amber:ai is in the Stable scope and compiles in the default build. It does not need the ai Cargo feature, which is empty. Read the notes on LLM below before building on it.

const { Tensor, LLM, AgentPipeline, embed, cosineSimilarity } = require("amber:ai");

Tensor

Tensor wraps a Float32Array and a shape. Operations do not copy the backing buffer when they can avoid it.

const a = new Tensor([1, 2, 3, 4], [2, 2]);
const b = new Tensor([5, 6, 7, 8], [2, 2]);

a.matmul(b).toArray(); // [[19, 22], [43, 50]]

const v1 = new Tensor([1, 2, 3]);
const v2 = new Tensor([4, 5, 6]);
v1.dot(v2);              // 32
v1.norm();               // 3.7416...
v1.cosineSimilarity(v2); // 0.9746...
new Tensor([2, 1, 0.1]).softmax().toArray();

Embeddings

const vector = embed("hello world");          // Float32Array, 64 dims by default
const other = embed("hello there", { dimensions: 128 });

embed is a deterministic, hashed-token embedding computed in Rust. It is fast and reproducible, but it is not a learned semantic model. Use it for tests, deduplication, or rough lexical similarity. For semantic search, load a real embedding model and feed its output into Tensor.

LLM

const llm = await LLM.load("./models/model.gguf");
const result = await llm.generate("Summarize this file.");

for await (const chunk of llm.generateStream("Write a haiku.")) {
  process.stdout.write(chunk);
}

When LLM.load can open the model file, generation runs locally through Candle. The default build runs on the CPU. CUDA and Metal need a source build with the cuda or metal Cargo feature, and those features are not CI-gated.

Important: when the model cannot be loaded, LLM.load does not throw. It returns an LLM backed by a built-in template generator that produces placeholder text, and placeholder JSON when you pass a schema. That output is not model output. Check that your model path loads before you trust a result.

AgentPipeline

const pipeline = new AgentPipeline({
  model: llm,
  tools: [{ name: "echo", execute: (input) => input }],
});

const step = await pipeline.step("hello");
// { status: "completed", output: "...", historyLength: 2 }

AgentPipeline records history and holds registered tools. step() sends the input to model.generate() and stores the reply. It does not plan or call tools by itself; your code decides when to call tool.execute.

Not in scope

The getters weights, tools, kv, bus, grammar, and checkpoint on amber:ai point to modules that are not classified in the scope page. Treat them as Experimental.