/**
 * WFDASH — Price List Match (v2a)
 *
 * Webhook-driven catalogue matcher. Called by the dashboard's
 * `/api/v1/quotes/[id]/items/[itemId]/match` route when the operator
 * hits the new "Help me find this" button on the process page.
 *
 * Inputs (POST body):
 *   - sku:          string (the buyer's SKU or partial code)
 *   - description:  string (free-text description from the operator)
 *   - pbUrl:        string (PocketBase base URL — passed in by Next.js
 *                   so the workflow stays portable across environments)
 *   - pbToken:      string (PocketBase admin JWT — Next.js refreshes
 *                   on every call)
 *   - limit:        number (default 4 — top-N to return)
 *
 * Behaviour:
 *   - Build a single combined PB filter that covers Tier 1 (exact SKU),
 *     Tier 2 (SKU substring), AND Tier 3 (search_text fuzzy). Run it
 *     as one HTTP query. Downstream, classify each row by what it
 *     actually matched (Tier 1 > 2 > 3), sort, cap at `limit`.
 *
 *   - Combining tiers into one query is safe because `classifyMatch`
 *     inspects the row, not the query — it correctly identifies which
 *     tier each individual row matches. Single-query beats three-query
 *     (faster, one round-trip, no item multiplication risk).
 *
 * Output:
 *   - { candidates: Candidate[] }   (Candidate = same shape the
 *     dashboard's Picker modal already consumes)
 *
 * v2b swap-in: add an LLM agent node between HTTP Request and Rank +
 * Cap that re-ranks the raw candidates semantically (e.g. "turbo"
 * matching turbine-adjacent items even without an exact token hit).
 * The webhook contract stays identical.
 */
import { workflow, trigger, node } from '@n8n/workflow-sdk';

const matchWebhook = trigger({
  type: 'n8n-nodes-base.webhook',
  version: 2,
  config: {
    name: 'Match Webhook',
    parameters: {
      httpMethod: 'POST',
      path: 'wf-price-list-match',
      responseMode: 'lastNode',
      options: {},
    },
  },
});

const buildFilter = node({
  type: 'n8n-nodes-base.code',
  version: 2,
  config: {
    name: 'Build Filter',
    parameters: {
      jsCode: `// Parse webhook payload + build a single combined PB filter that
// covers Tier 1 + Tier 2 + Tier 3. Downstream classifies each row.
//
// Always output data (even on error / no-input) so the HTTP Request
// and Rank+Cap nodes run regardless — they handle the empty case.

const src = ($json.body && typeof $json.body === 'object') ? $json.body : $json;

const sku = String(src.sku || '').trim();
const description = String(src.description || '').trim();
const pbUrl = String(src.pbUrl || '').replace(/\\/$/, '');
const pbToken = String(src.pbToken || '');
const limit = Math.max(1, Math.min(20, Number(src.limit) || 4));

function escapeForPbString(value) {
  return String(value || '').replace(/"/g, '\\\\"');
}

function normaliseSku(raw) {
  if (!raw) return '';
  return raw.toUpperCase().replace(/[^A-Z0-9]/g, '');
}

// Build the URL. When there's no usable input OR no PB creds, point
// at a known-empty PB filter so downstream nodes still execute.
let url;
if (!pbUrl) {
  url = 'about:blank';
} else if (!sku && !description) {
  // No input — point at an always-empty filter so the query returns
  // 0 items. PB accepts \`filter=id="__none__"\` cleanly.
  url = pbUrl + '/api/collections/price_list/records?page=1&perPage=1&filter=' + encodeURIComponent('id="__none__"');
} else {
  const normalised = normaliseSku(sku);
  const parts = [];
  if (sku) {
    // Tier 1 — exact equality on raw + canonical form.
    parts.push('sku = "' + escapeForPbString(sku) + '"');
    parts.push('sku_normalised = "' + escapeForPbString(normalised) + '"');
    // Tier 2 — substring on canonical + raw form.
    parts.push('sku_normalised ~ "' + escapeForPbString(normalised) + '"');
    parts.push('sku ~ "' + escapeForPbString(sku) + '"');
  }
  // Tier 3 — fuzzy search_text (sku + description combined so we hit
  // both brand-only and description-only entries).
  const searchQuery = (sku + ' ' + description).trim();
  if (searchQuery) {
    parts.push('search_text ~ "' + escapeForPbString(searchQuery) + '"');
  }
  const filter = '(' + parts.join(' || ') + ')';
  const fields = 'id,sku,description,cost_price,selling_price,unit,supplier,brand,category,subcategory,sku_normalised,updated,specifications,search_text';
  url = pbUrl + '/api/collections/price_list/records?page=1&perPage=30'
    + '&fields=' + encodeURIComponent(fields)
    + '&filter=' + encodeURIComponent(filter);
}

return [{ json: { url, pbToken, sku, description, limit } }];`,
    },
  },
});

const queryPb = node({
  type: 'n8n-nodes-base.httpRequest',
  version: 4.4,
  config: {
    name: 'Query PB',
    parameters: {
      method: 'GET',
      url: '={{ $json.url }}',
      sendHeaders: true,
      headerParameters: {
        parameters: [
          {
            name: 'Authorization',
            value: '={{ $json.pbToken }}',
          },
        ],
      },
      options: {
        timeout: 10000,
        // Continue even on non-2xx so Rank+Cap can return a friendly
        // empty response instead of throwing.
        response: {
          response: {
            neverError: true,
          },
        },
      },
    },
  },
});

const rankAndCap = node({
  type: 'n8n-nodes-base.code',
  version: 2,
  config: {
    name: 'Rank and Cap',
    parameters: {
      jsCode: `// Read HTTP response + meta from Build Filter. Classify each row's
// tier (independently — a single combined query returned everything),
// generate the rule-based "why" line, sort by tier, cap at limit.
//
// On any failure here we still return \`{ candidates: [] }\` so the
// dashboard always gets a parseable response shape.

function normaliseSku(raw) {
  if (!raw) return '';
  return raw.toUpperCase().replace(/[^A-Z0-9]/g, '');
}

function classifyMatch(rawInput, match) {
  const raw = (rawInput || '').trim();
  const norm = normaliseSku(raw);
  if (match.sku === raw || (norm && match.sku_normalised === norm)) return 1;
  if ((norm && (match.sku_normalised || '').includes(norm)) || (raw && (match.sku || '').includes(raw))) return 2;
  return 3;
}

function explainMatch(rawInput, match, tier) {
  const raw = (rawInput || '').trim();
  const inputLower = raw.toLowerCase();
  const inputWords = inputLower.split(/\\s+/).filter((w) => w.length >= 2);
  const bits = [match.sku || '(no sku)'];
  if (tier === 1) bits.push('exact SKU match');
  else if (tier === 2) bits.push('SKU substring');
  if (tier === 3) {
    const detailBits = [];
    if (match.brand && inputWords.some((w) => match.brand.toLowerCase().includes(w))) {
      detailBits.push('brand: ' + match.brand);
    }
    if (match.category && inputWords.some((w) => match.category.toLowerCase().includes(w))) {
      detailBits.push('category: ' + match.category);
    }
    if (match.supplier && inputWords.some((w) => match.supplier.toLowerCase().includes(w))) {
      detailBits.push('supplier: ' + match.supplier);
    }
    if (match.description) {
      const descLower = match.description.toLowerCase();
      const matchedWords = inputWords.filter((w) => descLower.includes(w));
      if (matchedWords.length > 0) {
        detailBits.push('description matches "' + matchedWords.slice(0, 3).join(', ') + '"');
      }
    }
    if (match.specifications && typeof match.specifications === 'object') {
      for (const [k, v] of Object.entries(match.specifications)) {
        if (typeof v === 'string' && inputWords.some((w) => v.toLowerCase().includes(w))) {
          detailBits.push(k + ': ' + v);
          break;
        }
      }
    }
    if (detailBits.length === 0) detailBits.push('"' + raw + '" found in catalogue text');
    bits.push(detailBits.join('; '));
  }
  if (match.supplier) bits.push('from ' + match.supplier);
  return bits.slice(0, 4).join(' \\u2014 ').slice(0, 240);
}

try {
  const meta = ($('Build Filter').first().json) || {};
  const sku = String(meta.sku || '');
  const description = String(meta.description || '');
  const limit = Math.max(1, Math.min(20, Number(meta.limit) || 4));
  const classifyKey = sku || description;

  const items = (Array.isArray($json.items) ? $json.items : []);

  const enriched = items.map((c) => {
    const tier = classifyMatch(classifyKey, c);
    return {
      id: c.id,
      sku: c.sku || '',
      description: c.description || '',
      brand: c.brand || '',
      category: c.category || '',
      supplier: c.supplier || '',
      cost_price: Number(c.cost_price) || 0,
      selling_price: Number(c.selling_price) || 0,
      unit: c.unit || '',
      tier,
      why: explainMatch(classifyKey, c, tier),
    };
  });

  const tierRank = (t) => (t === 1 ? 0 : t === 2 ? 1 : 2);
  enriched.sort((a, b) => tierRank(a.tier) - tierRank(b.tier));
  const top = enriched.slice(0, limit);

  return [{ json: { candidates: top, query: { sku, description } } }];
} catch (err) {
  return [{ json: { candidates: [], error: err && err.message ? err.message : String(err) } }];
}
`,
    },
  },
});

export default workflow('wf-price-list-match', 'WFDASH — Price List Match')
  .add(matchWebhook)
  .to(buildFilter)
  .to(queryPb)
  .to(rankAndCap);