schemalytics 0.1.3

DB schema extraction to semantic layer for self-service analytics with local LLMs

Schemalytics

Semantic Layer for Self-Service Analytics

Schemalytics transforms your database into an LLM-ready semantic layer that enables self-service analytics. It analyzes your schema, generates dimensional models, and creates comprehensive metadata that LLMs can use to write accurate SQL queries—all running locally with complete privacy.

The tool generates a complete dbt project as the implementation layer, following dimensional modeling best practices with medallion architecture (Bronze → Silver → Gold).

Key features:

• Semantic layer generation - LLM-ready metadata with metrics, relationships, and query patterns
• Self-service analytics - Enable natural language queries against your data
• Privacy-first - Runs on local LLMs (Ollama), no data leaves your machine
• Interactive refinement - Perfect your data model through natural language feedback
• Industry templates - Pre-configured patterns for e-commerce, SaaS, fintech, and more

Quick Start

1. Install prerequisites

# Install Ollama
curl -fsSL https://ollama.com/install.sh | sh
ollama pull qwen2.5-coder:7b

# Install Schemalytics  
pip install schemalytics

2. Generate semantic layer + dbt project

schemalytics generate \
  -c postgresql://localhost/mydb \
  -o ./dbt_project

3. Interactive refinement

• AI generates initial plan with concrete specifications
• You refine with natural language ("make revenue weekly", "add customer LTV")
• Approve when ready

What You Get

• Semantic layer (semantic_layer.yml) - Complete metadata for LLM-powered analytics
• Bronze models - Raw data staging layer
• Silver models - Facts and dimensions (star schema)
• Gold models - Pre-aggregated metrics for performance
• Documentation - Auto-generated schema.yml files with tests

License

Apache 2.0 • Built by NiChr0