QuerySUTRA 0.2.3

SUTRA: Structured-Unstructured-Text-Retrieval-Architecture - Creates multiple structured tables from ANY data

QuerySUTRA

SUTRA: Structured-Unstructured-Text-Retrieval-Architecture

Transform any data into structured, queryable databases with AI-powered entity extraction.

🎯 Key Features

✅ Multi-Table Creation - Automatically extracts entities and creates multiple related tables
✅ Smart Entity Extraction - Identifies people, contacts, events, organizations from unstructured data
✅ Natural Language Queries - Ask questions in plain English
✅ Multiple Data Formats - CSV, Excel, JSON, PDF, DOCX, TXT, SQL, DataFrames
✅ Direct SQL Access - Query without API costs
✅ Auto Visualization - Built-in charts and graphs
✅ Cloud Export - Save to MySQL, PostgreSQL, or local SQLite

📦 Installation

pip install QuerySUTRA

# With MySQL support
pip install QuerySUTRA[mysql]

# With PostgreSQL support
pip install QuerySUTRA[postgres]

# With all database support
pip install QuerySUTRA[all]

🚀 Quick Start

from sutra import SUTRA

# Initialize
sutra = SUTRA(api_key="your-openai-key")

# Upload any data - AI creates multiple structured tables!
sutra.upload("employee_story.pdf")

# View all created tables
sutra.tables()
# Output:
# 📋 TABLES IN DATABASE
# 1. employee_story_people (20 rows, 6 columns)
#    Columns: id, name, address, city, email, phone
# 2. employee_story_contacts (20 rows, 4 columns)
#    Columns: id, person_id, email, phone
# 3. employee_story_events (15 rows, 4 columns)
#    Columns: id, host_id, description, city

# View detailed schema
sutra.schema()

# Query with natural language
result = sutra.ask("Show all people from New York")
print(result.data)

# With visualization
result = sutra.ask("Show events by city", viz=True)

# Direct SQL (no API cost!)
result = sutra.sql("SELECT * FROM employee_story_people WHERE city='Dallas'")
print(result.data)

📊 How It Works

From Unstructured PDF to Structured Tables

Input: PDF with employee information

AI Automatically Creates:

📋 Created 3 structured tables:
  📊 employee_story_people: 20 rows, 6 columns
     - id, name, address, city, email, phone
  📊 employee_story_contacts: 20 rows, 4 columns
     - id, person_id, email, phone  
  📊 employee_story_events: 15 rows, 4 columns
     - id, host_id, description, city

💡 Usage Examples

1. Upload Different Formats

# CSV file
sutra.upload("sales_data.csv")

# Excel file
sutra.upload("quarterly_report.xlsx")

# PDF document (AI extracts entities!)
sutra.upload("company_directory.pdf")

# Word document
sutra.upload("meeting_notes.docx")

# Text file
sutra.upload("log_data.txt")

# DataFrame
import pandas as pd
df = pd.DataFrame({'name': ['Alice', 'Bob'], 'score': [95, 87]})
sutra.upload(df, name="test_scores")

2. View Your Data

# List all tables with details
sutra.tables()

# Show schema with data types
sutra.schema()

# Show schema for specific table
sutra.schema("employee_story_people")

# Preview data
sutra.peek("employee_story_people", n=10)

3. Query Your Data

# Natural language (uses OpenAI)
result = sutra.ask("What are the top 5 sales by region?")
print(result.data)

# With visualization
result = sutra.ask("Show sales trends by month", viz=True)

# Interactive mode (asks if you want viz)
result = sutra.interactive("Compare revenue across quarters")

# Direct SQL (free, no API!)
result = sutra.sql("SELECT city, COUNT(*) as count FROM employee_story_people GROUP BY city")
print(result.data)

4. Export Your Database

# Export to MySQL (local or cloud)
sutra.save_to_mysql(
    host="localhost",
    user="root",
    password="password",
    database="my_database"
)

# Export to PostgreSQL
sutra.save_to_postgres(
    host="mydb.amazonaws.com",
    user="admin",
    password="password",
    database="production_db"
)

# Export to SQLite file
sutra.export_db("backup.db", format="sqlite")

# Export to SQL dump
sutra.export_db("schema.sql", format="sql")

# Export to JSON
sutra.export_db("data.json", format="json")

# Export to Excel (all tables as sheets)
sutra.export_db("data.xlsx", format="excel")

# Complete backup
sutra.backup("./backups")

🔥 Advanced Features

Entity Extraction

QuerySUTRA automatically identifies and extracts:

• 👥 People - Names, addresses, contact info
• 📧 Contacts - Emails, phone numbers
• 📅 Events - Meetings, activities, locations
• 🏢 Organizations - Companies, departments
• 📍 Locations - Cities, addresses, coordinates

Multiple Table Relationships

# AI creates relational structure
sutra.upload("company_data.pdf")

# Result:
# people table with person_id
# contacts table with foreign key to person_id
# events table with host_id linking to people

Query Across Tables

# Natural language handles joins automatically
result = sutra.ask("Show all events hosted by people from Dallas")

# Or write SQL joins manually
result = sutra.sql("""
    SELECT e.description, p.name, p.city
    FROM employee_story_events e
    JOIN employee_story_people p ON e.host_id = p.id
    WHERE p.city = 'Dallas'
""")

📈 Visualization

# Auto-detect best chart type
result = sutra.ask("Show revenue by product", viz=True)

# Interactive charts with Plotly
# - Bar charts for categorical data
# - Line charts for time series  
# - Tables for detailed data
# - Pie charts for distributions

🌐 Cloud Database Integration

AWS RDS MySQL

sutra.save_to_mysql(
    host="mydb.xxxx.us-east-1.rds.amazonaws.com",
    user="admin",
    password="password",
    database="production",
    port=3306
)

Google Cloud SQL

sutra.save_to_postgres(
    host="35.123.456.789",
    user="postgres",
    password="password",
    database="analytics"
)

Heroku Postgres

sutra.save_to_postgres(
    host="ec2-xx-xxx-xxx-xxx.compute-1.amazonaws.com",
    user="username",
    password="password",
    database="dbname",
    port=5432
)

⚡ Performance Tips

# Use direct SQL for complex queries (faster, no API cost)
result = sutra.sql("SELECT * FROM data WHERE status='active'")

# Cache is automatic for repeated questions
result1 = sutra.ask("Show total sales")  # Calls API
result2 = sutra.ask("Show total sales")  # From cache ⚡

# Export results for reuse
result.data.to_csv("results.csv")

🔒 API Key Security

# Option 1: Pass directly (not recommended for production)
sutra = SUTRA(api_key="sk-...")

# Option 2: Environment variable (recommended)
import os
os.environ["OPENAI_API_KEY"] = "sk-..."
sutra = SUTRA()

# Option 3: .env file
# Create .env file with: OPENAI_API_KEY=sk-...
from dotenv import load_dotenv
load_dotenv()
sutra = SUTRA()

🎓 Complete Example

from sutra import SUTRA
import pandas as pd

# Initialize
sutra = SUTRA(api_key="your-openai-key")

# Upload PDF - creates multiple tables
sutra.upload("employee_directory.pdf")

# View what was created
tables_info = sutra.tables()
print(f"Created {len(tables_info)} tables")

# View detailed schema
sutra.schema()

# Query specific table
result = sutra.ask("How many people are in each city?", 
                   table="employee_directory_people")
print(result.data)

# Visualize
result = sutra.ask("Show distribution of people by city", viz=True)

# Export to MySQL
sutra.save_to_mysql("localhost", "root", "password", "company_db")

# Backup everything
sutra.backup("./backups")

# Close connection
sutra.close()

📚 Method Reference

Core Methods

Method	Description
upload(data, name)	Upload any data format, creates multiple tables
tables()	List all tables with row/column counts
schema(table)	Show detailed schema with data types
peek(table, n)	Preview first n rows
ask(question, viz)	Natural language query
sql(query, viz)	Direct SQL query
interactive(question)	Query with viz prompt

Export Methods

Method	Description
export_db(path, format)	Export database (sqlite/sql/json/excel)
save_to_mysql(...)	Save to MySQL database
save_to_postgres(...)	Save to PostgreSQL database
backup(path)	Complete backup with timestamp

🐛 Troubleshooting

Q: Only one table created instead of multiple?
A: Make sure you have OpenAI API key set. Without it, falls back to simple parsing.

Q: "No API key" error?
A: Set your OpenAI key: sutra = SUTRA(api_key="sk-...")

Q: PDF extraction failed?
A: Install PyPDF2: pip install PyPDF2

Q: MySQL export error?
A: Install extras: pip install QuerySUTRA[mysql]

📄 License

MIT License - see LICENSE file

🤝 Contributing

Contributions welcome! Open an issue or submit a PR.

📞 Support

• Issues: GitHub Issues
• Email: [e-mail]

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Made with ❤️ by Aditya Batta