schemalytics 0.2.0

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) by default; Anthropic supported via env var
• Agentic pipeline - Five focused AI agents infer industry, metrics, and table roles from schema metadata alone
• Interactive refinement - Review and refine the generated data model through natural language feedback

Quick Start

1. Install prerequisites

# Install Ollama (default provider)
curl -fsSL https://ollama.com/install.sh | sh
ollama pull gemma3-data

# Install Schemalytics
pip install schemalytics

2. Generate semantic layer + dbt project

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

3. Agentic pipeline + interactive refinement

• Agent 1 infers your industry and domain from schema metadata
• Agent 2 suggests metrics, goals, and reporting grain
• Agent 3 classifies each table as fact, dimension, bridge, or reference
• You review a consolidated summary and correct anything wrong
• Agent 4 generates a full modeling plan; you refine it with natural language ("make revenue weekly", "add customer LTV")
• Press Enter to approve and generate the dbt project

Optional: use Anthropic instead of Ollama

SCHEMALYTICS_LLM_PROVIDER=anthropic \
ANTHROPIC_API_KEY=sk-ant-... \
schemalytics generate -c postgresql://localhost/mydb -o ./dbt_project

What You Get

• Semantic layer (semantic_layer.yml) - Complete metadata for LLM-powered analytics
• Bronze models - Raw data staging layer (stg_<schema>_<table>)
• Silver models - Facts (fct_*) and dimensions (dim_*) in star schema
• Gold models - Pre-aggregated metrics (agg_<grain>_<metric>)
• Documentation - Auto-generated schema.yml files

CLI

# Full agentic pipeline
schemalytics generate -c postgresql://user:pass@localhost/db -o ./dbt_project

# Schema extraction only
schemalytics extract -c postgresql://user:pass@localhost/db -o schema.json

License

Apache 2.0 • Built by NiChr0