entity": {"type": "string", "description": "Entity name for 'probe'/'related'."}, "entities": {"type": "array", "items": {"type": "string"}, "description": "Entity names for 'reason'."}, "fact_id": {"type": "integer", "description": "Fact ID for 'update'/'remove'."}, "category": {"type": "string", "enum": ["user_pref", "project", "tool", "general"]}, "tags": {"type": "string", "description": "Comma-separated tags."}, "trust_delta": {"type": "number", "description": "Trust adjustment for 'update'."}, "min_trust": {"type": "number", "description": "Minimum trust filter (default: 0.3)."}, "limit": {"type": "integer", "description": "Max results (default: 10)."}, }, "required": ["action"], }, } FACT_FEEDBACK_SCHEMA = { "name": "fact_feedback", "description": ( "Rate a fact after using it. Mark 'helpful' if accurate, 'unhelpful' if outdated. " "This trains the memory — good facts rise, bad facts sink." ), "parameters": { "type": "object", "properties": { "action": {"type": "string", "enum": ["helpful", "unhelpful"]}, "fact_id": {"type": "integer", "description": "The fact ID to rate."}, }, "required": ["action", "fact_id"], }, } # Auto-extraction patterns (on_session_end): user preferences -> user_pref, decisions -> project. _PREF_PATTERNS = [ re.compile(r'\bI\s+(?:prefer|like|love|use|want|need)\s+(.+)', re.IGNORECASE), re.compile(r'\bmy\s+(?:favorite|preferred|default)\s+\w+\s+is\s+(.+)', re.IGNORECASE), re.compile(r'\bI\s+(?:always|never|usually)\s+(.+)', re.IGNORECASE), ] _DECISION_PATTERNS = [ re.compile(r'\bwe\s+(?:decided|agreed|chose)\s+(?:to\s+)?(.+)', re.IGNORECASE), re.compile(r'\bthe\s+project\s+(?:uses|needs|requires)\s+(.+)', re.IGNORECASE), ] def _load_plugin_config() -> dict: try: # Canonical loader: honors the managed-scope overlay + ${VAR} expansion. from hermes_cli.config import load_config_readonly all_config = load_config_readonly() return cfg_get(all_config, "plugins", "hermes-memory-store", default={}) or {} except Exception: return {} def _results(results: list) -> str: return json.dumps({"results": results, "count": len(results)}) class HolographicMemoryProvider(MemoryProvider): """Holographic memory with structured facts, entity resolution, and HRR retrieval.""" def __init__(self, config: dict | None = None): self._config = config or _load_plugin_config() self._store = None self._retriever = None self._min_trust = float(self._config.get("min_trust_threshold", 0.3)) @property def name(self) -> str: return "holographic" def is_available(self) -> bool: return True # SQLite is always available, numpy is optional def save_config(self, values, hermes_home): """Write config to config.yaml under plugins.hermes-memory-store.""" config_path = Path(hermes_home) / "config.yaml" try: import yaml # Raw read for the write-back round-trip: merged defaults must not # be persisted into the user's file. from hermes_cli.config import read_user_config_raw existing = read_user_config_raw(config_path) existing.setdefault("plugins", {})["hermes-memory-store"] = values with open(config_path, "w", encoding="utf-8") as f: yaml.dump(existing, f, default_flow_style=False) except Exception: pass def get_config_schema(self): from hermes_constants import display_hermes_home _default_db = f"{display_hermes_home()}/memory_store.db" return [ {"key": "db_path", "description": "SQLite database path", "default": _default_db}, {"key": "auto_extract", "description": "Auto-extract facts at session end", "default": "false", "choices": ["true", "false"]}, {"key": "default_trust", "description": "Default trust score for new facts", "default": "0.5"}, {"key": "hrr_dim", "description": "HRR vector dimensions", "default": "1024"}, ] def initialize(self, session_id: str, **kwargs) -> None: from hermes_constants import get_hermes_home _hermes_home = str(get_hermes_home()) db_path = self._config.get("db_path", _hermes_home + "/memory_store.db") # Expand $HERMES_HOME so configured paths resolve to the active profile's directory. if isinstance(db_path, str): db_path = db_path.replace("$HERMES_HOME", _hermes_home).replace("${HERMES_HOME}", _hermes_home) hrr_dim = int(self._config.get("hrr_dim", 1024)) self._store = MemoryStore( db_path=db_path, default_trust=float(self._config.get("default_trust", 0.5)), hrr_dim=hrr_dim, ) self._retriever = FactRetriever( store=self._store, temporal_decay_half_life=int(self._config.get("temporal_decay_half_life", 0)), hrr_weight=float(self._config.get("hrr_weight", 0.3)), hrr_dim=hrr_dim, ) self._session_id = session_id def system_prompt_block(self) -> str: if not self._store: return "" try: total = self._store._conn.execute("SELECT COUNT(*) FROM facts").fetchone()[0] except Exception: total = 0 if total == 0: return ( "# Holographic Memory\n" "Active. Empty fact store — proactively add facts the user would expect you to remember.\n" "Use fact_store(action='add') to store durable structured facts about people, projects, preferences, decisions.\n" "Use fact_feedback to rate facts after using them (trains trust scores)." ) return ( f"# Holographic Memory\n" f"Active. {total} facts stored with entity resolution and trust scoring.\n" f"Use fact_store to search, probe entities, reason across entities, or add facts.\n" f"Use fact_feedback to rate facts after using them (trains trust scores)." ) def prefetch(self, query: str, *, session_id: str = "") -> str: if not self._retriever or not query: return "" try: results = self._retriever.search(query, min_trust=self._min_trust, limit=5) if not results: return "" lines = [f"- [{r.get('trust_score', r.get('trust', 0)):.1f}] {r.get('content', '')}" for r in results] return "## Holographic Memory\n" + "\n".join(lines) except Exception as e: logger.debug("Holographic prefetch failed: %s", e) return "" def sync_turn(self, user_content: str, assistant_content: str, *, session_id: str = "") -> None: # Facts are stored explicitly via tools; on_session_end handles auto-extraction if configured. pass def get_tool_schemas(self) -> List[Dict[str, Any]]: return [FACT_STORE_SCHEMA, FACT_FEEDBACK_SCHEMA] def handle_tool_call(self, tool_name: str, args: Dict[str, Any], **kwargs) -> str: handler = self._TOOL_HANDLERS.get(tool_name) if handler is None: return tool_error(f"Unknown tool: {tool_name}") try: return handler(self, args) except KeyError as exc: return tool_error(f"Missing required argument: {exc}") except Exception as exc: return tool_error(str(exc)) def on_session_end(self, messages: List[Dict[str, Any]]) -> None: # is_truthy_value: the config schema declares auto_extract as a string # enum ("false"/"true"); plain truthiness would treat "false" as enabled. if is_truthy_value(self._config.get("auto_extract", False)) and self._store and messages: self._auto_extract_facts(messages) def on_memory_write(self, action: str, target: str, content: str) -> None: """Mirror built-in memory writes as facts.""" if action == "add" and self._store and content: try: category = "user_pref" if target == "user" else "general" self._store.add_fact(content, category=category) except Exception as e: logger.debug("Holographic memory_write mirror failed: %s", e) def shutdown(self) -> None: # Release the shared SQLite connection on the caller's thread: leaving # it to GC keeps the connection (and its write lock) alive on a # long-running gateway. close() is idempotent and refcount-guarded. if self._store is not None: try: self._store.close() except Exception as e: logger.debug("Holographic shutdown close() failed: %s", e) self._store = None self._retriever = None # -- Tool handlers ------------------------------------------------------- # KeyError from args[...] / Exception are turned into tool_error by handle_tool_call. def _handle_fact_store(self, args: dict) -> str: action = args["action"] handler = self._FACT_STORE_ACTIONS.get(action) if handler is None: return tool_error(f"Unknown action: {action}") return handler(self, args) def _act_add(self, args: dict) -> str: fact_id = self._store.add_fact(args["content"], category=args.get("category", "general"), tags=args.get("tags", "")) return json.dumps({"fact_id": fact_id, "status": "added"}) def _act_search(self, args: dict) -> str: return _results(self._retriever.search( args["query"], category=args.get("category"), min_trust=float(args.get("min_trust", self._min_trust)), limit=int(args.get("limit", 10)), )) def _act_entity(self, args: dict, method: str) -> str: """Shared body of 'probe' and 'related' (single-entity retriever queries).""" return _results(getattr(self._retriever, method)( args["entity"], category=args.get("category"), limit=int(args.get("limit", 10)), )) def _act_reason(self, args: dict) -> str: entities = args.get("entities", []) if not entities: return tool_error("reason requires 'entities' list") return _results(self._retriever.reason(entities, category=args.get("category"), limit=int(args.get("limit", 10)))) def _act_contradict(self, args: dict) -> str: return _results(self._retriever.contradict(category=args.get("category"), limit=int(args.get("limit", 10)))) def _act_update(self, args: dict) -> str: updated = self._store.update_fact( int(args["fact_id"]), content=args.get("content"), trust_delta=float(args["trust_delta"]) if "trust_delta" in args else None, tags=args.get("tags"), category=args.get("category"), ) return json.dumps({"updated": updated}) def _act_remove(self, args: dict) -> str: return json.dumps({"removed": self._store.remove_fact(int(args["fact_id"]))}) def _act_list(self, args: dict) -> str: facts = self._store.list_facts( category=args.get("category"), min_trust=float(args.get("min_trust", 0.0)), limit=int(args.get("limit", 10)), ) return json.dumps({"facts": facts, "count": len(facts)}) def _handle_fact_feedback(self, args: dict) -> str: return json.dumps(self._store.record_feedback(int(args["fact_id"]), helpful=args["action"] == "helpful")) _FACT_STORE_ACTIONS = { "add": _act_add, "search": _act_search, "probe": lambda self, args: self._act_entity(args, "probe"), "related": lambda self, args: self._act_entity(args, "related"), "reason": _act_reason, "contradict": _act_contradict, "update": _act_update, "remove": _act_remove, "list": _act_list, } _TOOL_HANDLERS = {"fact_store": _handle_fact_store, "fact_feedback": _handle_fact_feedback} # -- Auto-extraction (on_session_end) ------------------------------------ def _auto_extract_facts(self, messages: list) -> None: # Local import: the compressor module is heavier than this plugin and # only needed when auto_extract is on. from agent.context_compressor import ( _MERGED_PRIOR_CONTEXT_HEADER, _MERGED_SUMMARY_DELIMITER, is_compaction_summary_message, ) extracted = 0 for msg in messages: if msg.get("role") != "user": continue content = msg.get("content", "") # Compaction handoff summaries arrive as role="user" and reliably # match the decision patterns; skip them so the compactor's own # output is never stored as a durable fact. A merge-into-tail row # holds genuine prior user text BEFORE _MERGED_SUMMARY_DELIMITER # (prefixed with the header) and the summary AFTER it — harvest # only the pre-delimiter segment. if isinstance(content, str) and _MERGED_SUMMARY_DELIMITER in content: pre = content.split(_MERGED_SUMMARY_DELIMITER, 1)[0] if pre.startswith(_MERGED_PRIOR_CONTEXT_HEADER): pre = pre[len(_MERGED_PRIOR_CONTEXT_HEADER):] if pre.strip(): content = pre.strip() elif is_compaction_summary_message(msg): continue elif is_compaction_summary_message(msg): continue if not isinstance(content, str) or len(content) < 10: continue for patterns, category in ((_PREF_PATTERNS, "user_pref"), (_DECISION_PATTERNS, "project")): if any(p.search(content) for p in patterns): try: self._store.add_fact(content[:400], category=category) extracted += 1 except Exception: pass if extracted: logger.info("Auto-extracted %d facts from conversation", extracted) def register(ctx) -> None: """Register the holographic memory provider with the plugin system.""" ctx.register_memory_provider(HolographicMemoryProvider(config=_load_plugin_config()))