feat: Add local AI model support (Ollama, vLLM, LM Studio) with performance tracking

[Immutablemike]

Pull Request: Add Local AI Model Support to Open Lovable

🎯 Summary

This PR adds comprehensive support for local AI models to Open Lovable, enabling users to run code generation completely offline with Ollama, vLLM, and LM Studio. Includes performance tracking, analytics, and maintains full compatibility with existing cloud providers.

🚀 Features Added

Local Model Providers

• Ollama: Easy-to-use local models (llama3.2, deepseek-coder, etc.)
• vLLM: High-performance inference server for production
• LM Studio: User-friendly GUI for model management

Performance Tracking & Analytics

• Automatic tracking of generation time, success rate, and code quality
• Provider comparison dashboard
• Export capabilities for research and optimization
• File-based database for development, optional SQL for production

Developer Experience

• Zero-config fallback when local servers unavailable
• Comprehensive documentation and setup guides
• Environment variable configuration
• Model selection in existing UI dropdown

📁 Files Modified

Core Integration

• config/app.config.ts - Added local model configurations
• app/api/generate-ai-code-stream/route.ts - Provider support + tracking
• .env.example - Local model environment variables

Documentation & Guides

• docs/LOCAL_MODELS.md - Complete setup instructions
• LOCAL_INTEGRATION_SUMMARY.md - Implementation overview
• data/README.md - Database documentation

Analytics & Tracking

• lib/simple-database.ts - Performance tracking system
• app/api/local-models-analytics/route.ts - Analytics API
• data/schema.sql - SQLite database schema
• data/duckdb_schema.sql - Analytics database schema

Dependencies

• package.json - Added better-sqlite3, duckdb for tracking

🔧 Configuration

Environment Variables (.env.local)

# Ollama (recommended)
OLLAMA_ENABLED=true
OLLAMA_BASE_URL=http://localhost:11434/v1

# vLLM (high performance)
VLLM_ENABLED=true
VLLM_BASE_URL=http://localhost:8000/v1

# LM Studio (GUI)
LMSTUDIO_ENABLED=true
LMSTUDIO_BASE_URL=http://localhost:1234/v1

Model Setup Examples

# Ollama - Easy setup
curl -fsSL https://ollama.ai/install.sh | sh
ollama pull deepseek-coder:6.7b
ollama serve

# vLLM - Production server
pip install vllm
python -m vllm.entrypoints.openai.api_server \
  --model deepseek-ai/deepseek-coder-6.7b-instruct

# LM Studio - Download and start any model via GUI

📊 Performance Benefits

Benchmarks (Example Results)

Model	Provider	Avg Time	Success Rate	Cost
deepseek-coder:6.7b	ollama	4.1s	92%	Free
llama3.2:7b	ollama	3.2s	95%	Free
CodeLlama-7b	vllm	2.6s	88%	Free
gpt-4-turbo	openai	3.8s	98%	$0.03/req

Key Advantages

• Zero API costs for basic usage
• Privacy-first - code never leaves your machine
• Offline capability after initial setup
• Custom fine-tuned models support
• Hardware optimization for M1/RTX GPUs

🧪 Testing Instructions

1. Quick Test (Ollama)

# Install and start Ollama
ollama serve
ollama pull deepseek-coder:6.7b

# Configure environment
export OLLAMA_ENABLED=true

# Start Open Lovable
npm run dev

# Test in browser
# - Select "Ollama: DeepSeek Coder 6.7B" from model dropdown
# - Generate code as normal

2. Analytics Verification

# Initialize sample data
curl http://localhost:3000/api/local-models-analytics?action=init

# View performance stats
curl http://localhost:3000/api/local-models-analytics?action=stats

# Check tracking in data/clone_attempts.json

3. Fallback Testing

# Stop local servers
pkill ollama

# Verify graceful fallback to cloud providers
# Error handling logs should show retry logic

🎯 Use Cases

Individual Developers

• Learning: Experiment with different models without API costs
• Privacy: Keep proprietary code local during development
• Offline: Code generation during travel or poor connectivity

Teams & Organizations

• Cost Control: Reduce API expenses for high-volume usage
• Data Security: Sensitive code never transmitted externally
• Custom Models: Fine-tuned models for specific coding patterns
• Research: Performance analysis across different model types

Educational Institutions

• Budget-Friendly: Free AI-powered coding for students
• Experimentation: Compare model capabilities hands-on
• Air-Gapped: Secure environments without internet access

🔒 Security & Privacy

• No data transmission to external services (when using local models)
• Optional tracking can be disabled via environment variables
• Local database storage - no cloud analytics by default
• Standard OpenAI API compatibility - familiar security patterns

🚦 Backwards Compatibility

• Zero breaking changes to existing functionality
• Cloud providers unchanged - all existing models work identically
• Environment-based enablement - local models opt-in only
• Progressive enhancement - degrades gracefully if local servers unavailable

📈 Future Roadmap

Immediate Enhancements

• Model auto-discovery from running local servers
• Real-time performance monitoring dashboard
• Model switching based on prompt complexity
• Hardware utilization tracking

Research Opportunities

• Ensemble generation (multiple models for same prompt)
• Quality scoring using AST analysis
• Custom model recommendations based on project type
• Distributed inference across multiple local servers

🤝 Community Impact

This PR enables Open Lovable to serve a broader community:

• Cost-conscious developers using free local models
• Privacy-focused organizations requiring air-gapped development
• Researchers studying LLM performance characteristics
• Educational users needing budget-friendly AI coding assistance

🔧 Technical Implementation

Architecture Decisions

• OpenAI SDK compatibility - reuse existing AI SDK patterns
• Provider abstraction - local models treated identically to cloud
• Graceful degradation - automatic fallback on local server failures
• Performance tracking - lightweight JSON storage for development

Error Handling

• Connection failures → automatic retry with exponential backoff
• Model unavailable → fallback to alternative local model or cloud
• Database errors → continue operation, log for later analysis
• Invalid responses → standard validation and user feedback

Testing Strategy

• Unit tests for database operations
• Integration tests for provider switching
• Performance benchmarks across hardware configurations
• Documentation examples verified on clean installations

📋 Checklist

• Core functionality - Local model providers integrated
• Documentation - Complete setup guides and examples
• Performance tracking - Analytics and monitoring system
• Error handling - Graceful fallbacks and retry logic
• Backwards compatibility - No breaking changes
• Security - Local-first privacy architecture
• Testing - Manual verification on development setup
• Community testing - Feedback from different hardware configs
• Performance benchmarks - Cross-platform validation

🎉 Ready for Review

This implementation provides a complete local AI model integration that:

• Maintains Open Lovable's existing excellent UX
• Adds powerful new capabilities for privacy and cost savings
• Enables valuable research into local vs cloud model performance
• Opens doors for custom model fine-tuning and optimization

The code is production-ready and ready for community testing! 🚀

[Copilot]

Pull Request Overview

This PR adds comprehensive local AI model support to Open Lovable, enabling users to run code generation offline using Ollama, vLLM, and LM Studio. The implementation includes performance tracking infrastructure with two database options (file-based and SQL), extensive documentation, and maintains full backward compatibility with existing cloud providers.

Key Changes:

• Integration of three local AI providers (Ollama, vLLM, LM Studio) with OpenAI-compatible APIs
• Performance tracking system using file-based JSON storage and optional SQLite/DuckDB databases
• Comprehensive setup documentation and configuration examples

Reviewed Changes

Copilot reviewed 16 out of 17 changed files in this pull request and generated 5 comments.

Show a summary per file

File	Description
package.json	Added better-sqlite3 and duckdb dependencies for database tracking
lib/simple-database.ts	File-based JSON database implementation for performance tracking
lib/database.ts	SQLite/DuckDB database implementation (alternative to simple-database)
docs/LOCAL_MODELS.md	Complete setup guide for local model providers
data/schema.sql	SQLite schema for tracking websites and clone attempts
data/duckdb_schema.sql	DuckDB schema optimized for analytics queries
data/README.md	Database integration documentation and usage examples
config/app.config.ts	Local model configurations and display names
app/api/local-models-analytics/route.ts	Analytics API endpoint for performance data
app/api/generate-ai-code-stream/route.ts	Provider integration and performance tracking
PULL_REQUEST.md	Detailed PR description document
CLAUDE.md	AI assistant configuration file (unrelated to local models)
.windsurfrules	AI assistant configuration file (unrelated to local models)
.cursor/rules/design.mdc	AI assistant configuration file (unrelated to local models)
.superdesign/design_iterations/default_ui_darkmode.css	UI theme file (unrelated to local models)
.env.example	Environment variable examples for local providers

Comments suppressed due to low confidence (1)

.windsurfrules:1

• Duplicate line at the beginning of the file should be removed.

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[Copilot]

The component counting logic only matches 'export default function' but would miss other valid React component patterns like arrow functions ('export default'), named exports, or class components. Consider using a more comprehensive pattern or documenting this limitation.

Suggested change

	component_count: (attempt.generated_code?.match(/export default function/g) || []).length
	// Count common React component patterns: export default function/class/arrow, named exports, etc.
	component_count: (
	attempt.generated_code?.match(
	/export\s+(default\s+)?(function|class|const|let|var)\s+\w+|export\s+default\s*\(/g
	) || []
	).length

[Copilot]

Using as any to bypass TypeScript's type checking when the import statement is corrected (Comment 2) would eliminate the need for this type assertion. The current approach masks the underlying import issue.

[Copilot]

The tracking will not occur if generatedCode is empty, but tracking should happen even for failed attempts or empty responses to capture complete performance metrics. The condition should check websiteUrl && modelUsed only.

Suggested change

	if (websiteUrl && modelUsed && generatedCode) {
	if (websiteUrl && modelUsed) {

[Copilot]

Duplicate line at the beginning of the file should be removed.

Suggested change

When asked to design UI & frontend interface

[Copilot]

Duplicate line should be removed.

Suggested change

When asked to design UI & frontend interface