commit 89f1bae5e05450997be6525ce15825b3eb6f1083 Author: Emil Shanaty Date: Tue May 26 22:19:49 2026 +0300 Initial release: MoME — Mixture of Memory Experts plugin for Hermes Agent diff --git a/README.md b/README.md new file mode 100644 index 0000000..2bb0d2d --- /dev/null +++ b/README.md @@ -0,0 +1,116 @@ +# MoME — Mixture of Memory Experts for Hermes Agent + +Sparse-gated personal memory with an online-learning router. Replaces monolithic memory context with expert-routed retrieval — only relevant memory experts are activated per query. + +## Features + +- **4 Experts**: `identity`, `knowledge`, `projects`, `preferences` +- **Sparse Activation**: Only 1–2 experts are queried per turn (determined by the router) +- **Online Learning**: SGD classifier router learns which expert to route to based on usage patterns +- **Local-Only**: 100% offline, no API keys needed +- **Auto-Learning**: Regex-based fact extraction from user queries +- **Persistent**: JSON-based storage, survives restarts + +## Installation + +```bash +# 1. Clone the plugin into Hermes plugins directory +git clone https://github.com/emil28092005/hermes-plugin-mome.git \ + ~/.hermes/hermes-agent/plugins/memory/mome + +# 2. Install dependencies +pip install numpy scikit-learn + +# 3. Activate via Hermes memory setup +hermes memory setup +``` + +Select `mome` from the list of available memory providers. + +## Usage + +Once activated, MoME provides three tools: + +### `mome_search` +Search memory across experts. The router automatically selects which experts to query. + +```text +mome_search(query="what projects am I working on?", top_k=3) +mome_search(query="Python skills", expert="knowledge", top_k=5) +``` + +### `mome_store` +Store a fact directly into a specific expert. + +```text +mome_store(expert="identity", fact="I prefer dark mode") +``` + +### `mome_status` +Show expert sizes and router training state. + +```text +mome_status() +``` + +## Architecture + +``` +User Query + │ + ▼ +┌─────────────┐ +│ Router │ ← SGDClassifier (online learning) +│ (predict) │ +└──────┬──────┘ + │ top-2 experts selected + ▼ +┌──────┴──────┐ +│ Experts │ +│ identity │ ─── cosine similarity search +│ knowledge │ +│ projects │ +│ preferences │ +└──────┬──────┘ + │ relevant memories + ▼ +┌─────────────┐ +│ Context │ → injected into system prompt +└─────────────┘ +``` + +### Fact Extraction (Auto-Learning) + +On each turn, MoME automatically extracts facts from user queries via regex: + +- `меня зовут X` → identity +- `я живу в X` → identity +- `работаю над X` → projects +- `у меня проект X` → projects +- `знаю/использую X` → knowledge +- `нравится X` → preferences + +The router learns over time which experts to activate for which types of queries. + +## Dependencies + +- Python ≥ 3.10 +- `numpy` — vector operations +- `scikit-learn` — SGD router classifier + +Zero external API dependencies. Works fully offline. + +## Development + +```bash +# Test the engine standalone +python -c "from plugins.memory.mome.engine import MomeEngine; e = MomeEngine('/tmp/test_mome'); e.store_fact('identity', 'Test fact'); print(e.query('test'))" +``` + +## Author + +**Emil Shanaty** — [github.com/emil28092005](https://github.com/emil28092005) + +## License + +MIT diff --git a/__init__.py b/__init__.py new file mode 100644 index 0000000..c1eb638 --- /dev/null +++ b/__init__.py @@ -0,0 +1,283 @@ +""" +MoME — Mixture of Memory Experts plugin for Hermes Agent. + +Sparse-gated personal memory with online-learning router. +Replaces monolithic memory context with expert-routed retrieval. + +Activate:: + hermes memory setup + # then select "mome" from the list +""" + +from __future__ import annotations + +import json +import logging +import os +import threading +from pathlib import Path +from typing import Any, Dict, List, Optional + +from agent.memory_provider import MemoryProvider + +from .engine import EXPERT_NAMES, MomeEngine + +logger = logging.getLogger(__name__) + +# ─── Tool Schemas ───────────────────────────────────────────────────────── + +MEMORY_SEARCH_SCHEMA = { + "name": "mome_search", + "description": "Search MoME memory experts. Returns relevant facts routed to the right expert.", + "parameters": { + "type": "object", + "properties": { + "query": { + "type": "string", + "description": "What to search for.", + }, + "expert": { + "type": "string", + "enum": EXPERT_NAMES + ["all"], + "description": "Which expert to search (default: auto-routed).", + }, + "top_k": { + "type": "integer", + "description": "Max results (default: 3).", + }, + }, + "required": ["query"], + }, +} + +MEMORY_STORE_SCHEMA = { + "name": "mome_store", + "description": "Store a fact in a specific MoME memory expert.", + "parameters": { + "type": "object", + "properties": { + "expert": { + "type": "string", + "enum": EXPERT_NAMES, + "description": "Target expert.", + }, + "fact": { + "type": "string", + "description": "The fact to remember.", + }, + }, + "required": ["expert", "fact"], + }, +} + +MEMORY_STATUS_SCHEMA = { + "name": "mome_status", + "description": "Show MoME memory status — expert sizes and router learning state.", + "parameters": {"type": "object", "properties": {}}, +} + + +# ─── MoME Provider ───────────────────────────────────────────────────────── + +class MomeProvider(MemoryProvider): + """MoME — Mixture of Memory Experts for Hermes Agent. + + Sparse-gated personal memory with: + - 4 experts (identity, knowledge, projects, preferences) + - Online-learning SGD classifier router + - Regex-based fact extraction from user queries + """ + + def __init__(self): + self._engine: Optional[MomeEngine] = None + self._hermes_home: Optional[Path] = None + self._prefetch_result = "" + self._prefetch_lock = threading.Lock() + self._prefetch_thread: Optional[threading.Thread] = None + + @property + def name(self) -> str: + return "mome" + + def is_available(self) -> bool: + return True # 100% local, no API keys needed + + def get_config_schema(self) -> List[Dict[str, Any]]: + return [ + { + "key": "store_dir", + "description": ( + "Directory for MoME memory storage " + "(relative to HERMES_HOME)" + ), + "default": "mome_store", + "required": False, + }, + ] + + def initialize(self, session_id: str, **kwargs) -> None: + hermes_home = kwargs.get( + "hermes_home", + os.environ.get("HERMES_HOME", ""), + ) + if hermes_home: + self._hermes_home = Path(hermes_home) + else: + self._hermes_home = Path.home() / ".hermes" + + store_dir = self._hermes_home / "mome_store" + self._engine = MomeEngine(store_dir) + + logger.info( + "MoME initialized: %s (%d experts, %d total facts)", + store_dir, + len(self._engine.experts), + sum(e.count() for e in self._engine.experts.values()), + ) + + def system_prompt_block(self) -> str: + if not self._engine: + return "" + stats = self._engine.get_stats() + total = sum(stats.values()) + return ( + "## MoME Memory\n" + f"Active. Experts: " + f"{', '.join(f'{k}={v}' for k, v in stats.items())} " + f"total={total}\n" + "Use `mome_search` to find memories, " + "`mome_store` to save facts, " + "`mome_status` for expert info." + ) + + def prefetch(self, query: str, *, session_id: str = "") -> str: + if not self._engine: + return "" + context = self._engine.query(query) + if context: + return f"## MoME Memory Context\n{context}" + return "" + + def queue_prefetch(self, query: str, *, session_id: str = "") -> None: + if not self._engine: + return + + def _run() -> None: + try: + context = self._engine.query(query) + with self._prefetch_lock: + self._prefetch_result = context + except Exception as e: + logger.debug("MoME prefetch failed: %s", e) + + self._prefetch_thread = threading.Thread( + target=_run, daemon=True, name="mome-prefetch", + ) + self._prefetch_thread.start() + + def sync_turn( + self, + user_content: str, + assistant_content: str, + *, + session_id: str = "", + ) -> None: + if not self._engine: + return + try: + self._engine.extract_and_store(user_content) + except Exception as e: + logger.debug("MoME sync failed: %s", e) + + def get_tool_schemas(self) -> List[Dict[str, Any]]: + return [ + MEMORY_SEARCH_SCHEMA, + MEMORY_STORE_SCHEMA, + MEMORY_STATUS_SCHEMA, + ] + + def handle_tool_call( + self, + tool_name: str, + args: Dict[str, Any], + **kwargs, + ) -> str: + if not self._engine: + return json.dumps({"error": "MoME not initialized"}) + + if tool_name == "mome_search": + return self._handle_search(args) + elif tool_name == "mome_store": + return self._handle_store(args) + elif tool_name == "mome_status": + return self._handle_status() + + return json.dumps({"error": f"Unknown tool: {tool_name}"}) + + def _handle_search(self, args: Dict[str, Any]) -> str: + query = args.get("query", "") + expert_filter = args.get("expert", "all") + top_k = int(args.get("top_k", 3)) + + if expert_filter == "all": + selected = self._engine.router.predict(query, top_k=2) + results = [] + for name, conf in selected: + memories = self._engine.experts[name].search(query, top_k=top_k) + for m in memories: + results.append({ + "expert": name, + "memory": m, + "confidence": round(conf, 2), + }) + return json.dumps({"results": results, "count": len(results)}) + + elif expert_filter in self._engine.experts: + memories = self._engine.experts[expert_filter].search( + query, top_k=top_k, + ) + results = [ + {"expert": expert_filter, "memory": m} + for m in memories + ] + return json.dumps({"results": results, "count": len(results)}) + + return json.dumps({"error": f"Unknown expert: {expert_filter}"}) + + def _handle_store(self, args: Dict[str, Any]) -> str: + expert = args.get("expert", "") + fact = args.get("fact", "") + if expert in self._engine.experts and fact: + self._engine.experts[expert].write(fact) + return json.dumps({ + "result": f"Stored in [{expert}]", + "fact": fact, + }) + return json.dumps({ + "error": f"Invalid expert '{expert}' or empty fact", + }) + + def _handle_status(self) -> str: + stats = self._engine.get_stats() + router_status = ( + "trained" + if self._engine.router._fitted + else "untrained (default routing)" + ) + return json.dumps({ + "expert_counts": stats, + "total": sum(stats.values()), + "router": router_status, + "experts": EXPERT_NAMES, + }) + + def shutdown(self) -> None: + if self._prefetch_thread and self._prefetch_thread.is_alive(): + self._prefetch_thread.join(timeout=3.0) + self._engine = None + logger.info("MoME shut down") + + +def register(ctx) -> None: + """Register MoME as a Hermes memory provider plugin.""" + ctx.register_memory_provider(MomeProvider()) diff --git a/__pycache__/__init__.cpython-311.pyc b/__pycache__/__init__.cpython-311.pyc new file mode 100644 index 0000000..b5126b5 Binary files /dev/null and b/__pycache__/__init__.cpython-311.pyc differ diff --git a/__pycache__/engine.cpython-311.pyc b/__pycache__/engine.cpython-311.pyc new file mode 100644 index 0000000..44757fc Binary files /dev/null and b/__pycache__/engine.cpython-311.pyc differ diff --git a/engine.py b/engine.py new file mode 100644 index 0000000..c702616 --- /dev/null +++ b/engine.py @@ -0,0 +1,236 @@ +""" +MoME Engine — Mixture of Memory Experts core. +Sparse-gated personal memory with online-learning router. + +Requires: numpy, scikit-learn +""" + +from __future__ import annotations + +import json +import logging +import re +import threading +import time +import uuid +from pathlib import Path +from typing import Any, Dict, List, Optional + +import numpy as np +from sklearn.linear_model import SGDClassifier + +logger = logging.getLogger(__name__) + +# ─── Expert Names ────────────────────────────────────────────────────────── + +EXPERT_NAMES = ["identity", "knowledge", "projects", "preferences"] + +# ─── Tiny Embedder ───────────────────────────────────────────────────────── + +class TinyEmbedder: + """Минимальный bag-of-words эмбеддер без внешних зависимостей.""" + + def __init__(self, dim: int = 384): + self.dim = dim + self.word_vectors: Dict[str, np.ndarray] = {} + self.rng = np.random.RandomState(42) + + def _get_word_vec(self, word: str) -> np.ndarray: + if word not in self.word_vectors: + v = self.rng.randn(self.dim).astype(np.float32) + v /= np.linalg.norm(v) + 1e-8 + self.word_vectors[word] = v + return self.word_vectors[word] + + def embed(self, text: str) -> np.ndarray: + words = re.findall(r'\w+', text.lower()) + if not words: + return np.zeros(self.dim, dtype=np.float32) + vecs = [self._get_word_vec(w) for w in words] + vec = np.mean(vecs, axis=0).astype(np.float32) + vec /= np.linalg.norm(vec) + 1e-8 + return vec + + +# ─── Memory Expert ───────────────────────────────────────────────────────── + +class MemoryExpert: + """Хранилище памяти эксперта с векторным поиском.""" + + def __init__(self, name: str, embedder: TinyEmbedder, store_dir: Path): + self.name = name + self.embedder = embedder + self.path = store_dir / f"{name}.json" + self.memories: List[Dict[str, Any]] = [] + self._lock = threading.Lock() + self.load() + + def load(self) -> None: + if self.path.exists(): + try: + with open(self.path) as f: + data = json.load(f) + for m in data: + m["embedding"] = np.array(m["embedding"], dtype=np.float32) + self.memories = data + except Exception: + self.memories = [] + + def save(self) -> None: + data = [] + for m in self.memories: + entry = dict(m) + entry["embedding"] = m["embedding"].tolist() + data.append(entry) + self.path.parent.mkdir(parents=True, exist_ok=True) + with open(self.path, "w") as f: + json.dump(data, f, ensure_ascii=False, indent=2) + + def write(self, text: str) -> None: + with self._lock: + embedding = self.embedder.embed(text) + self.memories.append({ + "id": str(uuid.uuid4()), + "text": text, + "embedding": embedding, + "timestamp": time.time(), + "access_count": 0, + }) + self.save() + + def search(self, query: str, top_k: int = 3) -> List[str]: + with self._lock: + if not self.memories: + return [] + q_emb = self.embedder.embed(query) + scores = [float(np.dot(q_emb, m["embedding"])) for m in self.memories] + top_idx = np.argsort(scores)[::-1][:top_k] + results = [] + for i in top_idx: + self.memories[i]["access_count"] += 1 + results.append(self.memories[i]["text"]) + self.save() + return results + + def all(self) -> List[str]: + with self._lock: + return [m["text"] for m in self.memories] + + def count(self) -> int: + with self._lock: + return len(self.memories) + + def delete(self, text_contains: str) -> None: + with self._lock: + self.memories = [m for m in self.memories if text_contains not in m["text"]] + self.save() + + +# ─── Online Router ───────────────────────────────────────────────────────── + +class MemoryRouter: + """Online-learning роутер экспертов через SGDClassifier.""" + + def __init__(self, embedder: TinyEmbedder): + self.embedder = embedder + self.classifier = SGDClassifier( + loss='log_loss', penalty='l2', alpha=0.001, + learning_rate='adaptive', eta0=0.01, + warm_start=True, random_state=42, + ) + self._fitted = False + self._classes = np.array(EXPERT_NAMES) + + def predict(self, query: str, top_k: int = 2) -> List[tuple[str, float]]: + emb = self.embedder.embed(query).reshape(1, -1) + if not self._fitted: + return [(name, 0.5) for name in EXPERT_NAMES[:top_k]] + probs = self.classifier.predict_proba(emb)[0] + top_indices = np.argsort(probs)[::-1][:top_k] + return [(EXPERT_NAMES[i], float(probs[i])) for i in top_indices] + + def update(self, query: str, feedback: Dict[str, float]) -> None: + emb = self.embedder.embed(query).reshape(1, -1) + best_expert = max(feedback, key=feedback.get) + target = np.array([best_expert]) + if not self._fitted: + self.classifier.partial_fit(emb, target, classes=self._classes) + self._fitted = True + else: + self.classifier.partial_fit(emb, target) + + +# ─── MoME Engine ─────────────────────────────────────────────────────────── + +class MomeEngine: + """Ядро MoME: роутер + эксперты + извлечение фактов.""" + + def __init__(self, store_dir: Path): + self.store_dir = store_dir + self.store_dir.mkdir(parents=True, exist_ok=True) + self.embedder = TinyEmbedder(dim=384) + self.router = MemoryRouter(self.embedder) + self.experts = { + name: MemoryExpert(name, self.embedder, store_dir) + for name in EXPERT_NAMES + } + + def query(self, text: str, top_k: int = 2) -> str: + """Получить релевантный контекст. Возвращает форматированную строку.""" + selected = self.router.predict(text, top_k=top_k) + parts = [] + for name, confidence in selected: + results = self.experts[name].search(text, top_k=3) + if results: + lines = "\n".join(f"• {r}" for r in results) + parts.append(f"[{name.upper()}] (conf: {confidence:.2f}):\n{lines}") + return "\n\n".join(parts) if parts else "" + + def store_fact(self, expert: str, fact: str) -> bool: + """Сохранить факт в указанного эксперта.""" + if expert in self.experts and fact: + self.experts[expert].write(fact) + return True + return False + + def learn(self, query: str, expert_ratings: Dict[str, float]) -> None: + """Обучить роутер на feedback.""" + self.router.update(query, expert_ratings) + + def extract_and_store(self, query: str) -> int: + """Извлечь факты из запроса через regex и сохранить.""" + facts = [] + + name_match = re.search(r'(?:меня\s+зовут|мо[её]\s+имя)\s+([^,\.!?]+)', query, re.IGNORECASE) + if name_match: + facts.append(("identity", f"Меня зовут {name_match.group(1).strip()}.")) + + city_match = re.search(r'(?:я\s+(?:из|живу\s+в)\s+)([^,\.!?]+)', query, re.IGNORECASE) + if city_match: + facts.append(("identity", f"Я живу в {city_match.group(1).strip()}.")) + + work_match = re.search(r'(?:занимаюсь|работаю\s+над|делаю|пишу)\s+([^,\.!?]+)', query, re.IGNORECASE) + if work_match: + facts.append(("projects", f"Работает над {work_match.group(1).strip()}.")) + + project_match = re.search(r'(?:проект|мой\s+проект|у\s+меня\s+(?:есть\s+)?проект)\s+([^,\.!?]+)', query, re.IGNORECASE) + if project_match: + facts.append(("projects", f"Проект: {project_match.group(1).strip()}.")) + + skill_match = re.search(r'(?:знаю|умею|использую|пишу\s+на|стек|технологии?)\s*[\:\-]?\s*([^,\.!?]+)', query, re.IGNORECASE) + if skill_match: + facts.append(("knowledge", f"Знает/использует: {skill_match.group(1).strip()}.")) + + like_match = re.search(r'(?:нравится|люблю|предпочитаю)\s+([^,\.!?]+)', query, re.IGNORECASE) + if like_match: + facts.append(("preferences", f"Предпочитает {like_match.group(1).strip()}.")) + + stored = 0 + for expert, fact in facts: + if self.store_fact(expert, fact): + stored += 1 + logger.info(" 💾 [%s] запомнил: %s", expert, fact[:60]) + return stored + + def get_stats(self) -> Dict[str, int]: + return {name: expert.count() for name, expert in self.experts.items()} diff --git a/memory_store/identity.json b/memory_store/identity.json new file mode 100644 index 0000000..08e9eaa --- /dev/null +++ b/memory_store/identity.json @@ -0,0 +1,1962 @@ +[ + { + "id": "73c38d77-32c5-403f-8f62-7d1e972c3ac5", + "text": "Меня зовут Эмиль (Emil Shanaty).", + "embedding": [ + -0.022007036954164505, + 0.0005390773876570165, + 0.04902300983667374, + 0.01993601582944393, + 0.01439555175602436, + 0.09671340882778168, + 0.09360556304454803, + -0.025723077356815338, + -0.1279929280281067, + 0.11034682393074036, + -0.04125068709254265, + -0.036739569157361984, + 0.01279342919588089, + 0.03648484870791435, + 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Sparse-gated personal memory with online-learning router for Hermes Agent." +version: 0.1.0 +author: Emil Shanaty +homepage: https://github.com/emil28092005/hermes-plugin-mome +repository: https://github.com/emil28092005/hermes-plugin-mome +tags: [memory, mome, moe, experts, personalization, routing] +license: MIT +python: + requirements: + - numpy>=1.24 + - scikit-learn>=1.3