> ## Documentation Index
> Fetch the complete documentation index at: https://productlasso.com/docs/llms.txt
> Use this file to discover all available pages before exploring further.

# Data enrichment

> Turn partial product data into complete, sourced records with Lasso Enrich.

Lasso Enrich takes partial product records — as little as a product name or SKU — and fills in every missing field with sourced, cited data. Each value comes with a **basis** showing exactly where the information came from.

## How it works

1. **Pass in what you have** -- Provide one or more items with whatever data you already know (name, SKU, brand, etc.).
2. **Define the target schema** -- Tell Lasso what fields you want filled in (or use the default product schema).
3. **Lasso researches each product** -- The AI searches Lasso's database, finds the product, and extracts structured data.
4. **Get back complete records** -- Every field is filled in with citations, reasoning, and confidence scores.

## The basis

Every enriched field includes a `basis` that tells you:

* **citations** -- The sources used (URL, title, excerpt)
* **reasoning** -- How the AI determined the value
* **confidence** -- `high` or `low`

This transparency is critical for product data quality. You can verify values, filter by confidence, or show sources to your users.

## Schema options

Same as Search — use `schema_id`, inline `columns`, or omit both for the default product schema.

## Sync vs async

* **Sync** (default): For up to 5 items, Lasso processes and returns results in the same request.
* **Async**: For larger batches (>5 items) or when you pass `webhook_url`, Lasso returns `202` immediately and delivers results via webhook.

## Example: Search then Enrich

A common pattern is to search for products first, then enrich the results with additional detail.

<CodeGroup>
  ```typescript TypeScript theme={null}
  // 1. Search for products
  const search = await client.search({
    query: "best wireless mice for gaming 2025",
    columns: [
      { key: "name", label: "Name", type: "text" },
      { key: "brand", label: "Brand", type: "text" },
    ],
    max_results: 5,
  });

  // 2. Enrich with full detail
  const enriched = await client.enrich({
    items: search.results.map(r => ({ data: r.data })),
    columns: [
      { key: "name", label: "Name", type: "text" },
      { key: "brand", label: "Brand", type: "text" },
      { key: "price", label: "Price", type: "number" },
      { key: "dpi", label: "Max DPI", type: "number" },
      { key: "weight", label: "Weight (g)", type: "number" },
      { key: "features", label: "Features", type: "tags" },
      { key: "description", label: "Description", type: "text" },
    ],
  });

  for (const item of enriched.items) {
    console.log(`${item.data.name} — ${item.data.price}g, ${item.data.dpi} DPI`);
  }
  ```

  ```python Python theme={null}
  # 1. Search for products
  search = client.search(
      query="best wireless mice for gaming 2025",
      columns=[
          {"key": "name", "label": "Name", "type": "text"},
          {"key": "brand", "label": "Brand", "type": "text"},
      ],
      max_results=5,
  )

  # 2. Enrich with full detail
  enriched = client.enrich(
      items=[{"data": r["data"]} for r in search["results"]],
      columns=[
          {"key": "name", "label": "Name", "type": "text"},
          {"key": "brand", "label": "Brand", "type": "text"},
          {"key": "price", "label": "Price", "type": "number"},
          {"key": "dpi", "label": "Max DPI", "type": "number"},
          {"key": "weight", "label": "Weight (g)", "type": "number"},
          {"key": "features", "label": "Features", "type": "tags"},
          {"key": "description", "label": "Description", "type": "text"},
      ],
  )

  for item in enriched["items"]:
      print(f"{item['data']['name']} — {item['data']['price']}g, {item['data']['dpi']} DPI")
  ```
</CodeGroup>

## Credits

Credit cost depends on the **thinking** level you choose:

| Thinking | Model           | Credits/item | Best for                               |
| -------- | --------------- | ------------ | -------------------------------------- |
| `hard`   | Gemini 3.1 Pro  | **4**        | Complex or ambiguous products          |
| `medium` | Gemini 3.1 Lite | **2**        | General-purpose enrichment *(default)* |
| `low`    | Gemini 2.0      | **1**        | Fast, straightforward lookups          |

## Web search

By default, Lasso uses web search to research each product and provides a `basis` with citations for every enriched field. You can disable this with `web_search: false` — the AI will fill fields from its own knowledge without citations, which is faster and still accurate for well-known products.

## Next steps

* [Enrich API reference](/api-reference/enrich) -- Full parameter and response documentation.
* [Search](/learn/search) -- Discover products to enrich.
* [Glossary](/learn/glossary) -- Control terminology during enrichment with `use_glossary: true`.