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
115 lines
2.5 KiB
Markdown
115 lines
2.5 KiB
Markdown
# BaraQL — Abfragesprache-Referenz
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BaraQL ist eine SQL-kompatible Abfragesprache mit Erweiterungen für Graph-, Vektor- und Dokumentoperationen.
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## Datentypen
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| Typ | Beschreibung | Beispiel |
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|------|-------------|---------|
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| `null` | Nullwert | `null` |
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| `bool` | Boolean | `true`, `false` |
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| `int64` | 64-bit Ganzzahl | `42` |
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| `float64` | 64-bit Fließkomma | `3.14` |
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| `str` | UTF-8 String | `'hello'` |
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| `vector(n)` | Float32 Vektor | `VECTOR(768)` |
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| `json` | JSON-Dokument | `{"key": "value"}` |
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## Grundlegende Abfragen
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```sql
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SELECT * FROM users;
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SELECT name, age FROM users WHERE age > 18;
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SELECT * FROM users ORDER BY age DESC LIMIT 10;
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```
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## Vektor-Operationen
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```sql
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-- Distanzberechnungen
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SELECT cosine_distance(embedding, '[0.1, 0.2, 0.3]') AS dist FROM items;
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SELECT embedding <-> '[0.1, 0.2, 0.3]' AS dist FROM items;
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-- Hybride Suche
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SELECT hybrid_search('query', embedding, content, 10) AS result;
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```
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## Graph-Operationen
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```sql
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CREATE GRAPH social;
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DROP GRAPH social;
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-- Traversierung
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SELECT * FROM GRAPH_TABLE(social MATCH (n)-[r]->(m)
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ALGORITHM bfs START 1
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COLUMNS (id, node_label));
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-- PageRank
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SELECT * FROM GRAPH_TABLE(social ALGORITHM pagerank
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COLUMNS (id, node_label, rank));
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-- Community Detection
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SELECT * FROM GRAPH_TABLE(social ALGORITHM community
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COLUMNS (id, node_label, community));
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```
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## AI-Funktionen
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```sql
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-- Text in Chunks zerlegen
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SELECT chunk('Langer Text...', 1024, 128) AS result;
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-- Embedding generieren
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SELECT embed_text('Suchtext') AS result;
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-- Natural Language → SQL
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SELECT nl_to_sql('Zeige alle Benutzer über 25', 'users') AS result;
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-- Schema-Prompt für LLM
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SELECT schema_prompt('users') AS result;
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-- Cypher-Übersetzung
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SELECT cypher('MATCH (a)-[r]->(b) RETURN a.node_label') AS result;
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-- Knotenähnlichkeit
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SELECT similarity_nodes('social', 'jaccard') AS result;
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-- Graph-Embeddings
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SELECT node2vec_embed('social', 64) AS result;
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```
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## Joins
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```sql
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SELECT u.name, o.amount
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FROM users u
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INNER JOIN orders o ON u.id = o.user_id;
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```
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## Aggregation
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```sql
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SELECT department, COUNT(*), AVG(salary)
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FROM employees
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GROUP BY department
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HAVING COUNT(*) > 5;
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```
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## Index-Erstellung
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```sql
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CREATE INDEX idx_name ON users(name) USING btree;
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CREATE INDEX idx_vec ON docs(embedding) USING hnsw;
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CREATE INDEX idx_fts ON docs(content) USING fts;
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```
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## Multi-Tenant / RLS
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```sql
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CREATE POLICY tenant_policy ON orders
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FOR ALL USING (tenant_id = current_setting('app.tenant_id'));
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SET app.tenant_id = 'company-a';
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SELECT * FROM orders; -- Automatisch gefiltert
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```
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