AI Dev Kit
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  • Introduction
    • AI DevKit 3.7.0
    • Troubleshooting
    • FAQ
    • Update Logs
  • Provider Setup
    • OpenAI
    • Google Gemini
    • ElevenLabs
    • Ollama
    • OpenRouter
  • Editor Tools
    • Introduction
    • Editor Chat
    • Model Library
    • Voice Library
  • GEN Tasks
    • Introduction - Prefixes
    • Text/Content Generation
      • Structured Outputs (JSON Mode)
    • Chat Session
    • Image Generation
    • Video Generation
    • Sound FX Generation
    • Text to Speech (TTS)
    • Speech to Text (STT)
    • Voice Change
    • Audio Isolation
  • Advanced APIs (Pro)
    • Assistants API
      • How it works
      • Creating custom functions
      • Creating assistants API
    • Realtime API
  • Legacy API
    • OpenAI
      • 💬Chat completions
      • 🖼️Image operations
      • 🗣️Text to speech
      • 🎙️Speech to text
        • Recording real-time in Unity
      • 💾Files
      • 🔎Embeddings
      • 🛡️Moderations
      • ⚙️Fine-tuning
    • Google Gemini
      • 📝System instructions
      • 💬Text generation
      • ⚙️Fine-tuning
      • ▶️Fucntion calling
      • 🔎Embeddings
      • 🛡️Safety
      • 💻Code execution
  • Legacy Documents
    • AI DevKit 1.0 - 2.0
      • AI DevKit 2.0
      • AI DevKit 1.0
      • Preperation
      • Event Handlers
      • Scriptable Toolkits
        • Chat Streamer
        • Image Generator
        • Voice Transcriber
        • Voice Generator
      • Editor Tools
      • Troubleshooting (Legacy)
        • ❗Build Error: The name 'UnityMenu' does not exist in the current context
        • ❗The type or namespace name 'AndroidJavaObject' could not be found
        • ❗The type or namaspace name 'Plastic' does not exist
        • ❗Build Error: The name 'Asset Database' does not exist in the current context
        • ❗'ModelData.Create(Provider, string, UnixTime?, string)': not all code paths return a value
      • Code Generators
        • C# Script Generator
        • Unity Component Generator
      • Generation Menu
      • Editor Chat
      • Editor Vision (TTI, ITI)
      • Editor Speech (TTS)
      • Management Tools
        • Prompt History Viewer
        • AI Model Manager
        • TTS Voice Manager
        • OpenAI File Manager
        • OpenAI Assistant Manager
        • ElevenLabs Voice Library
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  • Embeddings Operations Overview:
  • Sample Code for Embeddings Requests:
  1. Legacy API
  2. OpenAI

Embeddings

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Last updated 10 months ago

Integrating OpenAI's Embeddings into your Unity project allows you to tap into advanced machine learning capabilities for understanding and processing natural language data. These embeddings transform text into high-dimensional vectors, enabling a wide range of applications like semantic search, text clustering, and more sophisticated natural language understanding tasks.

For a comprehensive understanding of the Embeddings API, including available models, parameter options, and usage scenarios, please refer to the .

Embeddings Operations Overview:

  • Semantic Text Analysis: Convert text into vector space to understand its underlying meaning and context.

  • Text Similarity and Clustering: Compare and cluster text data based on semantic similarity, ideal for organizing large datasets or creating recommendation systems.

Sample Code for Embeddings Requests:

To use embeddings in your project, you'll need to create an EmbeddingRequest and execute it asynchronously. Here's how you can do it:

Semantic Analysis Request:

Generate embeddings for given text to analyze its semantics. You need to specify the text and choose an appropriate model.

var request = new EmbeddingRequest.Builder()
    .SetModel(EmbeddingModel.TextEmbeddingAda002)
    .SetInput("The food was delicious and the waiter...")
    .SetEncodingFormat(EncodingFormat.Float)
    .Build();

var result = await request.ExecuteAsync();

float[] embeddings = result.EmbeddingVector;
from openai import OpenAI
client = OpenAI()

client.embeddings.create(
  model="text-embedding-ada-002",
  input="The food was delicious and the waiter...",
  encoding_format="float"
)

This request will return a high-dimensional vector representation of your text, which you can then use for further analysis or comparison against other vectors.

By integrating Embeddings into your Unity applications, you unlock powerful capabilities for advanced text analysis and interpretation, enhancing the intelligence and interactivity of your projects.

🔎
Embeddings API Reference