rag-construction

# RAG Construction

## Overview

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"

## Quick Start

```python
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"
    SENTENCE = "sentence"

@dataclass
class DocumentChunk:
    """A chunk of document text"""
    id: str
    document_id: str
    content: str
    metadata: Dict[str, Any]
    embedding: Optional[List[float]] = None
    token_count: int = 0
    position: int = 0

@dataclass
class Document:
    """Construction document"""
    id: str
    title: str
    doc_type: DocumentType
    content: str
    source: str
    metadata: Dict[str, Any] = field(default_factory=dict)
    chunks: List[DocumentChunk] = field(default_factory=list)
    created_at: datetime = field(default_factory=datetime.now)

@dataclass
class SearchResult:
    """Search result from vector store"""
    chunk: DocumentChunk
    score: float
    document_title: str
    doc_type: DocumentType

@datacla...