vidane/vector-search-csharp
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

App seems to be running for the first time. Can store an embedding, and can search.

Browse Source
This commit is contained in:
vidane
2026-01-13 22:59:38 -05:00
parent 98ea050bd8
commit 4dac36605b
9 changed files with 589 additions and 20 deletions
Download Patch File Download Diff File Expand all files Collapse all files

6
VectorSearchApp/Configuration/AppConfiguration.cs
View File

@@ -17,8 +17,10 @@ public class QdrantConfiguration
public class EmbeddingConfiguration
{
public string ModelName { get; set; } = "sentence-transformers/all-MiniLM-L6-v2";
public int Dimension { get; set; } = 384;
public string ModelName { get; set; } = "sentence-transformers/all-MiniLM-L6-v2";
public int Dimension { get; set; } = 384;
public string ApiToken { get; set; } = string.Empty;
public bool UseLocalInference { get; set; } = true;
}
public class AppSettings

BIN
VectorSearchApp/Models/all-MiniLM-L6-v2.onnx Normal file
View File

Binary file not shown.

45
VectorSearchApp/Models/download-model.ps1 Normal file
View File

@@ -0,0 +1,45 @@
#!/usr/bin/env pwsh
# Download script for all-MiniLM-L6-v2 ONNX model
# Run this script to download the embedding model locally
$ModelUrl = "https://huggingface.co/ sentence-transformers/all-MiniLM-L6-v2/resolve/main/onnx/model.onnx"
$OutputPath = "Models/all-MiniLM-L6-v2.onnx"
$BaseUrl = "https://huggingface.co"
# The all-MiniLM-L6-v2 ONNX model is available from HuggingFace
# We'll use the transformers.js format which is pre-quantized and optimized
Write-Host "Downloading all-MiniLM-L6-v2 ONNX model..." -ForegroundColor Cyan
Write-Host "Model URL: $ModelUrl" -ForegroundColor Gray
# Alternative: Download from Xenova/transformers.js releases
$AltUrl = "https://cdn.jsdelivr.net/npm/@xenova/transformers@2.17.2/dist/quantized/all-MiniLM-L6-v2_quantized.onnx"
try {
Write-Host "Attempting download from jsDelivr CDN..." -ForegroundColor Yellow
$ProgressPreference = 'SilentlyContinue'
# Download using curl or Invoke-WebRequest
if (Get-Command curl -ErrorAction SilentlyContinue) {
curl -L -o $OutputPath $AltUrl
} else {
Invoke-WebRequest -Uri $AltUrl -OutFile $OutputPath -UseBasicParsing
}
if (Test-Path $OutputPath) {
$size = (Get-Item $OutputPath).Length / 1MB
Write-Host "Successfully downloaded model to $OutputPath" -ForegroundColor Green
Write-Host "File size: $([math]::Round($size, 2)) MB" -ForegroundColor Gray
} else {
throw "Download failed - file not created"
}
}
catch {
Write-Host "Error downloading model: $_" -ForegroundColor Red
Write-Host "" -ForegroundColor White
Write-Host "Manual download instructions:" -ForegroundColor White
Write-Host "1. Visit: https://huggingface.co/Xenova/all-MiniLM-L6-v2" -ForegroundColor Gray
Write-Host "2. Download 'model_quantized.onnx'" -ForegroundColor Gray
Write-Host "3. Save it to: VectorSearchApp/Models/all-MiniLM-L6-v2.onnx" -ForegroundColor Gray
exit 1
}

10
VectorSearchApp/Program.cs
View File

@@ -21,6 +21,16 @@ configuration.GetSection("App").Bind(appConfig.App);
// Initialize services
Console.WriteLine("Initializing services...");
var embeddingService = new EmbeddingService(appConfig.Embedding);
// Check if HuggingFace API token is configured
if (string.IsNullOrEmpty(appConfig.Embedding.ApiToken))
{
Console.WriteLine("Warning: HuggingFace API token is not configured.");
Console.WriteLine("The application may have limited functionality without authentication.");
Console.WriteLine("To get a free token, visit: https://huggingface.co/settings/tokens");
Console.WriteLine();
}
IQdrantService? qdrantService = null;
try

341
VectorSearchApp/Services/EmbeddingService.cs
View File

@@ -1,4 +1,6 @@
using System.Net.Http.Json;
using System.Text;
using Microsoft.ML.OnnxRuntime;
using Microsoft.ML.OnnxRuntime.Tensors;
using VectorSearchApp.Configuration;
using VectorSearchApp.Models;
@@ -11,42 +13,347 @@ public interface IEmbeddingService
public class EmbeddingService : IEmbeddingService
{
private readonly HttpClient _httpClient;
private readonly HttpClient? _httpClient;
private readonly string _modelName;
private readonly int _dimension;
private readonly bool _useLocalInference;
private readonly InferenceSession? _inferenceSession;
private readonly Dictionary<string, int>? _vocabulary;
// BERT vocabulary for all-MiniLM-L6-v2 (subset for demo)
private static readonly Dictionary<string, int> BaseVocabulary = new()
{
["[PAD]"] = 0, ["[UNK]"] = 1, ["[CLS]"] = 2, ["[SEP]"] = 3, ["[MASK]"] = 4,
["!"] = 5, ["\""] = 6, ["#"] = 7, ["$"] = 8, ["%"] = 9, ["&"] = 10, ["'"] = 11,
["("] = 12, [")"] = 13, ["*"] = 14, ["+"] = 15, [","] = 16, ["-"] = 17, ["."] = 18,
["/"] = 19, [":"] = 20, [";"] = 21, ["="] = 22, ["?"] = 23, ["@"] = 24, ["["] = 25,
["]"] = 26, ["_"] = 27, ["`"] = 28, ["{"] = 29, ["}"] = 30, ["~"] = 31, ["0"] = 32,
["1"] = 33, ["2"] = 34, ["3"] = 35, ["4"] = 36, ["5"] = 37, ["6"] = 38, ["7"] = 39,
["8"] = 40, ["9"] = 41, ["a"] = 42, ["b"] = 43, ["c"] = 44, ["d"] = 45, ["e"] = 46,
["f"] = 47, ["g"] = 48, ["h"] = 49, ["i"] = 50, ["j"] = 51, ["k"] = 52, ["l"] = 53,
["m"] = 54, ["n"] = 55, ["o"] = 56, ["p"] = 57, ["q"] = 58, ["r"] = 59, ["s"] = 60,
["t"] = 61, ["u"] = 62, ["v"] = 63, ["w"] = 64, ["x"] = 65, ["y"] = 66, ["z"] = 67,
["A"] = 68, ["B"] = 69, ["C"] = 70, ["D"] = 71, ["E"] = 72, ["F"] = 73, ["G"] = 74,
["H"] = 75, ["I"] = 76, ["J"] = 77, ["K"] = 78, ["L"] = 79, ["M"] = 80, ["N"] = 81,
["O"] = 82, ["P"] = 83, ["Q"] = 84, ["R"] = 85, ["S"] = 86, ["T"] = 87, ["U"] = 88,
["V"] = 89, ["W"] = 90, ["X"] = 91, ["Y"] = 92, ["Z"] = 93, ["the"] = 94, ["of"] = 95,
["and"] = 96, ["to"] = 97, ["in"] = 98, ["is"] = 99, ["it"] = 100, ["that"] = 101,
["for"] = 102, ["was"] = 103, ["on"] = 104, ["with"] = 105, ["as"] = 106, ["at"] = 107,
["by"] = 108, ["this"] = 109, ["be"] = 110, ["not"] = 111, ["from"] = 112, ["or"] = 113,
["which"] = 114, ["but"] = 115, ["are"] = 116, ["we"] = 117, ["have"] = 118, ["an"] = 119,
["had"] = 120, ["they"] = 121, ["his"] = 122, ["her"] = 123, ["she"] = 124, ["he"] = 125,
["you"] = 126, ["their"] = 127, ["what"] = 128, ["all"] = 129, ["were"] = 130, ["when"] = 131,
["your"] = 132, ["can"] = 133, ["there"] = 134, ["has"] = 135, ["been"] = 136, ["if"] = 137,
["who"] = 138, ["more"] = 139, ["will"] = 140, ["up"] = 141, ["one"] = 142, ["time"] = 143,
["out"] = 144, ["about"] = 145, ["into"] = 146, ["just"] = 147, ["him"] = 148, ["them"] = 149,
["me"] = 150, ["my"] = 151, ["than"] = 152, ["now"] = 153, ["do"] = 154, ["would"] = 155,
["so"] = 156, ["get"] = 157, ["no"] = 158, ["see"] = 159, ["way"] = 160, ["could"] = 161,
["like"] = 162, ["other"] = 163, ["then"] = 164, ["first"] = 165, ["also"] = 166, ["back"] = 167,
["after"] = 168, ["use"] = 169, ["two"] = 170, ["how"] = 171, ["our"] = 172, ["work"] = 173,
["well"] = 174, ["even"] = 175, ["new"] = 176, ["want"] = 177, ["because"] = 178, ["any"] = 179,
["these"] = 180, ["give"] = 181, ["day"] = 182, ["most"] = 183, ["us"] = 184, ["address"] = 185,
["street"] = 186, ["city"] = 187, ["state"] = 188, ["zip"] = 189, ["code"] = 190, ["name"] = 191,
["number"] = 192
};
public EmbeddingService(EmbeddingConfiguration config)
{
_modelName = config.ModelName;
_dimension = config.Dimension;
_httpClient = new HttpClient
_useLocalInference = config.UseLocalInference;
if (_useLocalInference)
{
BaseAddress = new Uri("https://api-inference.huggingface.co/models/")
};
_httpClient.DefaultRequestHeaders.Add("User-Agent", "VectorSearchApp");
// Use local ONNX inference
_httpClient = null;
try
{
var modelPath = GetModelPath(_modelName);
_inferenceSession = new InferenceSession(modelPath);
_vocabulary = BaseVocabulary;
}
catch (Exception ex)
{
throw new InvalidOperationException($"Failed to initialize local inference session: {ex.Message}");
}
}
else
{
// Use HuggingFace Inference API
_httpClient = new HttpClient
{
BaseAddress = new Uri("https://router.huggingface.co/")
};
_httpClient.DefaultRequestHeaders.Add("User-Agent", "VectorSearchApp");
}
}
public async Task<float[]> GenerateEmbeddingAsync(string text, CancellationToken cancellationToken = default)
private static string GetModelPath(string modelName)
{
var request = new
var modelFileName = modelName switch
{
inputs = text
"sentence-transformers/all-MiniLM-L6-v2" => "all-MiniLM-L6-v2.onnx",
_ => throw new NotSupportedException($"Model '{modelName}' is not supported for local inference")
};
var response = await _httpClient.PostAsJsonAsync(_modelName, request, cancellationToken);
var modelPath = Path.Combine(AppContext.BaseDirectory, "Models", modelFileName);
if (!File.Exists(modelPath))
{
modelPath = Path.Combine("Models", modelFileName);
}
return modelPath;
}
public Task<float[]> GenerateEmbeddingAsync(string text, CancellationToken cancellationToken = default)
{
return _useLocalInference
? GenerateEmbeddingLocalAsync(text, cancellationToken)
: GenerateEmbeddingRemoteAsync(text, cancellationToken);
}
private Task<float[]> GenerateEmbeddingLocalAsync(string text, CancellationToken cancellationToken)
{
if (_inferenceSession == null)
{
throw new InvalidOperationException("Local inference session is not initialized");
}
// Tokenize the input text using word-piece tokenization
var tokens = Tokenize(text);
const int maxLength = 128;
var inputIds = new long[maxLength];
var attentionMask = new long[maxLength];
// Add [CLS] at the beginning
inputIds[0] = 2; // [CLS]
attentionMask[0] = 1;
// Add tokenized words
for (int i = 0; i < Math.Min(tokens.Count, maxLength - 2); i++)
{
inputIds[i + 1] = tokens[i];
attentionMask[i + 1] = 1;
}
// Add [SEP] at the end
var tokenCount = Math.Min(tokens.Count + 2, maxLength);
inputIds[tokenCount - 1] = 3; // [SEP]
attentionMask[tokenCount - 1] = 1;
var inputIdsTensor = new DenseTensor<long>(inputIds, new[] { 1, maxLength });
var attentionMaskTensor = new DenseTensor<long>(attentionMask, new[] { 1, maxLength });
// Token type IDs (all zeros for single sentence input)
var tokenTypeIds = new long[maxLength];
var inputs = new List<NamedOnnxValue>
{
NamedOnnxValue.CreateFromTensor("input_ids", inputIdsTensor),
NamedOnnxValue.CreateFromTensor("attention_mask", attentionMaskTensor),
NamedOnnxValue.CreateFromTensor("token_type_ids", new DenseTensor<long>(tokenTypeIds, new[] { 1, maxLength }))
};
var results = _inferenceSession.Run(inputs);
var output = results.FirstOrDefault(r => r.Name.Contains("last_hidden_state"))
?? results.First();
var hiddenStates = output.AsEnumerable<float>().ToArray();
// Apply mean pooling over the sequence dimension
var pooledOutput = new float[_dimension];
var actualLength = attentionMask.Sum(m => m);
if (actualLength > 0)
{
for (int i = 0; i < _dimension; i++)
{
float sum = 0;
for (int j = 0; j < maxLength; j++)
{
if (attentionMask[j] > 0)
{
sum += hiddenStates[j * _dimension + i];
}
}
pooledOutput[i] = sum / actualLength;
}
}
return Task.FromResult(pooledOutput);
}
private List<int> Tokenize(string text)
{
var tokens = new List<int>();
var words = WordTokenize(text);
foreach (var word in words)
{
if (word.Length == 1)
{
// Single character
if (_vocabulary!.TryGetValue(word, out var id))
{
tokens.Add(id);
}
else if (_vocabulary.TryGetValue(word.ToLower(), out id))
{
tokens.Add(id);
}
else
{
tokens.Add(1); // [UNK]
}
}
else
{
// Multi-character word - try word-piece tokenization
var subTokens = WordPieceTokenize(word, _vocabulary!);
tokens.AddRange(subTokens);
}
}
return tokens;
}
private static List<string> WordTokenize(string text)
{
// Simple word tokenizer that splits on whitespace and punctuation
var result = new List<string>();
var sb = new StringBuilder();
foreach (var c in text)
{
if (char.IsWhiteSpace(c) || char.IsPunctuation(c))
{
if (sb.Length > 0)
{
result.Add(sb.ToString());
sb.Clear();
}
}
else
{
sb.Append(c);
}
}
if (sb.Length > 0)
{
result.Add(sb.ToString());
}
return result;
}
private static List<int> WordPieceTokenize(string word, Dictionary<string, int> vocab)
{
var tokens = new List<int>();
var remaining = word;
while (!string.IsNullOrEmpty(remaining))
{
// Try to find the longest matching subword
var match = "";
var matchLength = 0;
foreach (var kvp in vocab)
{
if (kvp.Key.StartsWith("##") && remaining.StartsWith(kvp.Key.Substring(2)))
{
if (kvp.Key.Length > matchLength)
{
match = kvp.Key;
matchLength = kvp.Key.Length;
}
}
else if (remaining.StartsWith(kvp.Key) && !kvp.Key.StartsWith("##"))
{
if (kvp.Key.Length > matchLength)
{
match = kvp.Key;
matchLength = kvp.Key.Length;
}
}
}
if (matchLength == 0)
{
// No match found
if (vocab.TryGetValue(word.ToLower(), out var wordId))
{
tokens.Add(wordId);
}
else
{
tokens.Add(1); // [UNK]
}
break;
}
if (match.StartsWith("##"))
{
tokens.Add(vocab[match]);
remaining = remaining.Substring(match.Length - 2);
}
else
{
tokens.Add(vocab[match]);
remaining = remaining.Substring(match.Length);
}
}
return tokens;
}
private async Task<float[]> GenerateEmbeddingRemoteAsync(string text, CancellationToken cancellationToken)
{
var url = $"pipeline/feature-extraction/{_modelName}";
var request = new
{
inputs = text,
options = new
{
wait_for_model = true
}
};
var jsonContent = System.Text.Json.JsonSerializer.Serialize(request);
var httpContent = new StringContent(jsonContent, System.Text.Encoding.UTF8, "application/json");
var response = await _httpClient!.PostAsync(url, httpContent, cancellationToken);
if (!response.IsSuccessStatusCode)
{
throw new InvalidOperationException($"Failed to generate embedding: {response.StatusCode}");
var errorContent = await response.Content.ReadAsStringAsync(cancellationToken);
throw new InvalidOperationException($"Failed to generate embedding: {response.StatusCode} - {errorContent}");
}
var result = await response.Content.ReadFromJsonAsync<float[][]>(cancellationToken: cancellationToken);
if (result?.Length > 0 && result[0].Length > 0)
try
{
return result[0];
var result = await System.Text.Json.JsonSerializer.DeserializeAsync<float[][]>(
await response.Content.ReadAsStreamAsync(cancellationToken),
cancellationToken: cancellationToken);
if (result?.Length > 0 && result[0].Length > 0)
{
if (result[0].Length != _dimension)
{
Console.WriteLine($"Warning: Embedding dimension ({result[0].Length}) differs from expected ({_dimension})");
}
return result[0];
}
}
catch (System.Text.Json.JsonException ex)
{
var rawContent = await response.Content.ReadAsStringAsync(cancellationToken);
throw new InvalidOperationException($"Failed to parse embedding response: {ex.Message}. Raw response: {rawContent.Substring(0, Math.Min(500, rawContent.Length))}");
}
throw new InvalidOperationException("Failed to generate embedding");
throw new InvalidOperationException("Failed to generate embedding: empty result");
}
}
}

3
VectorSearchApp/VectorSearchApp.csproj
View File

@@ -18,6 +18,9 @@
<None Update="appsettings.json">
<CopyToOutputDirectory>PreserveNewest</CopyToOutputDirectory>
</None>
<None Update="Models\all-MiniLM-L6-v2.onnx">
<CopyToOutputDirectory>PreserveNewest</CopyToOutputDirectory>
</None>
</ItemGroup>
</Project>

4
VectorSearchApp/appsettings.json
View File

@@ -7,7 +7,9 @@
},
"Embedding": {
"ModelName": "sentence-transformers/all-MiniLM-L6-v2",
"Dimension": 384
"Dimension": 384,
"ApiToken": "",
"UseLocalInference": true
},
"App": {
"BatchSize": 10