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Phase 04 Completion Report

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Phase 4 Completion Report: Realtime Communication Foundation

Date Completed: 2025-11-21 03:45 Duration: ~2 hours Status: ✅ Successfully Completed (MVP Scope)

Executive Summary

Phase 4 established the realtime communication foundation for VoiceAssist by implementing a WebSocket-based streaming chat endpoint integrated with the QueryOrchestrator. The system now supports bidirectional real-time messaging with structured streaming responses, laying the groundwork for future voice features while maintaining a clear MVP scope.

Key Achievements:

  • ✅ WebSocket endpoint operational at /api/realtime/ws
  • ✅ QueryOrchestrator integration for clinical query processing
  • ✅ Message streaming protocol (message_start → message_chunk* → message_complete)
  • ✅ Connection management with keepalive (ping/pong)
  • ✅ Error handling and structured responses
  • ✅ Unit tests for WebSocket endpoint
  • ✅ Documentation updated (SERVICE_CATALOG.md)

MVP Scope Decisions:

  • ✅ Text-based streaming implemented
  • ✅ Query orchestration integrated
  • ⏸️ Full voice pipeline deferred to Phase 5+
  • ⏸️ Frontend voice UI deferred (backend-focused phase)
  • ⏸️ OpenAI Realtime API integration deferred
  • ⏸️ VAD and audio processing deferred

See also:

  • PHASE_STATUS.md (Phase 4 section)
  • docs/SERVICE_CATALOG.md
  • docs/ORCHESTRATION_DESIGN.md

Deliverables

1. WebSocket Realtime Endpoint ✅

Implementation:

  • Location: services/api-gateway/app/api/realtime.py
  • Endpoint: WS /api/realtime/ws
  • Features:
    • Connection establishment with welcome message
    • Message protocol with structured events
    • Streaming response in chunks
    • Ping/pong keepalive mechanism
    • Error handling with structured error responses

Protocol Design:

Client connects → Server sends "connected" event
Client sends "message" → Server processes → Streams response
  ↓
  message_start (with message_id)
  ↓
  message_chunk* (streamed incrementally)
  ↓
  message_complete (with citations)

Connection Manager:

  • Manages active WebSocket connections
  • Tracks client_id for each connection
  • Handles disconnection cleanup
  • Provides error messaging helpers

Testing:

  • ✅ WebSocket connection test passing
  • ✅ Message flow test passing
  • ✅ Ping/pong test passing
  • ✅ Error handling test passing
  • ✅ Integration test with QueryOrchestrator passing

2. QueryOrchestrator Integration ✅

Implementation:

  • Copied rag_service.py and llm_client.py to api-gateway services
  • Integrated QueryOrchestrator into realtime message handler
  • Query flow: WebSocket → QueryOrchestrator → LLMClient → Streaming Response

Current Behavior (Stub LLM):

  • Processes queries through QueryOrchestrator
  • Routes to cloud model stub (gpt-4o)
  • Returns formatted response: [CLOUD MODEL STUB: gpt-4o] You are a clinical decision support assistant. Answer this query: {query}
  • Simulates streaming by chunking response text

Future Integration Points:

  • Replace LLMClient stubs with real OpenAI/local LLM calls
  • Add PHI detection for routing decisions
  • Implement RAG search integration
  • Add citation generation from knowledge base

3. Message Streaming Protocol ✅

Implemented Event Types:

Client → Server:

  • message: User query with optional session_id and clinical_context_id
  • ping: Keepalive/heartbeat

Server → Client:

  • connected: Welcome message with client_id, protocol_version, capabilities
  • message_start: Marks beginning of response streaming
  • message_chunk: Incremental response content with chunk_index
  • message_complete: Final response with complete text and citations
  • pong: Keepalive response
  • error: Structured error with code and message

Protocol Version: 1.0 Capabilities (Phase 4): ["text_streaming"]

4. Supporting Services Integration ✅

QueryOrchestrator (app/services/rag_service.py):

  • Receives QueryRequest with query, session_id, clinical_context_id
  • Returns QueryResponse with answer, message_id, citations, timestamp
  • Stub implementation calls LLMClient
  • Ready for expansion in future phases

LLMClient (app/services/llm_client.py):

  • Provides unified interface for cloud and local models
  • Routing logic: PHI detected → local model, else → cloud model
  • Stub implementation returns formatted responses
  • Includes safety checks (prompt validation, token limits)

Testing Summary

Unit Tests (Phase 4) ✅

File: tests/unit/test_websocket_realtime.py

Tests implemented:

  • ✅ Connection establishment and welcome message
  • ✅ Complete message flow (start → chunks → complete)
  • ✅ Ping/pong keepalive
  • ✅ Unknown message type handling
  • ✅ QueryOrchestrator integration
  • ✅ Clinical context parameters
  • ✅ Empty message handling

Manual Testing:

  • ✅ WebSocket client test script (test_ws.py)
  • ✅ Verified streaming response with QueryOrchestrator
  • ✅ Confirmed message protocol compliance
  • ✅ Tested connection lifecycle (connect → message → disconnect)

Integration Status

Passing:

  • WebSocket endpoint responds correctly
  • QueryOrchestrator processes queries
  • LLMClient returns stub responses
  • Message streaming protocol works
  • Error handling functions

Known Issues:

  • Redis cache warnings (fastapi-cache async context manager)
    • Non-blocking, does not affect functionality
    • Will be addressed in future cache optimization

Architecture & Design Decisions

1. MVP Scope Definition

Included in Phase 4:

  • Text-based streaming chat
  • WebSocket protocol foundation
  • QueryOrchestrator integration
  • Structured message events
  • Connection management

Deferred to Future Phases:

  • Voice streaming and audio processing
  • OpenAI Realtime API integration
  • Voice Activity Detection (VAD)
  • Echo cancellation
  • Barge-in and turn-taking
  • Frontend voice UI components

Rationale:

  • Focus on backend foundation first
  • Ensure solid streaming protocol before adding voice complexity
  • Allow frontend development to proceed independently
  • Validate query orchestration flow before voice features

2. Integration Strategy

Current (Phase 4):

  • Realtime endpoint as part of API Gateway
  • Monolithic FastAPI application
  • Direct function calls to QueryOrchestrator
  • Shared database and Redis connections

Future (Phase 5+):

  • Consider extracting to separate voice-proxy service
  • Add voice-specific features (VAD, audio processing)
  • Integrate OpenAI Realtime API
  • Implement advanced streaming (server-sent events, audio chunks)

3. Protocol Design Choices

Event-based messaging:

  • Allows extensibility for future event types
  • Clean separation of concerns
  • Easy to add new capabilities (voice, video, screen sharing)

Incremental streaming:

  • Provides responsive user experience
  • Allows for real-time display of AI responses
  • Reduces perceived latency

Structured errors:

  • Machine-readable error codes
  • Consistent error format
  • Facilitates client-side error handling

Documentation Updates

Updated Files:

  • ✅ CURRENT_PHASE.md - Marked Phase 4 as In Progress, then Completed
  • ✅ PHASE_STATUS.md - Updated Phase 4 deliverables and status
  • ✅ docs/SERVICE_CATALOG.md - Added realtime endpoint documentation
    • Updated API Gateway endpoints
    • Expanded Voice Proxy Service section
    • Documented Phase 4 message protocol
    • Added implementation details

New Files:

  • ✅ services/api-gateway/app/api/realtime.py - WebSocket endpoint
  • ✅ services/api-gateway/app/services/rag_service.py - QueryOrchestrator
  • ✅ services/api-gateway/app/services/llm_client.py - LLM interface
  • ✅ tests/unit/test_websocket_realtime.py - WebSocket tests
  • ✅ test_ws.py - Manual WebSocket test client
  • ✅ docs/PHASE_04_COMPLETION_REPORT.md - This document

Known Limitations

Phase 4 Scope:

  • No voice streaming (text-only for now)
  • No audio processing (VAD, echo cancellation deferred)
  • No OpenAI Realtime API integration
  • No frontend voice UI (backend-focused phase)
  • Stub LLM responses (no real OpenAI/local LLM calls yet)

Technical:

  • QueryOrchestrator uses stub LLM (returns formatted test responses)
  • No RAG search integration (returns empty citations)
  • No PHI detection (assumes no PHI for routing)
  • No conversation persistence (messages not saved to DB)
  • No session management (client_id is transient UUID)

Testing:

  • Limited integration test coverage
  • No load testing or performance benchmarks
  • No WebSocket stress testing
  • Frontend integration not tested (no frontend yet)

Recommendations & Readiness for Phase 5

Recommendations

Immediate (Pre-Phase 5):

  1. Replace LLMClient stubs with real OpenAI API calls
  2. Integrate PHI detection for model routing
  3. Add conversation persistence to PostgreSQL
  4. Implement session management in Redis

Short-term (Phase 5):

  1. Add voice streaming capabilities (audio_chunk events)
  2. Integrate OpenAI Realtime API
  3. Implement VAD for voice activity detection
  4. Add audio processing (echo cancellation, noise reduction)

Long-term (Phase 6+):

  1. Extract voice-proxy to separate service if needed
  2. Add barge-in and turn-taking features
  3. Implement advanced streaming (multimodal)
  4. Add observability and monitoring

Phase 5 Readiness

✅ Ready:

  • WebSocket foundation is solid and tested
  • Message protocol is extensible
  • QueryOrchestrator integration works
  • Connection management is reliable
  • Error handling is structured

⏳ Prerequisites for Phase 5:

  • Real LLM integration (OpenAI API key configuration)
  • Audio processing library selection
  • Frontend voice UI design decisions
  • OpenAI Realtime API access and testing

🎯 Next Steps:

  1. Update Phase 5 scope based on MVP learnings
  2. Design voice streaming protocol extensions
  3. Select audio processing libraries
  4. Plan OpenAI Realtime API integration
  5. Begin frontend voice UI development

Conclusion

Phase 4 successfully established the realtime communication foundation for VoiceAssist. The WebSocket endpoint is operational, integrated with QueryOrchestrator, and provides a solid streaming protocol that can be extended for voice features in future phases.

Key Success Metrics:

  • ✅ WebSocket endpoint functional and tested
  • ✅ QueryOrchestrator integration working
  • ✅ Message streaming protocol implemented
  • ✅ Documentation comprehensive and up-to-date
  • ✅ Foundation ready for Phase 5 voice features

The system is ready to proceed with Phase 5: Voice Pipeline Integration.

Completion Date: 2025-11-21 03:45 Next Phase: Phase 5 - Voice Pipeline Integration Status: ✅ Phase 4 Complete - Ready for Phase 5

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