NaturalQuery 0.1.1

NaturalQuery

NL2SQL engine for .NET — convert natural language questions into SQL queries using LLMs.

"top 10 products by sales"  →  SELECT product AS label, SUM(amount) AS value FROM orders GROUP BY product ORDER BY value DESC LIMIT 10

What it does

NaturalQuery takes a natural language question, sends it to an LLM with your database schema, and returns:

• A validated SQL query
• A recommended chart type (bar, pie, line, table, etc.)
• Title, description, and follow-up suggestions

It then executes the query and returns structured results ready for visualization.

Quick Start

dotnet add package NaturalQuery

builder.Services.AddNaturalQuery(options =>
{
    options.Tables = new List<TableSchema>
    {
        new("orders", new[]
        {
            new ColumnDef("id", "string"),
            new ColumnDef("product", "string"),
            new ColumnDef("amount", "double"),
            new ColumnDef("status", "string", "Active, Cancelled, Refunded"),
            new ColumnDef("created_at", "string", "ISO 8601 timestamp"),
        })
    };
    options.TenantIdColumn = "tenant_id";
    options.TenantIdPlaceholder = "{TENANT_ID}";
})
.UseBedrockProvider("us.anthropic.claude-haiku-4-5-20251001-v1:0")
.UseAthenaExecutor("my_database", "my_workgroup", "s3://my-results/");

app.MapGet("/ask", async (string question, INaturalQueryEngine engine) =>
{
    var result = await engine.AskAsync(question, tenantId: "my-tenant");
    return Results.Ok(result);
});

Architecture

Question (natural language)
    ↓
ILlmProvider (AWS Bedrock / custom)
    ↓
SQL generation + validation
    ↓
IQueryExecutor (Amazon Athena / custom)
    ↓
QueryResult (data + chart type + metadata)

Built-in Providers

Custom Providers

Implement ILlmProvider for other LLMs (OpenAI, Ollama, etc.):

public class OpenAiProvider : ILlmProvider
{
    public async Task<LlmResponse> GenerateAsync(string systemPrompt, string userPrompt, CancellationToken ct)
    {
        // Your implementation
    }
}

builder.Services.AddNaturalQuery(options => { ... })
    .UseLlmProvider<OpenAiProvider>()
    .UseQueryExecutor<PostgresExecutor>();

Implement IQueryExecutor for other databases (PostgreSQL, BigQuery, etc.):

public class PostgresExecutor : IQueryExecutor
{
    public async Task<List<DataPoint>> ExecuteChartQueryAsync(string sql, CancellationToken ct) { ... }
    public async Task<List<Dictionary<string, string>>> ExecuteTableQueryAsync(string sql, CancellationToken ct) { ... }
}

Features

• Schema-aware: Auto-generates system prompts from your table definitions
• Multi-tenant: Built-in tenant isolation with placeholder replacement
• SQL validation: Blocks destructive queries (DELETE, DROP, etc.)
• CTE support: Handles WITH ... AS (...) queries for deduplication
• Chart type inference: LLM picks the best visualization for each question
• Customizable: Override the system prompt, add rules, block keywords

Configuration

options.Tables = new List<TableSchema> { ... };       // Your database schema
options.TenantIdColumn = "tenant_id";                  // Multi-tenant column
options.TenantIdPlaceholder = "{TENANT_ID}";           // Placeholder in SQL
options.MaxTokens = 1000;                              // LLM max tokens
options.Temperature = 0.1;                             // LLM temperature
options.CustomSystemPrompt = "...{TABLES_SCHEMA}...";  // Override prompt
options.AdditionalRules = new() { "Use UTC dates" };   // Extra instructions
options.ForbiddenSqlKeywords = new() { "UNION " };     // Extra blocked keywords

License

MIT