Florian Hartmann
b83546d833
Architecture (Agent 1):
- hermes_memory/tier2/{schema,facts,entities,relations,timeline}.py
- hermes_memory/tier3/{backend,chroma_backend,embedder}.py
- hermes_memory/graph/nx_store.py
- hermes_memory/api/memory_api.py (unified API)
- hermes_memory/cron/{consolidate,embed_queue,graph_refresh,prune}.py
- hermes_memory/config.py + pyproject.toml
Integration Plan (Agent 3):
- INTEGRATION_PLAN.md: Memory Provider Plugin strategy
- Hermes Core needs minimal changes
- sync_turn() + prefetch() hooks
- Skills integration via nextlevel_search/remember
Auto-Extraction (Agent 2):
- ARCHITECTURE.md: Full extraction pipeline docs
- Chunking, Pre-Filter, LLM Prompts, Classification
- Entity-Linking, Temporal Reasoning, Deduplication
All files: Python syntax checked, ECC standards applied.
172 lines
4.0 KiB
Markdown
172 lines
4.0 KiB
Markdown
# Hermes Memory Next Level — Integrations-Plan
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**Ziel:** Schrittweise Integration in Hermes Agent ohne Breaking Changes.
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---
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## 1. Memory Tool Erweiterung (tools/memory_tool.py)
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### Neue Actions
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```python
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# Bestehende Actions: add, replace, remove, read
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# Neue Actions für Tier 2/3/Graph:
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"fact_store" -> api.fact_store(content, category, confidence, source)
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"fact_query" -> api.fact_query(query, category, limit)
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"entity_ensure" -> api.entity_ensure(name, entity_type, aliases)
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"entity_link" -> api.entity_link(from_name, to_name, relation)
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"semantic_search"-> api.semantic_search(query, limit)
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"recall" -> api.recall(query, tiers)
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"stats" -> api.stats()
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```
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### Implementierung
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```python
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# In MemoryStore oder als neues Modul
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from hermes_memory import MemoryAPI
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class NextLevelMemoryStore:
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def __init__(self):
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self.api = MemoryAPI()
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def handle_action(self, action: str, **kwargs) -> dict:
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handler = getattr(self, f"_handle_{action}", None)
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if handler:
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return handler(**kwargs)
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return {"success": False, "error": f"Unknown action: {action}"}
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```
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---
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## 2. Session Search Tool (tools/session_search_tool.py)
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### Automatische Indexierung
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```python
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# Nach Discovery/Scroll: Indexiere Ergebnisse in Tier 3
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def _index_results(results: list, session_id: str):
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api = MemoryAPI()
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for r in results:
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api.semantic_index(
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text=r.get("content", "")[:1000],
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source_type="session",
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session_id=session_id,
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message_id=r.get("id")
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)
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```
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---
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## 3. Cronjob Scheduler (cron/scheduler.py)
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### Neue Jobs registrieren
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```python
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# In scheduler.py oder jobs.py:
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from hermes_memory.cron import run_consolidate, run_embed_queue, run_graph_refresh, run_prune
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MEMORY_CRON_JOBS = {
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"memory.consolidate": {
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"schedule": "0 3 * * *",
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"func": run_consolidate,
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},
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"memory.embed_queue": {
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"schedule": "*/5 * * * *",
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"func": run_embed_queue,
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},
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"memory.graph_refresh": {
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"schedule": "0 4 * * 0",
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"func": run_graph_refresh,
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},
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"memory.prune": {
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"schedule": "0 2 1 * *",
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"func": run_prune,
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},
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}
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```
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---
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## 4. Skills System
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### Skill-Manifest Erweiterung
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```json
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{
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"name": "project_tracker",
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"memory_tiers": ["tier2", "tier3"],
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"memory_queries": [
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"aktuelle Projekte",
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"technische Entscheidungen"
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]
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}
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```
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### Skill-Context Injection
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```python
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# In skill_manager_tool.py:
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def _inject_memory_context(skill_id: str, query: str) -> str:
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api = MemoryAPI()
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context = api.recall(query, tiers=["tier1", "tier2"], limit_per_tier=3)
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return format_memory_context(context)
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```
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---
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## 5. System Prompt Integration
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```python
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# In agent_init.py / prompt_builder.py:
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def build_memory_context(api: MemoryAPI) -> str:
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parts = []
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# Tier 1: Curated (frozen snapshot)
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parts.append(api.curated_get("memory"))
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parts.append(api.curated_get("user"))
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# Tier 2: Relevante Fakten
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facts = api.fact_query(category="user", limit=5, min_confidence=0.8)
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parts.append("## Bekannte Fakten\n" + format_facts(facts))
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# Graph: Zentrale Entitäten
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central = api.graph_central_entities(limit=5)
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parts.append("## Wichtige Entitäten\n" + format_entities(central))
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return "\n\n".join(parts)
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```
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---
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## 6. Migrationsschritte
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### Phase 1: Tier 2 (SQLite)
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- [ ] `hermes_memory/` in Hermes-Codebase kopieren
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- [ ] `memory_tool.py` um Tier-2-Actions erweitern
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- [ ] Schema-Initialisierung bei Agent-Start
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- [ ] Test: Fakten speichern & abfragen
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### Phase 2: Tier 3 (Chroma)
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- [ ] `chromadb` als optional dependency
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- [ ] Embedding-Queue implementieren
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- [ ] `semantic_search` Action hinzufügen
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- [ ] Session-Search-Integration
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### Phase 3: Graph (NetworkX)
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- [ ] Entity-Extraktion aus Sessions
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- [ ] Graph-Builder implementieren
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- [ ] Traversal-Tools hinzufügen
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### Phase 4: Unified API
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- [ ] Cross-Tier `recall()` implementieren
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- [ ] Skill-Memory-Adapter
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- [ ] Performance-Optimierung
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---
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*Integrations-Plan v1.0*
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