# Pipeline Organization and Categorization System
## Overview
This document defines the organizational structure for the AI engineering pipeline library, establishing a systematic approach to pipeline discovery, reuse, and composition.
## Directory Structure
```
pipeline_ex/
├── pipelines/ # Main pipeline library
│ ├── registry.yaml # Global pipeline registry
│ ├── data/ # Data processing pipelines
│ │ ├── cleaning/
│ │ ├── enrichment/
│ │ ├── transformation/
│ │ └── quality/
│ ├── model/ # Model development pipelines
│ │ ├── prompt_engineering/
│ │ ├── evaluation/
│ │ ├── comparison/
│ │ └── fine_tuning/
│ ├── code/ # Code generation pipelines
│ │ ├── api_generation/
│ │ ├── test_generation/
│ │ ├── documentation/
│ │ └── refactoring/
│ ├── analysis/ # Analysis pipelines
│ │ ├── codebase/
│ │ ├── security/
│ │ ├── performance/
│ │ └── dependencies/
│ ├── content/ # Content generation pipelines
│ │ ├── blog/
│ │ ├── tutorial/
│ │ ├── api_docs/
│ │ └── changelog/
│ ├── devops/ # DevOps pipelines
│ │ ├── ci_cd/
│ │ ├── deployment/
│ │ ├── monitoring/
│ │ └── infrastructure/
│ ├── components/ # Reusable components
│ │ ├── steps/ # Reusable step definitions
│ │ ├── prompts/ # Prompt templates
│ │ ├── functions/ # Gemini function definitions
│ │ ├── validators/ # Validation components
│ │ └── transformers/ # Data transformation components
│ └── templates/ # Pipeline templates
│ ├── basic/ # Simple pipeline patterns
│ ├── advanced/ # Complex pipeline patterns
│ └── enterprise/ # Production-grade patterns
├── examples/ # Example usage and demos
│ ├── tutorials/ # Step-by-step tutorials
│ └── case_studies/ # Real-world implementations
└── tests/ # Pipeline-specific tests
├── pipeline_tests/ # Integration tests for pipelines
└── component_tests/ # Unit tests for components
```
## Pipeline Registry Schema
The `registry.yaml` serves as the central catalog of all available pipelines:
```yaml
version: "1.0"
last_updated: "2025-06-30"
pipelines:
- id: "data-cleaning-standard"
name: "Standard Data Cleaning Pipeline"
category: "data/cleaning"
description: "Multi-stage data cleaning with validation"
version: "1.0.0"
tags: ["data", "cleaning", "validation"]
dependencies:
- "components/steps/validation"
- "components/transformers/data"
complexity: "medium"
estimated_tokens: 5000
providers: ["claude", "gemini"]
- id: "api-rest-generator"
name: "REST API Generator"
category: "code/api_generation"
description: "Generate complete REST API with tests"
version: "2.1.0"
tags: ["api", "code-generation", "rest"]
dependencies:
- "components/steps/code"
- "components/prompts/api"
complexity: "high"
estimated_tokens: 15000
providers: ["claude"]
```
## Categorization Taxonomy
### 1. Primary Categories
- **Data**: Pipelines focused on data manipulation and processing
- **Model**: AI/ML model development and optimization
- **Code**: Software development and code generation
- **Analysis**: System and code analysis workflows
- **Content**: Documentation and content creation
- **DevOps**: Infrastructure and deployment automation
### 2. Complexity Levels
- **Basic**: Single-step or simple multi-step pipelines
- **Medium**: Multi-step with conditional logic
- **High**: Complex workflows with parallel execution
- **Enterprise**: Production-grade with full error handling
### 3. Provider Requirements
- **Claude-only**: Requires Claude-specific features
- **Gemini-only**: Requires Gemini function calling
- **Multi-provider**: Can use either provider
- **Hybrid**: Requires both providers
## Component Classification
### Step Components
```yaml
# components/steps/validation/input_validator.yaml
component:
type: "step"
id: "input-validator"
name: "Input Validation Step"
description: "Validates input data against schema"
parameters:
schema:
type: "object"
description: "JSON Schema for validation"
strict:
type: "boolean"
default: true
outputs:
valid:
type: "boolean"
errors:
type: "array"
items:
type: "string"
```
### Prompt Templates
```yaml
# components/prompts/analysis/code_review.yaml
component:
type: "prompt"
id: "code-review-prompt"
name: "Code Review Prompt Template"
variables:
- code_content
- review_focus
- severity_level
template: |
Review the following code with focus on {review_focus}:
```
{code_content}
```
Provide feedback at {severity_level} level.
```
## Naming Conventions
### Pipeline Files
- Format: `{purpose}_{variant}_pipeline.yaml`
- Examples:
- `data_cleaning_standard_pipeline.yaml`
- `api_generation_rest_pipeline.yaml`
- `security_audit_comprehensive_pipeline.yaml`
### Component Files
- Format: `{function}_{type}.yaml`
- Examples:
- `input_validator.yaml`
- `json_transformer.yaml`
- `code_review_prompt.yaml`
### Version Tags
- Semantic versioning: `MAJOR.MINOR.PATCH`
- Beta versions: `X.Y.Z-beta.N`
- Release candidates: `X.Y.Z-rc.N`
## Discovery Mechanisms
### 1. CLI Commands
```bash
# List all pipelines
mix pipeline.list
# Search by category
mix pipeline.list --category data/cleaning
# Search by tags
mix pipeline.list --tags "api,rest"
# Show pipeline details
mix pipeline.info api-rest-generator
```
### 2. Web Interface (Future)
- Visual pipeline browser
- Dependency graph visualization
- Performance metrics dashboard
- Usage analytics
### 3. API Access
```elixir
# Pipeline discovery API
Pipeline.Registry.list_by_category("data/cleaning")
Pipeline.Registry.search(tags: ["api", "rest"])
Pipeline.Registry.get_details("api-rest-generator")
```
## Metadata Standards
Each pipeline must include:
1. Unique identifier
2. Descriptive name
3. Clear category placement
4. Version information
5. Dependency declarations
6. Performance estimates
7. Provider requirements
8. Comprehensive tags
## Migration Path
For existing pipelines:
1. Analyze current pipeline files
2. Categorize according to new taxonomy
3. Add required metadata
4. Update file locations
5. Register in central registry
6. Update references in code
## Governance
### Adding New Pipelines
1. Define clear purpose and category
2. Follow naming conventions
3. Include all required metadata
4. Add comprehensive tests
5. Document usage examples
6. Submit for review
### Deprecation Process
1. Mark as deprecated in registry
2. Add deprecation notice to file
3. Provide migration guide
4. Maintain for 2 major versions
5. Archive after removal
## Benefits
1. **Discoverability**: Easy to find relevant pipelines
2. **Reusability**: Clear component boundaries
3. **Maintainability**: Organized structure
4. **Scalability**: Supports growth
5. **Consistency**: Enforced standards
6. **Quality**: Review process
