AI

The AI - Chat Message node enables conversations with large language models (LLMs) from multiple providers including OpenAI, Anthropic, Google AI, and Groq. It supports dynamic message templates with variable interpolation, streaming responses, extended thinking, and AI actions (tool use).

The AI - Compute Embedding node converts text into high-dimensional vector representations (embeddings) that capture semantic meaning. These embeddings can be used for semantic search, similarity comparison, and vector context operations.

The AI - Data Extraction node uses large language models to extract structured data from unstructured text content. It can extract data matching a defined schema, optionally creating Records automatically upon extraction.

The AI - Image Generate node creates images based on text prompts using AI image generation models. It returns the generated image as a file that can be used in Experiences or stored.

Canonical model list for AI nodes, including deprecated model fallback mappings.

The AI - Router node evaluates input variables and configured route options, then chooses a single option to continue the run. Each option can optionally define a structured output schema for AI-generated data when that option is selected.

The AI - Text to Speech node generates realistic spoken audio from text input using OpenAI's text-to-speech models. It supports multiple voice options and quality levels, returning an audio file that can be used in Experiences or stored.

The AI - Transcribe node converts spoken audio into text using OpenAI's Whisper model. It supports various audio formats and can handle multiple languages.

The AI - Vector Context Add node stores text embeddings in a vector context, enabling later semantic search operations. It automatically computes embeddings and indexes them with optional labels for filtering.

The AI - Vector Context Query node performs semantic search over stored embeddings in a vector context. It returns the most similar content based on embedding distance, with optional filtering by labels.

The AI - Vector Context Remove node deletes embeddings from a vector context. It can remove specific embeddings by ID or remove all embeddings matching certain label criteria.