TramAI

tramai-embedding

Version: 0.3.1
Status: Stable
Role: Text embedding generation.

Purpose

The tramai-embedding module converts raw text into numerical vector arrays (embeddings). These embeddings power semantic search and clustering, forming the translation layer between plain text and the tramai-vectorstore-spi modules.

Core Concepts

EmbeddingModel

The core SPI interface. Translates lists of strings into lists of vectors (e.g. List<FloatArray>).

This module bundles standard integrations natively:

  • OpenAI Embeddings (text-embedding-3-small, text-embedding-ada-002)
  • Ollama Embeddings (Local models like nomic-embed-text)

Dependencies

// build.gradle.kts
dependencies {
    implementation("dev.tramai:tramai-embedding:0.3.1")
}

Quick Start

import dev.tramai.embedding.openai.OpenAiEmbeddingModel

val model = OpenAiEmbeddingModel(apiKey = "sk-...")

// Generate vectors
val embeddings = model.embedAll(listOf(
    "What is the capital of France?",
    "How do I sort an array in Kotlin?"
))

println("Vector dimensions: ${embeddings[0].size}")

When to use this module

  • You are configuring a RAG pipeline.
  • You need to calculate document similarity, cluster text, or perform semantic search.

When NOT to use this module

  • You are strictly generating text (chat/completions) without any vector search components.
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