json-paws 0.1.6

A library for generating structured JSON using GPT-4o.

jsonpaws

jsonpaws is a Python library designed to generate structured and consistent JSON outputs using GPT-4o. The library provides both analysis and synthesis modes to extract and generate structured JSON data from unstructured text or based on a given schema.

Features

• Analysis Mode: Extracts structured information from unstructured data. Ideal for data extraction and transformation tasks.
• Synthesis Mode: Generates realistic structured JSON data based on a specified schema. Perfect for creating synthetic datasets for simulations or testing.
• Customizable: Allows users to customize the OpenAI model and temperature settings for tailored data generation.
• Easy Integration: Designed for seamless integration into existing Python projects, with a straightforward API and minimal setup.

Installation

Install jsonpaws using pip:

pip install json_paws

Usage

Getting Started

To use jsonpaws, you'll need to have an OpenAI API key. You can set it as an environment variable or pass it directly to the library.

Setting the API Key

You can set the API key as an environment variable:

export OPENAI_API_KEY=your_api_key

Or pass it directly in your code:

import openai

openai.api_key = "your_api_key"

Importing jsonpaws

Start by importing the necessary components from the library:

from jsonpaws import JSONSchemaParser, PromptGenerator, ContentGenerator, JSONProcessor

Analysis Mode

In Analysis Mode, jsonpaws extracts structured data from unstructured text using a predefined JSON schema. This is useful for data extraction and transformation tasks.

Example

import json
from jsonpaws import JSONSchemaParser, PromptGenerator, ContentGenerator, JSONProcessor

# Set your OpenAI API key here
api_key = "YOUR_OPENAI_API_KEY"

# Define the JSON schema
json_schema = {
    "type": "object",
    "properties": {
        "report_date": {"type": "string"},
        "patients": {
            "type": "array",
            "items": {
                "type": "object",
                "properties": {
                    "id": {"type": "string"},
                    "firstName": {"type": "string"},
                    "lastName": {"type": "string"},
                    "age": {"type": "number", "minimum": 0, "maximum": 120},
                    "gender": {"type": "string", "enum": ["male", "female"]},
                    "diagnosis": {"type": "string"},
                    "medications": {
                        "type": "array",
                        "items": {"type": "string"}
                    }
                }
            }
        }
    }
}

# Initialize the schema parser
schema_parser = JSONSchemaParser(json_schema)

# For analysis mode
prompt_generator_analysis = PromptGenerator(mode='analysis')
content_generator_analysis = ContentGenerator(api_key=api_key, model='gpt-4', mode='analysis')
analysis_processor = JSONProcessor(schema_parser, prompt_generator_analysis, content_generator_analysis, mode='analysis')

# Example unstructured data
unstructured_data = """
Patient Report: John Doe is a 45-year-old male diagnosed with hypertension.
The notes mention that he needs a follow-up in 3 months.
"""

# Process the unstructured data
generated_json_analysis = analysis_processor.process(unstructured_data, schema=json_schema)

# Print the extracted structured JSON
print("Generated JSON (Analysis):", json.dumps(generated_json_analysis, indent=4))

Output

Generated JSON (Analysis): {
    "report_date": "",
    "patients": [
        {
            "id": "1",
            "name": "John Doe",
            "age": 45,
            "gender": "male",
            "diagnosis": "hypertension",
            "notes": "He needs a follow-up in 3 months."
        }
    ]
}

Synthesis Mode

In Synthesis Mode, jsonpaws generates realistic structured JSON data based on a specified schema. This mode is great for creating synthetic datasets for testing and simulations.

Example

import json
from jsonpaws import JSONSchemaParser, PromptGenerator, ContentGenerator, JSONProcessor

# Set your OpenAI API key here
api_key = "YOUR_OPENAI_API_KEY"

# Define the JSON schema
json_schema = {
    "type": "object",
    "properties": {
        "report_date": {"type": "string"},
        "patients": {
            "type": "array",
            "items": {
                "type": "object",
                "properties": {
                    "id": {"type": "string"},
                    "firstName": {"type": "string"},
                    "lastName": {"type": "string"},
                    "age": {"type": "number", "minimum": 0, "maximum": 120},
                    "gender": {"type": "string", "enum": ["male", "female"]},
                    "diagnosis": {"type": "string"},
                    "medications": {
                        "type": "array",
                        "items": {"type": "string"}
                    }
                }
            }
        }
    }
}

# Initialize the schema parser
schema_parser = JSONSchemaParser(json_schema)

# Instructions for synthesis mode
instructions = """
Generate a JSON with a report date and a list of patients, where each patient has fields like id, firstName, lastName, age, gender, diagnosis, and medications.
"""

# For synthesis mode
prompt_generator_synthesis = PromptGenerator(mode='synthesis')
content_generator_synthesis = ContentGenerator(api_key=api_key, model='gpt-4', mode='synthesis')
synthesis_processor = JSONProcessor(schema_parser, prompt_generator_synthesis, content_generator_synthesis, mode='synthesis')

# Generate the synthetic JSON data
generated_json_synthesis = synthesis_processor.process(data={}, schema=json_schema)

# Print the generated synthetic JSON
print("Generated JSON (Synthesis):", json.dumps(generated_json_synthesis, indent=4))

Output

Generated JSON (Synthesis): {
    "report_date": {
        "report_date": "2023-10-15",
        "patients": [
            {
                "id": "p001",
                "name": "John Doe",
                "age": 45,
                "gender": "male",
                "diagnosis": "Hypertension",
                "notes": "Patient advised to follow a low-sodium diet."
            },
            {
                "id": "p002",
                "name": "Jane Smith",
                "age": 34,
                "gender": "female",
                "diagnosis": "Type 2 Diabetes",
                "notes": "Monitor blood sugar levels regularly."
            },
            {
                "id": "p003",
                "name": "Alex Johnson",
                "age": 28,
                "gender": "other",
                "diagnosis": "Anxiety Disorder",
                "notes": "Recommended therapy sessions once a week."
            }
        ]
    },
    "patients": {
        "report_date": "2023-10-15",
        "patients": [
            {
                "id": "P001",
                "name": "John Doe",
                "age": 45,
                "gender": "male",
                "diagnosis": "Hypertension",
                "notes": "Patient advised to monitor blood pressure regularly."
            },
            {
                "id": "P002",
                "name": "Jane Smith",
                "age": 34,
                "gender": "female",
                "diagnosis": "Type 2 Diabetes",
                "notes": "Diet and exercise plan recommended."
            },
            {
                "id": "P003",
                "name": "Alex Johnson",
                "age": 28,
                "gender": "other",
                "diagnosis": "Anxiety Disorder",
                "notes": "Referred to a mental health specialist."
            },
            {
                "id": "P004",
                "name": "Emily Davis",
                "age": 60,
                "gender": "female",
                "diagnosis": "Osteoarthritis",
                "notes": "Physical therapy suggested."
            },
            {
                "id": "P005",
                "name": "Michael Brown",
                "age": 72,
                "gender": "male",
                "diagnosis": "Chronic Heart Failure",
                "notes": "Medication adjustments required."
            },
            {
                "id": "P006",
                "name": "Linda Wilson",
                "age": 51,
                "gender": "female",
                "diagnosis": "Hyperlipidemia",
                "notes": "Lifestyle changes discussed."
            },
            {
                "id": "P007",
                "name": "David Lee",
                "age": 39,
                "gender": "male",
                "diagnosis": "Asthma",
                "notes": "Inhaler usage reviewed."
            },
            {
                "id": "P008",
                "name": "Sophia Taylor",
                "age": 29,
                "gender": "female",
                "diagnosis": "Depression",
                "notes": "Follow-up in one month."
            },
            {
                "id": "P009",
                "name": "James Anderson",
                "age": 75,
                "gender": "male",
                "diagnosis": "Alzheimer's Disease",
                "notes": "Support for caregivers discussed."
            },
            {
                "id": "P010",
                "name": "Olivia Martinez",
                "age": 22,
                "gender": "female",
                "diagnosis": "Migraine",
                "notes": "Triggers identified and managed."
            }
        ]
    }
}

Customization

jsonpaws allows users to customize the OpenAI model and temperature settings:

• Model: You can specify the model you want to use (e.g., gpt-4o, gpt-4o-mini).
• Temperature: Control the randomness of the output. A higher temperature results in more random output.

Example

# Customize the content generator
content_generator_custom = ContentGenerator(
    api_key=api_key,
    model='gpt-4o',
    mode='synthesis',
    temperature=0.8  # Higher temperature for more randomness
)

# Use the custom content generator in your processor
synthesis_processor_custom = JSONProcessor(schema_parser, prompt_generator_synthesis, content_generator_custom, mode='synthesis')

# Generate the synthetic JSON data
generated_json_custom = synthesis_processor_custom.process(data={}, schema=json_schema)
print("Generated JSON (Custom):", json.dumps(generated_json_custom, indent=4))

Configuration

Set your OpenAI API key as an environment variable:

export OPENAI_API_KEY=your_api_key

Contributing

Contributions are welcome! Please submit a pull request or open an issue to discuss potential improvements or features.

License

This project is licensed under the MIT License.