# Phase 2: LLM Generation Methods - Implementation Complete

## Overview

This phase adds **LLM-powered automatic generation** for the 5 extended artifacts that were added in Phase 1. Now users can generate data models, research findings, API contracts, quality checklists, and quickstart guides automatically using AI.

## What Was Added

### 1. Jinja2 Prompt Templates (5 new files)

Created specialized prompts for each artifact type:

- **`templates/data_model.jinja2`** - Prompts LLM to generate database schemas
- **`templates/research.jinja2`** - Prompts LLM to document technology decisions
- **`templates/api_contract.jinja2`** - Prompts LLM to create API specifications
- **`templates/checklist.jinja2`** - Prompts LLM to validate specification quality
- **`templates/quickstart.jinja2`** - Prompts LLM to create developer guides

Each template:
- Supports multi-language output (pt-br, es, en, etc.)
- Provides detailed instructions and examples
- Returns structured JSON matching Pydantic schemas
- Follows the same pattern as existing templates (specification.jinja2, plan.jinja2)

### 2. ArtifactGenerator Module

New core module: `src/speckit/core/artifacts.py` (340 lines)

Provides generation methods:
```python
class ArtifactGenerator:
    def generate_data_model(spec, plan) -> DataModel
    def generate_research(plan) -> ResearchFindings
    def generate_api_contract(spec, plan) -> APIContract
    def generate_checklist(spec) -> QualityChecklist
    def generate_quickstart(spec, plan) -> QuickstartGuide
```

All methods include:
- Sync and async versions
- Language support via config
- Structured output using Pydantic
- Proper datetime and ID handling

### 3. SpecKit Class Updates

Added 10 new public methods to `SpecKit` class:

**Sync methods:**
- `kit.generate_data_model(spec, plan) -> DataModel`
- `kit.generate_research(plan) -> ResearchFindings`
- `kit.generate_api_contract(spec, plan) -> APIContract`
- `kit.generate_checklist(spec) -> QualityChecklist`
- `kit.generate_quickstart(spec, plan) -> QuickstartGuide`

**Async methods:**
- `kit.generate_data_model_async(spec, plan)`
- `kit.generate_research_async(plan)`
- `kit.generate_api_contract_async(spec, plan)`
- `kit.generate_checklist_async(spec)`
- `kit.generate_quickstart_async(spec, plan)`

All methods:
- Follow the same pattern as existing workflow methods
- Use lazy initialization for `ArtifactGenerator`
- Include complete docstrings with examples
- Support language configuration

## Usage Examples

### Complete Workflow with Extended Artifacts

```python
from speckit import SpecKit

kit = SpecKit("./my-project")

# Core workflow
spec = kit.specify("Add user authentication")
plan = kit.plan(spec)
tasks = kit.tasks(plan)

# Extended artifacts (NEW in Phase 2!)
data_model = kit.generate_data_model(spec, plan)
research = kit.generate_research(plan)
contract = kit.generate_api_contract(spec, plan)
checklist = kit.generate_checklist(spec)
quickstart = kit.generate_quickstart(spec, plan)

# Save all artifacts
kit.save(spec)
kit.save(plan)
kit.save(tasks)
kit.storage.save_data_model(data_model, spec.feature_id)
kit.storage.save_research(research, spec.feature_id)
kit.storage.save_api_contract(contract, spec.feature_id)
kit.storage.save_checklist(checklist, spec.feature_id)
kit.storage.save_quickstart(quickstart, spec.feature_id)
```

### Async Workflow

```python
import asyncio
from speckit import SpecKit

async def generate_all_artifacts():
    kit = SpecKit("./my-project")

    # Generate core artifacts
    spec = await kit.specify_async("Add user authentication")
    plan = await kit.plan_async(spec)
    tasks = await kit.tasks_async(plan)

    # Generate extended artifacts in parallel
    data_model, research, contract, checklist, quickstart = await asyncio.gather(
        kit.generate_data_model_async(spec, plan),
        kit.generate_research_async(plan),
        kit.generate_api_contract_async(spec, plan),
        kit.generate_checklist_async(spec),
        kit.generate_quickstart_async(spec, plan),
    )

    # Save everything
    kit.save(spec)
    kit.save(plan)
    kit.save(tasks)
    kit.storage.save_data_model(data_model, spec.feature_id)
    kit.storage.save_research(research, spec.feature_id)
    kit.storage.save_api_contract(contract, spec.feature_id)
    kit.storage.save_checklist(checklist, spec.feature_id)
    kit.storage.save_quickstart(quickstart, spec.feature_id)

asyncio.run(generate_all_artifacts())
```

### Individual Artifact Generation

```python
# Load existing artifacts
spec = kit.load_specification("001-user-auth")
plan = kit.load_plan("001-user-auth")

# Generate just the data model
data_model = kit.generate_data_model(spec, plan)
print(f"Generated {len(data_model.entities)} entities")
for entity in data_model.entities:
    print(f"  - {entity.name}: {len(entity.fields)} fields")

# Generate just the API contract
contract = kit.generate_api_contract(spec, plan)
print(f"Generated {len(contract.endpoints)} endpoints")
for endpoint in contract.endpoints:
    print(f"  - {endpoint.method} {endpoint.path}")
```

## Files Modified

| File | Changes | Lines |
|------|---------|-------|
| `src/speckit/core/artifacts.py` | Created | +340 |
| `src/speckit/speckit.py` | Added 10 methods + imports | +245 |
| `src/speckit/templates/data_model.jinja2` | Created | +70 |
| `src/speckit/templates/research.jinja2` | Created | +66 |
| `src/speckit/templates/api_contract.jinja2` | Created | +86 |
| `src/speckit/templates/checklist.jinja2` | Created | +94 |
| `src/speckit/templates/quickstart.jinja2` | Created | +102 |
| **Total** | **7 files** | **+1003 lines** |

## Comparison: Phase 1 vs Phase 2

### Phase 1 (Completed)
✅ Manual artifact creation and saving
```python
# User creates artifacts manually
data_model = DataModel(
    feature_id="001-auth",
    entities=[
        DataModelEntity(
            name="User",
            fields=[...]
        )
    ]
)
kit.storage.save_data_model(data_model, "001-auth")
```

### Phase 2 (This PR)
✅ LLM-powered automatic generation
```python
# LLM generates artifacts automatically
spec = kit.load_specification("001-auth")
plan = kit.load_plan("001-auth")
data_model = kit.generate_data_model(spec, plan)
kit.storage.save_data_model(data_model, "001-auth")
```

**Key Improvement**: Users no longer need to manually construct complex Pydantic models. The LLM analyzes the specification and plan, then generates comprehensive, well-structured artifacts automatically.

## Integration with Velospec Platform

This implementation enables Velospec to:

1. **Automatically generate extended artifacts** after spec/plan phases
2. **Trigger generation** via API endpoints
3. **Commit all artifacts** to Git repositories
4. **Display artifacts** in the UI

### Suggested Velospec Integration Points

```python
# backend/src/services/ai/plan.py
async def generate_plan(specification_id: str):
    # Existing plan generation
    plan = await spec_kit.plan_async(spec)

    # NEW: Generate extended artifacts after plan
    data_model = await spec_kit.generate_data_model_async(spec, plan)
    research = await spec_kit.generate_research_async(plan)
    contract = await spec_kit.generate_api_contract_async(spec, plan)

    # Save and commit all artifacts
    git_service.commit_artifacts([
        data_model.to_markdown(),
        research.to_markdown(),
        contract.to_markdown()
    ])

# backend/src/services/ai/specification.py
async def generate_specification(description: str):
    spec = await spec_kit.specify_async(description)

    # NEW: Generate quality checklist immediately
    checklist = await spec_kit.generate_checklist_async(spec)

    # Return both for UI display
    return {
        "specification": spec,
        "checklist": checklist
    }
```

## Testing

A comprehensive test script is included: `test_extended_artifacts_generation.py`

Run with:
```bash
cd /home/alairjt/workspace/suportly/spec-kit
python test_extended_artifacts_generation.py
```

This will:
1. Generate a complete feature specification
2. Create a technical plan
3. Generate all 5 extended artifacts
4. Save everything to disk
5. Display file structure and statistics

## Breaking Changes

**None.** This is a purely additive change.

- Existing code continues to work without modifications
- New methods are optional
- No changes to existing methods or signatures

## Backward Compatibility

✅ **Fully backward compatible**
- Phase 1 users can upgrade without code changes
- Manual artifact creation still works
- New generation methods are opt-in

## Next Steps (Phase 3)

### Velospec Backend Integration

1. **Update API endpoints** to trigger artifact generation
2. **Add database storage** for extended artifacts
3. **Implement Git commits** for all artifacts
4. **Create UI components** to display artifacts

### Suggested Timeline

- **Week 1**: Backend API integration
- **Week 2**: Git commit automation
- **Week 3**: UI components
- **Week 4**: Testing and deployment

## Performance Considerations

### LLM API Calls

Each generation method makes **1 LLM API call**:

- `generate_data_model()` - 1 call (~2-3 seconds)
- `generate_research()` - 1 call (~2-3 seconds)
- `generate_api_contract()` - 1 call (~2-3 seconds)
- `generate_checklist()` - 1 call (~2-3 seconds)
- `generate_quickstart()` - 1 call (~2-3 seconds)

**Total for all 5 artifacts**: ~10-15 seconds

**Optimization**: Use async methods and `asyncio.gather()` to generate all artifacts in parallel (~3-4 seconds total).

### Cost Estimation

Using Claude Sonnet 3.5:
- Input: ~2-3K tokens per call
- Output: ~1-2K tokens per call
- Cost per artifact: ~$0.01-0.02
- Total for all 5: ~$0.05-0.10 per feature

## Documentation

All methods include:
- Complete docstrings
- Type hints
- Usage examples
- Parameter descriptions

View with:
```python
from speckit import SpecKit
help(SpecKit.generate_data_model)
```

## Conclusion

Phase 2 **completes the automation** of the extended artifacts workflow. Users can now generate comprehensive documentation, schemas, and guides automatically using AI, matching the functionality of the bash version while providing a superior Python developer experience.

**Status**: ✅ **READY FOR MERGE**

---

**Created By**: Claude Code (Sonnet 4.5)
**Date**: 2025-12-26
**Project**: Velospec Platform - Extended Artifacts Phase 2
**Related**: Phase 1 PR #4 (Extended Artifacts Models)
