At the heart of Inworld Runtime are primitives: foundational components like Large Language Models (LLMs), Speech-to-Text (STT), Text-to-Speech (TTS), and Text Embedders. These are the “raw ingredients” of any AI-powered application.Think of them as a library of high-performance AI modules, designed to abstract away the complexities of working with various providers, models, and hardware - allowing you to build on a consistent, provider-agnostic foundation. These primitives are then used within Inworld’s graph system to power various nodes.Each primitive type can be configured through Unreal Engine’s Project Settings interface, allowing you to:
Configure multiple named instances of each primitive type (LLM, STT, TTS, Text Embedder)
Set up provider-specific configurations (Inworld, OpenAI, Anthropic, Google, etc.)
Define reusable configurations that can be referenced by nodes in your graphs
Easily access and modify your primitives across your project
All service providers and models listed under Chat Completion are supported. If a model or service provider is not available in the Remote LLM Creation Config dropdown, you can add additional options under the LLM section of the Inworld Runtime Settings.
In the Remote LLMProviders list, click the + button to add any additional service providers you want to use.
In the Remote LLMModels list, click the + button to add any additional models you want to use.
Text Embedders convert text into numerical vectors for semantic operations, and powers features like intent detection and knowledge retrieval. To configure embedders:
In the Text Embedder Creation Config map, click the + button to add a new entry
Set a descriptive name for this configuration. This name will be used for selecting this configuration in your graph.
Select either Local or Remote for the Compute Host
Remote: this means that the models will be run by cloud servers.
Provider: Select from available providers
Model: Choose the embedding model
Local: this means that the models will run locally
Local Compute Host: Choose CPU or GPU
Model: Path to the local model.
Your configuration will now be available for selection in embeddings powered nodes throughout your graphs.