Multistage Architectures

Introduction

Multi-stage architectures allow us to break down complex tasks into manageable steps. This improves modularity, debuggability, and performance.

Architectural Patterns

1. Sequential Pipeline

The most common pattern, where data flows linearly through a series of stages.

Input → Stage 1 → Stage 2 → Stage 3 → Output

class SequentialPipeline(dspy.Module):
    def forward(self, x):
        for stage in self.stages:
            x = stage(x)
        return x

2. Branching Architecture

Uses a router to direct the flow of execution based on the input or intermediate results.

           ┌─→ Stage A
Input → Router ─┤
           └─→ Stage B

class BranchingPipeline(dspy.Module):
    def forward(self, x):
        branch = self.router(x)
        if branch == 'A':
            return self.stage_a(x)
        else:
            return self.stage_b(x)

3. Iterative Architecture

Repeats a process until a stopping condition is met (e.g., quality threshold or max iterations).

4. Hierarchical Architecture

Composes pipelines within pipelines to handle very complex tasks.

Design Principles

• Clear Interfaces: Define standard input/output schemas for stages.
• Error Handling: Implement robust error detection and recovery strategies.
• Observability: Log intermediate outputs for debugging and optimization.