n8n Enriquecimiento Normalizer Workflow

import requests
import json

API_KEY = "your_scavio_api_key"

def normalize_scavio(raw: dict, platform: str) -> list[dict]:
    """Normalize Scavio response to canonical enrichment schema."""
    entries = []
    for item in raw.get("organic", []):
        entry = {
            "title": str(item.get("title", "")),
            "url": str(item.get("link", "")),
            "description": str(item.get("snippet", "")),
            "position": int(item.get("position", 0)),
            "source": f"scavio_{platform}",
        }
        # Numeric fields with type coercion
        if item.get("price") is not None:
            entry["price"] = float(item["price"])
        if item.get("rating") is not None:
            entry["rating"] = float(item["rating"])
        if item.get("reviews") is not None:
            entry["review_count"] = int(item["reviews"])
        if item.get("views") is not None:
            entry["view_count"] = int(item["views"])
        if item.get("score") is not None:
            entry["engagement_score"] = int(item["score"])
        entries.append(entry)
    return entries

def normalize_generic(raw: dict) -> list[dict]:
    """Fallback normalizer for unknown API formats."""
    # Try common response patterns
    items = raw.get("results", raw.get("items", raw.get("data", [])))
    if not isinstance(items, list):
        return []
    return [{"title": str(item.get("title", item.get("name", ""))), "url": str(item.get("url", item.get("link", ""))), "description": str(item.get("description", item.get("snippet", ""))), "source": "unknown"} for item in items]

def enrich_and_normalize(query: str, platform: str) -> list[dict]:
    """Search and normalize in one step."""
    res = requests.post(
        "https://api.scavio.dev/api/v1/search",
        headers={"x-api-key": API_KEY},
        json={"platform": platform, "query": query},
        timeout=15,
    )
    res.raise_for_status()
    return normalize_scavio(res.json(), platform)

# Downstream code always receives same schema
google = enrich_and_normalize("CRM software", "google")
amazon = enrich_and_normalize("CRM book", "amazon")
reddit = enrich_and_normalize("CRM recommendation", "reddit")

for source_name, results in [("google", google), ("amazon", amazon), ("reddit", reddit)]:
    print(f"{source_name}: {len(results)} entries")
    if results:
        print(f"  Fields: {list(results[0].keys())}")

const API_KEY = "your_scavio_api_key";

function normalizeScavio(raw, platform) {
  return (raw.organic ?? []).map((item) => {
    const entry = {
      title: String(item.title ?? ""),
      url: String(item.link ?? ""),
      description: String(item.snippet ?? ""),
      position: Number(item.position ?? 0),
      source: `scavio_${platform}`,
    };
    if (item.price != null) entry.price = Number(item.price);
    if (item.rating != null) entry.rating = Number(item.rating);
    if (item.reviews != null) entry.reviewCount = Number(item.reviews);
    if (item.views != null) entry.viewCount = Number(item.views);
    if (item.score != null) entry.engagementScore = Number(item.score);
    return entry;
  });
}

async function enrichAndNormalize(query, platform) {
  const res = await fetch("https://api.scavio.dev/api/v1/search", {
    method: "POST",
    headers: { "x-api-key": API_KEY, "content-type": "application/json" },
    body: JSON.stringify({ platform, query }),
  });
  if (!res.ok) throw new Error(`scavio ${res.status}`);
  return normalizeScavio(await res.json(), platform);
}

const google = await enrichAndNormalize("CRM software", "google");
const amazon = await enrichAndNormalize("CRM book", "amazon");
console.log(`Google: ${google.length} entries, Amazon: ${amazon.length} entries`);
if (google[0]) console.log("Fields:", Object.keys(google[0]).join(", "));