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/baraql.md

@@ -0,0 +1,114 @@
+# BaraQL — Abfragesprache-Referenz
+
+BaraQL ist eine SQL-kompatible Abfragesprache mit Erweiterungen für Graph-, Vektor- und Dokumentoperationen.
+
+## Datentypen
+
+| Typ | Beschreibung | Beispiel |
+|------|-------------|---------|
+| `null` | Nullwert | `null` |
+| `bool` | Boolean | `true`, `false` |
+| `int64` | 64-bit Ganzzahl | `42` |
+| `float64` | 64-bit Fließkomma | `3.14` |
+| `str` | UTF-8 String | `'hello'` |
+| `vector(n)` | Float32 Vektor | `VECTOR(768)` |
+| `json` | JSON-Dokument | `{"key": "value"}` |
+
+## Grundlegende Abfragen
+
+```sql
+SELECT * FROM users;
+SELECT name, age FROM users WHERE age > 18;
+SELECT * FROM users ORDER BY age DESC LIMIT 10;
+```
+
+## Vektor-Operationen
+
+```sql
+-- Distanzberechnungen
+SELECT cosine_distance(embedding, '[0.1, 0.2, 0.3]') AS dist FROM items;
+SELECT embedding <-> '[0.1, 0.2, 0.3]' AS dist FROM items;
+
+-- Hybride Suche
+SELECT hybrid_search('query', embedding, content, 10) AS result;
+```
+
+## Graph-Operationen
+
+```sql
+CREATE GRAPH social;
+DROP GRAPH social;
+
+-- Traversierung
+SELECT * FROM GRAPH_TABLE(social MATCH (n)-[r]->(m)
+    ALGORITHM bfs START 1
+    COLUMNS (id, node_label));
+
+-- PageRank
+SELECT * FROM GRAPH_TABLE(social ALGORITHM pagerank
+    COLUMNS (id, node_label, rank));
+
+-- Community Detection
+SELECT * FROM GRAPH_TABLE(social ALGORITHM community
+    COLUMNS (id, node_label, community));
+```
+
+## AI-Funktionen
+
+```sql
+-- Text in Chunks zerlegen
+SELECT chunk('Langer Text...', 1024, 128) AS result;
+
+-- Embedding generieren
+SELECT embed_text('Suchtext') AS result;
+
+-- Natural Language → SQL
+SELECT nl_to_sql('Zeige alle Benutzer über 25', 'users') AS result;
+
+-- Schema-Prompt für LLM
+SELECT schema_prompt('users') AS result;
+
+-- Cypher-Übersetzung
+SELECT cypher('MATCH (a)-[r]->(b) RETURN a.node_label') AS result;
+
+-- Knotenähnlichkeit
+SELECT similarity_nodes('social', 'jaccard') AS result;
+
+-- Graph-Embeddings
+SELECT node2vec_embed('social', 64) AS result;
+```
+
+## Joins
+
+```sql
+SELECT u.name, o.amount
+FROM users u
+INNER JOIN orders o ON u.id = o.user_id;
+```
+
+## Aggregation
+
+```sql
+SELECT department, COUNT(*), AVG(salary)
+FROM employees
+GROUP BY department
+HAVING COUNT(*) > 5;
+```
+
+## Index-Erstellung
+
+```sql
+CREATE INDEX idx_name ON users(name) USING btree;
+CREATE INDEX idx_vec ON docs(embedding) USING hnsw;
+CREATE INDEX idx_fts ON docs(content) USING fts;
+```
+
+## Multi-Tenant / RLS
+
+```sql
+CREATE POLICY tenant_policy ON orders
+FOR ALL USING (tenant_id = current_setting('app.tenant_id'));
+
+SET app.tenant_id = 'company-a';
+SELECT * FROM orders;  -- Automatisch gefiltert
+```