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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

2026-05-17 16:15:45 +03:00

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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

SELECT * FROM users;
SELECT name, age FROM users WHERE age > 18;
SELECT * FROM users ORDER BY age DESC LIMIT 10;

Vektor-Operationen

-- 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

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

-- 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

SELECT u.name, o.amount
FROM users u
INNER JOIN orders o ON u.id = o.user_id;

Aggregation

SELECT department, COUNT(*), AVG(salary)
FROM employees
GROUP BY department
HAVING COUNT(*) > 5;

Index-Erstellung

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

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

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