dimgigov
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
146 lines
3.4 KiB
Markdown
146 lines
3.4 KiB
Markdown
# Graph Engine
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Adjazenzlisten-Speicher mit eingebauten Algorithmen für Graph-Traversierung und -Analyse.
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Vollständig integriert in den SQL-Executor via `GRAPH_TABLE()`.
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## SQL — Graph DDL
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### Graph erstellen
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```sql
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CREATE GRAPH org_chart;
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```
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Erstellt automatisch zwei Tabellen:
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- `org_chart_nodes (id INTEGER PRIMARY KEY, node_label TEXT, properties TEXT)`
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- `org_chart_edges (source_id INTEGER, dest_id INTEGER, edge_label TEXT, weight REAL)`
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### Graph löschen
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```sql
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DROP GRAPH org_chart;
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```
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## SQL — Daten einfügen
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```sql
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-- Knoten
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INSERT INTO org_chart_nodes (id, node_label) VALUES (1, 'CEO');
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INSERT INTO org_chart_nodes (id, node_label) VALUES (2, 'VP');
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-- Kanten
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INSERT INTO org_chart_edges (source_id, dest_id, edge_label) VALUES (1, 2, 'manages');
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```
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Alle INSERTs werden automatisch mit dem nativen Graph-Objekt synchronisiert.
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## SQL — GRAPH_TABLE Abfragen
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### BFS (Breitensuche)
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```sql
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SELECT * FROM GRAPH_TABLE(org_chart MATCH (n)-[r]->(m)
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ALGORITHM bfs
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START 1 MAXDEPTH 2
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COLUMNS (id, node_label));
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```
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### DFS (Tiefensuche)
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```sql
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SELECT * FROM GRAPH_TABLE(org_chart MATCH (n)-[r]->(m)
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ALGORITHM dfs START 1
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COLUMNS (id, node_label));
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```
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### PageRank
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```sql
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SELECT id, node_label, rank FROM GRAPH_TABLE(org_chart
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ALGORITHM pagerank
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COLUMNS (id, node_label, rank))
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ORDER BY rank DESC;
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```
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### Community Detection (Louvain)
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```sql
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SELECT id, node_label, community FROM GRAPH_TABLE(org_chart
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ALGORITHM community
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COLUMNS (id, node_label, community));
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```
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### Kürzester Pfad (Shortest Path)
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```sql
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SELECT * FROM GRAPH_TABLE(org_chart
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ALGORITHM shortest_path
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START 1 END 3
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COLUMNS (id, node_label));
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```
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### Dijkstra (gewichtete kürzeste Pfade)
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```sql
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SELECT * FROM GRAPH_TABLE(org_chart
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ALGORITHM dijkstra START 1
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COLUMNS (id, node_label, distance));
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```
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## SQL-Funktionen
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### Knotenähnlichkeit
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```sql
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-- Jaccard-Ähnlichkeit zwischen allen Knotenpaaren
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SELECT similarity_nodes('social', 'jaccard') AS result;
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-- Adamic-Adar-Ähnlichkeit
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SELECT similarity_nodes('social', 'adamic_adar') AS result;
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```
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### Node2Vec Embeddings
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```sql
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-- Graphstruktur-Embeddings generieren (64 Dimensionen)
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SELECT node2vec_embed('social', 64) AS result;
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```
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## Cypher-Kompatibilität
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```sql
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-- Cypher-Syntax automatisch nach GRAPH_TABLE übersetzen
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SELECT cypher('MATCH (a)-[r]->(b) WHERE a.node_label = ''CEO'' RETURN b.node_label') AS result;
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```
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## Algorithmen
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| Algorithmus | Beschreibung | SQL |
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|-------------|--------------|-----|
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| `bfs` | Breitensuche | `ALGORITHM bfs` |
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| `dfs` | Tiefensuche | `ALGORITHM dfs` |
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| `dijkstra` | Gewichtete kürzeste Pfade | `ALGORITHM dijkstra` |
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| `pageRank` | Knoten-Wichtigkeit | `ALGORITHM pagerank` |
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| `louvain` | Community Detection | `ALGORITHM community` |
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| `shortestPath` | Kürzester Pfad | `ALGORITHM shortest_path START X END Y` |
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| `similarityNodes` | Knotenähnlichkeit | `similarity_nodes()` |
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| `node2vec` | Graph Embeddings | `node2vec_embed()` |
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## Native Nim API
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```nim
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import barabadb/graph/engine
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var g = newGraph()
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let alice = g.addNode("Person", {"name": "Alice"}.toTable)
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let bob = g.addNode("Person", {"name": "Bob"}.toTable)
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discard g.addEdge(alice, bob, "knows")
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let bfs = g.bfs(alice)
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let path = g.shortestPath(alice, bob)
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let ranks = g.pageRank()
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let communities = louvain(g)
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let similarities = g.similarityNodes(smJaccard)
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let embeddings = g.node2vec(64, 10, 5)
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```
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