# FAISSx: Next Steps

This document outlines the current status and next steps for the FAISSx project, which provides a high-performance vector database proxy using FAISS and ZeroMQ.

## Current Status

### Project Infrastructure (Complete ✅)
- [x] Project renamed from FAISS-Proxy to FAISSx
- [x] Directory structure reorganized (faissx, client, server, examples, data)
- [x] Build system configured (setup.py, MANIFEST.in)
- [x] Documentation updated
- [x] Basic Docker deployment
- [x] Modular code architecture with separate index implementations
- [x] **Project Organization Complete**: Consolidated all tests to unified `tests/` directory, removed scattered test locations
- [x] **Dependencies Modernized**: Updated `requirements.txt` to follow Python packaging best practices, eliminated duplication
- [x] **Documentation Streamlined**: Cleaned up `notes/` directory to focus on future planning, removed historical documentation
- [x] **Development Infrastructure**: Updated pytest.ini with modern configuration, updated dev dependencies to current versions

### Core Functionality (Complete ✅)
- [x] **Vector Reconstruction Fixed**: Resolved architectural issues, verified perfect floating-point precision
- [x] **Test Suite Consolidated**: All 64+ tests now in unified location with proper organization
- [x] **Project Structure Optimized**: Removed outdated files, cleaned up ignore configurations

### Server Implementation (Complete ✅)
- [x] Create ZeroMQ server application structure
- [x] Implement authentication with API keys
- [x] Create FAISS manager for vector operations
- [x] Implement basic binary protocol for CRUD routes for indices
- [x] Implement vector addition and search operations
- [x] Add tenant isolation
- [x] Create Docker container setup
- [x] Create comprehensive server documentation
- [x] **Authentication System Complete**: Fixed authentication bug in server.py, verified multi-tenant isolation, comprehensive test suite with 7 passing tests, production-ready

### Client Implementation (Complete ✅)
- [x] Create client package structure
- [x] Implement configuration management
- [x] Implement remote API client using ZeroMQ
- [x] Create IndexFlatL2 implementation with API parity
- [x] Add documentation for client usage
- [x] Implement drop-in replacement behavior
- [x] Create test suite for client functionality
- [x] Add support for all major index types
- [x] Improve code readability with descriptive variable names

### Packaging and Distribution (Complete ✅)
- [x] Publish to PyPI
- [x] Publish Docker images to GitHub Container Registry
- [x] Create automated build and test pipeline (GitHub Actions)

### Recent Optimizations (Complete ✅)
- [x] Persistence Layer Optimization
  - [x] Improved handling for both local and remote operation modes
  - [x] More robust vector reconstruction with multiple fallback strategies
  - [x] Better error handling for saving/loading indices
  - [x] Special handling for IDMap and IDMap2 classes
- [x] IndexPQ Optimization
  - [x] Added vector caching capability
  - [x] Improved training methods with fallbacks
  - [x] Enhanced remote operation with better batching
  - [x] Comprehensive vector reconstruction methods
- [x] IndexIVFScalarQuantizer Optimization
  - [x] Improved handling of training state inconsistencies
  - [x] Better vector addition with fallbacks
  - [x] Enhanced error handling
- [x] Index Modification Module Optimization
  - [x] Added robust vector extraction with multiple fallbacks
  - [x] Implemented batched processing for large vector operations
  - [x] Enhanced merge_indices and split_index functions
  - [x] Added detailed performance logging

## Next Milestones

### Server Enhancements
- [x] Add support for additional FAISS index types:
  - [x] IndexIVFFlat
  - [x] IndexHNSW
  - [x] IndexPQ
- [x] Implement index training endpoints
- [x] Add specialized search operations (range search, etc.)
- [ ] Server-side Improvements Needed:
  - [x] Implement missing vector reconstruction methods
  - [x] Add reset method for indices
  - [x] Implement merge_indices functionality
  - [x] Standardize response formats - COMPLETED (Implemented consistent JSON responses with timestamp and standardized field names)
  - [x] Fix training behavior inconsistencies - COMPLETED (Added proper training status detection and reporting)
  - [x] Add timeout handling for long-running operations - COMPLETED (Implemented TaskWorker with proper timeout management)
  - [x] Add support for binary indices - COMPLETED (Added support for IndexBinaryFlat, IndexBinaryIVF, and IndexBinaryHash)
  - [x] Add support for vector transformations (IndexPreTransform) - COMPLETED (Implemented transformation framework with PCA, OPQ, ITQ, and normalization support)

### Client-side Improvements (Medium Priority)
- [ ] **Reconnection logic**: Automatic reconnection with exponential backoff when server connection drops
  - Implement connection health monitoring with periodic heartbeats
  - Add configurable retry policies (max attempts, backoff strategy)
  - Handle graceful degradation to local mode when server unavailable
  - Preserve client state across reconnections
- [ ] **Progress tracking**: For long-running operations like large index training/clustering
  - Add callback system for operation progress updates
  - Implement progress bars for batch operations
  - Support cancellation of long-running remote operations
  - Provide ETA estimation for large dataset processing
- [ ] **Client-side configuration**: More granular config options for connection behavior, timeouts, etc.
  - Add timeout controls per operation type (search, add, train)
  - Implement connection pooling configuration
  - Support environment variable configuration overrides
  - Add debug/verbose logging configuration options
- [ ] **Connection pooling**: For multi-process applications that need shared connections
  - Implement thread-safe connection sharing
  - Add connection pool sizing and lifecycle management
  - Support connection reuse across multiple index instances
  - Handle connection cleanup on process termination
- [ ] **Client-side caching**: Cache frequently accessed vectors/results locally
  - Implement LRU cache for search results with configurable size limits
  - Add vector reconstruction caching for frequently accessed IDs
  - Support cache invalidation strategies (TTL, manual)
  - Provide cache hit/miss metrics and statistics

## Implementation Priorities

### High Priority - COMPLETED ✅
1. ~~Publish to PyPI~~ ✅ Done
2. ~~Support for additional index types (IndexIVFFlat)~~ ✅ Done
3. ~~Implement proper index training~~ ✅ Done
4. ~~Refactor code architecture for better maintainability~~ ✅ Done
5. ~~Create detailed documentation and examples~~ ✅ Done
   - [x] Comprehensive server documentation
   - [x] Client API documentation
   - [x] Advanced examples and tutorials

### Medium Priority (in progress)
1. Add more client-side features and FAISS compatibility
   - [x] Additional index types:
     - [x] IndexIVFPQ (IVF + Product Quantization)
     - [x] IndexScalarQuantizer (efficient scalar quantization)
     - [x] IndexIDMap/IndexIDMap2 (custom vector IDs)
     - [ ] Binary indices (IndexBinaryFlat, etc.) - moved to high priority
     - [ ] IndexPreTransform (vector transformations) - moved to high priority
   - [x] ~~Additional operations:~~ ✅ Done
     - [x] Vector reconstruction (reconstruct() and reconstruct_n())
     - [x] Custom ID support (add_with_ids())
     - [x] Parameter control (nprobe for IVF indices)
     - [x] Vector removal (remove_ids())
   - [x] ~~Advanced features:~~ ✅ Done
     - [x] Factory pattern (index_factory)
     - [✗] Metadata filtering (intentionally not implemented to maintain FAISS API compatibility)
     - [x] Direct index persistence (write_index/read_index)
     - [x] Index modification (merging, splitting)
   - [x] ~~Optimization controls:~~ ✅ Done
     - [x] Fine-grained parameters
     - [x] Memory management options
   - [x] Error recovery and reconnection capabilities
2. ~~Create benchmarking tools~~ ✅ Done
3. ~~Add performance optimizations~~ ✅ Done

### Current High Priority (in progress)
1. Server-side improvements:
   - [x] Implement missing vector reconstruction methods
   - [x] Add reset method for indices
   - [x] Implement merge_indices functionality
   - [x] Implement get_index_status and get_index_info methods
   - [x] Add parameter control methods (set_parameter and get_parameter)
   - [x] Standardize response formats
   - [x] Fix training behavior inconsistencies
   - [x] Add timeout handling for long-running operations
   - [x] Add server-side support for binary indices and IndexPreTransform
2. Additional index types for 100% FAISS compatibility:
   - [x] Binary indices (IndexBinaryFlat, IndexBinaryIVF, IndexBinaryHash)
   - [x] IndexPreTransform (vector transformations)

### Future Client-Side Optimizations
Once server-side improvements are completed, the following client-side optimizations should be prioritized:

1. **Binary indices remote mode optimization**:
   - Implement server-side binary vector operations
   - Optimize Hamming distance calculations on the server
   - Avoid sending large binary vector batches over the network

2. **IndexPreTransform remote mode optimization**:
   - Implement server-side transforms to avoid sending larger untransformed vectors over the network
   - Support transform serialization for transfer between client and server
   - Add server-side vector transformation endpoints
   - Implement distributed training for transforms that require significant computation (e.g., PCA)

These optimizations will significantly improve network efficiency and overall system performance for remote operation mode.

### Future Work (Low Priority)
The following items are deferred to future releases:

#### Server-Side Enhancements
1. GPU support for server-side operations
2. Advanced persistence optimizations for large indices:
   - Progressive/lazy loading
   - Optimized metadata storage with MessagePack
   - Index chunking and sharding
   - Compression for stored indices
   - Adaptive chunk sizing
3. Monitoring dashboard and performance metrics
4. Implement caching for frequently accessed indices
5. High availability configuration
6. Support for distributed indices

#### Ecosystem Extensions
1. Additional language clients (TypeScript, Go, etc.)

### Project Status v0.0.3 ✅
As of version 0.0.3, FAISSx has successfully implemented all high and medium priority features from the original plan, providing a complete drop-in replacement for FAISS with remote execution capabilities.

The implementation maintains 100% compatibility with FAISS API while adding powerful features like remote execution, index persistence, optimization controls, and memory management.

**COMPLETED IN LATEST SESSION** ✅
- **Project Organization Complete**: Unified test suite with 64+ tests in single location, modern pytest configuration
- **Dependencies Modernized**: Updated to current Python packaging standards, eliminated duplication and version conflicts
- **Vector Reconstruction Fixed**: Resolved architectural issues, verified perfect floating-point precision in reconstruction
- **Development Infrastructure**: Streamlined documentation, cleaned up project structure, updated development tools

**PREVIOUS COMPLETIONS** ✅
- **Authentication System Complete**: Multi-tenant authentication fully implemented and tested
- **Core FAISS Compatibility**: 100% API compatibility with comprehensive index type support
- **Advanced Features**: Vector transformations, binary indices, optimization controls, memory management
- **Production Ready**: Comprehensive test coverage, Docker deployment, PyPI distribution

The implementation maintains 100% compatibility with FAISS API while adding powerful features like remote execution, index persistence, optimization controls, and memory management.

**Authentication System Complete** ✅
- Multi-tenant authentication fully implemented and tested
- Supports both CLI auth-keys and JSON auth-file formats
- Production-ready with comprehensive test coverage
- Bug fixed in server.py authentication enforcement
- All 7 authentication tests passing including tenant isolation

Significant recent optimizations include:
- **Project Organization**: Unified test infrastructure, modern dependency management, streamlined documentation
- **Core Functionality**: Fixed vector reconstruction with perfect precision, consolidated test suite
- Robust persistence layer with vector caching
- Optimized IndexPQ implementation with fallbacks
- Enhanced IndexIVFScalarQuantizer with better error handling
- Improved index modification module with batched operations
- Complete authentication system implementation

The project is now **production-ready** with clean, maintainable structure and comprehensive testing.

## Get Involved

We welcome contributions to the FAISSx project. Here are some ways to get started:

1. Try out the current implementation and provide feedback
2. Help with additional index type implementation
3. Create examples and tutorials
4. Improve documentation
   - Server and client core documentation is complete
   - Help with advanced usage examples and tutorials
5. Add benchmarking and performance tests

## Decision Log

- **2024-12-19**: ✅ **Project Organization and Modernization Complete**
  - Consolidated all tests from scattered `client/tests/` and `server/tests/` to unified `tests/` directory
  - Updated pytest.ini with modern configuration including test markers and improved output options
  - Modernized requirements.txt to follow Python packaging best practices, eliminated dependency duplication
  - Updated development dependencies to current versions (pytest>=7.0.0, black>=23.0.0, mypy>=1.0.0, etc.)
  - Streamlined `notes/` directory to focus on future planning, removed historical documentation
  - Cleaned up `.cursorignore` to be Python-specific, removed JavaScript/Node.js entries
- **2024-12-19**: ✅ **Vector Reconstruction Architecture Fixed**
  - Resolved major architectural issue where tests incorrectly mixed local and remote mode operations
  - Fixed test_reconstruction.py to properly handle both local (direct FAISS) and remote (server) modes
  - Verified perfect floating-point precision in vector reconstruction with exact bit-for-bit accuracy
  - Comprehensive testing of both single vector (`reconstruct`) and batch (`reconstruct_n`) operations
- **2024-06-26**: ✅ Implemented comprehensive support for IndexPreTransform with:
  - Created transformations.py module with support for multiple transformation types (PCA, OPQ, ITQ, L2NORM)
  - Added specialized methods for transformation operations (get_transform_info, apply_transform)
  - Implemented compound index type parsing (e.g., "PCA32,L2", "OPQ8_32,IVF100,PQ8")
  - Enhanced training system to handle both transformation and base index training
- **2024-06-25**: ✅ Implemented server-side support for binary indices (IndexBinaryFlat, IndexBinaryIVF, and IndexBinaryHash)
- **2024-06-24**: ✅ Implemented timeout handling with TaskWorker for long-running operations
- **2024-06-24**: ✅ Standardized response formats with consistent JSON structure and timestamp
- **2024-06-24**: ✅ Added proper training status detection and reporting
- **2024-06-24**: ✅ Implemented missing API methods (get_vectors, search_and_reconstruct, range_search)
- **2024-06-21**: ✅ Implemented IndexPreTransform with modular vector transformation framework
- **2024-06-20**: ✅ Implemented binary indices framework with BinaryIndex base class, IndexBinaryFlat, IndexBinaryIVF, and IndexBinaryHash
- **2024-06-16**: ✅ Fixed API method inconsistencies in IVF-PQ implementation to use add_vectors consistently
- **2024-06-16**: ✅ Verified training state initialization in scalar quantizer to match FAISS behavior
- **2023-05-18**: ✅ Decided to split the project into server and client components
- **2023-05-18**: ✅ Selected ZeroMQ for the server implementation
- **2023-05-18**: ✅ Chose to implement a drop-in replacement client library for FAISS
- **2023-05-18**: ✅ Implemented tenant isolation for multi-application deployments
- **2023-05-25**: ✅ Completed test implementation for server and client components
- **2023-06-15**: ✅ Project renamed from FAISS-Proxy to FAISSx
- **2023-06-22**: ✅ Completed client implementation with IndexFlatL2 support
- **2023-07-15**: ✅ Added proper licensing and documentation to all components
- **2023-08-02**: ✅ Created comprehensive server documentation with API protocol details
- **2023-10-05**: ✅ Published package to PyPI
- **2023-10-10**: ✅ Created Docker images with multi-architecture support (AMD64/ARM64)
- **2023-10-15**: ✅ Set up GitHub Actions for automated Docker image builds
- **2023-10-20**: ✅ Improved server startup messaging for better clarity and consistency
- **2023-11-10**: ✅ Refactored client architecture to use modular index implementations
- **2023-11-15**: ✅ Improved code quality with descriptive variable names and linter compliance
- **2023-11-20**: ✅ Implemented nprobe parameter control for IVF indices to optimize search performance
- **2023-11-25**: ✅ Added IndexIDMap implementation for custom vector IDs and vector removal
- **2023-11-25**: ✅ Implemented vector reconstruction methods (reconstruct and reconstruct_n)
- **2023-11-26**: ✅ Added IndexIDMap2 implementation for updating vectors while maintaining their IDs
- **2023-11-26**: ✅ Created comprehensive example demonstrating usage of IndexIDMap and IndexIDMap2
- **2023-11-27**: ✅ Implemented index modification features for merging and splitting indices
- **2023-11-28**: ✅ Added optimization controls for fine-grained parameters and memory management
- **2023-11-29**: ✗ Decided not to implement metadata filtering to maintain strict compatibility with the FAISS API
- **2023-11-30**: ✓ Implemented basic client-side persistence optimizations while deferring advanced server-side persistence features to future releases
- **2023-12-01**: ✅ Added error recovery and reconnection capabilities with automatic retries
- **2024-01-15**: ✅ Optimized persistence layer with robust vector reconstruction and caching
- **2024-02-01**: ✅ Enhanced IndexPQ with comprehensive fallback mechanisms
- **2024-02-15**: ✅ Improved IndexIVFScalarQuantizer implementation
- **2024-03-01**: ✅ Optimized index modification module with batched processing

## Docker and Container Support

### Current Features
- [x] Official Docker images published to GitHub Container Registry
- [x] Multi-architecture support (AMD64/ARM64)
- [x] Slim image variant using multi-stage builds
- [x] Development container with volume-mounted source code
- [x] Docker Compose configuration for easy deployment

### Container Usage
```bash
# Pull and run the official image
docker run -p 45678:45678 ghcr.io/muxi-ai/faissx:latest

# Pull and run the slim variant
docker run -p 45678:45678 ghcr.io/muxi-ai/faissx:slim

# Run with persistence enabled
docker run -p 45678:45678 -v /path/to/data:/data -e FAISSX_DATA_DIR=/data ghcr.io/muxi-ai/faissx:latest

# Run with authentication
docker run -p 45678:45678 -e FAISSX_ENABLE_AUTH=true -e FAISSX_AUTH_KEYS="key1:tenant1,key2:tenant2" ghcr.io/muxi-ai/faissx:latest
```

### Development Setup
```bash
# Use Docker Compose to run the development container
docker-compose up

# Or build and run the development container manually
docker build -t faissx:dev -f Dockerfile.dev .
docker run -p 45678:45678 -v $(pwd):/app faissx:dev
```

## Client-Side Improvements
- [ ] Implement better reconnection logic
- [ ] Add progress reporting for large operations
- [ ] Implement client-side caching
- [ ] Add connection pooling

## Documentation
- [ ] Add examples for binary indices
- [ ] Add examples for vector transformations
- [ ] Document new server-side improvements
- [ ] Create API reference documentation
