validedi 0.3.4

A modern, configuration-driven X12 EDI parser and validator for healthcare transactions with optional LLM-powered explanations and structured data extraction

ValidEDI

A modern, configuration-driven X12 EDI parser and validator for healthcare transactions with optional LLM-powered explanations.

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What is ValidEDI?

ValidEDI is a Python library for parsing and validating healthcare EDI (Electronic Data Interchange) files. It supports the most common X12 transaction types used in healthcare:

• 837P - Professional Health Care Claims
• 837I - Institutional Health Care Claims
• 835 - Health Care Claim Payment/Remittance Advice
• 834 - Benefit Enrollment and Maintenance

Key Features

✅ Parse EDI Files - Convert X12 EDI into structured Python objects
✅ Validate Transactions - 60+ validation rules with plain-English error messages
✅ Configuration-Driven - All rules and codes in YAML files, not hardcoded
✅ LLM-Powered Explanations - Optional AI-powered plain-English reports (works with ANY LLM)
✅ Loop Navigation - Hierarchical loop structure for easy data access
✅ Type-Safe - Full Pydantic v2 models with type hints
✅ Extensible - Add custom validation rules and code sets
✅ Production-Ready - Thread-safe, well-tested, comprehensive error handling

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Quick Start

Installation

pip install validedi

Parse an EDI File

from validedi import parse

# Parse from file path
result = parse('claim.edi')

# Access parsed data
print(f"Transaction: {result.envelope.transaction_type}")
print(f"From: {result.envelope.sender_id}")
print(f"To: {result.envelope.receiver_id}")
print(f"Loops: {len(result.loops)}")

Validate an EDI File

from validedi import validate

# Validate the file
result = validate('claim.edi')

# Check results
if result.is_valid:
    print("✅ File is valid!")
else:
    print(f"❌ Found {result.error_count} errors")
    for error in result.errors:
        print(f"  • {error.message}")

Get Plain-English Explanations (Optional)

from validedi import parse, validate
from validedi.llm import explain

# Parse and validate
edi_result = parse('claim.edi')
val_result = validate(edi_result)

# Option 1: With your own LLM (OpenAI, Groq, Bedrock, Gemini, etc.)
from openai import OpenAI
client = OpenAI(api_key="your-key")

def my_llm(prompt: str) -> str:
    response = client.chat.completions.create(
        model="gpt-4",
        messages=[{"role": "user", "content": prompt}]
    )
    return response.choices[0].message.content

result = explain(edi_result, val_result, llm=my_llm)
print(result.report)

# Option 2: Without LLM (rule-based templates)
result = explain(edi_result, val_result)
print(result.report)

Ask Questions About Your EDI File

from validedi.llm import ask_followup

# Ask questions in plain English
answer = ask_followup(
    "What is the total billed amount?",
    edi_result,
    val_result,
    llm=my_llm
)
print(answer)

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Why ValidEDI?

vs. Other EDI Libraries

Feature	ValidEDI	Others
Configuration-Driven	✅ YAML files	❌ Hardcoded
Plain-English Errors	✅ Yes	❌ Technical codes
LLM Integration	✅ Any provider	❌ None
Loop Navigation	✅ Hierarchical	⚠️ Flat segments
Type Safety	✅ Pydantic v2	⚠️ Dicts
Extensible	✅ Easy	⚠️ Difficult
Modern Python	✅ 3.8+	⚠️ Legacy

Configuration-Driven Architecture

Unlike other libraries that hardcode validation rules, ValidEDI uses YAML configuration files:

# validedi/config/rules/rules_837.yaml
rules:
  - id: 'CLM01_REQUIRED'
    type: 'required_element'
    target: 'CLM01'
    severity: 'error'
    message: 'CLM01 (Patient Control Number) is blank'
    suggestion: 'CLM01 must be a unique identifier for the claim'

This means you can:

• Add new validation rules without changing code
• Customize rules for your specific needs
• Version control your validation logic
• Share configurations across teams

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Supported Transaction Types

837P - Professional Claims

Outpatient and office-based healthcare services billed with CPT codes.

Use Cases:

• Doctor office visits
• Outpatient procedures
• Laboratory services
• Durable medical equipment

837I - Institutional Claims

Inpatient and facility-based healthcare services billed with revenue codes.

Use Cases:

• Hospital inpatient stays
• Emergency room visits
• Skilled nursing facilities
• Home health services

835 - Remittance Advice

Payer explanation of claim payments and adjustments.

Use Cases:

• Payment reconciliation
• Claim status tracking
• Adjustment reason analysis
• Accounts receivable management

834 - Benefit Enrollment

Member insurance enrollment, changes, and terminations.

Use Cases:

• New member enrollment
• Coverage changes
• Dependent additions
• Terminations

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Documentation

Quick Start

• Quick Start Guide - Get started in 5 minutes
• Basic Usage Examples - Common use cases

Deep Dive

• Architecture Deep Dive - How ValidEDI works internally
• Configuration Guide - Customize validation rules

LLM Integration

• LLM Guide - AI-powered explanations
• LLM Examples - 8+ provider examples
• Interactive Chatbot - CLI chatbot

Advanced

• API Reference - Complete API documentation
• Custom Validation - Add your own rules
• Publishing Guide - Deploy to PyPI

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Examples

Parse and Navigate Loops

from validedi import parse

result = parse('claim.edi')

# Navigate hierarchical loops
for loop in result.loops:
    if loop.loop_id == '2000A':  # Billing Provider
        for segment in loop.segments:
            if segment.segment_id == 'NM1':
                print(f"Provider: {segment.elements[2]}")

Custom Validation

from validedi import validate

# Validate with custom rules
result = validate('claim.edi')

# Access detailed validation results
for error in result.errors:
    print(f"[{error.code}] {error.segment_id}: {error.message}")
    if error.suggestion:
        print(f"  Fix: {error.suggestion}")

Batch Processing

from validedi import parse, validate
import glob

for filepath in glob.glob('*.edi'):
    edi_result = parse(filepath)
    val_result = validate(edi_result)
    
    print(f"{filepath}: {val_result.summary}")

Interactive Chatbot

python examples/llm_chatbot.py claim.edi

You: What is the total billed amount?
Bot: The total billed amount is $4,250.00 across 3 claims.

You: Are there any errors?
Bot: There is 1 warning: CLM06 is missing on Claim #2.

You: quit
👋 Goodbye!

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Architecture Highlights

1. Configuration-Driven Design

All validation rules, code sets, and transaction definitions are in YAML files:

validedi/config/
├── transactions/     # Transaction definitions (837P, 837I, 835, 834)
├── rules/           # Validation rules
├── code_sets/       # Code value lists
└── registry.yaml    # Transaction registry

2. Hierarchical Loop Structure

EDI segments are organized into hierarchical loops for easy navigation:

Loop 2000A (Billing Provider)
  ├── Segment NM1 (Name)
  ├── Segment N3 (Address)
  └── Loop 2000B (Subscriber)
      ├── Segment NM1 (Name)
      └── Loop 2300 (Claim)
          ├── Segment CLM (Claim Info)
          └── Loop 2400 (Service Line)
              └── Segment SV1 (Service)

3. Type-Safe Models

Full Pydantic v2 models with type hints:

class ParsedEDI(BaseModel):
    envelope: EnvelopeMeta
    loops: List[Loop]
    segments: List[Segment]
    raw_content: str

class ValidationResult(BaseModel):
    is_valid: bool
    error_count: int
    errors: List[ValidationError]
    summary: str

4. LLM-Agnostic Integration

Works with ANY LLM through a simple callable interface:

# Works with OpenAI, Groq, Bedrock, Gemini, Anthropic, local models, etc.
def my_llm(prompt: str) -> str:
    return response

result = explain(edi_result, val_result, llm=my_llm)

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Validation Rules

ValidEDI includes 60+ validation rules across 4 categories:

Envelope Validation

• ISA/IEA segment presence and structure
• GS/GE functional group pairing
• ST/SE transaction set pairing
• Control number matching
• Segment count validation

Format Validation

• Date format (CCYYMMDD)
• NPI format and Luhn check
• ZIP code format
• Monetary amount format
• Element length validation

Business Rules

• Required entity presence (submitter, billing provider, payer)
• Charge total consistency
• Service line validation
• Diagnosis code format
• Procedure code validation

Code Set Validation

• Place of service codes
• Claim adjustment reason codes
• Entity identifier codes
• Relationship codes
• Coverage level codes

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Code Sets

ValidEDI includes comprehensive code sets:

• 200+ Adjustment Reason Codes (CARC)
• 50+ Segment Descriptions
• 30+ Entity Codes
• 35+ Date Qualifiers
• 13 Coverage Level Codes
• 10 Relationship Codes
• 8 Claim Status Codes
• Common ICD-10 Codes
• Common CPT Codes

All code sets are in YAML files and can be extended.

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LLM Integration

ValidEDI's LLM integration is provider-agnostic - works with ANY LLM:

Supported Providers (Examples Included)

1. OpenAI (GPT-4, GPT-3.5)
2. Groq (Llama 3.1, Mixtral, Gemma) - Free tier available
3. AWS Bedrock (Claude, Llama)
4. Google Gemini - Free tier available
5. Anthropic Claude
6. Azure OpenAI
7. Local Models (Ollama, LM Studio, etc.)
8. Custom Implementations

Features

• Plain-English explanations of EDI files
• Interactive Q&A about your data
• Validation error explanations with fix instructions
• Rule-based fallback (works without LLM)
• Zero dependencies (bring your own LLM)

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Requirements

• Python 3.8+
• pydantic >= 2.0
• pyyaml >= 6.0

Optional (for LLM features)

• openai (for OpenAI)
• groq (for Groq)
• anthropic (for Anthropic)
• google-generativeai (for Gemini)
• boto3 (for AWS Bedrock)

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Contributing

Contributions are welcome! Please see CONTRIBUTING.md for guidelines.

Areas for Contribution

• Additional transaction types (270/271, 276/277, 278, 997, 999)
• More validation rules
• Additional code sets
• Performance optimizations
• Documentation improvements
• Bug fixes

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License

MIT License - see LICENSE file for details.

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Support

• Documentation: docs/
• Examples: examples/
• Issues: GitHub Issues
• Discussions: GitHub Discussions

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Roadmap

v0.2.0 (Planned)

• Additional transaction types (270/271, 276/277)
• Performance optimizations
• Streaming parser for large files
• Web UI for validation

v0.3.0 (Planned)

• Real-time validation API
• Batch processing optimizations
• Custom rule DSL
• Report generation

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Acknowledgments

• Built with inspiration from Shaunak's X12-EDI-PARSER
• Validation rules based on X12 5010 implementation guides
• Code sets from HIPAA standards

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Quick Links

• Quick Start Guide
• Architecture Deep Dive
• LLM Integration Guide
• API Reference
• Examples

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