a5d34c001a
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
3.4 KiB
3.4 KiB
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
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
DROP GRAPH org_chart;
SQL — Daten einfügen
-- 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)
SELECT * FROM GRAPH_TABLE(org_chart MATCH (n)-[r]->(m)
ALGORITHM bfs
START 1 MAXDEPTH 2
COLUMNS (id, node_label));
DFS (Tiefensuche)
SELECT * FROM GRAPH_TABLE(org_chart MATCH (n)-[r]->(m)
ALGORITHM dfs START 1
COLUMNS (id, node_label));
PageRank
SELECT id, node_label, rank FROM GRAPH_TABLE(org_chart
ALGORITHM pagerank
COLUMNS (id, node_label, rank))
ORDER BY rank DESC;
Community Detection (Louvain)
SELECT id, node_label, community FROM GRAPH_TABLE(org_chart
ALGORITHM community
COLUMNS (id, node_label, community));
Kürzester Pfad (Shortest Path)
SELECT * FROM GRAPH_TABLE(org_chart
ALGORITHM shortest_path
START 1 END 3
COLUMNS (id, node_label));
Dijkstra (gewichtete kürzeste Pfade)
SELECT * FROM GRAPH_TABLE(org_chart
ALGORITHM dijkstra START 1
COLUMNS (id, node_label, distance));
SQL-Funktionen
Knotenähnlichkeit
-- 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
-- Graphstruktur-Embeddings generieren (64 Dimensionen)
SELECT node2vec_embed('social', 64) AS result;
Cypher-Kompatibilität
-- 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
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)