# Working Set Plan

**JJ Change ID**: ac236116
**JJ Message**: FEAT: Add voyage ai embedding provider support
**Date Created**: 2025-09-08
**Author**: Assistant

## Objective

Add Voyage AI as a new embedding provider option alongside the existing FastEmbed implementation, allowing users to choose between local FastEmbed models and cloud-based Voyage AI embeddings for their vector database operations.

## Requirements

- [ ] Add VOYAGE_AI as a new `EmbeddingProviderType` enum value
- [ ] Implement `VoyageAIProvider` class following the existing `EmbeddingProvider` interface
- [ ] Support async operations for Voyage AI API calls
- [ ] Handle API key authentication via environment variables (VOYAGE_API_KEY)
- [ ] Support multiple Voyage AI models (voyage-3.5, voyage-code-3, etc.)
- [ ] Add appropriate error handling for API failures and rate limiting
- [ ] Update factory pattern to create Voyage AI provider instances
- [ ] Add voyageai dependency to pyproject.toml
- [ ] Update settings to support Voyage AI configuration
- [ ] Maintain backward compatibility with existing FastEmbed usage
- [ ] Add comprehensive documentation with mermaid diagram showing integration points

## Technology Stack Decision

### Evaluated Options

1. **Official voyageai Python Client**
   - Pros: 
     - Official support and maintenance
     - Built-in async support with AsyncClient
     - Automatic retry logic and error handling
     - Environment variable API key detection
     - Well-documented and actively maintained
   - Cons:
     - External dependency and API calls
     - Requires internet connectivity
     - Usage costs
     - Potential rate limiting

2. **Direct HTTP API Integration**
   - Pros:
     - No additional dependencies beyond HTTP client
     - Full control over request/response handling
   - Cons:
     - More implementation complexity
     - Need to handle authentication, retries, errors manually
     - Maintenance overhead for API changes

3. **Custom Wrapper Around Multiple Providers**
   - Pros:
     - Could support multiple cloud embedding providers
   - Cons:
     - Over-engineering for current scope
     - Increased complexity without immediate benefit

### Selected Approach

**Official voyageai Python Client** - This provides the best balance of functionality, reliability, and maintainability. The official client handles authentication, async operations, retries, and error cases that we would otherwise need to implement manually.

## Implementation Milestones

1. **Milestone 1**: Add Voyage AI provider infrastructure - Test checkpoint: provider creation and basic authentication
2. **Milestone 2**: Implement embedding methods with proper async handling - Test checkpoint: successful embedding generation for documents and queries  
3. **Milestone 3**: Integration with existing factory and settings - Test checkpoint: end-to-end functionality with MCP server
4. **Milestone 4**: Documentation and final integration - Test checkpoint: complete documentation with mermaid diagram

## Success Criteria

- [ ] Users can set `EMBEDDING_PROVIDER=voyageai` and `VOYAGE_API_KEY=<key>` to use Voyage AI embeddings
- [ ] All existing FastEmbed functionality continues to work unchanged
- [ ] Voyage AI provider correctly implements all `EmbeddingProvider` interface methods
- [ ] Async operations work properly without blocking
- [ ] Error handling gracefully manages API failures and provides meaningful error messages
- [ ] Documentation includes detailed mermaid diagram showing integration architecture
- [ ] All tests pass and new functionality is covered by tests