join-agent 0.1.4

LLM-driven intelligent join key suggestion agent

Join Agent

LLM-driven intelligent data joining and relationship analysis agent. The JoinAgent uses large language models (LLMs) to analyze table structures, suggest optimal join strategies, and validate the quality of joins between datasets.

🌟 Features

Analyze table structures and sample data to identify potential join keys. Suggest optimal join strategies with reasoning and confidence scores. Validate join schema compatibility and data overlap. Supports multiple operations: golden_dataset – Identify join keys and build join order across multiple tables to create a golden dataset. manual_data_prep – Determine join keys and join type between two tables for manual data preparation. Integrates with SFN Blueprint’s AI handler for LLM-powered reasoning. Returns structured join plans including validated join types and overlap percentages.

📦 Installation

Prerequisites

• Python 3.11+
• Git
• uv – A fast Python package and environment manager.
  • For a quick setup on macOS/Linux, you can use:

    curl -LsSf https://astral.sh/uv/install.sh | sh
    

Setup

1. Clone the repository

   git clone https://github.com/stepfnAI/join_agent.git
   cd join_agent/
   git checkout main
   

2. Set up the virtual environment and install dependencies This command creates a .venv folder in the current directory and installs all required packages.

   uv sync --extra dev
   source .venv/bin/activate
   

3. Set up environment variables

   # Optional: Configure LLM provider (default: openai)
   export LLM_PROVIDER="your_llm_provider"
   
   # Optional: Configure LLM model (default: gpt-4.1-mini)
   export LLM_MODEL="your_llm_model"
   
   # Required: Your LLM API key (Note: If LLM provider is opeani then 'export OPENAI_API_KEY', if it antropic 'export ANTROPIC_API_KEY', use this accordingly as per LLM provider )
   export OPENAI_API_KEY="your_llm_api_key"
   

🚀 Quick Start

Basic Usage

This will support for detection of join keys from 2 to mutliple datsets for operation = "golden_dataset" it support for multiple table join for operation = "manual_data_prep" it will support for only 2 table join

from root directory -

python examples/goldendataset_usage.py
python examples/manualdataprep_usage.py

🧪 Testing

pytest -s tests/test_joinagent.py

📝 Prompt Management

All LLM prompts used by the JoinAgent are centralized in src/join_agent/constants.py for easy review and maintenance.

Prompt Types

Based upon operations there are 2 kinds of prompts:

• Golden_dataset_op_prompt: Template for analyzing join potential between multiple datasets purely based on column metadata
• Manual_data_prep_prompt: Template for analyzing join potential between multiple datasets considering column metadata, groupby fields, primary table

Benefits

• Easy Review: All prompts in one location for prompt engineering
• Version Control: Track prompt changes alongside code changes
• Maintainability: Update prompts without touching business logic
• Consistency: Standardized prompt formatting across the agent

🏗️ Architecture

The Target Synthesis Agent is built with a modular architecture:

• Core Components:

  • agent.py: Base agent implementation
  • models.py: Data models and schemas
  • constants.py: prompts
  • config.py: model configurations

• Dependencies:

  • sfn-blueprint: Core framework and utilities
  • pydantic: Data validation

📚 Documentation

For detailed documentation, visit: https://join-agent.readthedocs.io

🤝 Contributing

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

📧 Contact

• Email: [e-mail]
• GitHub: https://github.com/stepfnAI/join_agent