What Is a Vector Database?

// File: VectorSample.EmbeddingServices\OllamaEmbeddingClient.cs
public class OllamaEmbeddingClient: IDisposable
{
    

    private readonly HttpClient _http = new();
    private OllamaEmbeddingClientOptions _options;
    private string _model;
    private Uri _embedUri;

    public OllamaEmbeddingClient(OllamaEmbeddingClientOptions options)
    {

        _options = options;
        _model = _options.Model;
        var baseUri = new Uri(_options.BaseUrl);
        _embedUri = new Uri(baseUri,"api/embed");
    }


    public async Task<float[]> GetEmbeddingAsync(string text)
    {
        var request = new { model = _model, input = $"{text}" };

        var response = await _http.PostAsJsonAsync(_embedUri, request);
        var result = await response.Content.ReadFromJsonAsync<EmbeddingResponse>();

        return result?.Embeddings?.FirstOrDefault()?.ToArray() ?? throw new Exception("No embedding returned");
    }


    public void Dispose()
    {
        _http?.Dispose();
    }
}

// File: VectorSample.Database\VectorStore.cs

public async Task StoreDocumentAsync(string content, float[] vector, IDictionary<string, string> metadata)
{

    var dataSourceBuilder = new NpgsqlDataSourceBuilder(_connStr);
    dataSourceBuilder.UseVector();
    await using var dataSource = dataSourceBuilder.Build();

    using var conn = dataSource.CreateConnection();
    try
    {
        await conn.OpenAsync();

        await using var cmd = new NpgsqlCommand("""
        INSERT INTO documents (id, content, embedding, metadata)
        VALUES (@id, @content, @embedding, @metadata)
    """, conn);

        cmd.Parameters.AddWithValue("id", Guid.NewGuid());
        cmd.Parameters.AddWithValue("content", content);
        cmd.Parameters.AddWithValue("embedding", vector);
        // Properly defined JSONB param
        cmd.Parameters.Add(new NpgsqlParameter("metadata", JsonSerializer.Serialize(metadata))
        {
            NpgsqlDbType = NpgsqlDbType.Jsonb
        });

        await cmd.ExecuteNonQueryAsync();
    }
    finally
    {
        await conn.CloseAsync();
    }
}

// File: VectorSample.Database\VectorStore.cs

public async Task<List<string>> SearchAsync(float[] queryVector,int topK = 5)
{

    var dataSourceBuilder = new NpgsqlDataSourceBuilder(_connStr);
    dataSourceBuilder.UseVector();
    await using var dataSource = dataSourceBuilder.Build();
    

    var vector = new Pgvector.Vector(queryVector);
    using var conn = dataSource.CreateConnection();

    try
    {
        await conn.OpenAsync();
      
        await using var cmd = new NpgsqlCommand("""
            SELECT 
                content
            FROM documents
            ORDER BY embedding <=> @embedding
            LIMIT @topK
        """, conn);

        cmd.Parameters.AddWithValue("embedding", vector);
        cmd.Parameters.AddWithValue("topK", topK);


        var results = new List<string>();
        var reader = await cmd.ExecuteReaderAsync();
        while (await reader.ReadAsync())
            results.Add(reader.GetString(0));

        return results;
    }
    finally
    {
        await conn.CloseAsync();
    }
}

// File: VectorDb-Sample\VectorSample.RagCli\Program.cs
internal class Program
{

    private static string _connectionString = "Host=localhost;Port=54321;Username=admin;Password=admin;Database=vectordb";
    private static string _chatModel = "phi4";
    private static string _embedModel = "nomic-embed-text";

    private static string _ollamaUrl = "http://localhost:11434";


    static async Task Main(string[] args)
    {
        
        using var vectorStore = new VectorStore(new(_connectionString));
        using var chatClient = new OllamaChatClient(new(_ollamaUrl, _chatModel));
        using var embeddingClient = new OllamaEmbeddingClient(new(_ollamaUrl, _embedModel));

        Console.WriteLine("Enter your question (or 'exit' to quit):");


        while (true)
        {

            var firstChunkArrived = false;
            var spinnerFinished = false;
            Console.WriteLine();
            var question = AnsiConsole.Prompt(
                new TextPrompt<string>("[bold yellow]Question[/]:").AllowEmpty());

            if (string.Equals(question, "exit", StringComparison.OrdinalIgnoreCase))
                break;

            List<string> topDocs = new();

            StatusContext statusContext = null!;

            var _ = AnsiConsole.Status()
                .Spinner(Spinner.Known.Star)
                .StartAsync("Thinking...", async ctx => {

                    statusContext = ctx;
                

                    while (!firstChunkArrived)
                    {
                        await Task.Delay(100);
                    }

                    ctx.Status("Done Thinking");
                    ctx.Refresh();
                    spinnerFinished = true;
                    return;
                });




            statusContext.Status("Generating embedding...");
            statusContext.Refresh();
            var questionVector = await embeddingClient.GetEmbeddingAsync(question);

            statusContext.Status("Searching vector store...");
            statusContext.Refresh();

            topDocs = await vectorStore.SearchAsync(questionVector, 5);
            statusContext.Status("Thinking...");
            statusContext.Refresh();

            var prompt = $"""
                You are a helpful assistant. Use the following documents to answer the question.

                Documents:
                {string.Join("\n---\n", topDocs)}

                Question: {question}
                Answer:
                """;


            try
            {
                await foreach (var response in chatClient.ChatAsync(question, systemPrompt: prompt))
                {
                    if (!firstChunkArrived)
                    {
                        AnsiConsole.MarkupLine("[bold blue]Response:[/]");
                        firstChunkArrived = true;
                    }

                    while (!spinnerFinished)
                    {
                        await Task.Delay(100);
                    }
                    Console.Write($"{response}");
                }
            }
            catch(Exception ex)
            {
                firstChunkArrived = true;
                while (!spinnerFinished)
                {
                    await Task.Delay(100);
                }
                AnsiConsole.MarkupLine($"[red]Error:{Markup.Escape(ex.Message)}[/]");
            }
            Console.WriteLine();
        }
    }
}