DbRheo is a database operations and data analysis CLI agent that provides natural language database query execution, schema exploration, risk assessment capabilities, and Python-powered data analysis features.

# 1. Clone the repository
git clone https://github.com/Din829/DbRheo-CLI.git
cd DbRheo-CLI

# 2. Install dependencies
pip install -r requirements.txt


# 3. Environment setup
cp .env.example .env
# Set either GOOGLE_API_KEY or OPENAI_API_KEY in the .env file
# No need to modify other contents in .env.example
# Claude models are not recommended at this time as PromptCaching is not yet applied


# 4. Launch CLI
cd packages/cli
python cli.py

• Natural Language Query Processing: Database operation instructions in natural language
• Intelligent SQL Generation: Automatic generation of safe and optimized queries
• Automatic Schema Discovery: Dynamic analysis of database structures
• Risk Assessment System: Pre-detection and warnings for dangerous operations
• Python Code Execution: Data analysis, visualization, and automation script execution
• Data Export: Result output in CSV, JSON, and Excel formats

• Asynchronous Processing: High-performance async/await implementation
• Multi-Database Support: PostgreSQL, MySQL, SQLite compatibility
• Modular Design: Extensible plugin architecture
• Comprehensive Logging: Detailed operation history and debug information
• Intelligent Input: Automatic multi-line detection and paste processing
• Streaming Output: Real-time response display
• Internationalization: Multi-language support (Japanese, English)

• Python 3.9 or higher
• Node.js 20 or higher (only for Web UI development)

Currently supports 3 main database types (more can be added via adapter interface):

• PostgreSQL 12+ (via asyncpg driver)
• MySQL/MariaDB 8.0+ (via aiomysql driver)
• SQLite 3.35+ (via aiosqlite driver)

Note: Additional database types can be easily integrated through the adapter factory pattern. The system supports dynamic adapter registration and automatic driver detection.

git clone https://github.com/Din829/DbRheo-CLI.git
cd DbRheo-CLI

Alternative: https://dev.azure.com/HPSMDI/POC_Agent/_git/db-rheo-cli

# Install dependencies
pip install -r requirements.txt

# Install core package
cd packages/core
pip install -e .
cd ../..

# Install CLI package
cd packages/cli
pip install -e .
cd ../..

# Verify installation
pip show dbrheo-core dbrheo-cli

Note: You can run directly in development mode without installing packages.

# Copy configuration file
cp .env.example .env

# Edit the .env file and configure:
# - Google API key
# - Database connection information

The testdata/ directory contains sample datasets for testing the agent:

• adult.data: Adult Census Income dataset for data analysis testing
• adult.names: Dataset description and column information
• adult.test: Test dataset for validation
• Additional sample files for various testing scenarios

You can use these datasets to test DbRheo's data analysis capabilities and SQL generation features.

# Display help
/help

# Specify model
/model

DbRheo> Tell me about the structure of the users table
[Executing schema exploration...]
Structure of table 'users':
- id: INTEGER (Primary Key)
- name: VARCHAR(100)
- email: VARCHAR(255)
- created_at: TIMESTAMP

DbRheo> Show me the latest 10 users
[Generating SQL query...]
SELECT * FROM users ORDER BY created_at DESC LIMIT 10;
[Displaying execution results...]

DbRheo> Analyze and visualize sales data using Python
[Generating Python code...]
import pandas as pd
import matplotlib.pyplot as plt

# Retrieve sales data from database
df = pd.read_sql("SELECT * FROM sales", connection)

# Monthly sales aggregation
monthly_sales = df.groupby('month')['amount'].sum()

# Create graph
plt.figure(figsize=(10, 6))
monthly_sales.plot(kind='bar')
plt.title('Monthly Sales Trends')
plt.savefig('sales_analysis.png')

[Execution result: Generated graph file sales_analysis.png]

DbRheo> Create monthly aggregation of sales data
[Generating complex query...]
SELECT
    DATE_TRUNC('month', order_date) as month,
    SUM(amount) as total_sales
FROM orders
GROUP BY month
ORDER BY month;

DbRheo> Load the adult dataset from testdata and analyze income distribution
[Loading data from testdata/adult.data...]
[Generating analysis code...]
import pandas as pd

# Load the adult census dataset
df = pd.read_csv('testdata/adult.data', header=None)
# Apply column names from adult.names
df.columns = ['age', 'workclass', 'fnlwgt', 'education', ...]

# Analyze income distribution
income_dist = df['income'].value_counts()
print("Income Distribution:")
print(income_dist)

[Execution result: Income analysis completed]