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
135 lines
3.0 KiB
Markdown
135 lines
3.0 KiB
Markdown
# BaraDB — Schnellstart
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## Server starten
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```bash
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./build/baradadb
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```
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Der Server startet standardmäßig auf `localhost:9470`.
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## Verbindung via CLI
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```bash
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./build/baradadb --shell
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```
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## MCP Server (AI Agenten)
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```bash
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./build/baramcp --data-dir ./data
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```
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Der MCP Server startet im STDIO-Modus und stellt 3 Tools für AI-Agenten bereit: `query`, `vector_search`, `schema_inspect`.
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## Grundlegende Operationen
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### Tabelle erstellen
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```sql
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CREATE TABLE users (
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id INTEGER PRIMARY KEY,
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name TEXT,
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email TEXT,
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age INTEGER
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);
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```
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### Daten einfügen
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```sql
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INSERT INTO users (id, name, email, age) VALUES (1, 'Alice', 'alice@test.com', 30);
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INSERT INTO users (id, name, email, age) VALUES (2, 'Bob', 'bob@test.com', 25);
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```
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### Daten abfragen
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```sql
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SELECT name, age FROM users WHERE age > 18;
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```
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### Indizes erstellen
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```sql
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-- BTree Index
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CREATE INDEX idx_name ON users(name) USING btree;
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-- Volltext-Index
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CREATE INDEX idx_email_fts ON users(email) USING fts;
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-- Vektor-Index
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CREATE INDEX idx_vec ON items(embedding) USING hnsw;
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```
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## Vector Search
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```sql
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CREATE TABLE docs (id INTEGER PRIMARY KEY, content TEXT, embedding VECTOR(768));
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CREATE INDEX docs_vec ON docs(embedding) USING hnsw;
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-- Ähnlichkeitssuche
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SELECT id, cosine_distance(embedding, '[0.1, 0.2, ...]') AS dist
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FROM docs ORDER BY dist ASC LIMIT 10;
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```
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## Graph Engine
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```sql
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CREATE GRAPH social;
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INSERT INTO social_nodes (id, node_label) VALUES (1, 'Alice'), (2, 'Bob');
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INSERT INTO social_edges (source_id, dest_id) VALUES (1, 2);
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-- BFS Traversal
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SELECT * FROM GRAPH_TABLE(social MATCH (n)-[r]->(m) ALGORITHM bfs COLUMNS (id, node_label));
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-- PageRank
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SELECT * FROM GRAPH_TABLE(social ALGORITHM pagerank COLUMNS (id, node_label, rank));
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-- Community Detection (Louvain)
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SELECT * FROM GRAPH_TABLE(social ALGORITHM community COLUMNS (id, node_label, community));
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-- Kürzester Pfad
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SELECT * FROM GRAPH_TABLE(social ALGORITHM shortest_path START 1 END 2 COLUMNS (id, node_label));
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-- Knoten-Ähnlichkeit (Jaccard)
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SELECT similarity_nodes('social', 'jaccard') AS result;
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```
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## AI Pipeline
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```sql
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-- Text in Chunks zerlegen
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SELECT chunk('Langer Text hier...', 1024, 128) AS result;
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-- Embedding generieren (mit konfiguriertem externen Service)
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SELECT embed_text('Suchanfrage') AS result;
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-- Schema-Prompt für LLM generieren
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SELECT schema_prompt('users') AS result;
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-- Natural Language → SQL (mit konfiguriertem LLM)
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SELECT nl_to_sql('Zeige alle Benutzer über 25', 'users') AS result;
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-- Cypher zu BaraQL übersetzen
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SELECT cypher('MATCH (a)-[r]->(b) RETURN a.node_label, b.node_label') AS result;
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```
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## HTTP/REST API
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```bash
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curl -X POST http://localhost:9470/query \
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-H "Content-Type: application/json" \
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-d '{"query": "SELECT * FROM users"}'
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```
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## Konfiguration
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```bash
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# Umgebungsvariablen
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export BARADB_DATA_DIR=./data
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export BARADB_EMBED_ENDPOINT=http://localhost:11434/api/embeddings
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export BARADB_EMBED_MODEL=nomic-embed-text
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export BARADB_LLM_ENDPOINT=http://localhost:11434/api/generate
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export BARADB_LLM_MODEL=llama3
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```
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