Refactor and enhance config management

Refactored code to centralize configuration access through a single `AppSettings` instance in `ChatService` and `VectorSearchService`, improving maintainability and reducing verbosity. Introduced new configuration settings (`MaxTokensPerLine`, `MaxTokensPerParagraph`, `OverlapTokens`, `MaxChunksCount`) in `AppSettings.cs` and `appsettings.json` for enhanced flexibility in content processing. Adjusted existing settings usage (`MessageLimit`, `MessageExpiration`) to align with the new access method, and removed obsolete settings (`StoragePath`, `VectorDbPath`, `QueuePath`). These changes simplify the codebase, make the application more configurable and adaptable to different content characteristics, and allow for more controlled vector search operations.

SqlDatabaseVectorSearch/Services/ChatService.cs

@@ -9,6 +9,8 @@ namespace SqlDatabaseVectorSearch.Services;
 
 public class ChatService(IMemoryCache cache, IChatCompletionService chatCompletionService, IOptions<AppSettings> appSettingsOptions)
 {
+    private readonly AppSettings appSettings = appSettingsOptions.Value;
+
     public async Task<string> CreateQuestionAsync(Guid conversationId, string question)
     {
         var chat = new ChatHistory(cache.Get<ChatHistory?>(conversationId) ?? []);
@@ -77,12 +79,12 @@ public class ChatService(IMemoryCache cache, IChatCompletionService chatCompleti
 
     private Task UpdateCacheAsync(Guid conversationId, ChatHistory chat)
     {
-        if (chat.Count > appSettingsOptions.Value.MessageLimit)
+        if (chat.Count > appSettings.MessageLimit)
         {
-            chat = new ChatHistory(chat.TakeLast(appSettingsOptions.Value.MessageLimit));
+            chat = new ChatHistory(chat.TakeLast(appSettings.MessageLimit));
         }
 
-        cache.Set(conversationId, chat, appSettingsOptions.Value.MessageExpiration);
+        cache.Set(conversationId, chat, appSettings.MessageExpiration);
         return Task.CompletedTask;
     }
 }

SqlDatabaseVectorSearch/Services/VectorSearchService.cs

@@ -1,17 +1,21 @@
 using System.Text;
 using Microsoft.EntityFrameworkCore;
+using Microsoft.Extensions.Options;
 using Microsoft.SemanticKernel.Embeddings;
 using Microsoft.SemanticKernel.Text;
 using SqlDatabaseVectorSearch.DataAccessLayer;
 using SqlDatabaseVectorSearch.Models;
+using SqlDatabaseVectorSearch.Settings;
 using UglyToad.PdfPig;
 using UglyToad.PdfPig.DocumentLayoutAnalysis.TextExtractor;
 using Entities = SqlDatabaseVectorSearch.DataAccessLayer.Entities;
 
 namespace SqlDatabaseVectorSearch.Services;
 
-public class VectorSearchService(ApplicationDbContext dbContext, ITextEmbeddingGenerationService textEmbeddingGenerationService, ChatService chatService)
+public class VectorSearchService(ApplicationDbContext dbContext, ITextEmbeddingGenerationService textEmbeddingGenerationService, ChatService chatService, IOptions<AppSettings> appSettingsOptions)
 {
+    private readonly AppSettings appSettings = appSettingsOptions.Value;
+
     public async Task<Guid> ImportAsync(Stream stream, string name, Guid? documentId)
     {
         // Extract the contents of the file (current, only PDF are supported).
@@ -31,8 +35,8 @@ public class VectorSearchService(ApplicationDbContext dbContext, ITextEmbeddingG
         var document = new Entities.Document { Id = documentId.Value, Name = name, CreationDate = DateTimeOffset.UtcNow };
         dbContext.Documents.Add(document);
 
-        // Split the content into chunks of at most 1024 tokens and generate the embeddings for each one.
-        var paragraphs = TextChunker.SplitPlainTextParagraphs(TextChunker.SplitPlainTextLines(content, 300), 1024, 100);
+        // Split the content into chunks and generate the embeddings for each one.
+        var paragraphs = TextChunker.SplitPlainTextParagraphs(TextChunker.SplitPlainTextLines(content, appSettings.MaxTokensPerLine), appSettings.MaxTokensPerParagraph, appSettings.OverlapTokens);
         var embeddings = await textEmbeddingGenerationService.GenerateEmbeddingsAsync(paragraphs);
 
         foreach (var (paragraph, embedding) in paragraphs.Zip(embeddings, (p, e) => (p, e.ToArray())))
@@ -70,7 +74,7 @@ public class VectorSearchService(ApplicationDbContext dbContext, ITextEmbeddingG
 
         var chunks = await dbContext.DocumentChunks
             .OrderBy(c => EF.Functions.VectorDistance("cosine", c.Embedding, questionEmbedding.ToArray()))
-            .Take(5)
+            .Take(appSettings.MaxChunksCount)
             .ToListAsync();
 
         var answer = await chatService.AskQuestionAsync(question.ConversationId, chunks, reformulatedQuestion);

SqlDatabaseVectorSearch/Settings/AppSettings.cs

@@ -2,13 +2,15 @@
 
 public class AppSettings
 {
+    public int MaxTokensPerLine { get; init; } = 300;
+
+    public int MaxTokensPerParagraph { get; init; } = 1024;
+
+    public int OverlapTokens { get; init; } = 100;
+
+    public int MaxChunksCount { get; init; } = 5;
+
     public int MessageLimit { get; init; }
 
     public TimeSpan MessageExpiration { get; init; }
-
-    public required string StoragePath { get; init; }
-
-    public required string VectorDbPath { get; init; }
-
-    public required string QueuePath { get; init; }
 }

SqlDatabaseVectorSearch/appsettings.json

@@ -15,6 +15,10 @@
         }
     },
     "AppSettings": {
+        "MaxTokenPerLine": 300,
+        "MaxTokensPerParagraph": 1024,
+        "OverlapTokens": 100,
+        "MaxChunksCount": 5,
         "MessageLimit": 20,
         "MessageExpiration": "00:05:00"
     },