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/vector.md

@@ -0,0 +1,129 @@
+# Vektor-Suche
+
+Native HNSW und IVF-PQ Indizes für Ähnlichkeitssuche mit vollständiger SQL-Integration.
+
+## SQL — Vektor-Spalten
+
+```sql
+CREATE TABLE items (
+    id INT PRIMARY KEY,
+    embedding VECTOR(768)
+);
+```
+
+Der `VECTOR(n)`-Typ speichert float32-Arrays mit fester Dimension `n`.
+
+## Vektoren einfügen
+
+```sql
+INSERT INTO items (id, embedding) VALUES (1, '[0.1, 0.2, 0.3, ...]');
+```
+
+## Vektor-Distanzfunktionen
+
+```sql
+-- Kosinus-Distanz (0 = identisch, 1 = orthogonal)
+SELECT id, cosine_distance(embedding, '[0.1, 0.2, 0.3]') AS dist FROM items;
+
+-- Euklidische / L2 Distanz
+SELECT id, euclidean_distance(embedding, '[0.1, 0.2, 0.3]') AS dist FROM items;
+SELECT id, embedding <-> '[0.1, 0.2, 0.3]' AS dist FROM items;
+
+-- Inneres Produkt (negativ für Minimierung)
+SELECT id, inner_product(embedding, '[0.1, 0.2, 0.3]') AS dist FROM items;
+
+-- Manhattan / L1 Distanz
+SELECT id, l1_distance(embedding, '[0.1, 0.2, 0.3]') AS dist FROM items;
+```
+
+## Vektor-Indizes
+
+```sql
+-- HNSW-Index für approximative Nächste-Nachbarn-Suche
+CREATE INDEX idx_items_vec ON items(embedding) USING hnsw;
+
+-- Der Index wird bei INSERT und UPDATE automatisch aktualisiert
+```
+
+## Hybrid RAG Search
+
+```sql
+-- Kombinierte Vektor- + Volltext-Suche mit RRF-Reranking
+SELECT hybrid_search('AI query', embedding, content, 10) AS result;
+
+-- Gefilterte hybride Suche
+SELECT hybrid_search_filtered('AI query', embedding, content, 10, 'category', 'news') AS result;
+```
+
+## AI Pipeline
+
+### Text-Chunking
+
+```sql
+-- Text in überlappende Chunks zerlegen
+SELECT chunk('Langer Text hier...', 1024, 128) AS result;
+
+-- Ergebnis: [{"index":0, "text":"...", "size":124}, ...]
+```
+
+### Embedding-Generierung
+
+```sql
+-- Externen Embedding-Service aufrufen (konfiguriert via Umgebungsvariablen)
+SELECT embed_text('Suchtext hier') AS result;
+```
+
+Umgebungsvariablen für den Embedder:
+```bash
+export BARADB_EMBED_ENDPOINT=http://localhost:11434/api/embeddings
+export BARADB_EMBED_MODEL=nomic-embed-text
+```
+
+### Auto-Embedding bei INSERT
+
+Wenn eine VECTOR-Spalte NULL ist, aber eine TEXT-Spalte einen Wert hat, wird das Embedding automatisch generiert (falls ein Embedder konfiguriert ist).
+
+```sql
+CREATE TABLE docs (id INTEGER PRIMARY KEY, content TEXT, embedding VECTOR(768));
+CREATE INDEX docs_vec ON docs(embedding) USING hnsw;
+
+-- embedding wird automatisch gefüllt
+INSERT INTO docs (id, content) VALUES (1, 'Dieser Text wird automatisch embedded');
+```
+
+## Distanzmetriken
+
+| Metrik | SQL-Funktion | Beschreibung |
+|--------|-------------|-------------|
+| `cosine` | `cosine_distance(a, b)` | Kosinus-Distanz |
+| `euclidean` | `euclidean_distance(a, b)` / `<->` | L2-Distanz |
+| `dotproduct` | `inner_product(a, b)` | Negatives Skalarprodukt |
+| `manhattan` | `l1_distance(a, b)` | L1-Distanz |
+
+## Index-Typen
+
+### HNSW (Standard)
+
+```nim
+import barabadb/vector/engine
+var hnsw = newHNSWIndex(dimensions = 128, m = 16, efConstruction = 200)
+```
+
+### IVF-PQ
+
+```nim
+var ivfpq = newIVFPQIndex(dimensions = 128, numCentroids = 256, subQuantizers = 8)
+```
+
+## Native Nim API
+
+```nim
+import barabadb/vector/engine
+
+var idx = newHNSWIndex(dimensions = 128)
+idx.insert(1, @[1.0'f32, 0.0'f32, ...], {"category": "A"}.toTable)
+
+let results = idx.search(queryVector, k = 10)
+let filtered = idx.searchWithFilter(queryVector, k = 10,
+  filter = proc(meta: Table[string, string]): bool = return "category" in meta)
+```