import { pipeline, env } from '@xenova/transformers';

// ==============================|| Embeddings ||============================== //
//
// all-MiniLM-L6-v2 running IN PROCESS. No API key, no network call per query,
// no per-call cost.
//
// That is not an incidental choice. assistant/CLAUDE.md §2 blocked LLM work on
// exactly one ground: a hosted model needs a secret key and a static CRA build
// has nowhere to put one. A local embedding model has no key, so this plan is
// outside that blocker. Swapping to a hosted embedding model (text-embedding-3
// -small et al) re-opens §2 and needs its own decision — do not do it quietly.

export const MODEL_ID = 'Xenova/all-MiniLM-L6-v2';
export const DIMENSIONS = 384;

// Weights are cached on disk (mounted in docker-compose) so a container
// restart doesn't re-download 25MB.
env.cacheDir = process.env.TRANSFORMERS_CACHE || './.cache';
// Nothing here should reach the network except the one-time model fetch.
env.allowRemoteModels = true;

let extractor = null;
let loading = null;

// Loaded once, lazily, and shared. Concurrent callers await the same promise
// rather than each triggering their own model load.
const getExtractor = async () => {
  if (extractor) return extractor;
  if (!loading) {
    loading = pipeline('feature-extraction', MODEL_ID).then((p) => {
      extractor = p;
      return p;
    });
  }
  return loading;
};

export const warmUp = async () => {
  await getExtractor();
  return { model: MODEL_ID, dimensions: DIMENSIONS };
};

// Mean-pooled + L2-normalised sentence embedding.
//
// Normalisation matters: Chroma's cosine space assumes unit vectors, and
// queries must be embedded exactly the same way documents were. A mismatch
// doesn't error — it silently degrades every score, which is the worst kind of
// bug to chase. `seed` writes the model id into collection metadata so a
// mismatch is at least detectable.
export const embed = async (text) => {
  const pipe = await getExtractor();
  const output = await pipe(String(text || '').trim(), { pooling: 'mean', normalize: true });
  return Array.from(output.data);
};

export const embedMany = async (texts) => {
  const out = [];
  for (const t of texts) {
    // Sequential on purpose: batching MiniLM in-process gives no meaningful
    // speed-up at this corpus size (~700 vectors) and makes memory spikier.
    // eslint-disable-next-line no-await-in-loop
    out.push(await embed(t));
  }
  return out;
};