# Changelog

All notable changes to the AI API Service will be documented in this file.

The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/),
and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).

## [1.0.0] - 2025-10-01

### Added

#### Core Features
- **Multi-turn Chat API** - Conversational AI with context management supporting multiple LLM providers
- **RAG (Retrieval-Augmented Generation)** - Query documents with AI-powered vector retrieval
- **Image Generation** - Text-to-image using DALL-E or Stable Diffusion
- **Voice Synthesis** - Text-to-speech with multiple voice options via OpenAI TTS
- **Speech Recognition** - Audio transcription using Whisper
- **Document Ingestion** - Upload and process PDF, DOCX, TXT files with automatic chunking

#### Model Support
- OpenAI integration (GPT-4, GPT-3.5-turbo, DALL-E, TTS, Whisper)
- HuggingFace Inference API support (Mistral, Stable Diffusion, embeddings)
- Anthropic Claude models (Claude 3 Sonnet, Opus)
- Local model support (optional, via transformers)

#### Vector Database
- Pinecone adapter for production vector storage
- In-memory vector DB fallback for development
- Cosine similarity search
- Metadata filtering support

#### Authentication & Security
- API Key authentication with Bearer token support
- Role-based access control (default, premium, admin tiers)
- Token bucket rate limiting (configurable per tier)
- Input validation with TypeScript type safety

#### Observability
- Structured JSON logging with configurable log levels
- Prometheus-style metrics endpoint
- Health check endpoint with service status
- Request/response time tracking
- Model usage statistics

#### Background Processing
- Async document ingestion workers
- Configurable worker concurrency
- Webhook notifications for completion events
- Automatic text chunking with overlap

#### Developer Experience
- Comprehensive TypeScript types
- Auto-generated API clients
- Example curl scripts
- JavaScript/Node.js client library
- Full test suite with vitest
- Detailed API documentation

#### Deployment
- Docker support with multi-stage builds
- Docker Compose for local development
- Environment-based configuration
- Health checks and graceful shutdown
- Production-ready error handling

### API Endpoints

#### Health & Monitoring
- `GET /health` - Service health check with component status
- `GET /metrics` - Request metrics and usage statistics

#### Authentication
- `POST /auth/verify` - Validate API key and check rate limits

#### AI Chat
- `POST /ai/chat` - Multi-turn conversation with context
- `GET /ai/query` - Simple question answering

#### RAG
- `POST /rag/query` - Query with document retrieval
- `GET /rag/models` - List available LLM models

#### Image Generation
- `POST /image/generate` - Generate images from text prompts

#### Voice
- `POST /voice/synthesize` - Text to speech synthesis
- `POST /voice/transcribe` - Speech to text transcription

#### Documents
- `POST /upload` - Upload and ingest documents
- `GET /docs/:id/sources` - Retrieve document chunks
- `POST /webhook/events` - Ingestion completion webhooks

### Configuration

Environment variables for all services:
- LLM provider API keys (OpenAI, HuggingFace, Anthropic)
- Vector DB configuration (Pinecone)
- Rate limiting settings per tier
- Document processing parameters
- Worker configuration
- CORS and security settings

### Testing

- Unit tests for all core services
- Integration tests for API endpoints
- Mock implementations for external services
- Rate limiting validation
- Authentication flow tests
- Vector DB operations tests

### Documentation

- Comprehensive README with architecture diagram
- API reference with curl examples
- Environment variable guide
- Deployment instructions (Docker, Hugging Face Spaces, cloud providers)
- Scaling considerations and best practices
- Cost optimization guidelines
- Troubleshooting guide

### Known Limitations

- Maximum file upload size: 10MB (configurable)
- In-memory vector DB not suitable for production
- No built-in caching layer (add Redis for production)
- Synchronous API calls (streaming support coming soon)

### Future Roadmap

- Server-Sent Events (SSE) for streaming responses
- Redis caching layer for frequent queries
- Multi-language support for responses
- Fine-tuning pipeline integration
- Analytics dashboard
- Webhook integrations for third-party services
- GraphQL API support
- gRPC endpoints for high-performance use cases
- Kubernetes deployment manifests
- Auto-scaling configuration

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## Release Notes

This is the initial release of the AI API Service, a production-ready TypeScript API for integrating multiple AI capabilities into chatbots, LLM applications, and intelligent systems.

The service is built on Encore.ts for type-safe backend development and includes comprehensive documentation, tests, and deployment configurations.

For questions, issues, or contributions, please visit the GitHub repository.