Supported Backend Frameworks for LLM Generation in exercises-dataset: 6 Frameworks Explained

The exercises-dataset repository supports six backend frameworks for LLM generation: Express.js (Node.js), FastAPI (Python), ASP.NET Core (C#), Spring Boot (Java), Laravel (PHP), and Gin (Go).

When building REST APIs from the exercises dataset using Large Language Models, selecting the right backend framework determines your language ecosystem, package dependencies, and deployment workflow. The repository's Ask Your LLM feature in setup.html automates prompt generation with framework-specific metadata, enabling any LLM to produce runnable code tailored to your stack.

Where Framework Support Is Defined

Framework specifications live in the setup.html file as a JavaScript object called FRAMEWORK_META. Located at lines 78-86, this object maps framework identifiers to their configuration:

// From setup.html#L78-L86 - simplified structure
const FRAMEWORK_META = {
  express: {
    name: "Express.js",
    lang: "JavaScript",
    pkg: "express, pg / mysql2 / better-sqlite3",
    run: "node index.js"
  },
  fastapi: {
    name: "FastAPI", 
    lang: "Python",
    pkg: "fastapi, uvicorn, sqlalchemy, psycopg2-binary",
    run: "uvicorn main:app --reload"
  }
  // ... additional frameworks
};

This metadata feeds directly into the buildLlmPrompt() function (lines 95-112), which constructs the complete prompt sent to your LLM.

Complete Framework Reference Table

Framework	Language	Required Packages	Run Command
Express.js	JavaScript (Node.js)	express, pg / mysql2 / better-sqlite3	node index.js
FastAPI	Python	fastapi, uvicorn, sqlalchemy, psycopg2-binary	uvicorn main:app --reload
ASP.NET Core	C#	Npgsql / MySql.Data / Microsoft.Data.Sqlite	dotnet run

| Spring Boot | Java | spring-web, spring-data-jpa, database driver | mvn spring-boot:run | | Laravel | PHP | laravel/laravel, database driver | php artisan serve | | Gin | Go | gin-gonic/gin, database/sql + driver | go run main.go |

Database drivers adapt automatically based on your selected database engine (PostgreSQL, MySQL, or SQLite).

How LLM Prompt Generation Works

The supported backend frameworks for LLM generation integrate into a unified pipeline. When you select a framework and database in the UI, buildLlmPrompt() assembles:

• Dataset description: 1,324 exercises with multilingual fields and media paths
• SQL schema: Generated from the DB_SQL object for your chosen database
• Required endpoints: GET /exercises/:id, GET /exercises, plus filtering and pagination
• Technical requirements: Environment-variable DB connections, parameterized queries, CORS, validation, error handling, logging
• Framework packages: Inserted via ${fw.pkg} template substitution

JavaScript: Generating the Prompt

// From setup.html - selecting framework and building prompt
const fwKey = 'express';      // or 'fastapi', 'aspnet', 'spring', 'laravel', 'gin'
const dbKey = 'postgresql';   // or 'mysql', 'sqlite'

const prompt = buildLlmPrompt(fwKey, dbKey);
console.log(prompt);          // Copy this to ChatGPT, Claude, or Gemini

Python: Calling the LLM with Generated Prompt

import openai

# The prompt variable contains the framework-specific instructions

response = openai.ChatCompletion.create(
    model="gpt-4o",
    messages=[{"role": "user", "content": prompt}],
)
generated_code = response.choices[0].message.content
print(generated_code)

Go: Accessing Framework Metadata Directly

package main

import "fmt"

type Framework struct {
    Name string
    Lang string
    Pkg  string
    Run  string
}

var FRAMEWORK_META = map[string]Framework{
    "gin": {
        Name: "Gin (Go)",
        Lang: "Go",
        Pkg:  "gin-gonic/gin, database/sql + db driver",
        Run:  "go run main.go",
    },
    // Additional frameworks...
}

func main() {
    fw := FRAMEWORK_META["gin"]
    fmt.Printf("Framework: %s, Language: %s\n", fw.Name, fw.Lang)
    fmt.Printf("Packages: %s\nRun: %s\n", fw.Pkg, fw.Run)
}

Framework Selection Criteria

Choose your supported backend framework for LLM generation based on:

• Team expertise: Match your existing language proficiency

• Deployment target: Node.js and Python suit serverless; Java and C# excel in enterprise containers

• Performance needs: Go (Gin) and Rust-adjacent compiled languages offer lowest latency

• Ecosystem maturity: Express.js and Laravel provide extensive middleware ecosystems

The repository's prompt engineering ensures generated code follows idiomatic patterns for each framework regardless of your choice.

Key Files Supporting LLM Framework Generation

File	Path	Purpose
setup.html	/main/setup.html	Contains FRAMEWORK_META, DB_SQL, API_TEMPLATES, and buildLlmPrompt()
exercises.json	/data/exercises.json	Source dataset (1,324 records) for API generation
exercises.schema.json	/data/exercises.schema.json	Validation schema for generated backend models
README.md	/README.md	Usage documentation for the Ask Your LLM workflow

Summary

• Six frameworks are supported for LLM-generated backends: Express.js, FastAPI, ASP.NET Core, Spring Boot, Laravel, and Gin
• Framework metadata is centralized in setup.html inside the FRAMEWORK_META object (lines 78-86)
• The buildLlmPrompt() function (lines 95-112) constructs framework-specific prompts automatically
• Generated prompts include package lists, run commands, and technical requirements tailored to each stack
• Database drivers adapt per framework to support PostgreSQL, MySQL, and SQLite

Frequently Asked Questions

Which backend framework produces the smallest Docker image for LLM-generated APIs?

Gin (Go) typically yields the smallest container images due to Go's static compilation and minimal runtime dependencies. A compiled Gin binary often stays under 20 MB, compared to 100+ MB for Node.js or Python bases. However, Express.js and FastAPI images can be optimized using distroless or Alpine bases if Go isn't in your stack.

Can I modify the framework metadata to add unsupported frameworks?

Yes. Edit the FRAMEWORK_META object in setup.html following the existing structure: provide name, lang, pkg, and run properties. You must also extend API_TEMPLATES with endpoint patterns matching your new framework's conventions. The prompt builder will automatically incorporate your additions.

Does the LLM generate identical API structures across all frameworks?

The endpoint semantics remain identical (same URL patterns, response shapes, and query parameters), but implementation details follow each framework's idioms. Express.js uses middleware chains; FastAPI leverages Pydantic models and async handlers; Spring Boot generates annotation-driven controllers. The API_TEMPLATES object in setup.html encodes these structural variations.

Which database engines work with each backend framework?

All six frameworks support PostgreSQL, MySQL, and SQLite. The DB_SQL object in setup.html generates dialect-specific schema definitions, and FRAMEWORK_META.pkg includes the appropriate driver packages for each combination. Switching databases requires only changing the dbKey parameter in buildLlmPrompt()—no manual code changes needed.

{
  "mcpServers": {
    "instagit": {
      "command": "npx",
      "args": ["-y", "instagit@latest"]
    }
  }
}