import { pipeline, matmul } from "@huggingface/transformers";

// Create a feature extraction pipeline
const extractor = await pipeline("feature-extraction", "onnx-community/Qwen3-Embedding-0.6B-ONNX", {
    dtype: "fp32", // Options: "fp32", "fp16", "q8"
    // device: "webgpu",
});


function get_detailed_instruct(task_description, query) {
    return `Instruct: ${task_description}\nQuery:${query}`;
}

// Each query must come with a one-sentence instruction that describes the task
const task = 'Given a web search query, retrieve relevant passages that answer the query'

const queries = [
    get_detailed_instruct(task, 'What is the capital of China?'),
    get_detailed_instruct(task, 'Explain gravity')
]
// No need to add instruction for retrieval documents
const documents = [
    "The capital of China is Beijing.",
    "Gravity is a force that attracts two bodies towards each other. It gives weight to physical objects and is responsible for the movement of planets around the sun."
]
const input_texts = [...queries, ...documents]

const output = await extractor(input_texts, { pooling: "last_token", normalize: true });
const scores = await matmul(
    output.slice([0, queries.length]), // Query embeddings
    output.slice([queries.length, null]).transpose(1, 0), // Document embeddings
)
console.log(scores.tolist());
// [
//   [ 0.7645590305328369, 0.14142560958862305 ],
//   [ 0.13549776375293732, 0.599955141544342 ]
// ]