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Examples Overview

Hands-on examples demonstrating ArcadeDB Python bindings in real-world scenarios. Each example is self-contained, well-documented, and ready to run.

DSL-first examples

Current examples and docs use SQL/OpenCypher as the default approach for schema, CRUD, and graph operations.

Available Examples

🏁 Getting Started

Dataset Downloader Download and prepare datasets used by the examples (MovieLens, Stack Exchange, MSMARCO).

01 - Simple Document Store Foundation example covering document types, CRUD operations, comprehensive data types (DATE, DATETIME, DECIMAL, FLOAT, INTEGER, STRING, BOOLEAN, LIST OF STRING), and NULL value handling (INSERT NULL, UPDATE to NULL, IS NULL queries).

02 - Social Network Graph Complete graph modeling with vertices, edges, NULL handling, and dual query languages (SQL MATCH vs Cypher). Demonstrates 8 people with optional fields, 24 bidirectional edges, graph traversal, and comprehensive queries.

03 - Vector Search Semantic similarity search with HNSW (JVector) indexing. Demonstrates vector storage, index creation, and nearest neighbor search.

04 - CSV Import - Documents Production CSV import with automatic type inference by Java, NULL handling, and index optimization. Imports MovieLens dataset (36M+ records) with comprehensive performance analysis and result validation with actual data samples.

05 - CSV Import - Graph Production graph creation from MovieLens dataset. Comprehensive performance analysis of SQL pipelines, synchronous vs async, and index effects. Includes benchmark configurations, validation queries, and export/import roundtrip testing.

06 - Vector Search - Movie Recommendations Production-ready vector embeddings and HNSW (JVector) indexing for semantic movie search.

Quick Start

⚠️ Important: Always run examples from the examples/ directory.

cd bindings/python/examples/
python 01_simple_document_store.py

Learning Path

  1. Document Store (01) - Learn fundamentals
  2. Graph Operations (02) - Understand relationships
  3. Vector Search (03) - AI/ML integration
  4. CSV Import - Documents (04) - ETL to documents with MovieLens
  5. CSV Import - Graph (05) - Same data as graph with performance benchmarks
  6. Vector Search - Movies (06) - Semantic search and recommendations

Start with Simple Document Store!