2:I[7012,["4765","static/chunks/4765-f5afdf8061f456f3.js","9856","static/chunks/9856-3b185291364d9bef.js","6687","static/chunks/app/docs/%5B...slug%5D/page-e07536548216bee4.js"],"MarkdownRenderer"] 4:I[9856,["4765","static/chunks/4765-f5afdf8061f456f3.js","9856","static/chunks/9856-3b185291364d9bef.js","6687","static/chunks/app/docs/%5B...slug%5D/page-e07536548216bee4.js"],""] 5:I[4126,[],""] 7:I[9630,[],""] 8:I[4278,["9856","static/chunks/9856-3b185291364d9bef.js","8172","static/chunks/8172-b3a2d6fe4ae10d40.js","3185","static/chunks/app/layout-2814fa5d15b84fe4.js"],"HeadingProvider"] 9:I[1476,["9856","static/chunks/9856-3b185291364d9bef.js","8172","static/chunks/8172-b3a2d6fe4ae10d40.js","3185","static/chunks/app/layout-2814fa5d15b84fe4.js"],"Header"] a:I[3167,["9856","static/chunks/9856-3b185291364d9bef.js","8172","static/chunks/8172-b3a2d6fe4ae10d40.js","3185","static/chunks/app/layout-2814fa5d15b84fe4.js"],"Sidebar"] b:I[7409,["9856","static/chunks/9856-3b185291364d9bef.js","8172","static/chunks/8172-b3a2d6fe4ae10d40.js","3185","static/chunks/app/layout-2814fa5d15b84fe4.js"],"PageFrame"] 3:T2a6f, # 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 6:["slug","PHASE_04_COMPLETION_REPORT","c"] 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