Add AI Council architecture: Tier 2/3/Graph implementation + Integration Plan

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.

hermes_memory/tier3/backend.py

@@ -0,0 +1,35 @@
+"""Abstrakte Backend-Schnittstelle für Vektor-DBs."""
+
+from abc import ABC, abstractmethod
+from dataclasses import dataclass
+from typing import Dict, List
+
+
+@dataclass
+class SearchResult:
+    chunk_id: str
+    score: float
+    text: str
+    metadata: Dict
+
+
+class VectorBackend(ABC):
+    @abstractmethod
+    def index(self, chunks: List[str], payloads: List[Dict]) -> List[str]:
+        """Indexiert Chunks, gibt chunk_ids zurück."""
+        ...
+
+    @abstractmethod
+    def search(self, query_embedding: List[float], limit: int = 10, filters: Dict = None) -> List[SearchResult]:
+        """Semantische Suche mit Query-Embedding."""
+        ...
+
+    @abstractmethod
+    def delete(self, chunk_ids: List[str]) -> bool:
+        """Löscht Chunks anhand ihrer IDs."""
+        ...
+
+    @abstractmethod
+    def health(self) -> Dict:
+        """Gibt Status-Informationen zurück."""
+        ...