GitHub - kuklevSU10/-

DDC CWICR - Construction Work Items, Components & Resources
+ Pipelines n8n for calculating estimates based on descriptions, photos, and CAD (BIM)

⚡ n8n Workflows

Choose your input → Get cost estimate

📝 Text

Quick scope-to-estimate
from a short description

Input: Telegram / chat message
Output: Matched work items + estimate

📖 Documentation

📷 Photo / PDF

Site photos, scanned BOQ,
photo-PDF from the field

Input: Image or PDF pages
Output: Extracted scope → estimate

📖 Photo Docs · 📖 Universal Bot

🧊 CAD / BIM

Revit / IFC / DWG-based
quantification & estimating

Input: Model export
Output: 4D/5D estimate + breakdown

📖 Documentation

DataDrivenConstruction clients and users

📑 Table of Contents

Database & Data

About — What is DDC CWICR
Available Formats — Excel, Parquet, CSV, Qdrant
Data Schema — 85 fields structure
Field Groups — Classification, Resources, Labor, Machinery
Methodology — Resource-based costing principles
Historical Context — 100+ years of standards

n8n Workflows

n8n Workflows Overview — Choose your input type
Try It Now — Live Demo Bots — Test instantly in Telegram
Workflow 1: Text Estimator Bot — Telegram bot for text input
Workflow 2: Photo Cost Estimator — Web form with AI Vision
Workflow 3: Universal Bot — Text + Photo + PDF
Workflow 4: CAD/BIM Pipeline — Revit/IFC/DWG to estimate
Workflows Quick Start — Setup in 4 steps
⚠️ n8n 2.0+ Setup — Enable Execute Command node

CAD/BIM Pipeline Details

Prerequisites — Required components
Pipeline Stages — 10-stage processing
LLM Model Selection — OpenAI, Claude, Gemini, Grok
Output Files — HTML & Excel reports
Troubleshooting — Common issues

Vector Database

Vector Database — Semantic search with Qdrant
Releases — Download snapshots
Collections — 9 language collections
Docker Deployment — Self-hosted setup

Getting Started

Quick Start - Python — Tabular data & semantic search
Integration Use Cases — Entry to Advanced level

Community

Resources & Community — Links & channels
Consulting & Training — Professional services
Contributing — Submit your workflows
License — CC BY 4.0 & MIT
Support the Project — Sponsor & donate

About

DDC CWICR (Construction Work Items, Components & Resources) is an open database for construction cost estimation, covering the full spectrum of construction activities - from earthworks and concrete placement to specialized installation work.

The database draws on sources describing modern construction practices across Eurasia and the Asia-Pacific region, where a unified technical standardization ecosystem serves as a common engineering language for more than ten dynamically developing economies. DDC CWICR represents an effort to harmonize open standards by establishing a single regulatory framework for capital project management in multiple languages.

The structured data can be accessed through tabular formats (XLSX, CSV, Parquet) or queried conversationally via LLM, enabling specialists to integrate construction work descriptions (QDRANT vector database) into automated pipelines and workflows using plain language or concise queries.

Available Formats

Format	Extension	Size	Best For	Features
Excel	`.xlsx`	~150–400 MB	Manual analysis, filtering, pivots	Human-readable, full formatting
Parquet	`.parquet`	~55 MB	ETL pipelines, ML training, Big Data	Columnar, excellent compression
CSV	`.csv`	~1.3 GB	Database import, legacy systems	Universal compatibility
Qdrant	`.snapshot`	~1 GB	Semantic search, RAG, AI assistants	Pre-computed OpenAI embeddings

A live demo is available at openconstructionestimate.com, where you can explore the data and see the vector database in action for semantic search.

Data Schema

The database contains 85 fields organized into logical groups. Each record represents either a work item (rate) or a resource with full cost breakdown.

erDiagram
    RATE ||--o{ RESOURCE : contains
    RATE ||--o{ LABOR : requires
    RATE ||--o{ MACHINERY : uses
    RATE ||--o{ PRICE_VARIANT : has

    RATE {
        string rate_code PK "MEKA_KASA_KAKATO_KAME"
        string rate_original_name "Einbau von Trennwänden..."
        string rate_unit "100 m2"
        string category_type "BAUARBEITEN"
        string collection_name "Holzkonstruktionen"
        string department_name "TRENNWÄNDE..."
        string section_name "Einbau von Trennwänden..."
        text work_composition_text
    }

    RESOURCE {
        string resource_code PK "KAME-NE-KAME-KARI"
        string resource_name "Gipskartonplatten"
        string resource_unit "m2"
        float resource_quantity "632.0"
        float resource_price_per_unit_eur "5.02"
        float resource_cost_eur "3170.73"
        boolean is_material
        boolean is_abstract
    }

    LABOR {
        string resource_code FK
        float labor_hours_workers "172"
        float labor_hours_machinists "1.67"
        int count_workers_per_unit "172"
        int count_machinists_per_unit "2"
        float cost_of_working_hours "3088.11"
    }

    MACHINERY {
        string machine_class2_name "Krane"
        string machine_class3_name "Krane auf Fahrgestellen"
        float electricity_consumption_kwh "0.23"
        float price_machinist_wages "13.56"
        float total_value_machinery "64.18"
    }

    PRICE_VARIANT {
        float price_est_median "5.02"
        float price_est_min "3.03"
        float price_est_max "7.99"
        int position_count "24"
        string variable_parts "glasfaserverstärkt..."
    }

Field Groups

The 85 database fields are organized into logical groups that reflect the resource-based cost estimation methodology. Each group serves a specific function in the cost breakdown structure: from hierarchical classification and work item identification to detailed resource consumption, labor requirements, machinery costs, and aggregated totals. This modular structure allows users to query only the relevant fields for their task - whether generating a bill of materials, analyzing labor productivity, or building a complete cost estimate.

Classification - category_type, collection_code, collection_name, department_code, department_name, department_type, section_name, section_type, subsection_code, subsection_name

Work Item (Rate) - rate_code, rate_original_name, rate_final_name, rate_unit, row_type, is_scope, is_abstract, is_machine, is_labor, is_material, work_composition_text

Resources - resource_code, resource_name, resource_unit, resource_quantity, parameter_resource_quantity, resource_price_per_unit_eur_current, resource_cost_eur

Labor - count_workers_per_unit, count_engineers_per_unit, count_machinists_per_unit, count_total_people_per_unit, labor_hours_construction_workers, labor_hours_machinists, labor_hours_engineers, total_labor_hours_workers_machinists, total_labor_hours_all_personnel, cost_of_working_hours, count_people_per_day

Machinery - machine_class2_name, machine_class3_name, personnel_machinist_code, personnel_machinist_grade, price_machinist_wages, price_relocation_included, price_cost_without_wages, electricity_consumption_kwh_per_machine_hour, electricity_cost_per_unit, electricity_cost_total_sum, cost_machinist_sum, total_value_machinery_equipment

Price Variants - price_code_prefix, price_abstract_resource_common_start, price_abstract_resource_variable_parts, price_abstract_resource_position_count, price_abstract_resource_est_price_min, price_abstract_resource_est_price_max, price_abstract_resource_est_price_mean, price_abstract_resource_est_price_median, price_abstract_resource_unit, abstract_resource_tech_group

Aggregates - total_cost_per_position, total_material_cost_per_position, total_resource_cost_per_position, total_value_abstract_resources, materials_resource_cost_eur

Mass & Services - mass_name, mass_value, mass_unit, service_category, service_type, parameter_service_code, parameter_service_unit, parameter_service_name, parameter_service_quantity, service_cost_sum

Cost Calculation Formula

Component	Technology Norm	×	Regional Price	=	Cost
👷 Labor	172 hrs/100m²	×	€17.95/hr	=	€3,088.11
🧱 Materials	632 m²/100m²	×	€5.02/m²	=	€3,170.73
🚜 Equipment	1.67 hrs/100m²	×	€38.42/hr	=	€64.18
			Total	=	€7,725.91 per 100m²

Methodology

The key value of Resource-Based Costing is the separation of unchanging production technology from the volatile financial component. It is based on the physical "first principles" of construction:

Labor hours required for specific work
Material quantities per unit of work
Equipment time needed

Why it matters:

Transparency - Pricing without hidden markups, full resource breakdown
Auditability - Deep-dive capability for investment analysis and verification
Portability - Region-independent norms applicable across markets
Proven - Industry standard methodology established over 100+ years

flowchart TB
    subgraph Source["📦 Data Source"]
        CWICR[(DDC CWICR<br/>────────────<br/>55,719 Work Items<br/>27,672 Resources<br/>85 Fields per Record)]
    end

    subgraph Processing["⚙️ Processing Pipeline"]
        direction LR
        ETL[["🔄 ETL<br/>Extraction &<br/>Transformation"]]
        TRANS[["🌐 Translation<br/>9 Languages"]]
        EMBED[["🧠 Vectorization<br/>OpenAI 3072d"]]
        ETL --> TRANS --> EMBED
    end

    subgraph Outputs["📤 Output Formats"]
        XLSX[("📊 Excel<br/>.xlsx")]
        PARQUET[("⚡ Parquet<br/>.parquet")]
        CSV[("📄 CSV<br/>.csv")]
        QDRANT[("🔍 Qdrant<br/>.snapshot")]
    end

    subgraph Apps["🎯 Applications"]
        SEARCH["🔎 Semantic<br/>Search"]
        BIM["🏗️ BIM 5D<br/>Integration"]
        RAG["🤖 RAG<br/>Systems"]
        BI["📈 BI<br/>Analytics"]
    end

    Source --> Processing
    Processing --> XLSX & PARQUET & CSV & QDRANT
    XLSX & PARQUET & CSV --> BI & BIM
    QDRANT --> SEARCH & RAG & BIM

    style Source fill:#dbeafe,stroke:#2563eb,stroke-width:2px
    style Processing fill:#fef3c7,stroke:#d97706,stroke-width:2px
    style Outputs fill:#d1fae5,stroke:#059669,stroke-width:2px
    style Apps fill:#fce7f3,stroke:#db2777,stroke-width:2px

Historical Context

The construction work descriptions in this database are grounded in a resource-based standardization methodology with roots stretching from early 20th-century production norms to today's digital reference systems. Developed and refined continuously since the 1920s, this approach has seen especially robust evolution across the Eurasian region.

Throughout a hundred years of development, the system has transitioned from manual computations to machine-readable formats - yet its foundational principle remains intact: the precise measurement of physical resources required per unit of construction output. Modern implementations bridge historical normative data with real-time market pricing.

Regional adaptations of this methodology operate under various national designations: ENIR, GESN, FER, NRR, ESN, AzDTN, ShNQK, MKS ChT, SNT, BNbD, Dinh Muc, Ding'e.

⭐ If you want to see new updates and database versions and if you find our tools useful please give our repositories a star to see more similar applications for the construction industry. Star DDC workflow on GitHub and be instantly notified of new releases.

Integration

Use Cases

Entry Level - Cost Benchmarking, Price Indexation, Tender Estimation
Intermediate - Localization, ETL/BI Pipelines, CO₂ Calculation
Advanced - AI/ML Training, CAD (BIM) 5D, Deep-Dive Investment Audit

n8n Workflows — Detailed Description

Four production-ready workflows for automated construction cost estimation. Each workflow connects to the DDC CWICR vector database via Qdrant and uses AI models for intelligent parsing and matching.

#	Workflow	Input	Best For	Download
1	Text Estimator Bot	💬 Text	Quick estimates from text	JSON
2	Photo Estimator	📷 Photo	Site visits, visual inspections	JSON
3	Universal Bot	💬📷📄 All	Full-featured production use	JSON
4	CAD/BIM Pipeline	🏗️ Revit	BIM-based 4D/5D estimation	JSON

1️⃣ Text Estimator Bot

File: n8n_1_Telegram_Bot_Cost_Estimates_and_Rate_Finder_TEXT_DDC_CWICR.json

Telegram bot for text-based cost estimation. Describe construction works in natural language — the bot parses input, searches the vector database, and returns detailed cost breakdowns.

🤖 Try It Now — Live Demo Bots

Test the estimation workflows instantly in Telegram

@TextOpenConstructionEstimate_bot

Create complete cost estimates
from text descriptions

flowchart LR
    subgraph Input["💬 INPUT"]
        A[Telegram Message]
    end
    
    subgraph AI["🤖 AI PROCESSING"]
        B[Parse Text]
        C[Extract Work Items]
    end
    
    subgraph Search["🔍 VECTOR SEARCH"]
        D[Generate Embeddings]
        E[Qdrant Search]
        F[AI Rerank]
    end
    
    subgraph Output["📊 OUTPUT"]
        G[Calculate Costs]
        H[HTML Report]
        I[Excel Export]
    end
    
    A --> B --> C --> D --> E --> F --> G --> H
    G --> I
    
    style Input fill:#e0f2fe,stroke:#0284c7
    style AI fill:#fef3c7,stroke:#d97706
    style Search fill:#dcfce7,stroke:#16a34a
    style Output fill:#f3e8ff,stroke:#9333ea

How it works:

Step	Action	Technology
1	User sends text description	Telegram Bot API
2	AI parses and extracts work items	OpenAI / Claude / Gemini
3	Generate embeddings for each item	OpenAI `text-embedding-3-large`
4	Search matching rates in database	Qdrant vector search
5	AI reranks results for accuracy	LLM scoring
6	Calculate costs and generate report	HTML / Excel / PDF

Features:

Feature	Description
💬 Natural language input	Accepts any text format — lists, sentences, structured descriptions
🤖 Multi-LLM support	Works with OpenAI, Claude, or Gemini (switchable)
🔍 Semantic search	Finds best matches even with different wording
🌍 9 languages	DE, EN, RU, ES, FR, PT, ZH, AR, HI
📊 Multiple exports	HTML report, Excel spreadsheet, PDF document
✏️ Interactive editing	Modify quantities before final calculation

Required credentials:

Telegram Bot Token (from @BotFather)
OpenAI API Key (for embeddings + optional LLM)
Qdrant URL + API Key

2️⃣ Photo Cost Estimator

File: n8n_2_Photo_Cost_Estimate_DDC_CWICR.json

Web form interface for photo-based estimation. Upload a construction photo — AI Vision identifies elements, estimates dimensions, and calculates costs automatically.

flowchart TB
    subgraph Upload["📷 PHOTO UPLOAD"]
        A[Web Form]
        B[Select Region]
        C[Choose Work Type]
    end
    
    subgraph Vision["👁️ AI VISION"]
        D[GPT-4 Vision Analysis]
        E[Identify Elements]
        F[Estimate Dimensions]
        G[Detect Room Type]
    end
    
    subgraph Decompose["🔧 DECOMPOSITION"]
        H[Elements → Work Items]
        I[Calculate Quantities]
    end
    
    subgraph Price["💰 PRICING"]
        J[Vector Search]
        K[Match DDC Rates]
        L[Apply Regional Prices]
    end
    
    subgraph Report["📄 REPORT"]
        M[Generate HTML]
        N[Cost Breakdown]
    end
    
    A --> B --> C --> D
    D --> E --> F --> G
    G --> H --> I
    I --> J --> K --> L
    L --> M --> N
    
    style Upload fill:#dbeafe,stroke:#2563eb
    style Vision fill:#fef3c7,stroke:#d97706
    style Decompose fill:#dcfce7,stroke:#16a34a
    style Price fill:#fee2e2,stroke:#dc2626
    style Report fill:#f3e8ff,stroke:#9333ea

How it works:

Step	Action	Technology
1	User uploads photo via web form	n8n Form Trigger
2	AI Vision analyzes the image	GPT-4 Vision
3	Identify room type, elements, materials	Structured JSON extraction
4	Estimate dimensions from reference objects	AI reasoning (doors, tiles, etc.)
5	Decompose elements into work items	LLM processing
6	Price each work via vector search	Qdrant + OpenAI embeddings
7	Generate professional HTML report	Styled output

Features:

Feature	Description
📷 Photo analysis	GPT-4 Vision identifies construction elements
📐 Auto-dimensioning	Estimates sizes using reference objects (doors, tiles)
🏠 Room detection	Bathroom, kitchen, bedroom, exterior, etc.
🔨 Work type support	New construction / Renovation / Repair
🌍 9 regional databases	Prices localized to Berlin, Toronto, Paris, etc.
📄 Professional reports	Clean HTML output ready for clients

Required credentials:

OpenAI API Key (GPT-4 Vision + embeddings)
Qdrant URL + API Key

3️⃣ Universal Estimator Bot (Text + Photo + PDF)

File: n8n_3_Telegram_Bot_Cost_Estimates_and_Rate_Finder_TEXT_PHOTO_PDF_DDC_CWICR.json

Full-featured Telegram bot supporting all input types: text descriptions, construction photos, and PDF floor plans. The most comprehensive workflow for production use.

🤖 Try It Now — Live Demo Bots

Test the estimation workflows instantly in Telegram

📷 Universal Bot

@OpenConstructionEstimate_bot

Full-featured bot for text, photos, and PDF

flowchart TB
    subgraph Input["📥 MULTI-INPUT"]
        A[💬 Text Message]
        B[📷 Photo]
        C[📄 PDF Document]
    end
    
    subgraph Router["🔀 SMART ROUTER"]
        D{Detect Type}
    end
    
    subgraph TextPath["💬 TEXT PATH"]
        E[AI Parse Text]
        F[Extract Works]
    end
    
    subgraph PhotoPath["📷 PHOTO PATH"]
        G[Vision AI]
        H[Identify Elements]
        I[Decompose]
    end
    
    subgraph PDFPath["📄 PDF PATH"]
        J[Extract Pages]
        K[Vision Analysis]
        L[Parse Content]
    end
    
    subgraph Common["🔍 COMMON PIPELINE"]
        M[Generate Embeddings]
        N[Qdrant Search]
        O[AI Rerank]
        P[Calculate Costs]
    end
    
    subgraph Export["📤 EXPORT"]
        Q[HTML Report]
        R[Excel CSV]
        S[PDF Document]
    end
    
    A --> D
    B --> D
    C --> D
    D -->|Text| E --> F --> M
    D -->|Photo| G --> H --> I --> M
    D -->|PDF| J --> K --> L --> M
    M --> N --> O --> P
    P --> Q
    P --> R
    P --> S
    
    style Input fill:#e0f2fe,stroke:#0284c7
    style Router fill:#fef3c7,stroke:#d97706
    style TextPath fill:#dcfce7,stroke:#16a34a
    style PhotoPath fill:#fce7f3,stroke:#db2777
    style PDFPath fill:#f3e8ff,stroke:#9333ea
    style Common fill:#fee2e2,stroke:#dc2626
    style Export fill:#d1fae5,stroke:#059669

How it works:

Step	Action	Technology
1	User sends text, photo, or PDF	Telegram Bot API
2	Router detects input type	Content-type analysis
3a	Text: AI parses work items	OpenAI / Gemini
3b	Photo: Vision AI extracts elements	GPT-4 Vision / Gemini 2.0
3c	PDF: Extract and analyze pages	PDF processing + Vision
4	Semantic search in DDC CWICR	Qdrant vector database
5	AI reranking for best matches	LLM scoring
6	Interactive editing via bot menu	Telegram inline keyboards
7	Export results	HTML / Excel / PDF

17 Bot Actions:

Action	Description
`/start`	Language selection menu
Photo upload	Trigger AI vision analysis
Text message	Parse and extract work items
PDF upload	Process floor plans
Edit quantities	Modify before calculation
Add work	Manual work item entry
Calculate	Run full cost estimation
View details	Show resources for each item
Export Excel	Download CSV spreadsheet
Export PDF	Generate PDF report
Help	Show usage instructions
Refine	Re-analyze with corrections

Features:

Feature	Description
📷 Dual Vision AI	Gemini 2.0 Flash or GPT-4 Vision (configurable)
📄 PDF processing	Floor plans, scanned BOQ, documents
💬 Smart text parsing	Handles lists, tables, free-form text
🔍 AI reranking	Improves match accuracy
✏️ Full editing	Add, remove, modify work items
📊 Multi-format export	HTML, Excel, PDF
🌍 9 languages	Complete localization

Required credentials:

Telegram Bot Token
OpenAI API Key (embeddings)
Gemini API Key (Vision) or OpenAI GPT-4 Vision
Qdrant URL + API Key

4️⃣ CAD (BIM) Cost Estimation Pipeline

File: n8n_4_CAD_(BIM)_Cost_Estimation_Pipeline_4D_5D_with_DDC_CWICR.json

Automated cost estimation from Revit/IFC/DWG models. Extracts BIM data, classifies elements, decomposes into work items, and generates 4D/5D estimates with full resource breakdown.

flowchart TB
    subgraph INPUT["📁 INPUT<br/><i>CAD • photos • text description</i>"]
        CAD["📐 Project Input<br/>(text • photos • RVT / IFC / DWG)"]
    end

    subgraph EXTRACT["⚙️ EXTRACTION"]
        CONV["RvtExporter.exe / CAD Export  / ETL"]
        XLSX["📊 .XLSX<br/>(Raw Elements)"]
    end

    subgraph PREP["🔧 DATA PREPARATION"]
        PREP_AI["🤖 AI: Clean & Classify<br/><i>headers • types • categories</i>"]
    end

    subgraph STAGE_PLAN["📋 STAGES 1–3: Planning"]
        PLAN["🤖 Detect Project & Phases<br/><i>new / renovation / demolition</i><br/><i>small / medium / large</i><br/><i>elements → construction phases</i>"]
    end

    subgraph STAGE4["🔨 STAGE 4: Decomposition"]
        S4["🤖 Decompose Types to Works<br/><i>'Brick Wall 240mm' → masonry, mortar, plaster</i>"]
    end

    subgraph STAGE5["💰 STAGE 5: Pricing"]
        S5["🤖 Price via Vector DB<br/><i>OpenAI embeddings + Qdrant</i><br/><i>rate_code, unit_cost, resources</i>"]
    end

    subgraph STAGE75["✅ STAGE 7.5: Validation"]
        S75["🤖 CTO Review<br/><i>completeness • duplicates • missing works</i>"]
    end

    subgraph OUTPUT["📤 OUTPUT"]
        HTML["📄 HTML Report"]
        XLS["📊 XLS Report"]
    end

    CAD --> CONV --> XLSX
    XLSX --> PREP_AI --> PLAN --> S4 --> S5 --> S75
    S75 --> HTML & XLS

    style INPUT fill:#f4f4f5,stroke:#d4d4d8,color:#18181b
    style EXTRACT fill:#e0f2fe,stroke:#bae6fd,color:#0f172a
    style PREP fill:#ede9fe,stroke:#ddd6fe,color:#1e1b4b
    style STAGE_PLAN fill:#ecfdf5,stroke:#bbf7d0,color:#064e3b
    style STAGE4 fill:#fef9c3,stroke:#fef3c7,color:#78350f
    style STAGE5 fill:#fee2e2,stroke:#fecaca,color:#7f1d1d
    style STAGE75 fill:#e0f2f1,stroke:#bae5e1,color:#134e4a
    style OUTPUT fill:#eef2ff,stroke:#e0e7ff,color:#111827

n8n provides 400+ native integrations with platforms like Google Sheets, Notion, Slack, Airtable, databases (PostgreSQL, MongoDB), cloud storage, and more. Every node in this workflow is modular — you can:

🔄 Swap LLM providers (OpenAI ↔ Claude ↔ Gemini ↔ Grok)
📊 Connect to your ERP or project management system
📁 Export results to any destination (cloud storage, email, dashboards)
🔧 Modify any stage to match your estimation methodology

The workflow is yours to adapt. No restrictions. No licensing fees. Full control.

📋 Prerequisites

Component	Requirement	Description
n8n	v1.0+ (v2.0+ requires setup)	Workflow automation platform for orchestrating the estimation pipeline
Qdrant	Cloud or self-hosted instance	Vector database for semantic search across construction work items
OpenAI API	For embeddings (`text-embedding-3-large`)	Generates vector embeddings for BIM elements and cost database matching
LLM API	OpenAI / Claude / Gemini / xAI Grok	AI models for work item classification and estimate generation
DDC Converter	`RvtExporter.exe`	Extracts BIM data from Revit models to Excel/JSON for processing

Workflows Quick Start

Step 1: Import Workflow

n8n → New workflow → Import from File → Select JSON

Step 2: Configure Credentials

In the 🔑 TOKEN node, set your API keys:

{
  "bot_token": "YOUR_TELEGRAM_BOT_TOKEN",
  "OPENAI_API_KEY": "YOUR_OPENAI_KEY",
  "GEMINI_API_KEY": "YOUR_GEMINI_KEY",
  "QDRANT_URL": "http://localhost:6333",
  "QDRANT_API_KEY": ""
}

Step 3: Load DDC CWICR to Qdrant

Download snapshot from Releases and import:

curl -X POST "http://localhost:6333/collections/ddc_cwicr_en/snapshots/upload" \
  -H "Content-Type: multipart/form-data" \
  -F "snapshot=@EN_TORONTO_workitems_EMBEDDINGS_3072_DDC.snapshot"

Step 4: Activate & Test

Enable the workflow in n8n
For Telegram bots: send /start to your bot
For web forms: open the form URL provided by n8n

⚠️ n8n 2.0+ Setup Required

Starting from n8n version 2.0, the Execute Command node is disabled by default for security reasons.

Without the configuration below, workflows using Execute Command (especially CAD/BIM Pipeline) will not work — nodes will show with a question mark or won't be recognized.

Quick Fix

Windows (CMD) — run each time:

set NODES_EXCLUDE=[] && npx n8n

Permanent solution — create once:

Create file C:\Users\YOUR_USER\.n8n\.env with:

NODES_EXCLUDE=[]

Then just run npx n8n as usual.

Docker:

environment:
  - NODES_EXCLUDE=[]

Verify Setup

Start n8n
Click + → search for "Execute Command"
If the node appears → ✅ you're all set!

📚 More details: n8n 2.0 Breaking Changes

🌍 Supported Languages & Price Levels

Code	Language	Price Level	Currency	Qdrant Collection
`AR`	Arabic	Dubai	AED	`ddc_cwicr_ar`
`DE`	German	Berlin	EUR	`ddc_cwicr_de`
`EN`	English	Toronto	CAD	`ddc_cwicr_en`
`ES`	Spanish	Barcelona	EUR	`ddc_cwicr_es`
`FR`	French	Paris	EUR	`ddc_cwicr_fr`
`HI`	Hindi	Mumbai	INR	`ddc_cwicr_hi`
`PT`	Portuguese	São Paulo	BRL	`ddc_cwicr_pt`
`RU`	Russian	St. Petersburg	RUB	`ddc_cwicr_ru`
`ZH`	Chinese	Shanghai	CNY	`ddc_cwicr_zh`

📊 Pipeline Stages

The CAD/BIM workflow processes data through 10 stages:

Stage	Name	Description
0	Collect BIM Data	Extract elements from Revit via DDC Converter
1	Project Detection	AI identifies project type (Residential, Commercial, etc.)
2	Phase Generation	AI creates construction phases
3	Element Assignment	AI maps BIM types to phases
4	Work Decomposition	AI breaks types into work items ("Brick Wall" → masonry, mortar)
5	Vector Search	Find matching rates in DDC CWICR via Qdrant
6	Unit Mapping	Convert BIM units to rate units
7	Cost Calculation	Qty × Unit Price for each work item
7.5	Validation	CTO review for completeness and duplicates
8	Aggregation	Sum by phases and categories
9	Report Generation	Create HTML and Excel outputs

⚙️ LLM Model Selection

The workflow supports multiple AI providers. Enable your preferred model in the LLM Models section:

Model	Node Name	Status
OpenAI GPT-4o	`OpenAI LLM`	✅ Default
Claude Opus 4	`Anthropic Chat Model2`	Disabled
Gemini 2.5 Pro	`Google Gemini Chat Model`	Disabled
xAI Grok	`xAI Grok Chat Model1`	Disabled
DeepSeek	`DeepSeek Chat Model`	Disabled

To switch models: Enable the desired model node and Disable others.

📁 Output Files

Reports are saved to the project folder:

project_YYYY-MM-DD.html   ← Interactive report (opens in browser)
project_YYYY-MM-DD.xls    ← Excel-compatible spreadsheet

🔗 Qdrant Collections

The workflow automatically selects the correct collection based on language_code:

{LANG}_{CITY}_workitems_costs_resources_EMBEDDINGS_3072_DDC_CWICR

Example: DE_BERLIN_workitems_costs_resources_EMBEDDINGS_3072_DDC_CWICR

⚠️ Troubleshooting

Issue	Solution
"Execute Command missing" (n8n 2.0+)	Set `NODES_EXCLUDE=[]` environment variable. See n8n 2.0+ Setup
"No Excel file found"	Check `path_to_converter` and `project_file` paths
"Qdrant connection failed"	Verify Qdrant URL and API key in credentials
"Rate limit exceeded"	Reduce batch size or add delays between API calls
"No pricing found"	Check if the correct language collection exists in Qdrant
"Telegram webhook error"	Ensure workflow is active and webhook URL is accessible
"Vision API failed"	Verify Gemini or OpenAI Vision API key is valid

Vector Database

Ready-to-use Qdrant collections with OpenAI text-embedding-3-large embeddings for semantic search across construction work items.

Vector databases allow you to "talk" to your data in natural language – using simple sentences or short phrases instead of code or complex filters. This dramatically speeds up finding the right work item or cost line, even in very large datasets.

These Qdrant collections can be connected to application via modern automation and integration workflows (for example, low-code/no-code Workflow and Pipeline tools). You can build assistants that search, filter and explain construction work items, or integrate semantic search directly into your existing estimation and project-control tools.

Releases

Download QDRANT and CSV datasets (files larger than 1 gigabyte) from GitHub Releases.

Language	Region	CSV Files	Qdrant Snapshot
🇸🇦 Arabic	Dubai	`AR_DUBAI_*.csv`	`AR_DUBAI_*_EMBEDDINGS_3072_DDC.snapshot`
🇨🇳 Chinese	Shanghai	`ZH_SHANGHAI_*.csv`	`ZH_SHANGHAI_*_EMBEDDINGS_3072_DDC.snapshot`
🇩🇪 German	Berlin	`DE_BERLIN_*.csv`	`DE_BERLIN_*_EMBEDDINGS_3072_DDC.snapshot`
🇬🇧 English	Toronto	`EN_TORONTO_*.csv`	`EN_TORONTO_*_EMBEDDINGS_3072_DDC.snapshot`
🇪🇸 Spanish	Barcelona	`ES_BARCELONA_*.csv`	`ES_BARCELONA_*_EMBEDDINGS_3072_DDC.snapshot`
🇫🇷 French	Paris	`FR_PARIS_*.csv`	`FR_PARIS_*_EMBEDDINGS_3072_DDC.snapshot`
🇮🇳 Hindi	Mumbai	`HI_MUMBAI_*.csv`	`HI_MUMBAI_*_EMBEDDINGS_3072_DDC.snapshot`
🇧🇷 Portuguese	São Paulo	`PT_SAOPAULO_*.csv`	`PT_SAOPAULO_*_EMBEDDINGS_3072_DDC.snapshot`
🇷🇺 Russian	St. Petersburg	`RU_SPB_*.csv`	`RU_SPB_*_EMBEDDINGS_3072_DDC.snapshot`

Collections

🇸🇦 ddc_cwicr_ar (Arabic) · 🇨🇳 ddc_cwicr_zh (Chinese) · 🇩🇪 ddc_cwicr_de (German) · 🇬🇧 ddc_cwicr_en (English) · 🇪🇸 ddc_cwicr_es (Spanish) · 🇫🇷 ddc_cwicr_fr (French) · 🇮🇳 ddc_cwicr_hi (Hindi) · 🇧🇷 ddc_cwicr_pt (Portuguese) · 🇷🇺 ddc_cwicr_ru (Russian)

Each collection contains 55,719 vectors with full payload metadata.

Docker Deployment

# docker-compose.yml
services:
  qdrant:
    image: qdrant/qdrant:latest
    container_name: ddc-cwicr-qdrant
    ports:
      - "6333:6333"
      - "6334:6334"
    volumes:
      - qdrant_storage:/qdrant/storage
      - ./snapshots:/qdrant/snapshots
    environment:
      - QDRANT__LOG_LEVEL=INFO
    restart: unless-stopped

volumes:
  qdrant_storage:

# Start
docker-compose up -d

# Import snapshot
curl -X POST "http://localhost:6333/collections/ddc_cwicr_en/snapshots/upload" \
  -H "Content-Type: multipart/form-data" \
  -F "snapshot=@ddc_cwicr_en.snapshot"

# Dashboard: http://localhost:6333/dashboard

Quick Start

Python - Tabular Data

import pandas as pd

# Parquet (recommended)
df = pd.read_parquet("DDC_CWICR_EN.parquet")

# Excel
df = pd.read_excel("DDC_CWICR_EN.xlsx")

print(f"Records: {len(df):,} | Fields: {len(df.columns)}")
print(df[['rate_code', 'rate_original_name', 'rate_unit', 'total_cost_per_position']].head())

Python - Semantic Search

from qdrant_client import QdrantClient
from openai import OpenAI

client = QdrantClient("localhost", port=6333)
openai = OpenAI()

# Search by natural language
query = "reinforced concrete foundation pouring"
embedding = openai.embeddings.create(
    input=query, 
    model="text-embedding-3-large"
).data[0].embedding

results = client.search(
    collection_name="ddc_cwicr_en",
    query_vector=embedding, 
    limit=5
)

for r in results:
    print(f"[{r.score:.3f}] {r.payload['rate_code']}: {r.payload['rate_original_name']}")

Filtered Search

from qdrant_client.models import Filter, FieldCondition, MatchValue, Range

# By department
results = client.search(
    collection_name="ddc_cwicr_en",
    query_vector=embedding,
    query_filter=Filter(must=[
        FieldCondition(key="department_name", match=MatchValue(value="Concrete and Reinforced Concrete"))
    ]),
    limit=10
)

# By price range
results = client.search(
    collection_name="ddc_cwicr_en",
    query_vector=embedding,
    query_filter=Filter(must=[
        FieldCondition(key="price_est_median", range=Range(gte=1000, lte=50000))
    ]),
    limit=10
)

Resources & Community

Consulting & Training

We work with leading construction, engineering, consulting agencies, and technology firms around the world to help them implement open data principles, automate CAD/BIM processing, and build robust ETL pipelines. We actively support organizations seeking practical solutions for digital transformation and interoperability, focusing on data quality and classification challenges while driving the adoption of open and automated workflows.

If you would like to test this solution with your own data or are interested in adapting the workflow to real project tasks, feel free to contact us. Our team delivers hands-on workshops, provides strategic consulting, and develops prototypes tailored to real project processes.

Contributing

DDC CWICR is a free and open project dedicated to making the construction industry more efficient, transparent, and technologically advanced. We are actively looking for like-minded enthusiasts who share this mission. If you create useful solutions and are ready to share them with the community, we are here to help you be heard.

We invite you to submit your open source workflows, pipelines, and integrations based on DDC CWICR-tools that anyone can freely use in their work. The top solutions will be published with full author attribution on GitHub and announced through our newsletter and social media channels, reaching tens of thousands of professional subscribers. This places your name directly in front of an international community of estimators, BIM specialists, and project managers.

Together we are changing the industry. You can send your solution to info@datadrivenconstruction.io with the subject "DDC Open Workflow" or submit a Pull Request directly to our GitHub repositories.

Automate construction data processing with ready-made CAD-BIM n8n workflows:

License

Database (DDC CWICR) - CC BY 4.0. Free to use, share, and adapt commercially. Attribution: "DDC CWICR by DataDrivenConstruction"

Code (workflows, scripts) - MIT. Free to use, modify, and distribute without restrictions.

Support the Project

If you find this useful, please consider supporting:

Unlock the Power of Data in Construction
_{Move to full-cycle data management where only unified structured data & processes remain}

Name		Name	Last commit message	Last commit date
Latest commit History 136 Commits
.github		.github
0_n8n_Example_Cost_Calculation_Reports		0_n8n_Example_Cost_Calculation_Reports
AR___DDC_CWICR		AR___DDC_CWICR
DE___DDC_CWICR		DE___DDC_CWICR
EN___DDC_CWICR		EN___DDC_CWICR
ES___DDC_CWICR		ES___DDC_CWICR
FR___DDC_CWICR		FR___DDC_CWICR
HI___DDC_CWICR		HI___DDC_CWICR
PT___DDC_CWICR		PT___DDC_CWICR
RU___DDC_CWICR		RU___DDC_CWICR
ZH___DDC_CWICR		ZH___DDC_CWICR
DataDrivenConstruction_Book_2ndEdition_ArtemBoiko_2025_en-US.pdf		DataDrivenConstruction_Book_2ndEdition_ArtemBoiko_2025_en-US.pdf
LICENSE		LICENSE
README.md		README.md
n8n_1_Telegram_Bot_Cost_Estimates_and_Rate_Finder_TEXT_DDC_CWICR.json		n8n_1_Telegram_Bot_Cost_Estimates_and_Rate_Finder_TEXT_DDC_CWICR.json
n8n_2_Photo_Cost_Estimate_DDC_CWICR.json		n8n_2_Photo_Cost_Estimate_DDC_CWICR.json
n8n_3_Telegram_Bot_Cost_Estimates_and_Rate_Finder_TEXT_PHOTO_PDF_DDC_CWICR.json		n8n_3_Telegram_Bot_Cost_Estimates_and_Rate_Finder_TEXT_PHOTO_PDF_DDC_CWICR.json
n8n_4_CAD_(BIM)_Cost_Estimation_Pipeline_4D_5D_with_DDC_CWICR.json		n8n_4_CAD_(BIM)_Cost_Estimation_Pipeline_4D_5D_with_DDC_CWICR.json

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DDC CWICR - Construction Work Items, Components & Resources + Pipelines n8n for calculating estimates based on descriptions, photos, and CAD (BIM)

⚡ n8n Workflows

📝 Text

📷 Photo / PDF

🧊 CAD / BIM

📑 Table of Contents

Database & Data

n8n Workflows

CAD/BIM Pipeline Details

Vector Database

Getting Started

Community

About

Available Formats

Data Schema

Field Groups

Cost Calculation Formula

Methodology

Historical Context

Integration

Use Cases

n8n Workflows — Detailed Description

1️⃣ Text Estimator Bot

🤖 Try It Now — Live Demo Bots

2️⃣ Photo Cost Estimator

3️⃣ Universal Estimator Bot (Text + Photo + PDF)

🤖 Try It Now — Live Demo Bots

📷 Universal Bot

4️⃣ CAD (BIM) Cost Estimation Pipeline

📋 Prerequisites

Workflows Quick Start

Step 1: Import Workflow

Step 2: Configure Credentials

Step 3: Load DDC CWICR to Qdrant

Step 4: Activate & Test

⚠️ n8n 2.0+ Setup Required

Quick Fix

Verify Setup

🌍 Supported Languages & Price Levels

📊 Pipeline Stages

⚙️ LLM Model Selection

📁 Output Files

🔗 Qdrant Collections

⚠️ Troubleshooting

Vector Database

Releases

Collections

Docker Deployment

Quick Start

Python - Tabular Data

Python - Semantic Search

Filtered Search

Resources & Community

Consulting & Training

Contributing

License

Support the Project

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Releases

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DDC CWICR - Construction Work Items, Components & Resources
+ Pipelines n8n for calculating estimates based on descriptions, photos, and CAD (BIM)

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