GEO Optimizer: Make Your Content Visible to AI Engines

A pluggable framework for GEO (Generative Engine Optimization) in Go. Built-in strategies for common use cases, with full support for custom strategy registration.

Why this framework?

• 🧩 Pluggable Architecture - Register your own optimization strategies or use built-in ones
• 📦 5 Built-in Strategies - Structure, Schema, AnswerFirst, Authority, FAQ ready to use
• 🔌 Easy Extension - Implement the Strategy interface to add custom logic
• 🎯 Composable - Mix and match strategies for different optimization needs

Features

• Multi-Strategy Optimization: 5 built-in optimization strategies that can be combined
• LLM Abstraction: Clean interface supporting multiple LLM providers (GLM, extensible to others)
• Content Scoring: Rule-based GEO quality scoring system
• AI Platform Presets: Pre-configured preferences for ChatGPT, Perplexity, Google AI, and Claude
• Schema Markup: Automatic JSON-LD structured data generation
• Enterprise Ready: Multi-tenant architecture with enterprise isolation

Installation

go get github.com/Lin-Jiong-HDU/geo-optimizer

Quick Start

package main

import (
    "context"
    "fmt"
    "log"

    "github.com/Lin-Jiong-HDU/geo-optimizer/pkg/llm"
    "github.com/Lin-Jiong-HDU/geo-optimizer/pkg/models"
    "github.com/Lin-Jiong-HDU/geo-optimizer/pkg/optimizer"
)

func main() {
    // 1. Create LLM client
    client, err := llm.NewClient(llm.Config{
        Provider: llm.ProviderGLM,
        APIKey:   "your-api-key",
        Model:    "glm-4.7",
    })
    if err != nil {
        log.Fatal(err)
    }

    // 2. Create optimizer
    opt := optimizer.New(client)

    // 3. Build request
    req := &models.OptimizationRequest{
        Content: "Your content here...",
        Title:   "Content Title",
        Enterprise: models.EnterpriseInfo{
            CompanyName: "Your Company",
            ProductName: "Your Product",
        },
        Strategies: []models.StrategyType{
            models.StrategyStructure,
            models.StrategySchema,
        },
    }

    // 4. Execute optimization
    resp, err := opt.Optimize(context.Background(), req)
    if err != nil {
        log.Fatal(err)
    }

    // 5. Use results
    fmt.Printf("Score: %.2f -> %.2f\n", resp.ScoreBefore, resp.ScoreAfter)
    fmt.Println(resp.OptimizedContent)
}

Optimization Strategies

Built-in Strategies

Register Custom Strategies

The framework is designed for extensibility. Implement the Strategy interface to add your own optimization logic:

import strategiespkg "github.com/Lin-Jiong-HDU/geo-optimizer/pkg/optimizer/strategies"

// Define your custom strategy
type SEOStrategy struct {
    *strategiespkg.BaseStrategy
    keywords []string
}

func NewSEOStrategy(keywords []string) *SEOStrategy {
    return &SEOStrategy{
        BaseStrategy: strategiespkg.NewBaseStrategy("seo", "SEO Optimization"),
        keywords:     keywords,
    }
}

// Implement the Strategy interface
func (s *SEOStrategy) Validate(req *models.OptimizationRequest) bool {
    return len(s.keywords) > 0
}

func (s *SEOStrategy) BuildPrompt(req *models.OptimizationRequest) string {
    return fmt.Sprintf("Optimize for keywords: %v\n\n%s", s.keywords, req.Content)
}

// Register and use your strategy
func main() {
    opt := optimizer.New(client)

    // Register custom strategy
    opt.RegisterStrategy(NewSEOStrategy([]string{"cloud", "AI", "optimization"}))

    // Use it alongside built-in strategies
    req := &models.OptimizationRequest{
        Content:    "...",
        Strategies: []models.StrategyType{"seo", models.StrategyStructure},
    }

    resp, _ := opt.Optimize(ctx, req)
}

Strategy Interface

type Strategy interface {
    Name() string
    Type() models.StrategyType
    Validate(req *models.OptimizationRequest) bool
    Preprocess(content string, req *models.OptimizationRequest) string
    Postprocess(content string, req *models.OptimizationRequest) string
    BuildPrompt(req *models.OptimizationRequest) string
}

Architecture

pkg/
├── optimizer/         # Core optimization engine
│   └── strategies/    # Individual strategy implementations
├── llm/               # LLM abstraction layer
│   └── prompts/       # Prompt templates and builders
├── models/            # Data models (Request/Response)
├── analyzer/          # Content analysis and scoring
└── config/            # Configuration and AI platform presets

E2E Testing

An end-to-end testing CLI tool is available in examples/e2e/ for validating the complete optimization workflow.

cd examples/e2e

# Configure your GLM API key
cp .env.example .env
# Edit .env and add your GLM_API_KEY

# Run all tests
go run .

# Run specific scenario
go run . --scenario=basic
go run . --scenario=full_flow

# Verbose output
go run . --verbose

The E2E tests validate:

• Basic Functionality: LLM chat, content scoring, single strategy optimization
• Full Business Flow: Complete optimization pipeline with realistic enterprise data

Test reports are generated in examples/e2e/output/ with detailed Markdown summaries.

Documentation

See API.md for detailed API documentation.

Requirements

• Go 1.21+
• LLM API access (currently supports GLM)

License

This project is licensed under the MIT License - see the LICENSE file for details.

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.