Parallel Map Operation

The Parallel Map operation in DocETL applies multiple independent transformations to each item in the input data concurrently, maintaining a 1:1 input-to-output ratio while generating multiple fields simultaneously.

Similarity to Map Operation

The Parallel Map operation is very similar to the standard Map operation. The key difference is that Parallel Map allows you to define multiple prompts that run concurrently (without having to explicitly create a DAG), whereas a standard Map operation typically involves a single transformation.

Configuration

Each prompt in the Parallel Map operation is responsible for generating specific fields of the output. The prompts are executed concurrently, improving efficiency when working with multiple transformations.

The output schema should include all the fields generated by the individual prompts, ensuring that the results are combined into a single output item for each input.

Required Parameters

Parameter	Description
name	A unique name for the operation
type	Must be set to "parallel_map"
prompts	A list of prompt configurations (see below)
output	Schema definition for the combined output from all prompts

Each prompt configuration in the prompts list should contain:

• name: A unique name for the prompt
• prompt: The prompt template to use for the transformation
• output_keys: List of keys that this prompt will generate
• model (optional): The language model to use for this specific prompt

Optional Parameters

Parameter	Description	Default
model	The default language model to use	Falls back to default_model
optimize	Flag to enable operation optimization	True
recursively_optimize	Flag to enable recursive optimization	false
sample_size	Number of samples to use for the operation	Processes all data

Why use Parallel Map instead of multiple Map operations?

While you could achieve similar results with multiple Map operations, Parallel Map offers several advantages:

1. Concurrency: Prompts run in parallel, potentially reducing overall processing time.
2. Simplified Configuration: You define multiple transformations in a single operation, reducing pipeline complexity.
3. Unified Output: Results from all prompts are combined into a single output item, simplifying downstream operations.

🚀 Example: Processing Job Applications

Here's an example of a parallel map operation that processes job applications by extracting key information and evaluating candidates:

- name: process_job_application
  type: parallel_map
  prompts:
    - name: extract_skills
      prompt: "Given the following resume: '{{ input.resume }}', list the top 5 relevant skills for a software engineering position."
      output_keys:
        - skills
      model: gpt-4o-mini
    - name: calculate_experience
      prompt: "Based on the work history in this resume: '{{ input.resume }}', calculate the total years of relevant experience for a software engineering role."
      output_keys:
        - years_experience
      model: gpt-4o-mini
    - name: evaluate_cultural_fit
      prompt: "Analyze the following cover letter: '{{ input.cover_letter }}'. Rate the candidate's potential cultural fit on a scale of 1-10, where 10 is the highest."
      output_keys:
        - cultural_fit_score
      model: gpt-4o-mini
  output:
    schema:
      skills: list[string]
      years_experience: float
      cultural_fit_score: integer

This Parallel Map operation processes job applications by concurrently extracting skills, calculating experience, and evaluating cultural fit.

Advantages

1. Concurrency: Multiple transformations are applied simultaneously, potentially reducing overall processing time.
2. Simplicity: Users can define multiple transformations without needing to create explicit DAGs in the configuration.
3. Flexibility: Different models can be used for different prompts within the same operation.
4. Maintainability: Each transformation can be defined and updated independently, making it easier to manage complex operations.

Best Practices

1. Independent Transformations: Ensure that the prompts in a Parallel Map operation are truly independent of each other to maximize the benefits of concurrent execution.
2. Balanced Prompts: Try to design prompts that have similar complexity and execution times to optimize overall performance.
3. Output Schema Alignment: Ensure that the output schema correctly captures all the fields generated by the individual prompts.