Database Smart Query (DB Manager)

The Database Smart Query (DB Manager) example demonstrates how to create an AI-powered database query interface using Nanoservice-ts. This advanced example showcases how to build a workflow that allows users to query a PostgreSQL database using natural language prompts, which are then converted to SQL queries using AI.

Features

Interactive UI for database exploration
Natural language to SQL conversion using AI
Dynamic database schema introspection
Real-time query execution
Results visualization

Prerequisites

Before running this example, ensure you have:

A Nanoservice-ts project set up with the HTTP trigger
Node.js (v22 or later) and npm installed
Docker and Docker Compose installed
An OpenAI API key for the AI query generation

Environment Setup

1. Set Up Your Nanoservice-ts Project

If you haven’t created a project yet, you can do so with:

npx nanoctl@latest create project

Follow the prompts:

Provide a name for your project
Select “HTTP” as the trigger
Select “NodeJS” as the runtime
Choose “YES” when asked to install examples

2. Configure Environment Variables

Create a .env.local file in your project root with your OpenAI API key:

OPENAI_API_KEY=your_openai_api_key

Replace your_openai_api_key with your actual OpenAI API key.

3. Set Up the Required Infrastructure

The DB Manager example requires two infrastructure components that are already included in your project:

PostgreSQL Database

Navigate to the PostgreSQL infrastructure directory and start the containers:

# First, create the shared network if it doesn't exist
docker network create shared-network

# Navigate to the PostgreSQL directory
cd infra/postgresql

# Start the PostgreSQL containers
docker-compose up -d

This will start:

A PostgreSQL database with the sample dvdrental database
Adminer, a database management tool accessible at http://localhost:8080

Metrics Infrastructure (Optional but Recommended)

For monitoring your application, you can also start the metrics infrastructure:

# Navigate to the metrics directory
cd infra/metrics

# Start the metrics containers
docker-compose up -d

This will start:

Prometheus for metrics collection
Grafana for metrics visualization (accessible at http://localhost:3000)
Loki for log aggregation

4. Verify the Setup

To verify that everything is set up correctly:

Check that the PostgreSQL database is running:
```
docker ps | grep postgres
```
Access Adminer at http://localhost:8080 and log in with:
- System: PostgreSQL
- Server: postgres
- Username: postgres
- Password: example
- Database: dvdrental
You should see the dvdrental database with tables like actor, film, customer, etc.

Workflow Structure

The DB Manager workflow is defined in workflows/json/db-manager.json:

{
  "name": "Database Manager",
  "description": "This workflow is used to query a database using an AI Prompt UI",
  "version": "1.0.0",
  "trigger": {
    "http": {
      "method": "*",
      "path": "/:function?",
      "accept": "application/json"
    }
  },
  "steps": [
    {
      "name": "filter-request",
      "node": "@nanoservice-ts/if-else",
      "type": "module"
    }
  ],
  "nodes": {
    "filter-request": {
      "conditions": [
        {
          "type": "if",
          "steps": [
            {
              "name": "database-ui",
              "node": "database-ui",
              "type": "module"
            }
          ],
          "condition": "ctx.request.method.toLowerCase() === \"get\" && ctx.request.params.function === undefined"
        },
        {
          "type": "if",
          "steps": [
            {
              "name": "get-tables",
              "node": "postgres-query",
              "type": "module"
            }
          ],
          "condition": "ctx.request.method.toLowerCase() === \"get\" && ctx.request.params.function === \"tables\""
        },
        {
          "type": "if",
          "steps": [
            {
              "name": "get-table-columns",
              "node": "postgres-query",
              "type": "module"
            },
            {
              "name": "query-generator",
              "node": "query-generator",
              "type": "module"
            },
            {
              "name": "execute-query",
              "node": "postgres-query",
              "type": "module"
            },
            {
              "name": "create-response",
              "node": "mapper",
              "type": "module"
            }
          ],
          "condition": "ctx.request.method.toLowerCase() === \"post\" && ctx.request.params.function === \"execute-prompt\""
        },
        {
          "type": "else",
          "steps": [
            {
              "name": "method-not-allowed",
              "node": "error",
              "type": "module"
            }
          ]
        }
      ]
    },
    "database-ui": {
      "inputs": {}
    },
    "get-tables": {
      "inputs": {
        "host": "localhost",
        "port": 5432,
        "database": "dvdrental",
        "user": "postgres",
        "password": "example",
        "query": "select table_name from information_schema.tables where table_schema = 'public'"
      }
    },
    "get-table-columns": {
      "inputs": {
        "host": "localhost",
        "port": 5432,
        "database": "dvdrental",
        "user": "postgres",
        "password": "example",
        "query": "SELECT t1.column_name, COALESCE(t2.attname, '_') as primary_key, udt_name as data_type FROM information_schema.columns as t1 LEFT JOIN (SELECT a.attname FROM pg_index i JOIN pg_attribute a ON a.attrelid = i.indrelid AND a.attnum = ANY(i.indkey) WHERE  i.indrelid = '${ctx.request.body.table_name}'::regclass AND i.indisprimary) as t2 ON t1.column_name = t2.attname WHERE table_schema = 'public' AND table_name = '${ctx.request.body.table_name}';"
      }
    },
    "query-generator": {
      "inputs": {
        "table_name": "${ctx.request.body.table_name}",
        "columns": "js/ctx.response.data.data",
        "prompt": "${ctx.request.body.prompt}"
      }
    },
    "execute-query": {
      "inputs": {
        "host": "localhost",
        "port": 5432,
        "database": "dvdrental",
        "user": "postgres",
        "password": "example",
        "query": "${ctx.response.data.query}"
      }
    },
    "create-response": {
      "inputs": {
        "model": {
          "total": "${ctx.response.data.total}",
          "results": "js/ctx.response.data.data",
          "query": "${ctx.vars?.query}"
        }
      }
    }
  }
}

UI Rendering with If-Else Node

One of the most powerful features of Nanoservice-ts is the ability to serve different content based on request conditions. The DB Manager example demonstrates this by using the @nanoservice-ts/if-else node to render the UI and handle API requests within a single workflow.

How UI Rendering Works in Nanoservice-ts

In traditional web applications, you might have separate routes for serving HTML and handling API requests. In Nanoservice-ts, you can use conditional logic to handle both in a unified workflow:

Request Routing: The if-else node evaluates conditions based on the request method, path, and parameters.
Content Type Detection: The workflow determines whether to serve HTML (UI) or JSON (API response).
Node Selection: Based on the conditions, the appropriate node is executed.
Response Generation: The selected node generates the appropriate response (HTML or JSON).

The If-Else Node Structure

The if-else node in the DB Manager workflow is configured with multiple conditions:

"filter-request": {
  "conditions": [
    {
      "type": "if",
      "steps": [
        {
          "name": "database-ui",
          "node": "database-ui",
          "type": "module"
        }
      ],
      "condition": "ctx.request.method.toLowerCase() === \"get\" && ctx.request.params.function === undefined"
    },
    // Other conditions...
  ]
}

Each condition:

Has a type (“if” or “else”)
Contains a JavaScript expression in the condition field
Defines steps to execute when the condition is true

The Database-UI Node

The database-ui node is responsible for serving the HTML, CSS, and JavaScript for the UI. When a GET request is made to the root path (/db-manager), the if-else node routes the request to this node.

The node is implemented as a custom node that returns HTML content. The HTML includes:

HTML Structure: The basic structure of the page
CSS Styling: Styles for the UI components
React Components: JavaScript code for the interactive UI
API Integration: Code to communicate with the workflow’s API endpoints

Here’s a simplified example of how the database-ui node might be implemented:

import {
  type INanoServiceResponse,
  NanoService,
  NanoServiceResponse,
} from "@nanoservice-ts/runner";
import { type Context, GlobalError } from "@nanoservice-ts/shared";
import fs from 'fs';
import path from 'path';

type InputType = {};

export default class DatabaseUI extends NanoService<InputType> {
  constructor() {
    super();
    this.inputSchema = {};
  }

  async handle(ctx: Context, inputs: InputType): Promise<INanoServiceResponse> {
    const response: NanoServiceResponse = new NanoServiceResponse();

    try {
      // Read the HTML file
      const htmlPath = path.resolve(__dirname, './ui/index.html');
      const htmlContent = fs.readFileSync(htmlPath, 'utf8');
      
      // Set the content type to HTML
      response.headers = {
        'Content-Type': 'text/html',
      };
      
      // Return the HTML content
      response.setSuccess(htmlContent);
    } catch (error: unknown) {
      const nodeError = new GlobalError((error as Error).message);
      nodeError.setCode(500);
      nodeError.setStack((error as Error).stack);
      nodeError.setName(this.name);
      response.setError(nodeError);
    }

    return response;
  }
}

The Complete UI Rendering Flow

When a user accesses the DB Manager at http://localhost:4000/db-manager, the following happens:

The HTTP trigger receives the GET request

The if-else node evaluates the conditions:

ctx.request.method.toLowerCase() === "get" && ctx.request.params.function === undefined

Since this condition is true (it’s a GET request to the root path), the database-ui node is executed
The database-ui node returns HTML content with the appropriate Content-Type header
The browser renders the HTML, which includes the React application
The React application makes API requests to the other endpoints defined in the workflow

API Endpoints in the Same Workflow

The same workflow also handles API requests through different conditions:

GET /db-manager/tables: Lists available database tables

ctx.request.method.toLowerCase() === "get" && ctx.request.params.function === "tables"

POST /db-manager/execute-prompt: Generates and executes an SQL query

ctx.request.method.toLowerCase() === "post" && ctx.request.params.function === "execute-prompt"

Benefits of This Approach

Using the if-else node for UI rendering in Nanoservice-ts offers several advantages:

Unified Workflow: Both UI and API logic are contained in a single workflow
Simplified Deployment: No need for separate frontend and backend deployments
Shared Context: UI and API can share the same context and configuration
Conditional Logic: Complex routing can be implemented with JavaScript expressions
Content Negotiation: Different content types can be served based on request parameters

Implementing Your Own UI Rendering

To implement UI rendering in your own Nanoservice-ts workflows:

Create a UI Node: Implement a node that returns HTML content
Use the If-Else Node: Configure conditions based on request properties
Set Content-Type Headers: Ensure the correct Content-Type is set for HTML responses
Include Client-Side Code: Add JavaScript for client-side interactivity
Define API Endpoints: Add conditions for API endpoints in the same workflow

The Postgres-Query Node

The DB Manager example uses the postgres-query node to interact with the PostgreSQL database. This node is a custom implementation that allows executing SQL queries against the database.

Here’s a simplified version of the postgres-query node:

import {
  type INanoServiceResponse,
  type JsonLikeObject,
  NanoService,
  NanoServiceResponse,
} from "@nanoservice-ts/runner";
import { type Context, GlobalError } from "@nanoservice-ts/shared";
import pg from "pg";

type PostgresQueryInputs = {
  user: string;
  password: string;
  host: string;
  query: string;
  set_var?: boolean;
};

export default class PostgresQuery extends NanoService<PostgresQueryInputs> {
  constructor() {
    super();
    this.inputSchema = {
      $schema: "http://json-schema.org/draft-04/schema#",
      type: "object",
      properties: {
        user: { type: "string" },
        password: { type: "string" },
        host: { type: "string" },
        query: { type: "string" },
        set_var: { type: "boolean" },
      },
      required: ["user", "password", "host", "query"],
    };
    this.outputSchema = {};
  }

  async handle(ctx: Context, inputs: PostgresQueryInputs): Promise<INanoServiceResponse> {
    const response: NanoServiceResponse = new NanoServiceResponse();

    try {
      const { Client } = pg;
      const client = new Client({
        user: inputs.user,
        password: inputs.password,
        host: inputs.host,
        port: 5432,
        database: "dvdrental",
      });

      await client.connect();
      const result = await client.query(inputs.query);
      await client.end();

      response.setSuccess({
        total: result.rowCount,
        data: result.rows,
      });
    } catch (error: unknown) {
      const nodeError = new GlobalError((error as Error).message);
      nodeError.setCode(500);
      nodeError.setStack((error as Error).stack);
      nodeError.setName(this.name);
      response.setError(nodeError);
    }

    return response;
  }
}

The postgres-query node is used in three places in the workflow:

get-tables: To fetch the list of available tables from the database
get-table-columns: To fetch the columns of a specific table
execute-query: To execute the SQL query generated by the AI

Custom Node Implementations

The DB Manager example uses several custom nodes:

1. QueryGeneratorNode

This node uses OpenAI to generate an SQL query based on a natural language prompt and table schema:

import { createOpenAI } from "@ai-sdk/openai";
import {
  type INanoServiceResponse,
  NanoService,
  NanoServiceResponse,
  type ParamsDictionary,
} from "@nanoservice-ts/runner";
import { type Context, GlobalError } from "@nanoservice-ts/shared";
import { generateText } from "ai";

type InputType = {
  table_name: string;
  columns: Column[];
  prompt: string;
};

type Column = {
  column_name: string;
  data_type: string;
  primary_key: string;
};

export default class QueryGeneratorNode extends NanoService<InputType> {
  constructor() {
    super();
    this.inputSchema = {
      $schema: "http://json-schema.org/draft-04/schema#",
      type: "object",
      properties: {
        table_name: { type: "string" },
        columns: {
          type: "array",
          items: {
            type: "object",
            properties: {
              column_name: { type: "string" },
              data_type: { type: "string" },
              primary_key: { type: "string" },
            },
          },
        },
        prompt: { type: "string" },
      },
      required: ["table_name", "columns"],
    };
  }

  async handle(ctx: Context, inputs: InputType): Promise<INanoServiceResponse> {
    const response: NanoServiceResponse = new NanoServiceResponse();
    const { table_name: tableName, columns, prompt } = inputs;

    try {
      // Format column information
      const tableSchema = columns
        .map(
          (col) => `${col.column_name} (${col.data_type}${col.column_name === col.primary_key ? ", PRIMARY KEY" : ""})`,
        )
        .join(", ");

      // Generate SQL query using AI
      const openai = createOpenAI({
        compatibility: "strict",
        apiKey: process.env.OPENAI_API_KEY,
      });

      const ai_prompt = `Table: ${tableName}
            Schema: ${tableSchema}
            
            Generate a SQL query for the following request: ${prompt}
            
            Return ONLY the SQL query with no explanations, additional text or markdown code group.
            
            Double check the query to not include markdown code blocks or any other text that is not a valid SQL query.`;

      const { text: sqlQuery } = await generateText({
        model: openai("gpt-4o"),
        system: `You are a SQL expert. Generate only valid SQL queries without any explanations or markdown. 
            The query should be executable directly against a PostgreSQL database.`,
        prompt: ai_prompt,
      });

      if (ctx.vars === undefined) ctx.vars = {};
      ctx.vars.query = sqlQuery as unknown as ParamsDictionary;

      response.setSuccess({
        query: sqlQuery,
      });
    } catch (error: unknown) {
      const nodeError = new GlobalError((error as Error).message);
      nodeError.setCode(500);
      response.setError(nodeError);
    }

    return response;
  }
}

2. MapperNode

This node maps data from one format to another:

import {
  type INanoServiceResponse,
  type JsonLikeObject,
  NanoService,
  NanoServiceResponse,
} from "@nanoservice-ts/runner";
import { type Context, GlobalError } from "@nanoservice-ts/shared";

type InputType = {
  model: object;
};

export default class MapperNode extends NanoService<InputType> {
  constructor() {
    super();
    this.inputSchema = {
      $schema: "http://json-schema.org/draft-04/schema#",
      type: "object",
      properties: {
        model: { type: "object" },
      },
      required: ["model"],
    };
  }

  async handle(ctx: Context, inputs: InputType): Promise<INanoServiceResponse> {
    const response: NanoServiceResponse = new NanoServiceResponse();

    try {
      response.setSuccess(inputs.model as JsonLikeObject);
    } catch (error: unknown) {
      const nodeError = new GlobalError((error as Error).message);
      nodeError.setCode(500);
      nodeError.setStack((error as Error).stack);
      nodeError.setName(this.name);
      response.setError(nodeError);
    }

    return response;
  }
}

Running the Example

To run the DB Manager example:

Start your Nanoservice-ts project:
```
npm run dev
```
Open your browser and navigate to:
```
http://localhost:4000/db-manager
```
You should see the DB Manager UI with a dropdown to select a table and a text area to enter your natural language query.
Select a table (e.g., “film”) and enter a query like “Show me all films with a rating of PG-13”.
Click “Execute” to generate and run the SQL query.
The results will be displayed in a table below.

Troubleshooting

If you encounter issues:

Database Connection Issues:
- Ensure the PostgreSQL container is running: docker ps | grep postgres
- Check the connection parameters in the workflow JSON
- Verify that the dvdrental database is properly initialized
AI Query Generation Issues:
- Verify your OpenAI API key in the .env.local file
- Check the OpenAI API usage limits and quotas
- Examine the error messages in the console
UI Rendering Issues:
- Check the browser console for JavaScript errors
- Verify that the database-ui node is correctly returning HTML content
- Ensure the Content-Type header is set to text/html

Conclusion

The DB Manager example demonstrates the power and flexibility of Nanoservice-ts for building complex applications. By combining UI rendering, API endpoints, database access, and AI integration in a single workflow, you can create sophisticated applications with minimal code and configuration.

Key takeaways:

Unified Workflow: Both UI and API logic are contained in a single workflow
Conditional Logic: The if-else node enables complex routing and content negotiation
Custom Nodes: Specialized nodes like postgres-query and query-generator encapsulate specific functionality
AI Integration: Natural language processing enhances the user experience
Database Access: Direct database interaction without additional middleware

This example serves as a template for building your own database-driven applications with Nanoservice-ts.

Introduction

Examples

Fundamentals

CLI

Deployment

Database Smart Query (DB Manager)

Features

Prerequisites

Environment Setup

1. Set Up Your Nanoservice-ts Project

2. Configure Environment Variables

3. Set Up the Required Infrastructure

PostgreSQL Database

Metrics Infrastructure (Optional but Recommended)

4. Verify the Setup

Workflow Structure

UI Rendering with If-Else Node

How UI Rendering Works in Nanoservice-ts

The If-Else Node Structure

The Database-UI Node

The Complete UI Rendering Flow

API Endpoints in the Same Workflow

Benefits of This Approach

Implementing Your Own UI Rendering

The Postgres-Query Node

Custom Node Implementations

1. QueryGeneratorNode

2. MapperNode

Running the Example

Troubleshooting

Conclusion

Introduction

Examples

Fundamentals

CLI

Deployment

​Features

​Prerequisites

​Environment Setup

​1. Set Up Your Nanoservice-ts Project

​2. Configure Environment Variables

​3. Set Up the Required Infrastructure

​PostgreSQL Database

​Metrics Infrastructure (Optional but Recommended)

​4. Verify the Setup

​Workflow Structure

​UI Rendering with If-Else Node

​How UI Rendering Works in Nanoservice-ts

​The If-Else Node Structure

​The Database-UI Node

​The Complete UI Rendering Flow

​API Endpoints in the Same Workflow

​Benefits of This Approach

​Implementing Your Own UI Rendering

​The Postgres-Query Node

​Custom Node Implementations

​1. QueryGeneratorNode

​2. MapperNode

​Running the Example

​Troubleshooting

​Conclusion

Features

Prerequisites

Environment Setup

1. Set Up Your Nanoservice-ts Project

2. Configure Environment Variables

3. Set Up the Required Infrastructure

PostgreSQL Database

Metrics Infrastructure (Optional but Recommended)

4. Verify the Setup

Workflow Structure

UI Rendering with If-Else Node

How UI Rendering Works in Nanoservice-ts

The If-Else Node Structure

The Database-UI Node

The Complete UI Rendering Flow

API Endpoints in the Same Workflow

Benefits of This Approach

Implementing Your Own UI Rendering

The Postgres-Query Node

Custom Node Implementations

1. QueryGeneratorNode

2. MapperNode

Running the Example

Troubleshooting

Conclusion