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

# Chaining Reducto calls

You can build workflows by chaining multiple API calls together. The /extract and /split calls can take in a job\_id output from a parse endpoint preventing parsing the same document multiple times -- this can enable workflows that were previously too high latency or expensive to manage.

Common ways to chain calls are:

* Parse --> Split --> Extract
* Parse --> Extract

Below is an example using the [/extract](/api-reference/extract) and [/extract\_async](/api-reference/extract-async), where we'd like to extract two different sets of information from the same set of documents.

## Problem

Lets say we have users uploading documents of two types: W2 forms and Passports. We'd like to extract different sets of information from each set of documents:

1. For the passport: Passport number, name, and DOB.
2. For the W2: Total Wages, Calendar Year, Employer Name

Without pipelining, we would have to either provide a schema that has fields for both types of documents, or we would have to make three separate extraction calls. Without pipelining, this would mean a lot of extra latency and being charged 6x for the same jobs.

![Pipeline Diagram](https://cdn.reducto.ai/documentation_images/DocClassification.png)

## Example code snippet

The code snippet below walks through the approach.

1. Parse the document.
2. Use the Parse `job_id` to perform the classification extraction to determine if the document is a W-2 or Passport.
3. Use the Parse `job_id` to perform the data extraction for the corresponding type of document.

<CodeGroup>
  ```python Reducto invocation theme={null}
  from reducto import Reducto

  client = Reducto(api_key=os.environ.get('REDUCTO_API_KEY'), timeout=300)
  document_url = "https://support.adp.com/adp_payroll/content/hybrid/PDF/W2_Interactive.pdf"

  # Step 1: Parse
  parse_response = client.parse.run(document_url=document_url)
  job_id = parse_response.job_id

  # Step 2: Classify document type
  classification = client.extract.run(
      document_url=f"jobid://{job_id}",
      schema={
          "type": "object",
          "properties": {
              "document_type": {"type": "string", "enum": ["W2", "Passport", "Other"]}
          },
          "required": ["document_type"],
      },
  )
  print(classification.result)

  document_type = classification.result[0]["document_type"]

  # Step 3: Choose schema based on classification
  if document_type == "W2":
      schema = {
          "type": "object",
          "properties": {
              "total_wages": {"type": "number"},
              "calendar_year": {"type": "integer"},
              "employer_name": {"type": "string"}
          },
          "required": ["total_wages", "calendar_year", "employer_name"]
      }
  elif document_type == "Passport":
      schema = {
          "type": "object",
          "properties": {
              "passport_number": {"type": "string"},
              "name": {"type": "string"},
              "date_of_birth": {"type": "string"},
          },
      }
  else:
      raise ValueError(f"Unsupported document type: {document_type}")

  # Step 4: Extract structured fields
  extract_response = client.extract.run(
      document_url=f"jobid://{job_id}",
      schema=schema,
  )

  print(extract_response.result)
  ```

  ```python Result theme={null}
  [{'document_type': 'W2'}]
  [{'total_wages': 23500, 'calendar_year': 2020, 'employer_name': 'SAMPLE COMPANY INC'}]
  ```
</CodeGroup>