llm-document-extraction

LLM Document Extraction

Overview

Construction documents (RFIs, submittals, specs, contracts) contain critical data trapped in unstructured formats. This skill uses LLMs to extract structured data automatically.

"The construction industry is drowning in a flood of new data: the volume of information has grown from 15 zettabytes in 2015 to 181 zettabytes in 2025, and 90% of all existing data has been created in just the last few years." — Artem Boiko

Use Cases

Document Type	Extract
RFI	Question, response, dates, parties
Submittal	Product specs, approval status, materials
Contract	Parties, amounts, dates, scope, clauses
Specification	Materials, standards, requirements
Daily Report	Weather, labor, equipment, progress

Quick Start

from openai import OpenAI
import pdfplumber
import json

client = OpenAI()

def extract_from_pdf(pdf_path: str, extraction_schema: dict) -> dict:
    """Extract structured data from PDF using LLM"""

    # Extract text from PDF
    with pdfplumber.open(pdf_path) as pdf:
        text = "\n".join(page.extract_text() for page in pdf.pages)

    # Build extraction prompt
    prompt = f"""
    Extract the following information from this construction document.
    Return ONLY valid JSON matching the schema.

    Schema:
    {json.dumps(extraction_schema, indent=2)}

    Document:
    {text[:8000]}  # Truncate for context limits

    JSON Output:
    """

    response = client.chat.completions.create(
        model="gpt-4o",
        messages=[
            {"role": "system", "content": "You are a construction document analyst. Extract data accurately."},
            {"role": "user", "content": prompt}
        ],
        response_format={"type": "json_object"}
    )

    return json.loads(response.choices[0].message.content)

Extraction Schemas

RFI Schema

rfi_schema = {
    "rfi_number": "string",
    "date_submitted": "YYYY-MM-DD",
    "date_required": "YYYY-MM-DD",
    "from_company": "string",
    "to_company": "string",
    "subject": "string",
    "question": "string",
    "response": "string or null",
    "status": "open|closed|pending",
    "cost_impact": "boolean",
    "schedule_impact": "boolean",
    "attachments": ["list of attachment names"]
}

# Extract
rfi_data = extract_from_pdf("RFI-0042.pdf", rfi_schema)