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Rahul Kumar Jha
Rahul Kumar Jha

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Extract Structured Data from Car Listings Using AI in .NET 10

#csharp #ai #machinelearning #dotnet

🚗 From Chaos to Structure: Extracting Car Listings with AI

Ever struggled with parsing messy car listings from different sources? Imagine turning this:

"Check out this stylish Honda City 2018 model for sale, clocked only 30,000 km! Single owner, showroom condition, insurance valid. Yours for just ₹6.5 lakh."

Into this:

{
  "Make": "Honda",
  "Model": "City",
  "Year": 2018,
  "Mileage": 30000,
  "Price": 6.5,
  "AvailabilityType": "Sale",
  "Features": ["Single owner", "Showroom condition", "Insurance valid"],
  "OwnerCount": 1
}
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Let me show you how to build this in under 100 lines of C# using GitHub Models API! 🚀

🎯 The Problem

Car listings come in all shapes and sizes. Whether you're building a price comparison site, marketplace aggregator, or inventory management system, you need to:

  • Extract key details (make, model, year, mileage, price)
  • Handle different formats (sale, lease, rent)
  • Deal with missing information gracefully
  • Process data at scale

Manually parsing this is tedious. Let AI do the heavy lifting! 💪

🛠️ The Solution

We'll use:

  • GitHub Models - Free access to powerful AI models
  • Microsoft.Extensions.AI - Unified AI abstraction for .NET
  • .NET 10 - Latest and greatest

📦 Quick Setup

First, grab your free GitHub token from GitHub Models (no credit card needed!).

Create a new console app:

dotnet new console -n TextExtraction
cd TextExtraction
dotnet add package Microsoft.Extensions.AI.OpenAI
dotnet add package Microsoft.Extensions.Configuration.UserSecrets
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Store your token securely:

dotnet user-secrets init
dotnet user-secrets set "GitHubModels:Token" "your-github-token"
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🎨 Building the Model

Create a CarDetails.cs class to define what we want to extract:

using System.Text.Json.Serialization;

[JsonConverter(typeof(JsonStringEnumConverter))]
public enum AvailabilityType
{
    Sale,
    Lease,
    Rent
}

public class CarDetails
{
    public string Make { get; set; } = string.Empty;
    public string Model { get; set; } = string.Empty;
    public int? Year { get; set; }
    public double? Mileage { get; set; }
    public double? Price { get; set; }
    public AvailabilityType? AvailabilityType { get; set; }
    public double? PricePerMonth { get; set; }
    public double? PricePerDay { get; set; }
    public string[]? Features { get; set; }
    public string? Location { get; set; }
    public string ShortSummary { get; set; } = string.Empty;
    public int? OwnerCount { get; set; }
}
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Notice the nullable types? That's how we handle missing data elegantly! ✨

🧠 The Magic: AI-Powered Extraction

Here's the core extraction logic in Program.cs:

using Microsoft.Extensions.AI;
using OpenAI;
using System.ClientModel;

// Configure the client
var configuration = new ConfigurationBuilder()
    .AddUserSecrets<Program>()
    .Build();

var credential = new ApiKeyCredential(
    configuration["GitHubModels:Token"] ?? 
    throw new InvalidOperationException("Token not found")
);

IChatClient chatClient = new OpenAIClient(credential, new OpenAIClientOptions
{
    Endpoint = new Uri("https://models.inference.ai.azure.com")
}).GetChatClient("gpt-4o-mini")
  .AsIChatClient();

// Define extraction schema
var prompt = @"Extract the following details from the car listing and return ONLY a valid JSON object:
{
  ""Make"": ""string - car manufacturer/brand"",
  ""Model"": ""string - car model name"",
  ""Year"": number - manufacturing year,
  ""Mileage"": number - kilometers driven,
  ""Price"": number - price in lakhs,
  ""AvailabilityType"": ""string - one of: Sale, Lease, Rent"",
  ""Features"": ""array of strings - notable features"",
  ""ShortSummary"": ""string - brief summary in 10-15 words"",
  ""OwnerCount"": number - previous owners (null if not mentioned)
}
Return only the JSON object, no additional text.";

// Sample car listings
var carListings = new List<string>
{
    "Honda City 2018 for sale, only 30,000 km! Single owner, showroom condition. ₹6.5 lakh.",
    "Hyundai Creta SX 2020 — premium SUV with sunroof. Monthly lease at ₹22,000.",
    "Toyota Innova Crysta 2019 — spacious 7-seater, 40,000 km, rent at ₹2,500/day."
};

// Process each listing
foreach (var listing in carListings)
{
    var response = await chatClient.GetResponseAsync<CarDetails>(
        $"{prompt}\n\nCar Listing:\n{listing}"
    );

    if (response.TryGetResult(out CarDetails? carDetails) && carDetails != null)
    {
        Console.WriteLine($"✅ Extracted: {carDetails.Make} {carDetails.Model}");
        Console.WriteLine(JsonSerializer.Serialize(carDetails, 
            new JsonSerializerOptions { WriteIndented = true }));
    }
}
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🎬 Run It! 

dotnet run
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Output:

Processing car listings...

✅ Extracted: Honda City
{
  "Make": "Honda",
  "Model": "City",
  "Year": 2018,
  "Mileage": 30000,
  "Price": 6.5,
  "AvailabilityType": "Sale",
  "Features": ["Single owner", "Showroom condition"],
  "OwnerCount": 1
}

✅ Extracted: Hyundai Creta
{
  "Make": "Hyundai",
  "Model": "Creta SX",
  "Year": 2020,
  "AvailabilityType": "Lease",
  "PricePerMonth": 22000,
  "Features": ["Premium SUV", "Sunroof"]
}
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🚀 Level Up: Customization Ideas

1. Extract Different Fields

Add fuel type, transmission, color:

public string? FuelType { get; set; }  // Petrol/Diesel/Electric
public string? Transmission { get; set; }  // Manual/Automatic
public string? Color { get; set; }
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2. Process Real-Time Data

Connect to web scraping APIs or RSS feeds:

var listings = await FetchListingsFromApi("https://api.carmarket.com/listings");
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3. Add Validation

if (carDetails.Year < 1900 || carDetails.Year > DateTime.Now.Year)
{
    Console.WriteLine("⚠️ Invalid year detected");
}
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4. Export to Database

await dbContext.CarListings.AddAsync(carDetails);
await dbContext.SaveChangesAsync();
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5. Use a Better Model

For higher accuracy, switch to GPT-4o:

.GetChatClient("gpt-4o")  // More capable, slightly slower
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💡 Pro Tips

  1. Keep temperature low (default is good) for consistent extraction
  2. Be specific in prompts - define exact format you want
  3. Use nullable types - not all listings have all fields
  4. Batch process - handle multiple listings efficiently
  5. Monitor token usage - track costs with response.Usage

🎯 Real-World Applications

  • 🏪 Marketplace Aggregation: Consolidate listings from multiple sources
  • 💰 Price Intelligence: Track pricing trends across markets
  • 📊 Analytics Dashboards: Build insights from unstructured data
  • 🤖 Chatbots: Power car recommendation bots
  • 📱 Mobile Apps: Parse user-submitted listings

🔗 Get the Full Code

Grab the complete working example from GitHub:

👉 genai-dotnet-basic_llm_tasks/TextExtraction

The repo includes:

  • ✅ Full source code with comments
  • ✅ 9 example car listings
  • ✅ Configuration setup guide
  • ✅ Detailed README

🎓 What You Learned

  • Using GitHub Models API in .NET
  • Strongly-typed AI responses with GetResponseAsync<T>
  • Schema-based extraction with AI
  • Handling unstructured data gracefully
  • Building production-ready text extraction

🤔 What's Next?

Try extracting:

  • 📄 Resume data (name, skills, experience)
  • 🧾 Invoices (vendor, amounts, dates)
  • 📧 Emails (sender, subject, key points)
  • 🏠 Real estate listings
  • 🍕 Restaurant menus (dishes, prices, ingredients)

The same pattern works for ANY text extraction task!

💬 Let's Connect!

What will you build with text extraction? Drop a comment below! 👇

Found this helpful? Give it a ❤️ and follow for more .NET + AI content!

Tags: #dotnet #ai #machinelearning #csharp #github #opensource #textextraction #nlp #automation

GitHub Repo: https://github.com/Rahul1994jh/genai-dotnet-basic_llm_tasks

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