https://forloop.co.uk/blog/using-qdrant-for-embeddings-search-with-csharp

Program.cs

using System.Globalization;
using System.IO.Compression;
using System.Text.Json;
using Azure.AI.OpenAI;
using CsvHelper;
using CsvHelper.Configuration;
using CsvHelper.Configuration.Attributes;
using CsvHelper.TypeConversion;
using Qdrant.Client;
using Qdrant.Client.Grpc;

var embeddingsUrl =
	new Uri("https://cdn.openai.com/API/examples/data/vector_database_wikipedia_articles_embedded.zip");

var name = @"C:\vector_database_wikipedia_articles_embedded";

if (!Directory.Exists(name))
{
	using var httpClient = new HttpClient();
	await using (var stream = await httpClient.GetStreamAsync(embeddingsUrl))
	await using (var destination = new FileStream($"{name}.zip", FileMode.CreateNew))
		await stream.CopyToAsync(destination);

	ZipFile.ExtractToDirectory($"{name}.zip", name);
}

var records = ReadRecords(Path.Combine(name, "vector_database_wikipedia_articles_embedded.csv"));

var firstRecord = records.First();
var size = (ulong)firstRecord.ContentVector.Length;
var collectionName = "Articles";

var client = new QdrantClient("localhost");

try
{
	await client.DeleteCollectionAsync(collectionName);
}
catch (QdrantException)
{
	// swallow
}

await client.CreateCollectionAsync(collectionName,
	new VectorParamsMap
	{
		Map =
		{
			["title"] = new VectorParams { Distance = Distance.Cosine, Size = size },
			["content"] = new VectorParams { Distance = Distance.Cosine, Size = size },
		}
	});

var points = new List<PointStruct>(1000) { RecordToPointStruct(firstRecord) };

foreach (var record in records)
{
	points.Add(RecordToPointStruct(record));
	if (points.Count == 1000)
	{
		await client.UpsertAsync(collectionName, points);
		points.Clear();
	}
}

if (points.Any())
	await client.UpsertAsync(collectionName, points);

var count = await client.CountAsync(collectionName);
Console.WriteLine($"Count of points: {count}");

var openAiApiKey = "<insert your OpenAI API key>";
var openAIClient = new OpenAIClient(openAiApiKey);

var results = await Query(client, openAIClient, "modern art in Europe", collectionName);

foreach (var (point, i) in results.Select((point, i) => (point, i)))
	Console.WriteLine($"{i + 1}. {point.Payload["title"].StringValue} (Score: {Math.Round(point.Score, 3)})");

Console.WriteLine();

results = await Query(client, openAIClient, "Famous battles in Scottish history", collectionName, "content");

foreach (var (point, i) in results.Select((point, i) => (point, i)))
	Console.WriteLine($"{i + 1}. {point.Payload["title"].StringValue} (Score: {Math.Round(point.Score, 3)})");

return;

static async Task<IReadOnlyList<ScoredPoint>> Query(
	QdrantClient client,
	OpenAIClient openAIClient,
	string query,
	string collectionName,
	string vectorName = "title",
	ulong topK = 20)
{
	var response = await openAIClient.GetEmbeddingsAsync(new EmbeddingsOptions
		{
			Input = { query },
			DeploymentName = "text-embedding-ada-002"
		});
	return await client.SearchAsync(collectionName, response.Value.Data[0].Embedding, vectorName: vectorName, limit: topK);
}

static PointStruct RecordToPointStruct(CsvRecord record)
{
	return new PointStruct
	{
		Id = (ulong)record.Id,
		Vectors = new Dictionary<string, float[]>
		{
			["title"] = record.TitleVector,
			["content"] = record.ContentVector
		},
		Payload =
		{
			["url"] = record.Url,
			["title"] = record.Title,
			["text"] = record.Text
		}
	};
}

static IEnumerable<CsvRecord> ReadRecords(string name)
{
	using var reader = new StreamReader(name);
	var config = new CsvConfiguration(CultureInfo.InvariantCulture)
	{
		PrepareHeaderForMatch = args => string.Concat(args.Header.Select((x, i) =>
			i > 0 && char.IsUpper(x) ? "_" + x : x.ToString())).ToLowerInvariant()
	};
	using var csv = new CsvReader(reader, config);
	foreach (var record in csv.GetRecords<CsvRecord>())
		yield return record;
}

public record CsvRecord(
	int Id,
	string Url,
	string Title,
	string Text,
	[TypeConverter(typeof(StringToEmbeddingConverter))]
	float[] TitleVector,
	[TypeConverter(typeof(StringToEmbeddingConverter))]
	float[] ContentVector,
	int VectorId);

public class StringToEmbeddingConverter : DefaultTypeConverter
{
	public override object ConvertFromString(string? text, IReaderRow row, MemberMapData memberMapData) =>
		JsonSerializer.Deserialize<float[]>(text ?? throw new ArgumentNullException(nameof(text)))!;
}