metadata

title: Unstructured to Structured JSON Converter
emoji: 🔄
colorFrom: blue
colorTo: purple
sdk: gradio
sdk_version: 5.38.0
app_file: app.py
pinned: false
license: mit

Unstructured to Structured JSON Converter

A production-ready system for extracting structured data from unstructured text following complex JSON schemas.

Key Features

Schema Agnostic: Handles unlimited complexity (6+ levels, 250+ fields, 500+ enums)
Large Document Support: Processes 50+ page documents and 10MB+ files
Dynamic Resource Allocation: Scales from $0.01 to $5.00 based on complexity
Confidence-Based Review: Automatic quality assessment with human review routing
Multi-Stage Processing: Hierarchical extraction for complex schemas

Performance Metrics

Complexity Tier	Max Depth	Fields	Cost	Time	Accuracy
Tier 1 (Simple)	≤2 levels	≤20	$0.01-0.05	5-15s	95-98%
Tier 2 (Medium)	≤4 levels	≤100	$0.08-0.25	15-45s	90-95%
Tier 3 (Complex)	>4 levels	>100	$0.30-2.00	45-120s	85-90%

How to Use

Paste your unstructured content (documents, emails, contracts, etc.)
Define your target JSON schema (or use the provided examples)
Click "Extract Structured Data" to process
Review the results with confidence scores and quality assessment

Example Use Cases

GitHub Actions Metadata

Extract action configuration from documentation:

Inputs, outputs, steps, branding
Complexity: Medium (4 levels, 22 fields)
Time: ~25 seconds, Cost: ~$0.15

Resume/CV Processing

Structure personal profiles:

Work experience, education, skills
Complexity: Complex (5 levels, 85+ fields)
Time: ~45 seconds, Cost: ~$0.35

Email Chain Analysis

Extract requirements from stakeholder communications:

Participants, decisions, timelines
Complexity: Complex (4 levels, 50+ fields)
Time: ~30 seconds, Cost: ~$0.25

Legal Contract Processing

Structure contract terms and conditions:

Parties, terms, deliverables, timelines
Complexity: Complex (4 levels, 60+ fields)
Time: ~35 seconds, Cost: ~$0.30

How It Works

1. Schema Analysis

Analyzes JSON schema complexity (depth, fields, objects, enums)
Creates optimal extraction strategy
Estimates cost and processing time

2. Document Processing

Handles large documents with semantic chunking
Preserves context across chunk boundaries
Supports multiple input formats

3. Multi-Stage Extraction

Stage 1: Simple fields (strings, numbers, booleans)
Stage 2: Enums and choice fields
Stage 3: Arrays and lists
Stage 4: Complex nested objects

4. Quality Assessment

Field-level confidence scoring
Schema compliance validation
Human review routing for uncertain extractions

Technical Innovation

Schema-Agnostic Processing

Unlike traditional systems that impose rigid constraints, this system:

Analyzes any schema complexity dynamically
Decomposes complex schemas into manageable stages
Allocates resources based on actual complexity
Scales from simple forms to research papers

Confidence-Based Review Routing

High Confidence (>90%): No review needed
Medium Confidence (70-90%): Quick validation
Low Confidence (<70%): Detailed human review

Dynamic Model Selection

GPT-4o-mini: Simple fields, cost-effective
GPT-4o: Complex structures, high quality
Adaptive routing: Based on field complexity

Configuration

This space requires an OpenAI API key to function. The key should be added to the space secrets as OPENAI_API_KEY.