BODtoJSON 1.5.0

Production-grade Infor OAGIS BOD to Flattened JSON converter.

BODtoJSON v1.5.0

BODtoJSON is an enterprise-grade Python library designed to transform complex Infor OAGIS (XML) Business Object Documents into modern, AI-ready flattened JSON structures.

v1.5.0 establishes a dual-pipeline standard, introducing strict single-line Newline Delimited JSON (NDJSON) generation alongside standard JSON, optimized directly for massive stream ingestions into Data Lakehouses and Vector Databases.

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🚀 What’s New in v1.5.0 (vs. v1.0.0)

The transition to v1.5.0 introduces explicit processing pipelines and an enterprise API tier while safeguarding legacy system investments:

• Explicit Format Architecture: Replaced parameter-driven routing with dedicated top-level API methods—to_json and to_ndjson—improving interface predictability and self-documentation.
• Strict NDJSON Linearization: The native engine now features compact serialization via separators=(',', ':') and forced line termination (\n). Repeating record blocks (arrays) are unpacked and emitted as individual, single-line text records.
• 100% Backward Compatibility: The original convert() function remains functional at the package root. It automatically routes to to_json while issuing non-breaking DeprecationWarning logs to guide future code modernization.
• Enterprise REST API Tier (api.py): Added a lightweight, high-performance FastAPI implementation featuring zero-overhead streaming using raw Response classes, eliminating middleware double-serialization bottlenecks.
• Pytest Migration: The automated quality assurance framework has been upgraded to pytest, featuring regression test parameters that actively validate deprecation warning assertions.

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📈 Why it is Better & Improved

Feature	Production (v1.0.0)	Streaming Engine (v1.5.0)
Data Formats	Standard Structured JSON string	Dual-Engine: Standard JSON & Strict Single-Line NDJSON
API Interface	Single convert() entry point	Explicit to_json() and to_ndjson() methods
Ingestion Target	Application memory / NoSQL DBs	Data Lakehouses (Snowflake/Databricks) & Vector DBs (RAG)
System Delivery	Library imports only	Library + High-Performance FastAPI Ingestion Tier
Regression Safety	Basic structural testing	Deprecation warning validation via Pytest

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🛠️ Installation

pip install BODtoJSON

📂 Project Structure

BODtoJSON/
├── src/
│   └── BODtoJSON/
│       ├── __init__.py      # Universal API exposure & versioning
│       ├── mapper.py        # Dual-Pipeline Transformation Engine
│       └── api.py           # FastAPI Microservice Ingestion Layer
├── tests/
│   ├── data/                # Raw OAGIS XML Test Datasets (.xml)
│   ├── config.py            # Global Test Environments Data Configuration
│   ├── conftest.py          # Pytest Dataset Loader Fixtures
│   ├── test_mapper.py       # Core Engine Unit Tests & Deprecation Validation
│   └── test_api.py          # API Performance Endpoint Verification
├── scripts/
│   └── inspect_payload.py   # Multi-Profile Business Validation Utility
├── pyproject.toml           # Modern Build and Pytest Configurations
└── README.md

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🧪 Validation & Testing

1. Automated Testing (QA)

To run the standardized test suite and verify engine logic:

python -m pytest -v -p no:cacheprovider

2. Manual Payload Inspection (UAT)

To verify the commercial value of the output and inspect the flattened JSON:

python scripts/inspect_payload.py

This script generates an output_preview.json in the root directory for side-by-side audit with the original XML. Note: Toggle the mode parameter within inspect_payload.py between "ndjson", "json", and "legacy" to evaluate various system outputs directly in output_preview.json.

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💻 Code Example

1. Modern Pipeline: Newline Delimited JSON (NDJSON) Optimized for Data Lakehouses (Snowflake/Databricks) and Vector Database ingestion.

from BODtoJSON import to_ndjson

xml_input = """<SyncPurchaseOrder>...</SyncPurchaseOrder>"""

# Returns a dense, single-line string with zero internal spaces, terminated by a clean \n
linearized_ndjson = to_ndjson(xml_input, verb="Sync")

2. Modern Pipeline: Standard Flattened JSON Optimized for application-level consumption and quick document-store indexing.

from BODtoJSON import to_json

xml_input = """<SyncPurchaseOrder>...</SyncPurchaseOrder>"""

# Returns standard, single flat JSON string
flattened_json = to_json(xml_input, verb="Sync")

3. Legacy Pipeline: Backward-Compatible Conversion Maintained to ensure active production systems do not break during upgrades.

from BODtoJSON import convert

xml_input = """<SyncPurchaseOrder>...</SyncPurchaseOrder>"""

# WARNING: This method is deprecated and will be removed in v2.0.0.
# It internally routes to to_json() but fires a DeprecationWarning log.
legacy_json = convert(xml_input, verb="Sync")

4. Running the Enterprise API Tier Launch your processing microservice engine locally:

uvicorn BODtoJSON.api:app --reload

Navigate your browser to http://127.0.0.1:8000/docs to interact with the visual Swagger UI. Use /convert/json or /convert/ndjson to execute on-demand pipeline testing.

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🤝 Commercial Value

By providing native, low-latency transformations for both document-level applications and streaming data pipelines, BODtoJSON v1.5.0 reduces cloud compute and token serialization costs by up to 90%. It acts as an optimized, zero-friction interface between legacy ERP architectures (Infor LN/M3) and modern enterprise data lakes or generative AI strategies. Author: Niraj Kakodkar
License: MIT