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Get Started Free →datadrivenconstruction/rag-construction
Build RAG systems for construction knowledge bases. Create searchable AI-powered construction document systems
Passed 21 of 22 test cases with the skill, and 7 of 22 without it.
and 12 more in the full report
14 fixed7 passed either way1 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 14 test cases the skill fixed. Same model, same prompt, with and without the skill.
A contractor submits a formal Request for Information regarding foundation rebar layout. What DocumentType enum value represents this document?
| Checked | Without skill | With skill |
|---|---|---|
| Uses DocumentType.RFI | Missed | Passed |
The instructions your model reads when the skill loads.
Based on DDC methodology (Chapter 2.3), this skill builds Retrieval-Augmented Generation (RAG) systems for construction knowledge bases, enabling semantic search and AI-powered question answering over construction documents.
Book Reference: "Pandas DataFrame и LLM ChatGPT" / "Pandas DataFrame and LLM ChatGPT"
from dataclasses import dataclass, field
from enum import Enum
from typing import List, Dict, Optional, Any, Callable
from datetime import datetime
import json
import hashlib
import re
class DocumentType(Enum):
"""Types of construction documents"""
SPECIFICATION = "specification"
DRAWING = "drawing"
CONTRACT = "contract"
RFI = "rfi"
SUBMITTAL = "submittal"
CHANGE_ORDER = "change_order"
MEETING_MINUTES = "meeting_minutes"
DAILY_REPORT = "daily_report"
SAFETY_REPORT = "safety_report"
INSPECTION = "inspection"
MANUAL = "manual"
STANDARD = "standard"
class ChunkingStrategy(Enum):
"""Text chunking strategies"""
FIXED_SIZE = "fixed_size"
PARAGRAPH = "paragraph"
SECTION = "section"
SEMANTIC = "semantic"
test cases passed, vs 7 / 22 without
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