← Back to Dspex cognitive orchestration

04 REQUIREMENTS

Documentation for 04_REQUIREMENTS from the Dspex repository.

Requirements Document

Introduction

This document defines the requirements for DSPex Cognitive Orchestration Platform - an intelligent orchestration layer for DSPy that leverages Elixir’s coordination capabilities to add distributed intelligence, real-time adaptation, and production-grade reliability. The platform builds on Snakepit for Python process management while focusing on cognitive orchestration, variable coordination, and intelligent routing between native Elixir and Python implementations.

Requirements

Requirement 1

User Story: As a developer, I want to execute DSPy operations through a unified Elixir API, so that I can leverage ML capabilities without dealing with Python interop complexity.

Acceptance Criteria

  1. WHEN I call DSPex.execute/3 with a DSPy operation THEN the system SHALL intelligently route to the optimal implementation (native or Python)
  2. WHEN I chain multiple operations THEN the system SHALL automatically orchestrate them with optimal parallelization
  3. WHEN an operation fails THEN the system SHALL provide clear error messages in Elixir-idiomatic format
  4. IF both native and Python implementations exist THEN the system SHALL select based on performance characteristics and current load

Requirement 2

User Story: As a developer, I want compile-time type safety for DSPy signatures, so that I can catch errors early and have better IDE support.

Acceptance Criteria

  1. WHEN I define a signature using defsignature macro THEN the system SHALL parse and validate it at compile time
  2. WHEN I pass invalid inputs to a signature THEN the system SHALL provide clear type error messages
  3. WHEN I use a signature THEN the system SHALL provide autocomplete and type hints in my IDE
  4. WHEN converting between Elixir and Python types THEN the system SHALL handle the conversion transparently

Requirement 3

User Story: As a developer, I want any DSPy parameter to be optimizable as a variable, so that I can coordinate distributed optimization across my system.

Acceptance Criteria

  1. WHEN I register a parameter as a variable THEN the system SHALL track its optimization history
  2. WHEN multiple components want to optimize the same variable THEN the system SHALL coordinate their efforts
  3. WHEN a variable has dependencies THEN the system SHALL respect them during optimization
  4. WHEN optimization completes THEN observers SHALL be notified of the new value

Requirement 4

User Story: As a developer, I want intelligent LLM integration with multiple adapters, so that I can use the best provider for each use case.

Acceptance Criteria

  1. WHEN I make an LLM request THEN the system SHALL automatically select the optimal adapter based on requirements
  2. WHEN I need structured output THEN the system SHALL use InstructorLite adapter
  3. WHEN I need simple completions THEN the system SHALL use direct HTTP for lower latency
  4. WHEN I need complex DSPy operations THEN the system SHALL fallback to Python bridge

Requirement 5

User Story: As a developer, I want to define complex ML pipelines in Elixir, so that I can leverage Elixir’s concurrency for orchestration.

Acceptance Criteria

  1. WHEN I define a pipeline THEN the system SHALL analyze dependencies and parallelize execution
  2. WHEN a pipeline stage fails THEN the system SHALL handle partial results gracefully
  3. WHEN I request streaming THEN the system SHALL stream results as they become available
  4. WHEN monitoring a pipeline THEN the system SHALL provide real-time progress updates

Requirement 6

User Story: As an operations engineer, I want the system to learn and adapt from usage patterns, so that performance improves over time.

Acceptance Criteria

  1. WHEN similar operations are executed repeatedly THEN the system SHALL learn optimal strategies
  2. WHEN performance degrades THEN the system SHALL automatically adjust execution strategies
  3. WHEN new patterns emerge THEN the system SHALL adapt its routing decisions
  4. WHEN anomalies are detected THEN the system SHALL trigger appropriate adaptations

Requirement 7

User Story: As an operations engineer, I want comprehensive telemetry and monitoring, so that I can understand system behavior in production.

Acceptance Criteria

  1. WHEN any operation executes THEN the system SHALL emit detailed telemetry events
  2. WHEN performance patterns change THEN the system SHALL detect and report them
  3. WHEN errors occur THEN the system SHALL provide detailed context for debugging
  4. WHEN resources are constrained THEN the system SHALL provide early warnings

Requirement 8

User Story: As a developer, I want stateful session management, so that I can maintain context across multiple operations.

Acceptance Criteria

  1. WHEN I create a session THEN the system SHALL maintain state across operations
  2. WHEN using a session THEN the system SHALL prefer worker affinity for better cache utilization
  3. WHEN a session is idle THEN the system SHALL clean it up after the configured TTL
  4. WHEN querying a session THEN the system SHALL provide execution history and performance metrics

Requirement 9

User Story: As an operations engineer, I want production-grade reliability features, so that the system can handle failures gracefully.

Acceptance Criteria

  1. WHEN a Python worker crashes THEN the system SHALL restart it automatically
  2. WHEN an adapter fails repeatedly THEN the system SHALL circuit break to prevent cascading failures
  3. WHEN load exceeds capacity THEN the system SHALL queue requests up to configured limits
  4. WHEN critical errors occur THEN the system SHALL fall back to alternative implementations

Requirement 10

User Story: As a developer, I want seamless integration between native and Python implementations, so that I can mix them in the same pipeline.

Acceptance Criteria

  1. WHEN a pipeline contains both native and Python stages THEN data SHALL flow seamlessly between them
  2. WHEN switching implementations THEN the system SHALL handle type conversions automatically
  3. WHEN profiling a pipeline THEN the system SHALL show performance breakdown by implementation type
  4. WHEN optimizing THEN the system SHALL consider both native and Python options

Requirement 11

User Story: As a developer, I want high-performance native implementations for common operations, so that simple operations have minimal latency.

Acceptance Criteria

  1. WHEN executing simple operations like signatures and templates THEN latency SHALL be under 1ms
  2. WHEN using native implementations THEN memory usage SHALL be predictable and bounded
  3. WHEN native implementations exist THEN they SHALL be functionally equivalent to Python versions
  4. WHEN benchmarking THEN native implementations SHALL be at least 10x faster than Python bridge

Requirement 12

User Story: As a developer, I want intelligent execution strategies based on task analysis, so that complex operations are optimized automatically.

Acceptance Criteria

  1. WHEN submitting a task THEN the orchestrator SHALL analyze its requirements and complexity
  2. WHEN similar tasks have been executed THEN the system SHALL use learned strategies
  3. WHEN strategies fail THEN the system SHALL try fallback approaches automatically
  4. WHEN new strategies succeed THEN the system SHALL remember them for future use