Ashmatics FDA Pipeline

Version: 0.1.0 | Last Updated: 2025-11-29

Copyright 2025 Asher Informatics PBC - Proprietary and Confidential

FDA 510(k) and De Novo document extraction, enrichment, and structuring pipeline for the Ashmatics Knowledge Base platform.

Overview

The Ashmatics FDA Pipeline provides a comprehensive solution for extracting structured metadata from FDA regulatory documents (510(k) summaries and De Novo decision summaries). It transforms unstructured PDF documents into MongoDB-ready structured documents suitable for AI/ML analysis and knowledge base integration.

Key Features

• PDF Parsing: High-quality document parsing using DocLing
• Metadata Extraction: Regex-based extraction with LLM validation
• Table Processing: Multi-page table consolidation and classification
• Predicate Extraction: Multi-source predicate device identification
• AI/ML Data Extraction: Training data and performance metrics extraction
• Domain Knowledge: Product code-aware extraction with confidence scoring
• Batch Processing: Concurrent document processing with structured output

Installation

Prerequisites

• Python 3.12+
• uv package manager (recommended)
• Access to JFK-Ashmatics private repositories

Install from GitHub

# Using uv (recommended)
uv add "ashmatics-fda-pipeline @ git+https://github.com/JFK-Ashmatics/ashmatics-fda-pipeline.git"

# With optional dependencies
uv add "ashmatics-fda-pipeline[all] @ git+https://github.com/JFK-Ashmatics/ashmatics-fda-pipeline.git"

Development Installation

git clone https://github.com/JFK-Ashmatics/ashmatics-fda-pipeline.git
cd ashmatics-fda-pipeline
uv sync --all-extras

Quick Start

CLI Usage

# Process a batch of PDFs
fda-pipeline process /path/to/pdfs --output /path/to/output

# Process a single document
fda-pipeline process-single /path/to/K123456.pdf

# Show version
fda-pipeline version

Python API

from ashmatics_fda_pipeline import FDA510kPipeline, PipelineConfig

# Configure pipeline
config = PipelineConfig(
    enable_llm_validation=True,
    enable_performance_extraction=True,
    llm_provider="azure_openai",
)

# Create pipeline
pipeline = FDA510kPipeline(config)

# Process single document
result = await pipeline.process_single(Path("K123456.pdf"))

print(f"K-Number: {result.k_number}")
print(f"Manufacturer: {result.metadata['manufacturer']}")

# Process batch
results = await pipeline.process_batch([Path("K123456.pdf"), Path("K234567.pdf")])

Architecture

ashmatics_fda_pipeline/
├── __init__.py          # Public API exports
├── config.py            # PipelineConfig dataclass
├── pipeline.py          # FDA510kPipeline main class
├── pipeline_registry.py # Factory pattern for pipelines
├── cli.py               # Typer CLI entry point
│
├── extractors/          # Metadata extraction
│   ├── base.py          # DocumentExtractor ABC
│   ├── metadata_extractor.py  # FDA510kExtractor
│   └── llm_validator.py       # LLM validation
│
├── enrichers/           # Content enrichment
│   ├── table_classifier.py       # Table classification
│   ├── table_consolidator.py     # Multi-page table merge
│   ├── predicate_extractor.py    # Predicate device extraction
│   ├── training_data_extractor.py    # AI/ML training data
│   └── performance_data_extractor.py # Validation results
│
├── mappers/             # Schema mapping
│   ├── base.py          # DocumentMapper ABC
│   └── document_mapper.py # RegulatoryDocumentMapper
│
├── storage/             # Output management
│   └── batch_output.py  # BatchOutputManager
│
└── domain_knowledge/    # FDA domain patterns
    ├── __init__.py      # DomainKnowledge, DocumentPatternLoader
    ├── document_patterns.py
    ├── ai_device_expectations.yaml
    ├── 510k_summary_document_patterns.yaml
    └── de_novo_document_patterns.yaml

Configuration

Environment Variables

# Azure OpenAI (default LLM provider)
AZURE_OPENAI_API_KEY=your-api-key
AZURE_OPENAI_ENDPOINT=https://your-endpoint.openai.azure.com/
AZURE_OPENAI_DEPLOYMENT_NAME=gpt-4o

# OpenAI (alternative)
OPENAI_API_KEY=your-api-key

# Anthropic (alternative)
ANTHROPIC_API_KEY=your-api-key

PipelineConfig Options

@dataclass
class PipelineConfig:
    # LLM configuration
    enable_llm_validation: bool = True
    llm_provider: str = "azure_openai"
    enable_performance_extraction: bool = True

    # Processing
    max_concurrent: int = 3
    batch_size: int = 5

    # Output
    enable_batch_output: bool = True
    write_markdown: bool = False
    save_figures: bool = True
    save_tables: bool = True

Output Structure

When enable_batch_output=True, the pipeline creates a structured output:

batch-YYYYMMDD-HHMMSS/
├── manifest.json          # Batch metadata and processing summary
├── K123456/
│   ├── K123456_parsed.md  # Parsed markdown
│   ├── K123456_metadata.json
│   ├── K123456_mongo_doc.json
│   ├── figures/
│   │   ├── figure_1.png
│   │   └── figure_metadata.json
│   └── tables/
│       ├── table_1.md
│       ├── table_1.json
│       └── table_classifications.json
└── K234567/
    └── ...

Dependencies

Core Ashmatics Packages

• ashmatics-tools: Base utilities, parsers, LLM clients
• ashmatics-datamodels: Shared Pydantic data models

Optional Dependencies

# LLM enrichment (Azure OpenAI, OpenAI, Anthropic)
uv add ashmatics-fda-pipeline[llm]

# Azure storage support
uv add ashmatics-fda-pipeline[azure]

# MongoDB support
uv add ashmatics-fda-pipeline[mongodb]

# All optional dependencies
uv add ashmatics-fda-pipeline[all]

Development

Running Tests

# Run all tests
uv run pytest

# With coverage
uv run pytest --cov=ashmatics_fda_pipeline

# Run specific test
uv run pytest tests/unit/test_extractors.py -v

Code Quality

# Format code
uv run ruff format .

# Lint
uv run ruff check .

# Type check
uv run mypy src/

License

Copyright 2025 Asher Informatics PBC. All rights reserved.

This software is proprietary and confidential. Unauthorized copying, modification, distribution, or use is strictly prohibited.

See LICENSE for details.

Support

For licensing inquiries: legal@asherinformatics.com

For technical support: engineering@asherinformatics.com