docs: add German (DE) documentation + update all docs for Sessions 10-12

New German documentation (docs/de/):
- index.md, quickstart.md, installation.md
- baraql.md, graph.md, vector.md, mcp.md

Updated English documentation:
- changelog.md: all Sessions 10-12 features
- graph.md: SQL GRAPH_TABLE, CREATE GRAPH, all 8 algorithms, Cypher, similarity_nodes, node2vec
- vector.md: hybrid RAG, chunk(), embed_text(), auto-embed, nl_to_sql(), schema_prompt()
- baraql.md: new AI & Cross-Modal Functions section, updated keyword tables
- mcp.md: MCP Server documentation (new file)
- index.md: added German (DE) language link

docs/de/graph.md

@@ -0,0 +1,145 @@
+# Graph Engine
+
+Adjazenzlisten-Speicher mit eingebauten Algorithmen für Graph-Traversierung und -Analyse.
+Vollständig integriert in den SQL-Executor via `GRAPH_TABLE()`.
+
+## SQL — Graph DDL
+
+### Graph erstellen
+
+```sql
+CREATE GRAPH org_chart;
+```
+
+Erstellt automatisch zwei Tabellen:
+- `org_chart_nodes (id INTEGER PRIMARY KEY, node_label TEXT, properties TEXT)`
+- `org_chart_edges (source_id INTEGER, dest_id INTEGER, edge_label TEXT, weight REAL)`
+
+### Graph löschen
+
+```sql
+DROP GRAPH org_chart;
+```
+
+## SQL — Daten einfügen
+
+```sql
+-- Knoten
+INSERT INTO org_chart_nodes (id, node_label) VALUES (1, 'CEO');
+INSERT INTO org_chart_nodes (id, node_label) VALUES (2, 'VP');
+
+-- Kanten
+INSERT INTO org_chart_edges (source_id, dest_id, edge_label) VALUES (1, 2, 'manages');
+```
+
+Alle INSERTs werden automatisch mit dem nativen Graph-Objekt synchronisiert.
+
+## SQL — GRAPH_TABLE Abfragen
+
+### BFS (Breitensuche)
+
+```sql
+SELECT * FROM GRAPH_TABLE(org_chart MATCH (n)-[r]->(m)
+    ALGORITHM bfs
+    START 1 MAXDEPTH 2
+    COLUMNS (id, node_label));
+```
+
+### DFS (Tiefensuche)
+
+```sql
+SELECT * FROM GRAPH_TABLE(org_chart MATCH (n)-[r]->(m)
+    ALGORITHM dfs START 1
+    COLUMNS (id, node_label));
+```
+
+### PageRank
+
+```sql
+SELECT id, node_label, rank FROM GRAPH_TABLE(org_chart
+    ALGORITHM pagerank
+    COLUMNS (id, node_label, rank))
+ORDER BY rank DESC;
+```
+
+### Community Detection (Louvain)
+
+```sql
+SELECT id, node_label, community FROM GRAPH_TABLE(org_chart
+    ALGORITHM community
+    COLUMNS (id, node_label, community));
+```
+
+### Kürzester Pfad (Shortest Path)
+
+```sql
+SELECT * FROM GRAPH_TABLE(org_chart
+    ALGORITHM shortest_path
+    START 1 END 3
+    COLUMNS (id, node_label));
+```
+
+### Dijkstra (gewichtete kürzeste Pfade)
+
+```sql
+SELECT * FROM GRAPH_TABLE(org_chart
+    ALGORITHM dijkstra START 1
+    COLUMNS (id, node_label, distance));
+```
+
+## SQL-Funktionen
+
+### Knotenähnlichkeit
+
+```sql
+-- Jaccard-Ähnlichkeit zwischen allen Knotenpaaren
+SELECT similarity_nodes('social', 'jaccard') AS result;
+
+-- Adamic-Adar-Ähnlichkeit
+SELECT similarity_nodes('social', 'adamic_adar') AS result;
+```
+
+### Node2Vec Embeddings
+
+```sql
+-- Graphstruktur-Embeddings generieren (64 Dimensionen)
+SELECT node2vec_embed('social', 64) AS result;
+```
+
+## Cypher-Kompatibilität
+
+```sql
+-- Cypher-Syntax automatisch nach GRAPH_TABLE übersetzen
+SELECT cypher('MATCH (a)-[r]->(b) WHERE a.node_label = ''CEO'' RETURN b.node_label') AS result;
+```
+
+## Algorithmen
+
+| Algorithmus | Beschreibung | SQL |
+|-------------|--------------|-----|
+| `bfs` | Breitensuche | `ALGORITHM bfs` |
+| `dfs` | Tiefensuche | `ALGORITHM dfs` |
+| `dijkstra` | Gewichtete kürzeste Pfade | `ALGORITHM dijkstra` |
+| `pageRank` | Knoten-Wichtigkeit | `ALGORITHM pagerank` |
+| `louvain` | Community Detection | `ALGORITHM community` |
+| `shortestPath` | Kürzester Pfad | `ALGORITHM shortest_path START X END Y` |
+| `similarityNodes` | Knotenähnlichkeit | `similarity_nodes()` |
+| `node2vec` | Graph Embeddings | `node2vec_embed()` |
+
+## Native Nim API
+
+```nim
+import barabadb/graph/engine
+
+var g = newGraph()
+let alice = g.addNode("Person", {"name": "Alice"}.toTable)
+let bob = g.addNode("Person", {"name": "Bob"}.toTable)
+discard g.addEdge(alice, bob, "knows")
+
+let bfs = g.bfs(alice)
+let path = g.shortestPath(alice, bob)
+let ranks = g.pageRank()
+let communities = louvain(g)
+let similarities = g.similarityNodes(smJaccard)
+let embeddings = g.node2vec(64, 10, 5)
+```