> ## 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.

# Create table

> Create a new extraction table and start processing.

## Request body

<ParamField body="schema_id" type="string" required>
  The ID of the schema to use for extraction.
</ParamField>

<ParamField body="name" type="string" required>
  A name for the table.
</ParamField>

<ParamField body="file_ids" type="string[]">
  Array of file IDs (from the Files API) to extract data from. Provide exactly one of `file_ids`, `file_urls`, or `source_text`.
</ParamField>

<ParamField body="file_urls" type="string[]">
  Array of publicly accessible URLs to extract data from.
</ParamField>

<ParamField body="source_text" type="string">
  Raw text content to extract data from.
</ParamField>

<ParamField body="additional_context" type="string">
  Extra context to guide the extraction process.
</ParamField>

<ParamField body="enhancement_context" type="string">
  Context for AI enhancement of extracted data.
</ParamField>

<ParamField body="webhook_url" type="string">
  URL to receive webhook notifications when processing completes.
</ParamField>

## Response

Returns the created table object with status `processing`.

<ResponseField name="id" type="string">Unique table identifier.</ResponseField>
<ResponseField name="name" type="string">Table name.</ResponseField>
<ResponseField name="schema_id" type="string">Schema used for extraction.</ResponseField>
<ResponseField name="status" type="string">Always `processing` for a new table.</ResponseField>
<ResponseField name="progress" type="integer">Extraction progress (0-100).</ResponseField>
<ResponseField name="total_rows" type="integer">Number of rows extracted so far (0 initially).</ResponseField>
<ResponseField name="source_type" type="string">`files` or `text`.</ResponseField>
<ResponseField name="additional_context" type="string | null">Extraction context.</ResponseField>
<ResponseField name="enhancement_context" type="string | null">Enhancement context.</ResponseField>
<ResponseField name="error_message" type="string | null">Error message if processing failed.</ResponseField>
<ResponseField name="files" type="array">Uploaded file metadata.</ResponseField>
<ResponseField name="created_at" type="string">ISO 8601 timestamp.</ResponseField>
<ResponseField name="updated_at" type="string">ISO 8601 timestamp.</ResponseField>

<RequestExample>
  ```typescript TypeScript theme={null}
  const table = await client.tables.create({
    schema_id: "schema_abc123",
    name: "Q1 Product Catalog",
    file_ids: ["file_xyz789"],
    webhook_url: "https://your-server.com/webhooks",
  });

  console.log(table.id);     // "tbl_..."
  console.log(table.status); // "processing"
  ```

  ```python Python theme={null}
  table = client.tables.create(
      schema_id="schema_abc123",
      name="Q1 Product Catalog",
      file_ids=["file_xyz789"],
      webhook_url="https://your-server.com/webhooks",
  )

  print(table["id"])      # "tbl_..."
  print(table["status"])  # "processing"
  ```

  ```bash cURL theme={null}
  curl -X POST "https://hub.banditshq.com/api/v1/tables" \
    -H "Authorization: Bearer lasso_..." \
    -H "Content-Type: application/json" \
    -d '{
      "schema_id": "schema_abc123",
      "name": "Q1 Product Catalog",
      "file_ids": ["file_xyz789"],
      "webhook_url": "https://your-server.com/webhooks"
    }'
  ```
</RequestExample>

<ResponseExample>
  ```json Response theme={null}
  {
    "id": "tbl_abc123",
    "name": "Q1 Product Catalog",
    "schema_id": "schema_xyz789",
    "status": "processing",
    "progress": 0,
    "total_rows": 0,
    "source_type": "files",
    "additional_context": null,
    "enhancement_context": null,
    "error_message": null,
    "files": [
      { "name": "catalog.pdf", "path": "company_123/file_xyz/catalog.pdf", "size": 1048576 }
    ],
    "created_at": "2025-03-10T14:30:00.000Z",
    "updated_at": "2025-03-10T14:30:00.000Z"
  }
  ```
</ResponseExample>

## Waiting for completion

Tables are processed asynchronously. Use the SDK's built-in polling helper or configure a webhook.

<CodeGroup>
  ```typescript TypeScript theme={null}
  const completed = await client.tables.waitForCompletion(table.id, {
    intervalMs: 3000,
    timeoutMs: 300000,
  });

  console.log(completed.status);     // "completed"
  console.log(completed.total_rows); // 42
  ```

  ```python Python theme={null}
  completed = client.tables.wait_for_completion(
      table["id"],
      interval_s=3,
      timeout_s=300,
  )

  print(completed["status"])      # "completed"
  print(completed["total_rows"])  # 42
  ```
</CodeGroup>