Advanced: Customizing Optimization

You can customize the optimization process for specific operations using the ``optimizer_config in your pipeline.

Global Configuration

The following options can be applied globally to all operations in your pipeline during optimization:

• num_retries: The number of times to retry optimizing if the LLM agent fails. Default is 1.

• sample_sizes: Override the default sample sizes for each operator type. Specify as a dictionary with operator types as keys and integer sample sizes as values.

Default sample sizes:

SAMPLE_SIZE_MAP = {
    "reduce": 40,
    "map": 5,
    "resolve": 100,
    "equijoin": 100,
    "filter": 5,
}

Equijoin Configuration

• target_recall: Change the default target recall (default is 0.95).

Resolve Configuration

• target_recall: Specify the target recall for the resolve operation.

Reduce Configuration

• synthesize_resolve: Set to False if you definitely don't want a resolve operation synthesized or want to turn off this rewrite rule.

Map Configuration

• force_chunking_plan: Set to True if you want the the optimizer to force plan that breaks up the input documents into chunks.

Example Configuration

Here's an example of how to use the optimizer_config in your pipeline:

optimizer_config:
  num_retries: 2
  sample_sizes:
    map: 10
    reduce: 50
  reduce:
    synthesize_resolve: false
  map:
    force_chunking_plan: true

operations:
  - name: extract_medications
    type: map
    optimize: true
    # ... other configuration ...

  - name: summarize_prescriptions
    type: reduce
    optimize: true
    # ... other configuration ...
# ... rest of the pipeline configuration ...

This configuration will:

1. Retry optimization up to 2 times for each operation if the LLM agent fails.
2. Use custom sample sizes for map (10) and reduce (50) operations.
3. Prevent the synthesis of resolve operations for reduce operations.
4. Force a chunking plan for map operations.