📖 Part of the RAG Series: This is Part 5 - production integration patterns:
In Part 4a, we built the foundation: ONNX embeddings and Qdrant storage. In Part 4b, we covered the search UI and hybrid search implementation. Now we'll make it production-ready with automatic indexing (zero-touch content updates) via FileSystemWatcher.
Semantic search is powerful but traditional full-text search still excels at exact phrases and technical terms. The solution? Use both.
Why Hybrid? Different approaches have different strengths:
We use Reciprocal Rank Fusion to combine results from multiple search sources:
flowchart TB
A[User Query: 'docker containers'] --> B[PostgreSQL Full-Text Search]
A --> C[Semantic Vector Search]
B --> D["Results:<br/>1. 'Docker Basics' (rank 1)<br/>2. 'Containerizing Apps' (rank 2)<br/>3. 'Docker Compose' (rank 3)"]
C --> E["Results:<br/>1. 'Containerizing Apps' (rank 1)<br/>2. 'Kubernetes Guide' (rank 2)<br/>3. 'Docker Basics' (rank 3)"]
D --> F[RRF Algorithm]
E --> F
F --> G["Combined Results:<br/>1. 'Containerizing Apps'<br/> (1/61 + 1/62 = 0.0328)<br/>2. 'Docker Basics'<br/> (1/61 + 1/63 = 0.0322)<br/>3. 'Docker Compose'<br/> (1/63 = 0.0159)"]
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style B stroke:#3b82f6,stroke-width:2px
style C stroke:#6366f1,stroke-width:2px
style D stroke:#3b82f6,stroke-width:2px
style E stroke:#6366f1,stroke-width:2px
style F stroke:#ec4899,stroke-width:4px
style G stroke:#8b5cf6,stroke-width:2px
The RRF formula: score = Σ(1 / (k + rank))
k = 60 (constant to prevent early ranks dominating)rank = position in that search method's resultsWhy RRF works:
public class HybridSearchService : IHybridSearchService
{
private readonly ISemanticSearchService _semanticSearchService;
private const int RrfConstant = 60;
public async Task<List<SearchResult>> SearchAsync(
string query,
string language = "en",
int limit = 10,
CancellationToken cancellationToken = default)
{
// Execute both searches in parallel
var semanticResults = await _semanticSearchService.SearchAsync(
query, limit * 2, cancellationToken);
// Filter by language and apply RRF
var filteredResults = semanticResults
.Where(r => r.Language == language)
.ToList();
return ApplyReciprocalRankFusion(filteredResults)
.Take(limit)
.ToList();
}
private List<SearchResult> ApplyReciprocalRankFusion(List<SearchResult> results)
{
var rrfScores = new Dictionary<string, RrfScore>();
for (int i = 0; i < results.Count; i++)
{
var result = results[i];
var key = $"{result.Slug}_{result.Language}";
if (!rrfScores.ContainsKey(key))
rrfScores[key] = new RrfScore { Result = result };
// RRF formula: 1 / (k + rank)
rrfScores[key].Score += 1.0 / (RrfConstant + i + 1);
}
return rrfScores.Values
.OrderByDescending(x => x.Score)
.Select(x => x.Result)
.ToList();
}
}
Note: This shows semantic search only. In production, execute PostgreSQL full-text search in parallel and include those results in the RRF calculation.
If you've already implemented PostgreSQL full-text search (as covered here), adding semantic search is straightforward:
// Program.cs
services.AddSemanticSearch(configuration);
services.AddSingleton<IHybridSearchService, HybridSearchService>();
[HttpGet("search/hybrid")]
public async Task<IActionResult> HybridSearch(string query, string language = "en")
{
var results = await _hybridSearchService.SearchAsync(query, language);
return PartialView("_SearchResults", results);
}
The most powerful feature: automatic indexing. Save a blog post, it's immediately searchable - no manual intervention.
flowchart TB
A[Save Markdown File] --> B[FileSystemWatcher Detects Change]
B --> C{File in Main Directory?}
C -->|Yes| D[Save to Database]
C -->|No| E[Save to Database Only]
D --> F[Create BlogPostDocument]
F --> G[Generate Embedding via ONNX]
G --> H[Store in Qdrant]
H --> I[Post Searchable Immediately]
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Key Design Decision: Only index files in the main Markdown directory, not subdirectories (translated/, drafts/, comments/). This keeps the search index clean.
The blog already has a MarkdownDirectoryWatcherService. We extend it to trigger semantic indexing:
// In MarkdownDirectoryWatcherService.cs
private async Task OnChangedAsync(WaitForChangedResult e)
{
if (e.Name == null) return;
await retryPolicy.ExecuteAsync(async () =>
{
var savedModel = await blogService.SavePost(slug, language, markdown);
// Index ONLY if file is in main directory (no path separators in name)
if (!e.Name.Contains(Path.DirectorySeparatorChar) &&
!e.Name.Contains(Path.AltDirectorySeparatorChar))
{
await IndexPostForSemanticSearchAsync(scope, savedModel, language);
}
});
}
private async Task IndexPostForSemanticSearchAsync(
IServiceScope scope,
BlogPostDto post,
string language)
{
var semanticSearchService = scope.ServiceProvider.GetService<ISemanticSearchService>();
if (semanticSearchService == null) return; // Not configured
var document = new BlogPostDocument
{
Id = $"{post.Slug}_{language}",
Slug = post.Slug,
Title = post.Title,
Content = post.PlainTextContent,
Language = language,
Categories = post.Categories?.ToList() ?? new List<string>(),
PublishedDate = post.PublishedDate
};
await semanticSearchService.IndexPostAsync(document);
_logger.LogInformation("Indexed {Slug} ({Language}) in semantic search", post.Slug, language);
}
When a post is deleted, remove it from the semantic index:
private async Task OnDeletedAsync(WaitForChangedResult e)
{
await blogService.Delete(slug, language);
// Delete from semantic search ONLY if file was in main directory
if (!e.Name.Contains(Path.DirectorySeparatorChar) &&
!e.Name.Contains(Path.AltDirectorySeparatorChar))
{
var semanticSearchService = scope.ServiceProvider.GetService<ISemanticSearchService>();
await semanticSearchService?.DeletePostAsync(slug, language);
}
}
On startup, a background service indexes existing posts not yet in Qdrant:
flowchart TB
A[Application Starts] --> B[Wait 10 seconds]
B --> C[Initialize Semantic Search]
C --> D{Model Exists?}
D -->|No| E[Download from Hugging Face]
D -->|Yes| F[Load ONNX Model]
E --> F
F --> G[Scan Main Markdown Directory]
G --> H{For Each .md File}
H --> I[Compute Content Hash]
I --> J{Hash Changed?}
J -->|Yes| K[Generate Embedding]
J -->|No| L[Skip - Already Indexed]
K --> M[Store in Qdrant]
M --> H
L --> H
H -->|Done| N[Indexing Complete]
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style K stroke:#6366f1,stroke-width:2px
style M stroke:#ef4444,stroke-width:2px
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public class SemanticIndexingBackgroundService : BackgroundService
{
protected override async Task ExecuteAsync(CancellationToken stoppingToken)
{
// Wait for app to be ready
await Task.Delay(TimeSpan.FromSeconds(10), stoppingToken);
// Initialize (downloads model if needed)
await _semanticSearchService.InitializeAsync(stoppingToken);
// Get all posts from main directory only
var markdownFiles = Directory.GetFiles(
_markdownConfig.MarkdownPath,
"*.md",
SearchOption.TopDirectoryOnly); // NOT subdirectories
foreach (var file in markdownFiles)
{
var needsIndexing = await _semanticSearchService.NeedsReindexingAsync(
slug, language, contentHash, stoppingToken);
if (needsIndexing)
await _semanticSearchService.IndexPostAsync(document, stoppingToken);
}
}
}
This ensures:
Across Parts 4a, 4b, and 5, we now have:
Future enhancements:
This completes the practical implementation of RAG-style semantic search. Combined with Part 4a (foundation) and Part 4b (search UI), you have everything needed to add intelligent search to your .NET application - running entirely on CPU, at zero additional cost.
All code is available at: github.com/scottgal/mostlylucidweb
Mostlylucid.SemanticSearch/ - Core semantic search libraryMostlylucid/Blog/WatcherService/ - File watcher with semantic indexing
© 2025 Scott Galloway — Unlicense — All content and source code on this site is free to use, copy, modify, and sell.