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/cron/embed_queue.py

@@ -0,0 +1,45 @@
+"""Embedding-Job-Queue für Tier 3."""
+
+import logging
+import time
+from hermes_memory.api.memory_api import MemoryAPI
+from hermes_memory.tier2.schema import connect
+from hermes_memory.config import load_config
+from pathlib import Path
+
+logger = logging.getLogger(__name__)
+
+
+def run_embed_queue(profile: str = "default", batch_size: int = 50) -> dict:
+    """Verarbeitet pending Embeddings aus der Queue."""
+    config = load_config(profile)
+    db_path = Path(config["tier2"]["db_path"].format(
+        HERMES_HOME=Path.home() / ".hermes",
+        profile=profile,
+    ))
+    conn = connect(db_path)
+    rows = conn.execute(
+        "SELECT id, fact_id, content, source_type, session_id, message_id FROM embedding_queue WHERE processed = 0 ORDER BY queued_at LIMIT ?",
+        (batch_size,),
+    ).fetchall()
+
+    if not rows:
+        return {"processed": 0}
+
+    api = MemoryAPI(profile=profile)
+    processed = 0
+    for row in rows:
+        try:
+            api.semantic_index(
+                text=row["content"],
+                source_type=row["source_type"],
+                session_id=row["session_id"],
+                message_id=row["message_id"],
+            )
+            conn.execute("UPDATE embedding_queue SET processed = 1 WHERE id = ?", (row["id"],))
+            processed += 1
+        except Exception as e:
+            logger.error("Embedding failed for queue id %s: %s", row["id"], e)
+
+    conn.commit()
+    return {"processed": processed}