schema-description-agent 0.1.5

LLM-driven agent for describing data tables based on domain schemas

Schema Description Agent

The Schema Description Agent is a Python-based tool that automatically generates descriptions for tables and their columns. It analyzes the structure and content of a data file, and then uses a Large Language Model (LLM) to produce accurate and concise documentation.

Features

• Statistical Analysis: Automatically calculates key statistics for each column, such as row count, column count, duplicate rows, missing cells, and more.
• AI-Powered Descriptions: Leverages LLMs to generate human-readable descriptions for tables and columns based on the statistical analysis.
• Configurable: Easily configure the AI provider, model, and other parameters.
• Extensible: Built on a modular framework (sfn_blueprint) that allows for easy extension and integration.

Installation

Prerequisites

• uv – package & environment manager
  Please refer to the official installation guide for the most up-to-date instructions.
  For quick setup on macOS/Linux, you can currently use:

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

• Git

Steps

1. Clone the repository:

   git clone https://github.com/stepfnAI/schema_description_agent.git
   cd schema_description_agent
   git switch review
   

2. Create virtual environment and install dependencies:

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

3. Clone and install the blueprint dependency: The agent requires the sfn_blueprint library. Clone it into a sibling directory.

   cd ../
   git clone https://github.com/stepfnAI/sfn_blueprint.git
   cd sfn_blueprint
   git switch dev
   uv pip install -e .
   

4. Return to the agent directory:

   cd ../schema_description_agent
   

5. ** set environment variables:**

   export OPENAI_API_KEY='your_openai_api_key'
   

Configuration

You can configure the agent in two ways: using a .env file for project-specific settings or by exporting environment variables for more dynamic, shell-level control. Settings loaded via export will take precedence over those in a .env file.

Available Settings

The following table details the configuration options available:

Environment Variable	Description	Default
OPENAI_API_KEY or ANTHROPIC_API_KEY	(Required) Your OpenAI API key.	None
ai_provider_schema_description	The AI provider to use for generating schema descriptions.	openai
model_name_schema_description	The specific AI model to use for schema descriptions.	gpt-4o
temperature_schema_description	AI model temperature (e.g., 0.0 to 2.0).	0.3
max_tokens_schema_description	Maximum tokens for the AI response.	4000

Method 1: Using a .env File (Recommended)

For consistent configuration within your project, create a file named .env in the root directory and add your settings. This method is ideal for storing API keys and project-wide defaults.

1. Create a file named .env in the root of your project.
2. Add the key-value pairs for the settings you wish to override.

Example .env file:

# .env

# --- Required Settings ---
# Provide the API key for the provider you select below.
# For this example, we are using Anthropic.
ANTHROPIC_API_KEY="sk-your-anthropic-api-key-here"

# --- Optional Overrides for the Schema Description Agent ---
# Switch the AI provider to Anthropic
AI_PROVIDER_SCHEMA_DESCRIPTION="anthropic"

# Use a different model from the new provider
MODEL_NAME_SCHEMA_DESCRIPTION="claude-3-haiku-20240307"

# Use a higher temperature for potentially more descriptive responses
TEMPERATURE_SCHEMA_DESCRIPTION=0.7```

Testing

To run the tests, use the following command from the root of the schema_description_agent directory:

# Run all tests
pytest tests/ -s

# test agent    
pytest tests/test_agent.py -s

# test agent with sample data
pytest tests/test_agent_with_data.py -s

Usage

Here is a simple example of how to use the agent:

python examples/basic_usage.py

from schema_description_agent import SchemaDescriptionAgent, SchemaDescriptionConfig

# Create a custom configuration
config = SchemaDescriptionConfig(
    ai_provider_schema_description="anthropic",
    model_name_schema_description="claude-3-opus-20240229",
    temperature_schema_description=0.5
)

# Create an instance of the agent with the custom configuration
agent = SchemaDescriptionAgent(config=config)