pdf-structify 0.1.1

Extract structured data from PDFs using LLMs with sklearn-like API

pdf-structify

Extract structured data from PDFs using LLMs with a scikit-learn-like API.

pdf-structify makes it easy to extract structured, tabular data from PDF documents using Large Language Models. It handles PDF splitting, schema detection, and data extraction with progress tracking and checkpoint/resume support.

Features

• Scikit-learn-like API: Familiar fit(), transform(), fit_transform() interface
• Automatic Schema Detection: Let the LLM analyze your documents and detect extractable fields
• Natural Language Schema Definition: Describe what you want to extract in plain English
• Progress Bars: Beautiful, informative progress tracking with rich
• Checkpoint/Resume: Never lose progress - automatically resume from interruptions
• Two-Layer Prompt System: Strict JSON enforcement for reliable extraction
• PDF Splitting: Automatically split large PDFs into manageable chunks

Installation

pip install pdf-structify

Quick Start

3-Line Extraction

from structify import Pipeline

pipeline = Pipeline.quick_start()
results = pipeline.fit_transform("my_pdfs/")
results.to_csv("output.csv")

From Natural Language Description

from structify import Pipeline

pipeline = Pipeline.from_description("""
    Extract research findings from academic papers:
    - Author names and publication year
    - The country being studied
    - Main numerical finding (coefficient or percentage)
    - Statistical significance (p-value)
    - Methodology used (regression, RCT, etc.)
""")

results = pipeline.fit_transform("research_papers/")

With Custom Schema

from structify import Pipeline, SchemaBuilder

schema = SchemaBuilder.create(
    name="financial_metrics",
    fields=[
        {"name": "company", "type": "string", "required": True},
        {"name": "year", "type": "integer", "required": True},
        {"name": "revenue", "type": "float"},
        {"name": "profit_margin", "type": "float"},
        {"name": "sector", "type": "categorical",
         "options": ["Tech", "Finance", "Healthcare", "Energy"]}
    ],
    focus_on=["financial statements", "annual reports"],
    skip=["legal disclaimers", "boilerplate text"]
)

pipeline = Pipeline.from_schema(schema)
results = pipeline.fit_transform("annual_reports/")

Resume After Interruption

from structify import Pipeline

# If interrupted, just run again - automatically resumes!
pipeline = Pipeline.resume("my_pdfs/")
results = pipeline.transform("my_pdfs/")

Configuration

Environment Variables

export GEMINI_API_KEY="your-api-key"

Or in Code

from structify import Config

Config.set(
    gemini_api_key="your-api-key",
    pages_per_chunk=10,
    temperature=0.1,
    max_retries=5
)

Or from .env File

from structify import Config
Config.from_env()  # Loads from .env file

Components

PDFSplitter

Split large PDFs into smaller chunks:

from structify import PDFSplitter

splitter = PDFSplitter(pages_per_chunk=10)
splitter.transform("large_documents/", output_path="chunks/")

SchemaDetector

Automatically detect extractable fields:

from structify import SchemaDetector

detector = SchemaDetector(sample_ratio=0.1, max_samples=30)
schema = detector.fit_transform("documents/")
print(schema.fields)

LLMExtractor

Extract data using a schema:

from structify import LLMExtractor, Schema

extractor = LLMExtractor(schema=my_schema, deduplicate=True)
results = extractor.fit_transform("documents/")

Progress Tracking

pdf-structify provides beautiful progress bars:

╭─────────────────── Structify Pipeline ───────────────────╮
│ Stage 2/3: Data Extraction                               │
╰──────────────────────────────────────────────────────────╯
Processing papers ━━━━━━━━━━━━━━━━━ 45% 12/25 papers
  Current: "Economic_Study.pdf" part 3/8
  → Found 24 records

Output Formats

# CSV
results.to_csv("output.csv")

# JSON
results.to_json("output.json")

# Parquet
results.to_parquet("output.parquet")

# Excel
results.to_excel("output.xlsx")

Requirements

• Python 3.10+
• Google Gemini API key

Dependencies

• google-generativeai
• pypdf
• rich
• pydantic
• pandas
• python-dotenv
• pyyaml

License

MIT License - see LICENSE file for details.

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.