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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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BaraDB — Schnellstart

Server starten

./build/baradadb

Der Server startet standardmäßig auf localhost:9470.

Verbindung via CLI

./build/baradadb --shell

MCP Server (AI Agenten)

./build/baramcp --data-dir ./data

Der MCP Server startet im STDIO-Modus und stellt 3 Tools für AI-Agenten bereit: query, vector_search, schema_inspect.

Grundlegende Operationen

Tabelle erstellen

CREATE TABLE users (
    id INTEGER PRIMARY KEY,
    name TEXT,
    email TEXT,
    age INTEGER
);

Daten einfügen

INSERT INTO users (id, name, email, age) VALUES (1, 'Alice', 'alice@test.com', 30);
INSERT INTO users (id, name, email, age) VALUES (2, 'Bob', 'bob@test.com', 25);

Daten abfragen

SELECT name, age FROM users WHERE age > 18;

Indizes erstellen

-- BTree Index
CREATE INDEX idx_name ON users(name) USING btree;

-- Volltext-Index
CREATE INDEX idx_email_fts ON users(email) USING fts;

-- Vektor-Index
CREATE INDEX idx_vec ON items(embedding) USING hnsw;

Vector Search

CREATE TABLE docs (id INTEGER PRIMARY KEY, content TEXT, embedding VECTOR(768));
CREATE INDEX docs_vec ON docs(embedding) USING hnsw;

-- Ähnlichkeitssuche
SELECT id, cosine_distance(embedding, '[0.1, 0.2, ...]') AS dist
FROM docs ORDER BY dist ASC LIMIT 10;

Graph Engine

CREATE GRAPH social;
INSERT INTO social_nodes (id, node_label) VALUES (1, 'Alice'), (2, 'Bob');
INSERT INTO social_edges (source_id, dest_id) VALUES (1, 2);

-- BFS Traversal
SELECT * FROM GRAPH_TABLE(social MATCH (n)-[r]->(m) ALGORITHM bfs COLUMNS (id, node_label));

-- PageRank
SELECT * FROM GRAPH_TABLE(social ALGORITHM pagerank COLUMNS (id, node_label, rank));

-- Community Detection (Louvain)
SELECT * FROM GRAPH_TABLE(social ALGORITHM community COLUMNS (id, node_label, community));

-- Kürzester Pfad
SELECT * FROM GRAPH_TABLE(social ALGORITHM shortest_path START 1 END 2 COLUMNS (id, node_label));

-- Knoten-Ähnlichkeit (Jaccard)
SELECT similarity_nodes('social', 'jaccard') AS result;

AI Pipeline

-- Text in Chunks zerlegen
SELECT chunk('Langer Text hier...', 1024, 128) AS result;

-- Embedding generieren (mit konfiguriertem externen Service)
SELECT embed_text('Suchanfrage') AS result;

-- Schema-Prompt für LLM generieren
SELECT schema_prompt('users') AS result;

-- Natural Language → SQL (mit konfiguriertem LLM)
SELECT nl_to_sql('Zeige alle Benutzer über 25', 'users') AS result;

-- Cypher zu BaraQL übersetzen
SELECT cypher('MATCH (a)-[r]->(b) RETURN a.node_label, b.node_label') AS result;

HTTP/REST API

curl -X POST http://localhost:9470/query \
  -H "Content-Type: application/json" \
  -d '{"query": "SELECT * FROM users"}'

Konfiguration

# Umgebungsvariablen
export BARADB_DATA_DIR=./data
export BARADB_EMBED_ENDPOINT=http://localhost:11434/api/embeddings
export BARADB_EMBED_MODEL=nomic-embed-text
export BARADB_LLM_ENDPOINT=http://localhost:11434/api/generate
export BARADB_LLM_MODEL=llama3

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