datus-agent 0.2.0

AI-powered SQL Agent for data engineering (Compiled Version)

🎯 Overview

Datus is an AI-powered agent that transforms data engineering and metric management into a conversational experience.

With the Datus Agent you can:

• Simplify Data Engineering Development:
  • Enable data engineers to develop and debug using natural language, reducing entry barriers and increasing productivity.
• Standardize and Manage Metrics:
  • Extract and unify metrics consistently, ensuring your BI and AI tools always access accurate and reliable definitions.
• Self-Improving:
  • Convert iterative CoT reasoning workflows into structured datasets, enabling SFT and RL for ongoing, automatic improvements in model accuracy and performance.

✨ Why Choose Datus Agent?

🚀 Key Features

💬 Conversational Data Engineering

• Natural Language Workflows - Use / to execute complex task in plain language
• Intelligent SQL Generation - !gen creates optimized SQL with !fix for instant corrections
• Live Workflow Monitoring - !darun_screen shows real-time execution status
• Schema Intelligence - !sl provides smart table and column recommendations

📈 Smart Metrics Management

• Automated Metric Generation - !gen_metrics extracts business metrics from your queries
• Semantic Model Creation - !gen_semantic_model builds comprehensive data models
• Streaming Analytics - Real-time metric generation with !gen_metrics_stream variants
• Context-Aware Operations - !set manages different workflow contexts

🔄 Self-Improving AI System

• Reasoning Mode - !reason provides step-by-step analysis with detailed CoT for complex problems
• Standard log Output - Comprehensively record the user’s reasoning process to generate high-value data for subsequent model refinement and evolution

💡 Use Cases

Data Pipeline Development

# Natural language query execution
!reason "create a pipeline that aggregates daily sales by region"

# View recommended tables
!sl
# Schema linking found: sales_data, regions, daily_transactions

# Generate and refine SQL
!gen
# Generated: SELECT region_id, DATE(sale_date) as day, SUM(amount)...

!fix add product category grouping
# Updated SQL with category dimension added

Metric Standardization

# Check existing metrics
@subject

# Generate new metrics from analysis
!gen_metrics_stream
# Streaming metric generation...
# ✓ Monthly Active Users (MAU)
# ✓ Average Order Value (AOV)
# ✓ Customer Lifetime Value (CLV)

# Create semantic model
!gen_semantic_model
# Generated comprehensive data model with relationships

Intelligent Debugging

# Start debugging session
!dastart "debug ETL memory error"

# Explore context
@context_screen
# Visual display of current tables, schemas, and resources

# Run reasoning analysis
!reason_stream
# Analyzing: Large dataset (10TB) without partitioning detected
# Suggesting: Date-based partitioning, chunked processing

# Apply fix
!fix implement suggested partitioning stratege

Get more

• 🚦 Quick Start
• 🤝 Contribution
• 📝 Release Notes
• 🌱 Good First Issue
• 🏗️ Architecture