🚀 Quick Start

Get up and running with Ryoma in under 5 minutes! This guide covers the essentials to start analyzing data with AI.

📋 Prerequisites

  • Python 3.9+ - Ryoma requires Python 3.9 or higher

  • API Key - OpenAI API key or other supported LLM provider

  • Database (optional) - PostgreSQL, MySQL, SQLite, or other supported databases

📦 Installation

Basic Installation

pip install ryoma_ai

With Database Support

# PostgreSQL support
pip install ryoma_ai[postgres]

# Snowflake support
pip install ryoma_ai[snowflake]

# All database connectors
pip install ryoma_ai[all]

🎯 Quick Start Options

Option 2: Programmatic Usage

For integration into your applications:

from ryoma_ai.agent.sql import SqlAgent
from ryoma_ai.datasource.postgres import PostgresDataSource

# Set up data source
datasource = PostgresDataSource(
    connection_string="postgresql://user:pass@localhost:5432/db"
)

# Create SQL agent (uses default InMemoryStore)
agent = SqlAgent(
    model="gpt-4o",
    mode="enhanced",
    datasource=datasource
)

# Ask questions in natural language
response = agent.stream("Show me the top 10 customers by revenue this month")
print(response)

Option 3: Pandas Agent

For DataFrame analysis:

from ryoma_ai.agent.pandas import PandasAgent
import pandas as pd

# Create sample data
df = pd.DataFrame({
    'customer_id': [1, 2, 3, 4, 5],
    'revenue': [1000, 2500, 1800, 3200, 900],
    'region': ['North', 'South', 'East', 'West', 'North']
})

# Create pandas agent (uses default InMemoryStore)
agent = PandasAgent("gpt-4o")
agent.add_dataframe(df)

# Analyze with natural language
result = agent.stream("What's the average revenue by region?")
print(result)

🚀 Advanced Features

Enhanced SQL Agent with Profiling

from ryoma_ai.agent.sql import SqlAgent
from ryoma_ai.datasource.postgres import PostgresDataSource

# Connect to database with automatic profiling
datasource = PostgresDataSource(
    connection_string="postgresql://user:pass@localhost:5432/db"
)

# Use ReFoRCE mode for state-of-the-art performance
agent = SqlAgent(
    model="gpt-4",
    mode="reforce",  # Advanced self-refinement
    safety_config={
        "enable_validation": True,
        "max_retries": 3
    }
)
agent.add_datasource(datasource)

# Complex queries with automatic optimization
response = agent.stream("""
Find customers who made purchases in the last 30 days,
group by region, and show the top 3 products by revenue
""")

🔧 Configuration

Environment Variables

# Set your API key
export OPENAI_API_KEY="your-api-key-here"

# Optional: Configure other providers
export ANTHROPIC_API_KEY="your-anthropic-key"

Agent Configuration

# Custom configuration
agent = SqlAgent(
    model="gpt-4",
    mode="enhanced",
    model_parameters={
        "temperature": 0.1,
        "max_tokens": 2000
    },
    safety_config={
        "enable_validation": True,
        "allowed_operations": ["SELECT", "WITH"],
        "max_rows": 10000
    }
)

✅ Verify Installation

Run this quick test to ensure everything is working:

from ryoma_ai.agent.sql import SqlAgent
from ryoma_ai.datasource.sqlite import SqliteDataSource

# Create in-memory SQLite database
datasource = SqliteDataSource(":memory:")

# Test agent creation
agent = SqlAgent("gpt-3.5-turbo", mode="enhanced")
agent.add_datasource(datasource)

print("✅ Ryoma is ready to use!")

🎯 Next Steps

Now that you have Ryoma running, explore these advanced features:

🆘 Need Help?