server/llm/fake/fake.go

package fake

import (
	"context"
	"fmt"
	"time"

	"github.com/iomodo/staff/llm"
)

// FakeProvider implements a fake LLM provider for testing
type FakeProvider struct {
	responses []string
	index     int
}

// NewFakeProvider creates a new fake provider with predefined responses
func NewFakeProvider() *FakeProvider {
	responses := []string{
		`## Task Solution

I've analyzed the task requirements and here's my proposed solution:

### Implementation Plan
1. Create the necessary data structures
2. Implement the core business logic
3. Add proper error handling
4. Write comprehensive tests
5. Update documentation

### Code Changes
- Add new functions to handle the requirements
- Update existing modules for compatibility
- Implement proper validation
- Add logging for debugging

### Testing Strategy
- Unit tests for all new functions
- Integration tests for the workflow
- Performance tests for scalability
- Edge case validation

### Files to Create/Modify
- src/main.go - Core implementation
- src/handlers.go - Request handlers
- src/models.go - Data models
- tests/ - Test files
- docs/ - Documentation updates

### Dependencies
No new external dependencies required.

### Deployment Notes
- Backward compatible changes
- No database migrations needed
- Can be deployed incrementally

This solution addresses all the requirements while maintaining code quality and system stability.`,

		`## Comprehensive Task Analysis

After careful consideration, I recommend the following approach:

### Technical Architecture
- **Backend**: Implement using existing Go patterns
- **Database**: Utilize current PostgreSQL setup
- **API**: RESTful endpoints with proper versioning
- **Security**: OAuth2 authentication with JWT tokens

### Development Steps
1. **Phase 1**: Core functionality implementation
2. **Phase 2**: User interface development
3. **Phase 3**: Testing and optimization
4. **Phase 4**: Documentation and deployment

### Risk Assessment
- **Low Risk**: Well-defined requirements
- **Medium Risk**: Timeline constraints
- **Mitigation**: Incremental development approach

### Resource Requirements
- Development time: 2-3 weeks
- Testing phase: 1 week
- Documentation: 2-3 days

### Success Metrics
- Performance benchmarks met
- All test cases passing
- User acceptance criteria satisfied
- Code coverage > 90%

This solution provides a robust foundation for future enhancements while meeting immediate business needs.`,

		`## Strategic Implementation Proposal

### Executive Summary
This task requires a comprehensive solution that balances technical excellence with business objectives.

### Solution Overview
- **Approach**: Agile development methodology
- **Technology Stack**: Current tech stack enhancement
- **Timeline**: 3-4 week delivery cycle
- **Team**: Cross-functional collaboration

### Technical Specifications
- Clean architecture principles
- Microservices design patterns
- Event-driven communication
- Comprehensive monitoring and logging

### Implementation Details
1. **Requirements Analysis**: Complete stakeholder alignment
2. **System Design**: Scalable and maintainable architecture
3. **Development**: Test-driven development approach
4. **Quality Assurance**: Automated testing pipeline
5. **Deployment**: Blue-green deployment strategy

### Business Impact
- Improved user experience
- Enhanced system reliability
- Reduced operational overhead
- Increased development velocity

### Next Steps
1. Stakeholder review and approval
2. Resource allocation confirmation
3. Development sprint planning
4. Implementation kickoff

This solution ensures long-term success while delivering immediate value to the organization.`,
	}

	return &FakeProvider{
		responses: responses,
		index:     0,
	}
}

// ChatCompletion implements the LLM interface
func (f *FakeProvider) ChatCompletion(ctx context.Context, req llm.ChatCompletionRequest) (*llm.ChatCompletionResponse, error) {
	// Simulate API delay
	time.Sleep(500 * time.Millisecond)

	// Get the next response (cycle through responses)
	response := f.responses[f.index%len(f.responses)]
	f.index++

	return &llm.ChatCompletionResponse{
		ID:      fmt.Sprintf("fake-response-%d", f.index),
		Object:  "chat.completion",
		Created: time.Now().Unix(),
		Model:   req.Model,
		Choices: []llm.ChatCompletionChoice{
			{
				Index: 0,
				Message: llm.Message{
					Role:    llm.RoleAssistant,
					Content: response,
				},
				FinishReason: "stop",
			},
		},
		Usage: llm.Usage{
			PromptTokens:     100,
			CompletionTokens: 300,
			TotalTokens:      400,
		},
	}, nil
}

// CreateEmbeddings implements the LLM interface (not used in current implementation)
func (f *FakeProvider) CreateEmbeddings(ctx context.Context, req llm.EmbeddingRequest) (*llm.EmbeddingResponse, error) {
	return &llm.EmbeddingResponse{
		Object: "list",
		Data: []llm.Embedding{
			{
				Object:    "embedding",
				Index:     0,
				Embedding: make([]float64, 1536), // OpenAI embedding size
			},
		},
		Model: req.Model,
		Usage: llm.Usage{
			PromptTokens: 50,
			TotalTokens:  50,
		},
	}, nil
}

// Close implements the LLM interface
func (f *FakeProvider) Close() error {
	// Nothing to close for fake provider
	return nil
}