myAi/Apis/rag-api/Services/DocumentClassifier.cs

using System.Text.RegularExpressions;
using Api.Services.Contracts;
using Rag.Models;

namespace Api.Services;

public sealed class DocumentClassifier : IDocumentClassifier
{
    private static readonly HashSet<string> KnownTypes = new(StringComparer.OrdinalIgnoreCase)
    {
        "cv", "job", "article", "contract", "invoice", "product", "documentation", "unknown"
    };

    public Task<DocumentClassification> ClassifyAsync(string text, string? providedType, string? providedTitle, CancellationToken ct)
    {
        if (!string.IsNullOrWhiteSpace(providedType))
        {
            var normalized = NormalizeType(providedType);
            return Task.FromResult(new DocumentClassification
            {
                DocumentType = normalized,
                Confidence = KnownTypes.Contains(normalized) && normalized != "unknown" ? 1.0 : 0.6,
                Title = BuildTitle(providedTitle, text, normalized)
            });
        }

        // Keyword-frequency heuristic: count how many characteristic terms each document
        // type contributes to the text, then pick the type with the highest hit count.
        var lower = text.ToLowerInvariant();
        var scores = new Dictionary<string, int>(StringComparer.OrdinalIgnoreCase)
        {
            ["cv"] = Count(lower, "curriculum vitae", "resume", "work experience", "professional experience", "education", "skills", "technologies", "linkedin", "github"),
            ["job"] = Count(lower, "job description", "requirements", "responsibilities", "qualifications", "apply", "we are looking", "salary", "benefits", "remote", "hybrid"),
            ["contract"] = Count(lower, "agreement", "contract", "party", "parties", "liability", "termination", "confidentiality", "governing law"),
            ["invoice"] = Count(lower, "invoice", "vat", "subtotal", "total", "amount due", "due date", "billing"),
            ["documentation"] = Count(lower, "api", "endpoint", "configuration", "install", "usage", "parameters", "response", "request"),
            ["product"] = Count(lower, "features", "pricing", "sku", "product", "specification", "warranty")
        };

        var best = scores.OrderByDescending(x => x.Value).First();
        var type = best.Value <= 0 ? "unknown" : best.Key;
        // Confidence baseline 0.45 + 0.08 per matched keyword term, capped at 0.95.
        // Zero hits → 0.25 (effectively unknown).
        var confidence = best.Value <= 0 ? 0.25 : Math.Min(0.95, 0.45 + best.Value * 0.08);

        return Task.FromResult(new DocumentClassification
        {
            DocumentType = type,
            Confidence = confidence,
            Title = BuildTitle(providedTitle, text, type)
        });
    }

    private static int Count(string lower, params string[] terms) => terms.Count(term => lower.Contains(term));

    private static string NormalizeType(string value)
    {
        var cleaned = Regex.Replace(value.Trim().ToLowerInvariant(), "[^a-z0-9_-]", "-");
        return string.IsNullOrWhiteSpace(cleaned) ? "unknown" : cleaned;
    }

    private static string BuildTitle(string? providedTitle, string text, string documentType)
    {
        if (!string.IsNullOrWhiteSpace(providedTitle)) return providedTitle.Trim();
        var firstLine = text.Split('.', '\n', '\r').Select(x => x.Trim()).FirstOrDefault(x => x.Length > 20);
        if (!string.IsNullOrWhiteSpace(firstLine)) return firstLine.Length <= 120 ? firstLine : firstLine[..120];
        return $"{documentType} document";
    }
}