Text Embedding

Generate vector embeddings for single text inputs using .GENEmbed().

Basic Usage

float[] embedding = await "Hello, world!"
    .GENEmbed()
    .ExecuteAsync();

Debug.Log($"Embedding dimensions: {embedding.Length}");

Configuration

Model Selection

// OpenAI - Small (1536 dimensions, faster)
float[] embedding = await "Search query"
    .GENEmbed()
    .SetModel(OpenAIModel.TextEmbedding3Small)
    .ExecuteAsync();

// OpenAI - Large (3072 dimensions, more accurate)
float[] embedding = await "Search query"
    .GENEmbed()
    .SetModel(OpenAIModel.TextEmbedding3Large)
    .ExecuteAsync();

// Google
float[] embedding = await "Search query"
    .GENEmbed()
    .SetModel(GoogleModel.TextEmbedding004)
    .ExecuteAsync();

Unity Integration Examples

Example 1: Document Search Engine

public class DocumentSearchEngine : MonoBehaviour
{
    [System.Serializable]
    public class Document
    {
        public string id;
        public string content;
        public float[] embedding;
    }
    
    private List<Document> documents = new();
    
    public async UniTask IndexDocument(string id, string content)
    {
        float[] embedding = await content
            .GENEmbed()
            .SetModel(OpenAIModel.TextEmbedding3Small)
            .ExecuteAsync();
        
        documents.Add(new Document
        {
            id = id,
            content = content,
            embedding = embedding
        });
    }
    
    public async UniTask<List<Document>> Search(string query, int topK = 5)
    {
        float[] queryEmbedding = await query
            .GENEmbed()
            .SetModel(OpenAIModel.TextEmbedding3Small)
            .ExecuteAsync();
        
        var scores = documents
            .Select(doc => new
            {
                doc,
                score = CosineSimilarity(queryEmbedding, doc.embedding)
            })
            .OrderByDescending(x => x.score)
            .Take(topK)
            .Select(x => x.doc)
            .ToList();
        
        return scores;
    }
    
    float CosineSimilarity(float[] a, float[] b)
    {
        float dot = 0, magA = 0, magB = 0;
        for (int i = 0; i < a.Length; i++)
        {
            dot += a[i] * b[i];
            magA += a[i] * a[i];
            magB += b[i] * b[i];
        }
        return dot / (Mathf.Sqrt(magA) * Mathf.Sqrt(magB));
    }
}

Example 2: FAQ Matcher

public class FAQMatcher : MonoBehaviour
{
    [System.Serializable]
    public class FAQ
    {
        public string question;
        public string answer;
        public float[] embedding;
    }
    
    private List<FAQ> faqs = new();
    
    async void Start()
    {
        await LoadFAQs();
    }
    
    async UniTask LoadFAQs()
    {
        var faqData = new[]
        {
            ("How do I jump?", "Press the Space key to jump"),
            ("How do I save?", "Press Esc and click Save Game"),
            ("How do I pause?", "Press Esc to pause the game")
        };
        
        foreach (var (question, answer) in faqData)
        {
            float[] embedding = await question
                .GENEmbed()
                .ExecuteAsync();
            
            faqs.Add(new FAQ
            {
                question = question,
                answer = answer,
                embedding = embedding
            });
        }
    }
    
    public async UniTask<string> GetAnswer(string userQuestion)
    {
        float[] queryEmbed = await userQuestion
            .GENEmbed()
            .ExecuteAsync();
        
        FAQ bestMatch = null;
        float bestScore = float.MinValue;
        
        foreach (var faq in faqs)
        {
            float similarity = CosineSimilarity(queryEmbed, faq.embedding);
            if (similarity > bestScore)
            {
                bestScore = similarity;
                bestMatch = faq;
            }
        }
        
        return bestMatch?.answer ?? "Sorry, I don't understand.";
    }
}

Example 3: Content Deduplication

public class ContentDeduplicator : MonoBehaviour
{
    private const float SIMILARITY_THRESHOLD = 0.95f;
    
    public async UniTask<bool> IsDuplicate(string newContent, List<string> existingContent)
    {
        float[] newEmbed = await newContent.GENEmbed().ExecuteAsync();
        
        foreach (var existing in existingContent)
        {
            float[] existEmbed = await existing.GENEmbed().ExecuteAsync();
            float similarity = CosineSimilarity(newEmbed, existEmbed);
            
            if (similarity >= SIMILARITY_THRESHOLD)
                return true;
        }
        
        return false;
    }
}

Example 4: Smart Categorization

public class SmartCategorizer : MonoBehaviour
{
    private Dictionary<string, float[]> categories = new();
    
    async void Start()
    {
        // Define categories
        categories["Combat"] = await "Fighting, weapons, battles".GENEmbed().ExecuteAsync();
        categories["Exploration"] = await "Discovery, travel, adventure".GENEmbed().ExecuteAsync();
        categories["Puzzle"] = await "Logic, problem solving, thinking".GENEmbed().ExecuteAsync();
    }
    
    public async UniTask<string> Categorize(string content)
    {
        float[] contentEmbed = await content.GENEmbed().ExecuteAsync();
        
        string bestCategory = null;
        float bestScore = float.MinValue;
        
        foreach (var (category, embed) in categories)
        {
            float similarity = CosineSimilarity(contentEmbed, embed);
            if (similarity > bestScore)
            {
                bestScore = similarity;
                bestCategory = category;
            }
        }
        
        return bestCategory;
    }
}

Provider Support

OpenAI

// Small model (1536 dimensions)
float[] embedding = await text
    .GENEmbed()
    .SetModel(OpenAIModel.TextEmbedding3Small)
    .ExecuteAsync();

// Large model (3072 dimensions)
float[] embedding = await text
    .GENEmbed()
    .SetModel(OpenAIModel.TextEmbedding3Large)
    .ExecuteAsync();

Google

float[] embedding = await text
    .GENEmbed()
    .SetModel(GoogleModel.TextEmbedding004)
    .ExecuteAsync();

Similarity Calculation

Cosine Similarity

Most common method for comparing embeddings:

float CosineSimilarity(float[] a, float[] b)
{
    float dot = 0, magA = 0, magB = 0;
    
    for (int i = 0; i < a.Length; i++)
    {
        dot += a[i] * b[i];
        magA += a[i] * a[i];
        magB += b[i] * b[i];
    }
    
    return dot / (Mathf.Sqrt(magA) * Mathf.Sqrt(magB));
}

Returns value between -1 and 1:

• 1.0: Identical

• 0.5+: Similar

• 0.0: Unrelated

• -1.0: Opposite

Euclidean Distance

Alternative method:

float EuclideanDistance(float[] a, float[] b)
{
    float sum = 0;
    for (int i = 0; i < a.Length; i++)
    {
        float diff = a[i] - b[i];
        sum += diff * diff;
    }
    return Mathf.Sqrt(sum);
}

Lower distance = more similar.

Best Practices

✅ Good Practices

// ✅ Cache embeddings
Dictionary<string, float[]> cache = new();

// ✅ Use appropriate model
// Small for speed, Large for accuracy
await text.GENEmbed()
    .SetModel(OpenAIModel.TextEmbedding3Small)
    .ExecuteAsync();

// ✅ Normalize text before embedding
string normalized = text.ToLower().Trim();
float[] embed = await normalized.GENEmbed().ExecuteAsync();

// ✅ Batch when possible (see Batch Embedding)

❌ Bad Practices

// ❌ Don't embed in Update()
void Update()
{
    await text.GENEmbed().ExecuteAsync();  // NO!
}

// ❌ Don't generate unnecessarily
// Cache and reuse embeddings

// ❌ Don't compare embeddings from different models
// Always use same model for comparison

Use Cases

Use Case	Description
Semantic Search	Find relevant documents
FAQ Matching	Match questions to answers
Deduplication	Detect similar content
Categorization	Classify content
Recommendations	Suggest similar items
Clustering	Group related items

Performance Tips

// ✅ Good - cache embeddings
Dictionary<string, float[]> embedCache = new();

async UniTask<float[]> GetCachedEmbedding(string text)
{
    if (!embedCache.ContainsKey(text))
    {
        embedCache[text] = await text.GENEmbed().ExecuteAsync();
    }
    return embedCache[text];
}

// ✅ Good - use smaller model when appropriate
await text.GENEmbed()
    .SetModel(OpenAIModel.TextEmbedding3Small)  // Faster
    .ExecuteAsync();

Next Steps

• Batch Embedding - Process multiple texts