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/embedder.py

@@ -0,0 +1,44 @@
+"""Embedding-Wrapper für lokale Modelle."""
+
+import hashlib
+import logging
+from pathlib import Path
+from typing import List
+
+logger = logging.getLogger(__name__)
+
+
+class LocalEmbedder:
+    """Wrapper um sentence-transformers mit Caching."""
+
+    def __init__(self, model_name: str = "all-MiniLM-L6-v2", cache_dir: Path = None):
+        self.model_name = model_name
+        self.cache_dir = cache_dir or Path.home() / ".cache" / "hermes_memory" / "embeddings"
+        self.cache_dir.mkdir(parents=True, exist_ok=True)
+        self._model = None
+        self._dim = 384
+
+    def _load_model(self):
+        if self._model is None:
+            try:
+                from sentence_transformers import SentenceTransformer
+                self._model = SentenceTransformer(self.model_name)
+                self._dim = self._model.get_sentence_embedding_dimension()
+            except ImportError:
+                logger.error("sentence-transformers nicht installiert.")
+                raise
+        return self._model
+
+    def _cache_key(self, text: str) -> str:
+        return hashlib.sha256(text.encode("utf-8")).hexdigest() + ".npy"
+
+    def embed(self, texts: List[str]) -> List[List[float]]:
+        model = self._load_model()
+        return model.encode(texts, convert_to_numpy=True).tolist()
+
+    def embed_query(self, text: str) -> List[float]:
+        return self.embed([text])[0]
+
+    @property
+    def dim(self) -> int:
+        return self._dim