/* RAG Document Assistant — retrieval engine (client-side port).
 *
 * JavaScript port of the Python `rag.py` TF-IDF path: chunk -> embed (TF-IDF)
 * -> cosine retrieve -> grounded answer with citations, and an honest
 * "I don't know" below a similarity threshold. Runs entirely in the browser,
 * so the Hugging Face Static Space is free and needs no key or server.
 *
 * Source: https://github.com/furqunali  (full Python version adds neural
 * sentence-transformers embeddings + optional LLM generation).
 */
(function (global) {
  "use strict";

  function chunkText(text, source, chunkSize, overlap) {
    chunkSize = chunkSize || 600; overlap = overlap == null ? 100 : overlap;
    text = String(text || "").replace(/\s+/g, " ").trim();
    if (!text) return [];
    const sentences = text.split(/(?<=[.!?])\s+/);
    const chunks = []; let buf = "", idx = 0;
    for (const sent of sentences) {
      if (buf.length + sent.length + 1 <= chunkSize || !buf) {
        buf = (buf + " " + sent).trim();
      } else {
        chunks.push({ text: buf, source, index: idx++ });
        const tail = overlap ? buf.slice(-overlap) : "";
        buf = (tail + " " + sent).trim();
      }
    }
    if (buf) chunks.push({ text: buf, source, index: idx });
    return chunks;
  }

  const tokenize = (s) => (String(s || "").toLowerCase().match(/[a-z0-9]+/g) || []);

  function TfidfEmbedder() { this.vocab = {}; this.idf = null; }
  TfidfEmbedder.prototype.fit = function (corpus) {
    const df = {};
    corpus.forEach((d) => { new Set(tokenize(d)).forEach((t) => df[t] = (df[t] || 0) + 1); });
    const terms = Object.keys(df).sort();
    this.vocab = {}; terms.forEach((t, i) => this.vocab[t] = i);
    const n = corpus.length;
    this.idf = terms.map((t) => Math.log((1 + n) / (1 + df[t])) + 1.0);
    return this;
  };
  TfidfEmbedder.prototype.embed = function (texts) {
    const V = this.idf.length;
    return texts.map((text) => {
      const vec = new Float64Array(V), toks = tokenize(text), counts = {};
      toks.forEach((t) => { const j = this.vocab[t]; if (j !== undefined) counts[j] = (counts[j] || 0) + 1; });
      if (toks.length) for (const j in counts) vec[j] = (counts[j] / toks.length) * this.idf[j];
      return vec;
    });
  };

  function cosine(a, b) {
    let dot = 0, na = 0, nb = 0;
    for (let i = 0; i < a.length; i++) { dot += a[i] * b[i]; na += a[i] * a[i]; nb += b[i] * b[i]; }
    return dot / (Math.sqrt(na) * Math.sqrt(nb) + 1e-9);
  }

  function Retriever(chunks, embedder) {
    this.chunks = chunks; this.embedder = embedder;
    this.matrix = chunks.length ? embedder.embed(chunks.map((c) => c.text)) : [];
  }
  Retriever.prototype.query = function (question, k) {
    k = k || 4;
    if (!this.chunks.length) return [];
    const q = this.embedder.embed([question])[0];
    const scored = this.matrix.map((v, i) => ({ chunk: this.chunks[i], score: cosine(v, q) }));
    scored.sort((a, b) => b.score - a.score);
    return scored.slice(0, k);
  };

  const IDK = "I don't have enough information in the provided documents to answer that. " +
    "Try rephrasing, or add a document that covers this topic.";

  function answer(question, hits, threshold) {
    threshold = threshold == null ? 0.15 : threshold;
    const top = hits.length ? hits[0].score : 0.0;
    if (!hits.length || top < threshold)
      return { answer: IDK, grounded: false, citations: [], top_score: round(top) };
    const citations = hits.map((h) => ({
      source: h.chunk.source, chunk: h.chunk.index, score: round(h.score),
      preview: h.chunk.text.slice(0, 200) + (h.chunk.text.length > 200 ? "…" : ""),
    }));
    return { answer: hits[0].chunk.text, grounded: true, mode: "extractive",
      citations, top_score: round(top) };
  }
  const round = (x) => Math.round(x * 1000) / 1000;

  function buildRetriever(namedTexts) {
    let chunks = [];
    namedTexts.forEach(([name, text]) => { chunks = chunks.concat(chunkText(text, name)); });
    if (!chunks.length) return null;
    const emb = new TfidfEmbedder().fit(chunks.map((c) => c.text));
    return new Retriever(chunks, emb);
  }

  const SAMPLE_DOCS = [["company_handbook.md", `# Acme Demo Co. Customer Handbook (Fictional Sample)

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## Data Privacy
We store only the account information necessary to provide the service. Customer data is never sold to third parties. You can request deletion of your account and associated data at any time by contacting support.`]];

  const API = { chunkText, TfidfEmbedder, cosine, Retriever, answer, buildRetriever, SAMPLE_DOCS };
  if (typeof module !== "undefined" && module.exports) module.exports = API;
  else global.RAG = API;
})(typeof window !== "undefined" ? window : globalThis);