DSP Layered Architecture Design

Document Type: Knowledge Base - Architecture Design Pattern
Created: 2025-10-20
Last Updated: 2025-10-20
Confidence Level: High
Source: Framework architecture best practices and DSP implementation strategy
Purpose: Define the layered architecture for AirsDSP implementation

Overview

This document defines the layered architecture for AirsDSP, where the core DSP framework serves as the foundation with progressively higher-level building blocks constructed on top. This approach provides flexibility, maintainability, and accessibility for users at different expertise levels.

The Layered Architecture Vision

Architectural Principle

Core Concept: Build AirsDSP as a series of composable layers, where each layer provides increasing abstraction while building on the solid foundation of lower layers.

┌─────────────────────────────────────────────────────┐
│          Layer 4: Application Systems               │
│       (Complete solutions for end users)            │
└─────────────────────────────────────────────────────┘
                       ↑
┌─────────────────────────────────────────────────────┐
│       Layer 3: Task-Specific Pipelines              │
│     (Domain-optimized implementations)              │
└─────────────────────────────────────────────────────┘
                       ↑
┌─────────────────────────────────────────────────────┐
│         Layer 2: Pattern Library                    │
│      (Reusable composition patterns)                │
└─────────────────────────────────────────────────────┘
                       ↑
┌─────────────────────────────────────────────────────┐
│        Layer 1: Core DSP Framework                  │
│    (Fundamental primitives and operations)          │
└─────────────────────────────────────────────────────┘

Design Philosophy

1. Separation of Concerns: Each layer has a distinct responsibility
2. Progressive Abstraction: Higher layers provide convenience, lower layers provide control
3. Composability: Components at any layer can be combined
4. Flexibility: Users can choose their abstraction level
5. Maintainability: Changes in lower layers benefit all layers above

Layer 1: Core DSP Framework

Purpose

Provide the fundamental building blocks for DSP implementation - the primitives that cannot be broken down further.

Components

Core Operations

// The three fundamental DSP operations
pub trait Demonstrate {
    fn load_examples(&self, examples: &[Example]) -> Result<Demonstrations>;
    fn bootstrap_pipeline_awareness(&self) -> Result<()>;
}

pub trait Search {
    fn retrieve(&self, query: &Query, context: &Context) -> Result<RetrievalResults>;
    fn configure_retrieval_model(&mut self, config: RMConfig) -> Result<()>;
}

pub trait Predict {
    fn generate(&self, input: &PredictInput, context: &Context) -> Result<Prediction>;
    fn configure_language_model(&mut self, config: LMConfig) -> Result<()>;
}

Pipeline Composition

pub struct Pipeline {
    stages: Vec<Box<dyn Stage>>,
    context: Context,
    config: PipelineConfig,
}

impl Pipeline {
    pub fn new() -> Self;
    pub fn demonstrate(self, examples: Vec<Example>) -> Self;
    pub fn predict(self, stage: impl Predict + 'static) -> Self;
    pub fn search(self, stage: impl Search + 'static) -> Self;
    pub fn execute(&self, input: &str) -> Result<String>;
}

Context Management

pub struct Context {
    history: Vec<StageOutput>,
    demonstrations: Demonstrations,
    metadata: HashMap<String, Value>,
}

impl Context {
    pub fn add_stage_output(&mut self, output: StageOutput);
    pub fn get_stage_output(&self, stage_name: &str) -> Option<&StageOutput>;
    pub fn get_all_context(&self) -> String;
}

Integration Interfaces

// Language Model Integration
pub trait LanguageModel {
    fn generate(&self, prompt: &str, config: &GenerationConfig) -> Result<String>;
}

// Retrieval Model Integration
pub trait RetrievalModel {
    fn search(&self, query: &str, config: &RetrievalConfig) -> Result<Vec<Document>>;
}

Characteristics

Characteristic	Description
Abstraction Level	Minimal - close to the implementation
Flexibility	Maximum - full control over composition
Complexity	High - requires deep understanding
Use Cases	Novel research, custom patterns, maximum control
Target Users	Expert developers, researchers

Example Usage

// Direct use of core primitives - maximum flexibility
use airsdsp_core::{Pipeline, PredictStage, SearchStage, DemonstrateStage};

let custom_pipeline = Pipeline::new()
    .demonstrate(DemonstrateStage::new(custom_examples))
    .predict(PredictStage::new("custom_analysis", analysis_config))
    .search(SearchStage::new("custom_retrieval", retrieval_config))
    .predict(PredictStage::new("custom_synthesis", synthesis_config))
    .execute(input)?;

Core Module Structure

airsdsp-core/
├── src/
│   ├── lib.rs
│   ├── pipeline/
│   │   ├── mod.rs
│   │   ├── builder.rs
│   │   ├── executor.rs
│   │   └── stage.rs
│   ├── operations/
│   │   ├── mod.rs
│   │   ├── demonstrate.rs
│   │   ├── search.rs
│   │   └── predict.rs
│   ├── context/
│   │   ├── mod.rs
│   │   ├── context.rs
│   │   └── demonstrations.rs
│   ├── integration/
│   │   ├── mod.rs
│   │   ├── language_model.rs
│   │   └── retrieval_model.rs
│   └── types/
│       ├── mod.rs
│       ├── example.rs
│       ├── query.rs
│       └── prediction.rs
└── Cargo.toml

Layer 2: Pattern Library

Purpose

Provide reusable composition patterns that encode common and proven DSP pipeline structures.

Components

Chain-of-Thought Pattern

pub struct ChainOfThoughtPattern {
    num_reasoning_steps: usize,
    demonstrations: Vec<Example>,
    stage_configs: HashMap<usize, StageConfig>,
}

impl ChainOfThoughtPattern {
    pub fn new() -> Self;
    pub fn with_steps(mut self, num: usize) -> Self;
    pub fn with_demonstrations(mut self, examples: Vec<Example>) -> Self;
    pub fn with_stage_config(mut self, step: usize, config: StageConfig) -> Self;
    pub fn build(self) -> Result<Pipeline>;
}

Usage:

use airsdsp_patterns::ChainOfThoughtPattern;

let cot_pipeline = ChainOfThoughtPattern::new()
    .with_steps(3)
    .with_demonstrations(math_examples)
    .with_stage_config(0, StageConfig {
        name: "identify_problem",
        prompt_template: Some("..."),
    })
    .build()?;

ReAct Pattern

pub struct ReActPattern {
    max_iterations: usize,
    demonstrations: Vec<Example>,
    termination_condition: TerminationCondition,
}

impl ReActPattern {
    pub fn new() -> Self;
    pub fn with_max_iterations(mut self, max: usize) -> Self;
    pub fn with_demonstrations(mut self, examples: Vec<Example>) -> Self;
    pub fn with_termination(mut self, condition: TerminationCondition) -> Self;
    pub fn build(self) -> Result<Pipeline>;
}

Usage:

use airsdsp_patterns::ReActPattern;

let react_pipeline = ReActPattern::new()
    .with_max_iterations(3)
    .with_demonstrations(search_examples)
    .with_termination(TerminationCondition::HasAnswer)
    .build()?;

Multi-Hop Reasoning Pattern

pub struct MultiHopPattern {
    initial_search_config: SearchConfig,
    entity_extraction_config: PredictConfig,
    follow_up_search_config: SearchConfig,
    max_hops: usize,
}

impl MultiHopPattern {
    pub fn new() -> Self;
    pub fn with_max_hops(mut self, max: usize) -> Self;
    pub fn build(self) -> Result<Pipeline>;
}

Hierarchical Reasoning Pattern

pub struct HierarchicalPattern {
    overview_first: bool,
    depth_levels: usize,
    breadth_per_level: usize,
}

Conversational Pattern

pub struct ConversationalPattern {
    context_window: usize,
    reference_resolution: bool,
    history_summarization: bool,
}

Available Patterns

Pattern	Purpose	Use Cases
ChainOfThought	Step-by-step reasoning	Math, logic, analysis
ReAct	Iterative thought-action-observation	Research, investigation
MultiHop	Multi-stage information gathering	Complex QA, fact verification
Hierarchical	General-to-specific reasoning	Research, exploration
Conversational	Context-aware dialogue	Chat, multi-turn QA
Hybrid	Combined patterns	Complex multi-dimensional tasks

Characteristics

Characteristic	Description
Abstraction Level	Medium - encodes common patterns
Flexibility	High - configurable and customizable
Complexity	Moderate - requires pattern understanding
Use Cases	Common reasoning patterns, best practices
Target Users	Intermediate to advanced developers

Pattern Module Structure

airsdsp-patterns/
├── src/
│   ├── lib.rs
│   ├── chain_of_thought/
│   │   ├── mod.rs
│   │   ├── pattern.rs
│   │   └── examples.rs
│   ├── react/
│   │   ├── mod.rs
│   │   ├── pattern.rs
│   │   └── examples.rs
│   ├── multi_hop/
│   │   ├── mod.rs
│   │   ├── pattern.rs
│   │   └── examples.rs
│   ├── hierarchical/
│   │   ├── mod.rs
│   │   └── pattern.rs
│   ├── conversational/
│   │   ├── mod.rs
│   │   └── pattern.rs
│   └── hybrid/
│       ├── mod.rs
│       └── combinators.rs
└── Cargo.toml (depends on airsdsp-core)

Layer 3: Task-Specific Pipelines

Purpose

Provide ready-to-use, domain-optimized pipelines for specific task types with pre-configured demonstrations and optimizations.

Components

Math Solver

pub struct MathSolver {
    pipeline: Pipeline,
    problem_types: HashMap<String, ProblemTypeHandler>,
}

impl MathSolver {
    pub fn new() -> Result<Self>;
    pub fn solve(&self, problem: &str) -> Result<String>;
    pub fn solve_with_steps(&self, problem: &str) -> Result<SolutionWithSteps>;
    pub fn add_problem_type(&mut self, handler: ProblemTypeHandler);
}

Pre-configured for: - Arithmetic problems - Algebra problems - Geometry problems - Word problems - Calculus problems

Question Answering System

pub struct QuestionAnswering {
    pipeline: Pipeline,
    retrieval_config: RetrievalConfig,
}

impl QuestionAnswering {
    pub fn new() -> Result<Self>;
    pub fn answer(&self, question: &str) -> Result<Answer>;
    pub fn answer_with_sources(&self, question: &str) -> Result<AnswerWithSources>;
    pub fn configure_retrieval(&mut self, config: RetrievalConfig);
}

Pre-configured for: - Factual questions - Definitional questions - Explanatory questions - Comparison questions

Multi-Hop Question Answering

pub struct MultiHopQA {
    pipeline: Pipeline,
    max_hops: usize,
    retrieval_strategy: RetrievalStrategy,
}

impl MultiHopQA {
    pub fn new() -> Result<Self>;
    pub fn answer(&self, question: &str) -> Result<Answer>;
    pub fn answer_with_reasoning_trace(&self, question: &str) -> Result<AnswerWithTrace>;
}

Pre-configured for: - Multi-step reasoning questions - Questions requiring entity resolution - Questions requiring information synthesis

Code Analyzer

pub struct CodeAnalyzer {
    pipeline: Pipeline,
    supported_languages: Vec<Language>,
}

impl CodeAnalyzer {
    pub fn new() -> Result<Self>;
    pub fn analyze(&self, code: &str, language: Language) -> Result<Analysis>;
    pub fn suggest_fixes(&self, code: &str, language: Language) -> Result<Vec<Fix>>;
    pub fn explain_code(&self, code: &str, language: Language) -> Result<Explanation>;
}

Pre-configured for: - Syntax analysis - Bug detection - Code explanation - Fix suggestions

Legal Analyzer

pub struct LegalAnalyzer {
    pipeline: Pipeline,
    jurisdiction: Jurisdiction,
    precedent_database: PrecedentDB,
}

impl LegalAnalyzer {
    pub fn new(jurisdiction: Jurisdiction) -> Result<Self>;
    pub fn analyze_contract(&self, contract: &str) -> Result<ContractAnalysis>;
    pub fn find_precedents(&self, query: &str) -> Result<Vec<Precedent>>;
    pub fn assess_risk(&self, situation: &str) -> Result<RiskAssessment>;
}

Research Assistant

pub struct ResearchAssistant {
    pipeline: Pipeline,
    citation_style: CitationStyle,
    quality_filter: QualityFilter,
}

impl ResearchAssistant {
    pub fn new() -> Result<Self>;
    pub fn research_topic(&self, topic: &str) -> Result<ResearchReport>;
    pub fn summarize_papers(&self, papers: Vec<Paper>) -> Result<Summary>;
    pub fn synthesize_findings(&self, findings: Vec<Finding>) -> Result<Synthesis>;
}

Characteristics

Characteristic	Description
Abstraction Level	High - domain-specific solutions
Flexibility	Medium - configurable within domain
Complexity	Low - simple API, complex internally
Use Cases	Specific task domains
Target Users	Application developers, domain experts

Task Pipeline Module Structure

airsdsp-tasks/
├── src/
│   ├── lib.rs
│   ├── math/
│   │   ├── mod.rs
│   │   ├── solver.rs
│   │   ├── problem_types.rs
│   │   └── demonstrations/
│   │       ├── arithmetic.rs
│   │       ├── algebra.rs
│   │       └── word_problems.rs
│   ├── qa/
│   │   ├── mod.rs
│   │   ├── simple_qa.rs
│   │   ├── multi_hop_qa.rs
│   │   └── demonstrations/
│   │       └── factual_qa.rs
│   ├── code/
│   │   ├── mod.rs
│   │   ├── analyzer.rs
│   │   ├── languages/
│   │   │   ├── rust.rs
│   │   │   ├── python.rs
│   │   │   └── javascript.rs
│   │   └── demonstrations/
│   │       └── code_analysis.rs
│   ├── legal/
│   │   ├── mod.rs
│   │   ├── analyzer.rs
│   │   └── demonstrations/
│   │       └── legal_analysis.rs
│   └── research/
│       ├── mod.rs
│       ├── assistant.rs
│       └── demonstrations/
│           └── research_patterns.rs
└── Cargo.toml (depends on airsdsp-patterns)

Layer 4: Application Systems

Purpose

Provide complete, production-ready systems that compose multiple task pipelines with routing, orchestration, and user-facing features.

Components

Multi-Task Assistant

pub struct MultiTaskAssistant {
    task_classifier: Pipeline,
    router: TaskRouter,
    pipelines: HashMap<TaskType, Box<dyn TaskPipeline>>,
    default_pipeline: Pipeline,
    config: AssistantConfig,
}

impl MultiTaskAssistant {
    pub fn new() -> Result<Self>;
    pub fn process(&self, input: &str) -> Result<Response>;
    pub fn process_with_context(&self, input: &str, context: UserContext) -> Result<Response>;
    pub fn add_pipeline(&mut self, task_type: TaskType, pipeline: Box<dyn TaskPipeline>);
    pub fn configure(&mut self, config: AssistantConfig);
}

Features: - Automatic task classification - Intelligent routing to specialized pipelines - Context management across interactions - Fallback handling - Usage tracking and analytics

Domain Expert Systems

pub struct DomainExpertSystem {
    domain: Domain,
    specialized_pipelines: Vec<Pipeline>,
    knowledge_base: KnowledgeBase,
    reasoning_engine: ReasoningEngine,
}

impl DomainExpertSystem {
    pub fn new(domain: Domain) -> Result<Self>;
    pub fn consult(&self, query: &str) -> Result<ExpertResponse>;
    pub fn explain_reasoning(&self, query: &str) -> Result<ReasoningTrace>;
    pub fn update_knowledge(&mut self, knowledge: Knowledge);
}

Pre-built Domains: - Medical diagnosis support - Legal consultation - Financial analysis - Technical troubleshooting - Educational tutoring

Conversational Agent

pub struct ConversationalAgent {
    conversation_history: ConversationHistory,
    personality: PersonalityConfig,
    task_pipelines: HashMap<TaskType, Pipeline>,
    context_manager: ContextManager,
}

impl ConversationalAgent {
    pub fn new() -> Result<Self>;
    pub fn chat(&mut self, message: &str) -> Result<ChatResponse>;
    pub fn reset_conversation(&mut self);
    pub fn set_personality(&mut self, personality: PersonalityConfig);
    pub fn get_conversation_summary(&self) -> Result<Summary>;
}

Features: - Multi-turn conversation handling - Context accumulation - Reference resolution - Personality configuration - Task switching within conversation

Research Platform

pub struct ResearchPlatform {
    research_assistant: ResearchAssistant,
    qa_system: QuestionAnswering,
    summarizer: DocumentSummarizer,
    citation_manager: CitationManager,
}

impl ResearchPlatform {
    pub fn new() -> Result<Self>;
    pub fn research_topic(&self, topic: &str) -> Result<ResearchReport>;
    pub fn answer_research_question(&self, question: &str) -> Result<Answer>;
    pub fn summarize_literature(&self, query: &str) -> Result<LiteratureReview>;
    pub fn manage_citations(&mut self, papers: Vec<Paper>) -> Result<Bibliography>;
}

Characteristics

Characteristic	Description
Abstraction Level	Highest - complete solutions
Flexibility	Lower - opinionated design
Complexity	Minimal - plug-and-play
Use Cases	End-user applications
Target Users	Application users, minimal configuration

System Module Structure

airsdsp-systems/
├── src/
│   ├── lib.rs
│   ├── multi_task/
│   │   ├── mod.rs
│   │   ├── assistant.rs
│   │   ├── router.rs
│   │   └── classifier.rs
│   ├── domain_expert/
│   │   ├── mod.rs
│   │   ├── system.rs
│   │   └── domains/
│   │       ├── medical.rs
│   │       ├── legal.rs
│   │       └── financial.rs
│   ├── conversational/
│   │   ├── mod.rs
│   │   ├── agent.rs
│   │   ├── context_manager.rs
│   │   └── personality.rs
│   └── research/
│       ├── mod.rs
│       ├── platform.rs
│       └── components/
│           ├── summarizer.rs
│           └── citation_manager.rs
└── Cargo.toml (depends on airsdsp-tasks)

Cross-Layer Comparison

Feature Comparison Matrix

Feature	Layer 1: Core	Layer 2: Patterns	Layer 3: Tasks	Layer 4: Systems
Abstraction	Minimal	Medium	High	Highest
Flexibility	Maximum	High	Medium	Lower
Ease of Use	Expert	Moderate	Easy	Very Easy
Customization	Full	Configurable	Limited	Minimal
Learning Curve	Steep	Moderate	Gentle	Minimal
Development Time	Longest	Moderate	Quick	Quickest
Target Users	Researchers	Developers	App Builders	End Users
Documentation Needs	Deep technical	Pattern guides	API docs	User guides

Usage Comparison

Layer 1 Example: Core Primitives

use airsdsp_core::{Pipeline, PredictStage, SearchStage};

// Expert level - full control, high complexity
let pipeline = Pipeline::new()
    .demonstrate(custom_examples)
    .predict(PredictStage::new("stage1", config1))
    .search(SearchStage::new("search1", search_config))
    .predict(PredictStage::new("stage2", config2));

Layer 2 Example: Patterns

use airsdsp_patterns::ChainOfThoughtPattern;

// Intermediate level - pattern-based, configurable
let pipeline = ChainOfThoughtPattern::new()
    .with_steps(3)
    .with_demonstrations(examples)
    .build()?;

Layer 3 Example: Task Pipelines

use airsdsp_tasks::MathSolver;

// Application level - task-specific, simple API
let solver = MathSolver::new()?;
let answer = solver.solve("What is 15% of 240?")?;

Layer 4 Example: Complete Systems

use airsdsp_systems::MultiTaskAssistant;

// End-user level - just works, minimal setup
let assistant = MultiTaskAssistant::new()?;
let response = assistant.process("What is 15% of 240?")?;

Benefits of Layered Architecture

For Framework Development

1. Clear Development Path

• ✅ Build foundation first (Layer 1)
• ✅ Add patterns incrementally (Layer 2)
• ✅ Develop task pipelines (Layer 3)
• ✅ Compose systems (Layer 4)

2. Incremental Delivery

• ✅ Can ship Layer 1 as "core" release
• ✅ Add patterns as separate releases
• ✅ Task pipelines can be community contributions
• ✅ Systems can be specialized packages

3. Independent Testing

• ✅ Unit test core primitives
• ✅ Integration test patterns
• ✅ End-to-end test task pipelines
• ✅ System test complete applications

4. Performance Optimization

• ✅ Optimize core execution
• ✅ Optimize pattern compositions
• ✅ Optimize task-specific demonstrations
• ✅ Optimize system-level routing

For Library Users

1. Multiple Entry Points

• ✅ Experts can use core directly
• ✅ Developers can use patterns
• ✅ App builders can use task pipelines
• ✅ End users can use complete systems

2. Progressive Learning

• ✅ Start with high-level systems
• ✅ Learn patterns as needed
• ✅ Drop down to core for advanced needs
• ✅ Gradual complexity curve

3. Customization Options

• ✅ Configure systems without rebuilding
• ✅ Customize task pipelines with patterns
• ✅ Create novel patterns from core
• ✅ Mix layers as needed

4. Ecosystem Growth

• ✅ Community can contribute patterns
• ✅ Domain experts can build task pipelines
• ✅ Application developers can share systems
• ✅ Everyone benefits from core improvements

For Maintenance

1. Isolation of Changes

• ✅ Core changes rarely break patterns
• ✅ Pattern changes don't affect core
• ✅ Task pipeline updates independent
• ✅ System updates don't affect lower layers

2. Clear Responsibility

• ✅ Core team maintains Layer 1
• ✅ Pattern library team maintains Layer 2
• ✅ Domain teams maintain Layer 3
• ✅ Application teams maintain Layer 4

3. Versioning Strategy

• ✅ Core has stable API with semantic versioning
• ✅ Patterns can evolve independently
• ✅ Task pipelines can have faster iteration
• ✅ Systems can track dependencies clearly

Implementation Strategy

Phase 1: Core Foundation (Months 1-3)

Goal: Solid, well-tested core primitives

Deliverables: - Core DSP operations (Demonstrate, Search, Predict) - Pipeline composition and execution - Context management - LM/RM integration interfaces - Comprehensive core documentation - Core test suite

Success Criteria: - All core operations functional - Zero compiler warnings - 90%+ test coverage - Performance benchmarks established

Phase 2: Pattern Library (Months 4-6)

Goal: Encode proven composition patterns

Deliverables: - Chain-of-Thought pattern - ReAct pattern - Multi-Hop pattern - Hierarchical pattern - Conversational pattern - Pattern documentation and examples - Pattern test suite

Success Criteria: - All patterns buildable from core - Pattern performance validates DSP benchmarks - Clear pattern selection guidelines - Examples for each pattern

Phase 3: Task Pipelines (Months 7-9)

Goal: Ready-to-use domain solutions

Deliverables: - MathSolver pipeline - QuestionAnswering pipeline - MultiHopQA pipeline - CodeAnalyzer pipeline (basic) - Task-specific demonstrations - API documentation - Task pipeline test suite

Success Criteria: - Each pipeline performs well on domain tasks - Simple, intuitive APIs - Comprehensive demonstrations - Performance metrics documented

Phase 4: Application Systems (Months 10-12)

Goal: Complete production-ready systems

Deliverables: - MultiTaskAssistant system - Domain expert system (one domain) - ConversationalAgent system - System documentation - Deployment guides - System test suite

Success Criteria: - Systems handle real-world usage - Graceful error handling - Production-ready quality - User documentation complete

Crate Organization

Workspace Structure

airsdsp/
├── Cargo.toml (workspace)
├── crates/
│   ├── airsdsp-core/
│   │   ├── Cargo.toml
│   │   └── src/
│   ├── airsdsp-patterns/
│   │   ├── Cargo.toml (depends on core)
│   │   └── src/
│   ├── airsdsp-tasks/
│   │   ├── Cargo.toml (depends on patterns)
│   │   └── src/
│   └── airsdsp-systems/
│       ├── Cargo.toml (depends on tasks)
│       └── src/
├── examples/
│   ├── core_usage/
│   ├── pattern_usage/
│   ├── task_usage/
│   └── system_usage/
└── docs/
    ├── core/
    ├── patterns/
    ├── tasks/
    └── systems/

Dependency Graph

airsdsp-systems
    ↓ depends on
airsdsp-tasks
    ↓ depends on
airsdsp-patterns
    ↓ depends on
airsdsp-core
    ↓ depends on
[External dependencies: LM/RM clients, etc.]

Version Compatibility

# airsdsp-core
[package]
name = "airsdsp-core"
version = "0.1.0"

# airsdsp-patterns
[package]
name = "airsdsp-patterns"
version = "0.1.0"

[dependencies]
airsdsp-core = "0.1"  # Major version compatibility

# airsdsp-tasks
[package]
name = "airsdsp-tasks"
version = "0.1.0"

[dependencies]
airsdsp-patterns = "0.1"  # Major version compatibility

# airsdsp-systems
[package]
name = "airsdsp-systems"
version = "0.1.0"

[dependencies]
airsdsp-tasks = "0.1"  # Major version compatibility

Real-World Analogies

Programming Language Analogy

Layer 1 (Core)      ≈ Assembly/Machine Code
Layer 2 (Patterns)  ≈ Standard Library (std)
Layer 3 (Tasks)     ≈ Frameworks (web, async, etc.)
Layer 4 (Systems)   ≈ Applications built with frameworks

Building Construction Analogy

Layer 1 (Core)      ≈ Raw materials (bricks, cement, wood)
Layer 2 (Patterns)  ≈ Building components (walls, floors, roofs)
Layer 3 (Tasks)     ≈ Rooms (kitchen, bedroom, bathroom)
Layer 4 (Systems)   ≈ Complete buildings (house, office, mall)

Cooking Analogy

Layer 1 (Core)      ≈ Basic techniques (chop, sauté, boil)
Layer 2 (Patterns)  ≈ Fundamental recipes (sauces, stocks, dough)
Layer 3 (Tasks)     ≈ Specific dishes (pizza, pasta, soup)
Layer 4 (Systems)   ≈ Complete meals/menus

Precedents in Successful Frameworks

PyTorch

torch (core)
  → torch.nn (patterns/modules)
    → torchvision (domain models)
      → PyTorch Lightning (systems)

React Ecosystem

React Core (primitives)
  → React Hooks (patterns)
    → Component Libraries (reusable components)
      → Complete Applications

Rust Async Ecosystem

std::future (core)
  → futures crate (patterns/utilities)
    → tokio/async-std (runtimes)
      → axum/actix-web (frameworks)

Documentation Strategy

Layer-Specific Documentation

Layer 1: Core Documentation

• Audience: Expert developers, researchers
• Content: Technical specifications, API reference, architecture deep-dives
• Format: rustdoc, architecture documents, technical guides

Layer 2: Pattern Documentation

• Audience: Intermediate developers
• Content: Pattern descriptions, when to use, configuration options, examples
• Format: Pattern guides, tutorials, comparison tables

Layer 3: Task Documentation

• Audience: Application developers
• Content: API reference, usage examples, domain-specific guidance
• Format: API docs, how-to guides, cookbook recipes

Layer 4: System Documentation

• Audience: End users, integrators
• Content: User guides, setup instructions, deployment guides
• Format: User manuals, quick-start guides, FAQs

Documentation Types (Diátaxis Framework)

Following Diátaxis for each layer:

Layer	Tutorials	How-To Guides	Reference	Explanation
Core	Basic pipeline	Custom stages	API docs	Architecture
Patterns	Using patterns	Pattern selection	Pattern API	Pattern theory
Tasks	Task tutorials	Task customization	Task API	Domain concepts
Systems	Quick-start	System config	System API	System design

Key Architectural Principles

1. Explicit Over Implicit

• ✅ Layer boundaries are clear
• ✅ Dependencies are explicit
• ✅ Abstractions are visible

2. Composition Over Inheritance

• ✅ Layers compose, not inherit
• ✅ Components are composable
• ✅ Mix layers as needed

3. Simplicity at Each Layer

• ✅ Core is simple primitives
• ✅ Patterns are simple compositions
• ✅ Tasks are simple APIs
• ✅ Systems are simple to use

4. Performance at Every Layer

• ✅ Core is optimized for speed
• ✅ Patterns minimize overhead
• ✅ Tasks optimize for domain
• ✅ Systems optimize for throughput

5. Rust Idioms Throughout

• ✅ Zero-cost abstractions
• ✅ Explicit error handling
• ✅ Type safety
• ✅ Ownership and borrowing

Key Takeaways

For Architecture

1. ✅ Layered architecture provides flexibility - users choose abstraction level
2. ✅ Each layer has clear responsibility - separation of concerns
3. ✅ Layers build on each other - progressive abstraction
4. ✅ Composability at every level - mix and match components
5. ✅ Proven pattern in successful frameworks - PyTorch, React, Rust ecosystem

For Implementation

1. ✅ Build foundation first - core must be solid
2. ✅ Add layers incrementally - each layer adds value
3. ✅ Test independently - each layer has own tests
4. ✅ Document appropriately - different docs for different audiences
5. ✅ Version carefully - maintain compatibility

For Users

1. ✅ Start at appropriate layer - match expertise level
2. ✅ Can always go deeper - drop down layers as needed
3. ✅ Progressive learning - gradually understand lower layers
4. ✅ Customize at any level - flexibility throughout
5. ✅ Ecosystem benefits - improvements at any layer help all

References

Related Knowledge Base Documents

• DSP Framework Core: dsp_framework_core.md
• DSP Pipeline Architecture Examples: dsp_pipeline_architecture_examples.md
• DSP Reasoning Strategies: dsp_reasoning_strategies_implementation.md
• DSP Multi-Task System: dsp_multi_task_system_architecture.md
• DSP/DSPy Comparative Evolution: dsp_dspy_comparative_evolution.md

Framework Architecture References

• PyTorch architecture and layer separation
• React ecosystem and component composition
• Rust async ecosystem layering (futures → tokio → axum)

Design Principles

• Separation of Concerns
• Progressive Disclosure
• Composition over Inheritance
• Explicit over Implicit

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Document Status: Complete
Implementation Readiness: High - Provides clear architectural roadmap
Next Steps: Use this architecture to structure AirsDSP development roadmap and crate organization