diff --git a/.gitignore b/.gitignore index 41ac1fea..62f4cf39 100644 --- a/.gitignore +++ b/.gitignore @@ -73,3 +73,6 @@ backends/charts/advanced-backend/env-configmap.yaml extras/openmemory-mcp/data/* .env.backup.* +backends/advanced/nginx.conf +backends/advanced/Caddyfile + diff --git a/CLAUDE.md b/CLAUDE.md index 8ee8193c..9c06c934 100644 --- a/CLAUDE.md +++ b/CLAUDE.md @@ -4,7 +4,7 @@ This file provides guidance to Claude Code (claude.ai/code) when working with co ## Project Overview -Friend-Lite is at the core an AI-powered personal system - various devices, incuding but not limited to wearables from OMI can be used for at the very least audio capture, speaker specific transcription, memory extraction and retriaval. +Friend-Lite is at the core an AI-powered personal system - various devices, including but not limited to wearables from OMI can be used for at the very least audio capture, speaker specific transcription, memory extraction and retrieval. On top of that - it is being designed to support other services, that can help a user with these inputs such as reminders, action items, personal diagnosis etc. This supports a comprehensive web dashboard for management. @@ -74,36 +74,40 @@ cp .env.template .env # Configure API keys # Manual test execution (for debugging) source .env && export DEEPGRAM_API_KEY && export OPENAI_API_KEY uv run pytest tests/test_integration.py::test_full_pipeline_integration -v -s -``` - -#### Speaker Recognition Tests -```bash -cd extras/speaker-recognition -# Requires .env file with HF_TOKEN and DEEPGRAM_API_KEY -cp .env.template .env # Configure tokens +# Leave test containers running for debugging (don't auto-cleanup) +CLEANUP_CONTAINERS=false source .env && export DEEPGRAM_API_KEY && export OPENAI_API_KEY +uv run pytest tests/test_integration.py::test_full_pipeline_integration -v -s -# Run speaker recognition test suite -./run-test.sh +# Manual cleanup when needed +docker compose -f docker-compose-test.yml down -v ``` +#### Test Configuration Flags +- **CLEANUP_CONTAINERS** (default: true): Automatically stop and remove test containers after test completion + - Set to `false` for debugging: `CLEANUP_CONTAINERS=false ./run-test.sh` +- **REBUILD** (default: true): Force rebuild containers with latest code changes +- **FRESH_RUN** (default: true): Start with clean database and fresh containers +- **TRANSCRIPTION_PROVIDER** (default: deepgram): Choose transcription provider (deepgram or parakeet) + +#### Test Environment Variables +Tests use isolated test environment with overridden credentials: +- **Test Database**: `test_db` (MongoDB on port 27018, separate from production) +- **Test Ports**: Backend (8001), Qdrant (6337/6338), WebUI (3001) +- **Test Credentials**: + - `AUTH_SECRET_KEY`: test-jwt-signing-key-for-integration-tests + - `ADMIN_EMAIL`: test-admin@example.com + - `ADMIN_PASSWORD`: test-admin-password-123 +- **API Keys**: Loaded from `.env` file (DEEPGRAM_API_KEY, OPENAI_API_KEY) +- **Test Settings**: `DISABLE_SPEAKER_RECOGNITION=true` to prevent segment duplication + #### Test Script Features - **Environment Compatibility**: Works with both local .env files and CI environment variables -- **Simplified Configuration**: Uses environment variables directly, no temporary .env.test files -- **Docker Cleanup**: Uses lightweight Alpine container for reliable permission-free cleanup -- **Automatic Cleanup**: Stops and removes test containers after execution +- **Isolated Test Environment**: Separate ports and database prevent conflicts with running services +- **Automatic Cleanup**: Configurable via CLEANUP_CONTAINERS flag (default: true) - **Colored Output**: Clear progress indicators and error reporting - **Timeout Protection**: 15-minute timeout for advanced backend, 30-minute for speaker recognition -- **Fresh Testing**: Uses CACHED_MODE=False for clean test environments - -#### Debugging Integration Tests -For advanced debugging, you can still use the cached mode approach: - -1. **Edit tests/test_integration.py**: Set CACHED_MODE = True -2. **Run test manually**: `uv run pytest tests/test_integration.py -v -s --tb=short` -3. **Debug containers**: `docker logs advanced-backend-friend-backend-test-1 --tail=100` -4. **Test endpoints**: `curl -X GET http://localhost:8001/health` -5. **Clean up**: `docker compose -f docker-compose-test.yml down -v` +- **Fresh Testing**: Clean database and containers for each test run ### Mobile App Development ```bash @@ -122,7 +126,7 @@ npm run web ```bash # ASR Services cd extras/asr-services -docker compose up parakeet # Offline ASR with Parakeet +docker compose up parakeet-asr # Offline ASR with Parakeet # Speaker Recognition (with tests) cd extras/speaker-recognition @@ -136,17 +140,11 @@ docker compose up --build ## Architecture Overview -### Core Structure -- **backends/advanced-backend/**: Primary FastAPI backend with real-time audio processing - - `src/main.py`: Central FastAPI application with WebSocket audio streaming - - `src/auth.py`: Email-based authentication with JWT tokens - - `src/memory/`: LLM-powered conversation memory system using mem0 - - `webui/`: React-based web dashboard for conversation and user management - ### Key Components - **Audio Pipeline**: Real-time Opus/PCM → Application-level processing → Deepgram/Mistral transcription → memory extraction - **Wyoming Protocol**: WebSocket communication uses Wyoming protocol (JSONL + binary) for structured audio sessions -- **Application-Level Processing**: Centralized processors for audio, transcription, memory, and cropping +- **Unified Pipeline**: Job-based tracking system for all audio processing (WebSocket and file uploads) +- **Job Tracker**: Tracks pipeline jobs with stage events (audio → transcription → memory) and completion status - **Task Management**: BackgroundTaskManager tracks all async tasks to prevent orphaned processes - **Unified Transcription**: Deepgram/Mistral transcription with fallback to offline ASR services - **Memory System**: Pluggable providers (Friend-Lite native or OpenMemory MCP) @@ -203,105 +201,6 @@ Optional: - Efficient storage utilization for speech-only content - Automatic quality filtering without manual intervention -### Versioned Transcript and Memory System - -**Version Architecture**: -- **`transcript_versions`**: Array of transcript processing attempts with timestamps and providers -- **`memory_versions`**: Array of memory extraction attempts with different models/prompts -- **`active_transcript_version`**: Pointer to currently displayed transcript -- **`active_memory_version`**: Pointer to currently active memory extraction - -**Reprocessing Capabilities**: -- **Transcript Reprocessing**: Re-run speech-to-text with different providers or settings -- **Memory Reprocessing**: Re-extract memories using different LLM models or prompts -- **Version Management**: Switch between different processing results -- **Backward Compatibility**: Legacy fields auto-populated from active versions - -**Data Consistency**: -- All reprocessing operations use `conversation_id` (not `audio_uuid`) -- DateTime objects stored as ISO strings for MongoDB/JSON compatibility -- Legacy field support ensures existing integrations continue working - -### Database Schema Details - -**Collections Overview**: -- **`audio_chunks`**: All audio sessions by `audio_uuid` (always created) -- **`conversations`**: Speech-detected conversations by `conversation_id` (created conditionally) -- **`users`**: User accounts and authentication data - -**Speech-Driven Schema**: -```javascript -// audio_chunks collection (always created) -{ - "_id": ObjectId, - "audio_uuid": "uuid", // Primary identifier - "user_id": ObjectId, - "client_id": "user_suffix-device_name", - "audio_file_path": "/path/to/audio.wav", - "created_at": ISODate, - "transcript": "fallback transcript", // For non-speech audio - "segments": [...], // Speaker segments - "has_speech": boolean, // Speech detection result - "speech_analysis": {...}, // Detection metadata - "conversation_id": "conv_id" | null // Link to conversations collection -} - -// conversations collection (speech-detected only) -{ - "_id": ObjectId, - "conversation_id": "conv_uuid", // Primary identifier for user-facing operations - "audio_uuid": "audio_uuid", // Link to audio_chunks - "user_id": ObjectId, - "client_id": "user_suffix-device_name", - "created_at": ISODate, - - // Versioned Transcript System - "transcript_versions": [ - { - "version_id": "uuid", - "transcript": "text content", - "segments": [...], // Speaker diarization - "provider": "deepgram|mistral|parakeet", - "model": "nova-3|voxtral-mini-2507", - "created_at": ISODate, - "processing_time_seconds": 12.5, - "metadata": {...} - } - ], - "active_transcript_version": "uuid", // Points to current version - - // Versioned Memory System - "memory_versions": [ - { - "version_id": "uuid", - "memory_count": 5, - "transcript_version_id": "uuid", // Which transcript was used - "provider": "friend_lite|openmemory_mcp", - "model": "gpt-4o-mini|ollama-llama3", - "created_at": ISODate, - "processing_time_seconds": 45.2, - "metadata": {...} - } - ], - "active_memory_version": "uuid", // Points to current version - - // Legacy Fields (auto-populated from active versions) - "transcript": "text", // From active_transcript_version - "segments": [...], // From active_transcript_version - "memories": [...], // From active_memory_version - "memory_count": 5 // From active_memory_version -} -``` - -**Key Architecture Benefits**: -- **Clean Separation**: Raw audio storage vs user-facing conversations -- **Speech Filtering**: Only meaningful conversations appear in UI -- **Version History**: Complete audit trail of processing attempts -- **Backward Compatibility**: Legacy fields ensure existing code works -- **Reprocessing Support**: Easy to re-run with different providers/models -- **Service Decoupling**: Conversation creation independent of memory processing -- **Error Isolation**: Memory service failures don't affect conversation storage - ## Authentication & Security - **User System**: Email-based authentication with MongoDB ObjectId user IDs @@ -330,7 +229,7 @@ DEEPGRAM_API_KEY=your-deepgram-key-here # Optional: PARAKEET_ASR_URL=http://host.docker.internal:8767 # Optional: TRANSCRIPTION_PROVIDER=deepgram -# Memory Provider (New) +# Memory Provider MEMORY_PROVIDER=friend_lite # or openmemory_mcp # Database @@ -347,7 +246,7 @@ CORS_ORIGINS=http://localhost:3000,http://localhost:5173 ### Memory Provider Configuration -Friend-Lite now supports two pluggable memory backends: +Friend-Lite supports two pluggable memory backends: #### Friend-Lite Memory Provider (Default) ```bash @@ -378,81 +277,6 @@ OPENMEMORY_TIMEOUT=30 OPENAI_API_KEY=your-openai-key-here ``` -#### OpenMemory MCP Interface Patterns - -**Important**: OpenMemory MCP stores memories **per-app**, not globally. Understanding this architecture is critical for proper integration. - -**App-Based Storage Architecture:** -- All memories are stored under specific "apps" (namespaces) -- Generic endpoints (`/api/v1/memories/`) return empty results -- App-specific endpoints (`/api/v1/apps/{app_id}/memories`) contain the actual memories - -**Hardcoded Values and Configuration:** -```bash -# Default app name (configurable via OPENMEMORY_CLIENT_NAME) -Default: "friend_lite" - -# Hardcoded metadata (NOT configurable) -"source": "friend_lite" # Always hardcoded in Friend-Lite - -# User ID for OpenMemory MCP server -OPENMEMORY_USER_ID=openmemory # Configurable -``` - -**API Interface Pattern:** -```python -# 1. App Discovery - Find app by client_name -GET /api/v1/apps/ -# Response: {"apps": [{"id": "uuid", "name": "friend_lite", ...}]} - -# 2. Memory Creation - Uses generic endpoint but assigns to app -POST /api/v1/memories/ -{ - "user_id": "openmemory", - "text": "memory content", - "app": "friend_lite", # Uses OPENMEMORY_CLIENT_NAME - "metadata": { - "source": "friend_lite", # Hardcoded - "client": "friend_lite" # Uses OPENMEMORY_CLIENT_NAME - } -} - -# 3. Memory Retrieval - Must use app-specific endpoint -GET /api/v1/apps/{app_id}/memories?user_id=openmemory&page=1&size=10 - -# 4. Memory Search - Must use app-specific endpoint with search_query -GET /api/v1/apps/{app_id}/memories?user_id=openmemory&search_query=keyword&page=1&size=10 -``` - -**Friend-Lite Integration Flow:** -1. **App Discovery**: Query `/api/v1/apps/` to find app matching `OPENMEMORY_CLIENT_NAME` -2. **Fallback**: If client app not found, use first available app -3. **Operations**: All memory operations use the app-specific endpoints with discovered `app_id` - -**Testing OpenMemory MCP Integration:** -```bash -# Configure .env file with OpenMemory MCP settings -cp .env.template .env -# Edit .env to set MEMORY_PROVIDER=openmemory_mcp and configure OPENMEMORY_* variables - -# Start OpenMemory MCP server -cd extras/openmemory-mcp && docker compose up -d - -# Run integration tests (reads configuration from .env file) -cd backends/advanced && ./run-test.sh - -# Manual testing - Check app structure -curl -s "http://localhost:8765/api/v1/apps/" | jq - -# Test memory creation -curl -X POST "http://localhost:8765/api/v1/memories/" \ - -H "Content-Type: application/json" \ - -d '{"user_id": "openmemory", "text": "test memory", "app": "friend_lite"}' - -# Retrieve memories (replace app_id with actual ID from apps endpoint) -curl -s "http://localhost:8765/api/v1/apps/{app_id}/memories?user_id=openmemory" | jq -``` - ### Transcription Provider Configuration Friend-Lite supports multiple transcription services: @@ -480,295 +304,40 @@ OLLAMA_BASE_URL=http://ollama:11434 SPEAKER_SERVICE_URL=http://speaker-recognition:8085 ``` -## Transcription Architecture - -### Provider System -Friend-Lite supports multiple transcription providers: - -**Online Providers (API-based):** -- **Deepgram**: High-quality transcription using Nova-3 model with real-time streaming -- **Mistral**: Voxtral models for transcription with REST API processing - -**Offline Providers (Local processing):** -- **Parakeet**: Local speech recognition service available in extras/asr-services - -**Provider Interface:** -The transcription system handles: -- Connection management and health checks -- Audio format handling (streaming vs batch) -- Error handling and reconnection -- Unified transcript format normalization - -## Wyoming Protocol Implementation - -### Overview -The system uses Wyoming protocol for WebSocket communication between mobile apps and backends. Wyoming is a peer-to-peer protocol for voice assistants that combines JSONL headers with binary audio payloads. - -### Protocol Format -``` -{JSON_HEADER}\n - -``` - -### Supported Events - -#### Audio Session Events -- **audio-start**: Signals the beginning of an audio recording session - ```json - {"type": "audio-start", "data": {"rate": 16000, "width": 2, "channels": 1}, "payload_length": null} - ``` - -- **audio-chunk**: Contains raw audio data with format metadata - ```json - {"type": "audio-chunk", "data": {"rate": 16000, "width": 2, "channels": 1}, "payload_length": 320} - <320 bytes of PCM/Opus audio data> - ``` - -- **audio-stop**: Signals the end of an audio recording session - ```json - {"type": "audio-stop", "data": {"timestamp": 1234567890}, "payload_length": null} - ``` - -### Backend Implementation - -#### Advanced Backend (`/ws_pcm`) -- **Full Wyoming Protocol Support**: Parses all Wyoming events for session management -- **Session Tracking**: Only processes audio chunks when session is active (after audio-start) -- **Conversation Boundaries**: Uses audio-start/stop events to define conversation segments -- **Backward Compatibility**: Fallback to raw binary audio for older clients - -#### Simple Backend (`/ws`) -- **Minimal Wyoming Support**: Parses audio-chunk events, ignores others -- **Opus Processing**: Handles Opus-encoded audio chunks from Wyoming protocol -- **Graceful Degradation**: Falls back to raw Opus packets for compatibility - -### Mobile App Integration - -Mobile apps should implement Wyoming protocol for proper session management: - -```javascript -// Start audio session -const audioStart = { - type: "audio-start", - data: { rate: 16000, width: 2, channels: 1 }, - payload_length: null -}; -websocket.send(JSON.stringify(audioStart) + '\n'); - -// Send audio chunks -const audioChunk = { - type: "audio-chunk", - data: { rate: 16000, width: 2, channels: 1 }, - payload_length: audioData.byteLength -}; -websocket.send(JSON.stringify(audioChunk) + '\n'); -websocket.send(audioData); - -// End audio session -const audioStop = { - type: "audio-stop", - data: { timestamp: Date.now() }, - payload_length: null -}; -websocket.send(JSON.stringify(audioStop) + '\n'); -``` +## Quick API Reference -### Benefits -- **Clear Session Boundaries**: No timeout-based conversation detection needed -- **Structured Communication**: Consistent protocol across all audio streaming -- **Future Extensibility**: Room for additional event types (pause, resume, metadata) -- **Backward Compatibility**: Works with existing raw audio streaming clients - -## Memory System Architecture - -### Overview -Friend-Lite supports two pluggable memory backends that can be selected via configuration: - -#### 1. Friend-Lite Memory Provider (`friend_lite`) -The sophisticated in-house memory implementation with full control and customization: - -**Features:** -- Custom LLM-powered memory extraction with enhanced prompts -- Individual fact storage (no JSON blobs) -- Smart deduplication algorithms -- Intelligent memory updates (ADD/UPDATE/DELETE decisions) -- **Semantic search** with relevance threshold filtering -- **Memory count API** with total count tracking from native Qdrant -- Direct Qdrant vector storage with accurate similarity scoring -- Custom memory prompts and processing -- No external dependencies - -**Architecture Flow:** -1. **Audio Input** → Transcription via Deepgram/Parakeet -2. **Memory Extraction** → LLM processes transcript using custom prompts -3. **Fact Parsing** → XML/JSON parsing into individual memory entries -4. **Deduplication** → Smart algorithms prevent duplicate memories -5. **Vector Storage** → Direct Qdrant storage with embeddings -6. **Memory Updates** → LLM-driven action proposals (ADD/UPDATE/DELETE) - -#### 2. OpenMemory MCP Provider (`openmemory_mcp`) -Thin client that delegates all memory processing to external OpenMemory MCP server: - -**Features:** -- Professional memory extraction (handled by OpenMemory) -- Battle-tested deduplication (handled by OpenMemory) -- Semantic vector search (handled by OpenMemory) -- ACL-based user isolation (handled by OpenMemory) -- Cross-client compatibility (Claude Desktop, Cursor, Windsurf) -- Web UI for memory management at http://localhost:8765 - -**Architecture Flow:** -1. **Audio Input** → Transcription via Deepgram/Parakeet -2. **MCP Delegation** → Send enriched transcript to OpenMemory MCP server -3. **External Processing** → OpenMemory handles extraction, deduplication, storage -4. **Result Mapping** → Convert MCP results to Friend-Lite MemoryEntry format -5. **Client Management** → Automatic user context switching via MCP client - -### Memory Provider Comparison - -| Feature | Friend-Lite | OpenMemory MCP | -|---------|-------------|----------------| -| **Processing** | Custom LLM extraction | Delegates to OpenMemory | -| **Deduplication** | Custom algorithms | OpenMemory handles | -| **Vector Storage** | Direct Qdrant | OpenMemory handles | -| **Search Features** | Semantic search with threshold filtering | Semantic search with relevance scoring | -| **Memory Count** | Native Qdrant count API | Varies by OpenMemory support | -| **Dependencies** | Qdrant + MongoDB | External OpenMemory server | -| **Customization** | Full control | Limited to OpenMemory features | -| **Cross-client** | Friend-Lite only | Works with Claude Desktop, Cursor, etc | -| **Web UI** | Friend-Lite WebUI with advanced search | OpenMemory UI + Friend-Lite WebUI | -| **Memory Format** | Individual facts | OpenMemory format | -| **Setup Complexity** | Medium | High (external server required) | - -### Switching Memory Providers - -You can switch providers by changing the `MEMORY_PROVIDER` environment variable: +### Common Endpoints +- **GET /health**: Basic application health check +- **GET /readiness**: Service dependency validation +- **WS /ws_pcm**: Primary audio streaming endpoint (Wyoming protocol + raw PCM fallback) +- **GET /api/conversations**: User's conversations with transcripts +- **GET /api/memories/search**: Semantic memory search with relevance scoring +- **POST /auth/jwt/login**: Email-based login (returns JWT token) +### Authentication Flow ```bash -# Switch to OpenMemory MCP -echo "MEMORY_PROVIDER=openmemory_mcp" >> .env +# 1. Get auth token +curl -s -X POST \ + -H "Content-Type: application/x-www-form-urlencoded" \ + -d "username=admin@example.com&password=your-password-here" \ + http://localhost:8000/auth/jwt/login -# Switch back to Friend-Lite -echo "MEMORY_PROVIDER=friend_lite" >> .env +# 2. Use token in API calls +curl -s -H "Authorization: Bearer YOUR_TOKEN" \ + http://localhost:8000/api/conversations ``` -**Note:** Existing memories are not automatically migrated between providers. Each provider maintains its own memory storage. - -### OpenMemory MCP Setup - -To use the OpenMemory MCP provider: - +### Development Reset Commands ```bash -# 1. Start external OpenMemory MCP server -cd extras/openmemory-mcp -docker compose up -d - -# 2. Configure Friend-Lite to use OpenMemory MCP +# Reset all data (development only) cd backends/advanced -echo "MEMORY_PROVIDER=openmemory_mcp" >> .env +sudo rm -rf data/ -# 3. Start Friend-Lite backend +# Reset Docker volumes +docker compose down -v docker compose up --build -d ``` -### When to Use Each Provider - -**Use Friend-Lite when:** -- You want full control over memory processing -- You need custom memory extraction logic -- You prefer fewer external dependencies -- You want to customize memory prompts and algorithms -- You need individual fact-based memory storage - -**Use OpenMemory MCP when:** -- You want professional, battle-tested memory processing -- You need cross-client compatibility (Claude Desktop, Cursor, etc.) -- You prefer to leverage external expertise rather than maintain custom logic -- You want access to OpenMemory's web interface -- You're already using OpenMemory in other tools - -## Versioned Processing System - -### Overview - -Friend-Lite implements a comprehensive versioning system for both transcript and memory processing, allowing multiple processing attempts with different providers, models, or settings while maintaining a clean user experience. - -### Version Data Structure - -**Transcript Versions**: -```json -{ - "transcript_versions": [ - { - "version_id": "uuid", - "transcript": "processed text", - "segments": [...], - "provider": "deepgram|mistral|parakeet", - "model": "nova-3|voxtral-mini-2507", - "created_at": "2025-01-15T10:30:00Z", - "processing_time_seconds": 12.5, - "metadata": { - "confidence_scores": [...], - "speaker_diarization": true - } - } - ], - "active_transcript_version": "uuid" -} -``` - -**Memory Versions**: -```json -{ - "memory_versions": [ - { - "version_id": "uuid", - "memory_count": 5, - "transcript_version_id": "uuid", - "provider": "friend_lite|openmemory_mcp", - "model": "gpt-4o-mini|ollama-llama3", - "created_at": "2025-01-15T10:32:00Z", - "processing_time_seconds": 45.2, - "metadata": { - "prompt_version": "v2.1", - "extraction_quality": "high" - } - } - ], - "active_memory_version": "uuid" -} -``` - -### Reprocessing Workflows - -**Transcript Reprocessing**: -1. Trigger via API: `POST /api/conversations/{conversation_id}/reprocess-transcript` -2. System creates new transcript version with different provider/model -3. New version added to `transcript_versions` array -4. User can activate any version via `activate-transcript` endpoint -5. Legacy `transcript` field automatically updated from active version - -**Memory Reprocessing**: -1. Trigger via API: `POST /api/conversations/{conversation_id}/reprocess-memory` -2. Specify which transcript version to use as input -3. System creates new memory version using specified transcript -4. New version added to `memory_versions` array -5. User can activate any version via `activate-memory` endpoint -6. Legacy `memories` field automatically updated from active version - -### Legacy Field Compatibility - -**Automatic Population**: -- `transcript`: Auto-populated from active transcript version -- `segments`: Auto-populated from active transcript version -- `memories`: Auto-populated from active memory version -- `memory_count`: Auto-populated from active memory version - -**Backward Compatibility**: -- Existing API clients continue working without modification -- WebUI displays active versions by default -- Advanced users can access version history and switch between versions - ## Development Notes ### Package Management @@ -822,418 +391,15 @@ The system includes comprehensive health checks: ### Cursor Rule Integration Project includes `.cursor/rules/always-plan-first.mdc` requiring understanding before coding. Always explain the task and confirm approach before implementation. +## Extended Documentation -## API Reference - -### Health & Status Endpoints -- **GET /health**: Basic application health check -- **GET /readiness**: Service dependency validation (MongoDB, Qdrant, etc.) -- **GET /api/metrics**: System metrics and debug tracker status (Admin only) -- **GET /api/processor/status**: Processor queue status and health (Admin only) -- **GET /api/processor/tasks**: All active processing tasks (Admin only) -- **GET /api/processor/tasks/{client_id}**: Processing task status for specific client (Admin only) - -### WebSocket Endpoints -- **WS /ws_pcm**: Primary audio streaming endpoint (Wyoming protocol + raw PCM fallback) -- **WS /ws**: Simple audio streaming endpoint (Opus packets + Wyoming audio-chunk events) - -### Memory & Conversation Debugging -- **GET /api/admin/memories**: All memories across all users with debug stats (Admin only) -- **GET /api/memories/unfiltered**: User's memories without filtering -- **GET /api/memories/search**: Semantic memory search with relevance scoring -- **GET /api/conversations**: User's conversations with transcripts -- **GET /api/conversations/{conversation_id}**: Specific conversation details -- **POST /api/conversations/{conversation_id}/reprocess-transcript**: Re-run transcript processing -- **POST /api/conversations/{conversation_id}/reprocess-memory**: Re-extract memories with different parameters -- **GET /api/conversations/{conversation_id}/versions**: Get all transcript and memory versions -- **POST /api/conversations/{conversation_id}/activate-transcript**: Switch to a different transcript version -- **POST /api/conversations/{conversation_id}/activate-memory**: Switch to a different memory version - -### Client Management -- **GET /api/clients/active**: Currently active WebSocket clients -- **GET /api/users**: List all users (Admin only) - -### File Processing -- **POST /api/process-audio-files**: Upload and process audio files (Admin only) - - Note: Processes files sequentially, may timeout for large files - - Client timeout: 5 minutes, Server processing: up to 3x audio duration + 60s - - Example usage: - ```bash - # Step 1: Read .env file for ADMIN_EMAIL and ADMIN_PASSWORD - # Step 2: Get auth token - # Step 3: Use token in file upload - curl -X POST \ - -H "Authorization: Bearer YOUR_TOKEN_HERE" \ - -F "files=@/path/to/audio.wav" \ - -F "device_name=test-upload" \ - http://localhost:8000/api/process-audio-files - ``` - -### Authentication -- **POST /auth/jwt/login**: Email-based login (returns JWT token) -- **GET /users/me**: Get current authenticated user -- **GET /api/auth/config**: Authentication configuration - -### Step-by-Step API Testing Guide - -When testing API endpoints that require authentication, follow these steps: - -#### Step 1: Read credentials from .env file -```bash -# Use the Read tool to view the .env file and identify credentials -# Look for: -# ADMIN_EMAIL=admin@example.com -# ADMIN_PASSWORD=your-password-here -``` - -#### Step 2: Get authentication token -```bash -curl -s -X POST \ - -H "Content-Type: application/x-www-form-urlencoded" \ - -d "username=admin@example.com&password=your-password-here" \ - http://localhost:8000/auth/jwt/login -``` -This returns: -```json -{"access_token":"eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9...","token_type":"bearer"} -``` - -#### Step 3: Use the token in API calls -```bash -# Extract the token from the response above and use it: -curl -s -H "Authorization: Bearer eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9..." \ - http://localhost:8000/api/conversations - -# For reprocessing endpoints: -curl -s -X POST \ - -H "Authorization: Bearer eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9..." \ - -H "Content-Type: application/json" \ - http://localhost:8000/api/conversations/{conversation_id}/reprocess-transcript -``` - -**Important**: Always read the .env file first using the Read tool rather than using shell commands like `grep` or `cut`. This ensures you see the exact values and can copy them accurately. - -#### Step 4: Testing Reprocessing Endpoints -Once you have the auth token, you can test the reprocessing functionality: - -```bash -# Get list of conversations to find a conversation_id -curl -s -H "Authorization: Bearer YOUR_TOKEN" \ - http://localhost:8000/api/conversations - -# Test transcript reprocessing (uses conversation_id) -curl -s -X POST \ - -H "Authorization: Bearer YOUR_TOKEN" \ - -H "Content-Type: application/json" \ - http://localhost:8000/api/conversations/YOUR_CONVERSATION_ID/reprocess-transcript - -# Test memory reprocessing (uses conversation_id and transcript_version_id) -curl -s -X POST \ - -H "Authorization: Bearer YOUR_TOKEN" \ - -H "Content-Type: application/json" \ - -d '{"transcript_version_id": "VERSION_ID"}' \ - http://localhost:8000/api/conversations/YOUR_CONVERSATION_ID/reprocess-memory - -# Get transcript and memory versions -curl -s -H "Authorization: Bearer YOUR_TOKEN" \ - http://localhost:8000/api/conversations/YOUR_CONVERSATION_ID/versions - -# Activate a specific transcript version -curl -s -X POST \ - -H "Authorization: Bearer YOUR_TOKEN" \ - -H "Content-Type: application/json" \ - -d '{"transcript_version_id": "VERSION_ID"}' \ - http://localhost:8000/api/conversations/YOUR_CONVERSATION_ID/activate-transcript - -# Activate a specific memory version -curl -s -X POST \ - -H "Authorization: Bearer YOUR_TOKEN" \ - -H "Content-Type: application/json" \ - -d '{"memory_version_id": "VERSION_ID"}' \ - http://localhost:8000/api/conversations/YOUR_CONVERSATION_ID/activate-memory -``` - -### Development Reset Endpoints -Useful endpoints for resetting state during development: - -#### Data Cleanup -- **DELETE /api/admin/memory/delete-all**: Delete all memories for the current user -- **DELETE /api/memories/{memory_id}**: Delete a specific memory -- **DELETE /api/conversations/{conversation_id}**: Delete a specific conversation (keeps original audio file in audio_chunks) -- **DELETE /api/chat/sessions/{session_id}**: Delete a chat session and all its messages -- **DELETE /api/users/{user_id}**: Delete a user (Admin only) - - Optional query params: `delete_conversations=true`, `delete_memories=true` - -#### Quick Reset Commands -```bash -# Reset all data (development only) -cd backends/advanced -sudo rm -rf data/ - -# Reset Docker volumes -docker compose down -v -docker compose up --build -d -``` - - -## Speaker Recognition Service Features - -### Speaker Analysis & Visualization -The speaker recognition service now includes advanced analysis capabilities: - -#### Embedding Analysis (/speakers/analysis endpoint) -- **2D/3D Visualization**: Interactive embedding plots using UMAP, t-SNE, or PCA -- **Clustering Analysis**: Automatic clustering using DBSCAN or K-means -- **Speaker Similarity Detection**: Identifies speakers with similar embeddings -- **Quality Metrics**: Embedding separation quality and confidence scores -- **Interactive Controls**: Adjustable analysis parameters and visualization options - -Access via: `extras/speaker-recognition/webui` → Speakers → Embedding Analysis tab - -#### Live Inference Feature (/infer-live page) -Real-time speaker identification and transcription: -- **WebRTC Audio Capture**: Live microphone access with waveform visualization -- **Deepgram Streaming**: Real-time transcription with speaker diarization -- **Live Speaker ID**: Identifies enrolled speakers in real-time using internal service -- **Session Statistics**: Live metrics for words, speakers, and confidence scores -- **Configurable Settings**: Adjustable confidence thresholds and audio parameters - -Access via: `extras/speaker-recognition/webui` → Live Inference - -### Technical Implementation - -#### Backend (Python) -- **Analysis Utils**: `src/simple_speaker_recognition/utils/analysis.py` - - UMAP/t-SNE dimensionality reduction - - DBSCAN/K-means clustering - - Cosine similarity analysis - - Quality metrics calculation -- **API Endpoint**: `/speakers/analysis` - Returns processed embedding analysis -- **Dependencies**: Added `umap-learn` for dimensionality reduction - -#### Frontend (React/TypeScript) -- **EmbeddingPlot Component**: Interactive Plotly.js visualizations -- **LiveAudioCapture Component**: WebRTC audio recording with waveform -- **DeepgramStreaming Service**: WebSocket integration for real-time transcription -- **InferLive Page**: Complete live inference interface - -### Usage Instructions - -#### Setting up Live Inference -1. Navigate to Live Inference page -2. Configure Deepgram API key in settings -3. Adjust speaker identification settings (confidence threshold) -4. Start live session to begin real-time transcription and speaker ID - -**Technical Details:** -- **Audio Processing**: Uses browser's native sample rate (typically 44.1kHz or 48kHz) -- **Buffer Retention**: 120 seconds of audio for improved utterance capture -- **Real-time Updates**: Live transcription with speaker identification results - -#### Using Speaker Analysis -1. Go to Speakers page → Embedding Analysis tab -2. Select analysis method (UMAP, t-SNE, PCA) -3. Choose clustering algorithm (DBSCAN, K-means) -4. Adjust similarity threshold for speaker detection -5. View interactive plots and quality metrics - -### Deployment Notes -- Requires Docker rebuild to pick up new Python dependencies -- Frontend dependencies (Plotly.js) already included -- Live inference requires Deepgram API key for streaming transcription -- Speaker identification uses existing enrolled speakers from database - -### Live Inference Troubleshooting -- **"NaN:NaN" timestamps**: Fixed in recent updates, ensure you're using latest version -- **Poor speaker identification**: Try adjusting confidence threshold or re-enrolling speakers -- **Audio processing delays**: Check browser console for sample rate detection logs -- **Buffer overflow issues**: Extended to 120-second retention for better performance -- **"extraction_failed" errors**: Usually indicates audio buffer timing issues - check console logs for buffer availability - -## Distributed Self-Hosting Architecture - -Friend-Lite supports distributed deployment across multiple machines, allowing you to separate GPU-intensive services from lightweight backend components. This is ideal for scenarios where you have a dedicated GPU machine and want to run the main backend on a VPS or Raspberry Pi. - -### Architecture Patterns - -#### Single Machine (Default) -All services run on one machine using Docker Compose - ideal for development and simple deployments. - -#### Distributed GPU Setup -**GPU Machine (High-performance):** -- LLM services (Ollama with GPU acceleration) -- ASR services (Parakeet with GPU) -- Speaker recognition service -- Deepgram fallback can remain on backend machine - -**Backend Machine (Lightweight - VPS/RPi):** -- Friend-Lite backend (FastAPI) -- React WebUI -- MongoDB -- Qdrant vector database - -### Networking with Tailscale - -Tailscale VPN provides secure, encrypted networking between distributed services: - -**Benefits:** -- **Zero configuration networking**: Services discover each other automatically -- **Encrypted communication**: All inter-service traffic is encrypted -- **Firewall friendly**: Works behind NATs and firewalls -- **Access control**: Granular permissions for service access -- **CORS support**: Built-in support for Tailscale IP ranges (100.x.x.x) - -**Installation:** -```bash -# On each machine -curl -fsSL https://tailscale.com/install.sh | sh -sudo tailscale up -``` - -### Distributed Service Configuration - -#### GPU Machine Services -```bash -# .env on GPU machine -OLLAMA_BASE_URL=http://0.0.0.0:11434 # Expose to Tailscale network -SPEAKER_SERVICE_URL=http://0.0.0.0:8085 - -# Enable GPU acceleration for Ollama -docker run -d --gpus=all -p 11434:11434 ollama/ollama:latest -``` - -#### Backend Machine Configuration -```bash -# .env on backend machine -OLLAMA_BASE_URL=http://100.x.x.x:11434 # GPU machine Tailscale IP -SPEAKER_SERVICE_URL=http://100.x.x.x:8085 # GPU machine Tailscale IP - -# Parakeet ASR services can also be distributed (if using offline ASR) -# PARAKEET_ASR_URL=http://100.x.x.x:8767 - -# CORS automatically supports Tailscale IPs (no configuration needed) -``` - -#### Service URL Examples - -**Common remote service configurations:** -```bash -# LLM Processing (GPU machine) -OLLAMA_BASE_URL=http://100.64.1.100:11434 -OPENAI_BASE_URL=http://100.64.1.100:8080 # For vLLM/OpenAI-compatible APIs - -# Speech Recognition (GPU machine) -# PARAKEET_ASR_URL=http://100.64.1.100:8767 # If using Parakeet ASR -SPEAKER_SERVICE_URL=http://100.64.1.100:8085 - -# Database services (can be on separate machine) -MONGODB_URI=mongodb://100.64.1.200:27017 # Database name: friend-lite -QDRANT_BASE_URL=http://100.64.1.200:6333 -``` - -### Deployment Steps - -#### 1. Set up Tailscale on all machines -```bash -# Install and connect each machine to your Tailscale network -curl -fsSL https://tailscale.com/install.sh | sh -sudo tailscale up -``` - -#### 2. Deploy GPU services -```bash -# On GPU machine - start GPU-accelerated services -cd extras/asr-services && docker compose up parakeet -d -cd extras/speaker-recognition && docker compose up --build -d - -# Start Ollama with GPU support -docker run -d --gpus=all -p 11434:11434 \ - -v ollama:/root/.ollama \ - ollama/ollama:latest -``` - -#### 3. Configure backend machine -```bash -# Update .env with Tailscale IPs of GPU machine -OLLAMA_BASE_URL=http://[gpu-machine-tailscale-ip]:11434 -SPEAKER_SERVICE_URL=http://[gpu-machine-tailscale-ip]:8085 - -# Start lightweight backend services -docker compose up --build -d -``` - -#### 4. Verify connectivity -```bash -# Test service connectivity from backend machine -curl http://[gpu-machine-ip]:11434/api/tags # Ollama -curl http://[gpu-machine-ip]:8085/health # Speaker recognition -``` - -### Performance Considerations - -**Network Latency:** -- Tailscale adds minimal latency (typically <5ms between nodes) -- LLM inference: Network time negligible compared to GPU processing -- ASR streaming: Use local fallback for latency-sensitive applications - -**Bandwidth Usage:** -- Audio streaming: ~128kbps for Opus, ~512kbps for PCM -- LLM requests: Typically <1MB per conversation -- Memory embeddings: ~3KB per memory vector - -**Processing Time Expectations:** -- Transcription (Deepgram): 2-5 seconds for 4-minute audio -- Transcription (Parakeet): 5-10 seconds for 4-minute audio -- Memory extraction (OpenAI GPT-4o-mini): 30-40 seconds for typical conversation -- Memory extraction (Ollama local): 45-90 seconds depending on model and GPU -- Full pipeline (4-min audio): 40-60 seconds with cloud services, 60-120 seconds with local models - -### Security Best Practices - -**Tailscale Access Control:** -```json -{ - "acls": [ - { - "action": "accept", - "src": ["tag:backend"], - "dst": ["tag:gpu:11434", "tag:gpu:8085", "tag:gpu:8767"] - } - ], - "tagOwners": { - "tag:backend": ["your-email@example.com"], - "tag:gpu": ["your-email@example.com"] - } -} -``` - -**Service Isolation:** -- Run GPU services in containers with limited network access -- Use Tailscale subnet routing for additional security -- Monitor service access logs for unauthorized requests - -### Troubleshooting Distributed Setup - -**Common Issues:** -- **CORS errors**: Tailscale IPs are automatically supported, but verify CORS_ORIGINS if using custom IPs -- **Service discovery**: Use `tailscale ip` to find machine IPs -- **Port conflicts**: Ensure services use different ports on shared machines -- **Authentication**: Services must be accessible without authentication for inter-service communication - -**Debugging Commands:** -```bash -# Check Tailscale connectivity -tailscale ping [machine-name] -tailscale status - -# Test service endpoints -curl http://[tailscale-ip]:11434/api/tags -curl http://[tailscale-ip]:8085/health - -# Check Docker networks -docker network ls -docker ps --format "table {{.Names}}\t{{.Ports}}" -``` +For detailed technical documentation, see: +- **[@docs/wyoming-protocol.md](docs/wyoming-protocol.md)**: WebSocket communication protocol details +- **[@docs/memory-providers.md](docs/memory-providers.md)**: In-depth memory provider comparison and setup +- **[@docs/versioned-processing.md](docs/versioned-processing.md)**: Transcript and memory versioning details +- **[@docs/api-reference.md](docs/api-reference.md)**: Complete endpoint documentation with examples +- **[@docs/speaker-recognition.md](docs/speaker-recognition.md)**: Advanced analysis and live inference features +- **[@docs/distributed-deployment.md](docs/distributed-deployment.md)**: Multi-machine deployment with Tailscale ## Notes for Claude Check if the src/ is volume mounted. If not, do compose build so that code changes are reflected. Do not simply run `docker compose restart` as it will not rebuild the image. diff --git a/Docs/getting-started.md b/Docs/getting-started.md new file mode 100644 index 00000000..2f647b7b --- /dev/null +++ b/Docs/getting-started.md @@ -0,0 +1,731 @@ +# Getting Started + +# Friend-Lite Backend Quickstart Guide + +> 📖 **New to friend-lite?** This is your starting point! After reading this, continue with [architecture.md](./architecture.md) for technical details. + +## Overview + +Friend-Lite is an eco-system of services to support "AI wearable" agents/functionality. +At the moment, the basic functionalities are: +- Audio capture (via WebSocket, from OMI device, files, or a laptop) +- Audio transcription +- **Advanced memory system** with pluggable providers (Friend-Lite native or OpenMemory MCP) +- **Enhanced memory extraction** with individual fact storage and smart updates +- **Semantic memory search** with relevance threshold filtering and live results +- Action item extraction +- Modern React web dashboard with live recording and advanced search features +- Comprehensive user management with JWT authentication + +**Core Implementation**: See `src/advanced_omi_backend/main.py` for the complete FastAPI application and WebSocket handling. + +## Prerequisites + +- Docker and Docker Compose +- API keys for your chosen providers (see setup script) + +## Quick Start + +### Step 1: Interactive Setup (Recommended) + +Run the interactive setup wizard to configure all services with guided prompts: +```bash +cd backends/advanced +./init.sh +``` + +**The setup wizard will guide you through:** +- **Authentication**: Admin email/password setup +- **Transcription Provider**: Choose Deepgram, Mistral, or Offline (Parakeet) +- **LLM Provider**: Choose OpenAI or Ollama for memory extraction +- **Memory Provider**: Choose Friend-Lite Native or OpenMemory MCP +- **Optional Services**: Speaker Recognition and other extras +- **Network Configuration**: Ports and host settings + +**Example flow:** +``` +🚀 Friend-Lite Interactive Setup +=============================================== + +► Authentication Setup +---------------------- +Admin email [admin@example.com]: john@company.com +Admin password (min 8 chars): ******** + +► Speech-to-Text Configuration +------------------------------- +Choose your transcription provider: + 1) Deepgram (recommended - high quality, requires API key) + 2) Mistral (Voxtral models - requires API key) + 3) Offline (Parakeet ASR - requires GPU, runs locally) + 4) None (skip transcription setup) +Enter choice (1-4) [1]: 1 + +Get your API key from: https://console.deepgram.com/ +Deepgram API key: dg_xxxxxxxxxxxxx + +► LLM Provider Configuration +---------------------------- +Choose your LLM provider for memory extraction: + 1) OpenAI (GPT-4, GPT-3.5 - requires API key) + 2) Ollama (local models - requires Ollama server) + 3) Skip (no memory extraction) +Enter choice (1-3) [1]: 1 +``` + +### Step 2: HTTPS Setup (Optional) + +For microphone access and secure connections, set up HTTPS: +```bash +cd backends/advanced +./setup-https.sh 100.83.66.30 # Your Tailscale/network IP +``` + +This creates SSL certificates and configures nginx for secure access. + +### Step 3: Start the System + +**Start all services:** +```bash +cd backends/advanced +docker compose up --build -d +``` + +This starts: +- **Backend API**: `http://localhost:8000` +- **Web Dashboard**: `http://localhost:5173` +- **MongoDB**: `localhost:27017` +- **Qdrant**: `localhost:6333` + +### Step 4: Optional Services + +**If you configured optional services during setup, start them:** + +```bash +# OpenMemory MCP (if selected) +cd ../../extras/openmemory-mcp && docker compose up -d + +# Parakeet ASR (if selected for offline transcription) +cd ../../extras/asr-services && docker compose up parakeet -d + +# Speaker Recognition (if enabled) +cd ../../extras/speaker-recognition && docker compose up --build -d +``` + +### Manual Configuration (Alternative) + +If you prefer manual configuration, copy the `.env.template` file to `.env` and configure the required values: + +**Required Environment Variables:** +```bash +AUTH_SECRET_KEY=your-super-secret-jwt-key-here +ADMIN_PASSWORD=your-secure-admin-password +ADMIN_EMAIL=admin@example.com +``` + +**Memory Provider Configuration:** +```bash +# Memory Provider (Choose One) +# Option 1: Friend-Lite Native (Default - Recommended) +MEMORY_PROVIDER=friend_lite + +# Option 2: OpenMemory MCP (Cross-client compatibility) +# MEMORY_PROVIDER=openmemory_mcp +# OPENMEMORY_MCP_URL=http://host.docker.internal:8765 +# OPENMEMORY_CLIENT_NAME=friend_lite +# OPENMEMORY_USER_ID=openmemory +``` + +**LLM Configuration (Choose One):** +```bash +# Option 1: OpenAI (Recommended for best memory extraction) +LLM_PROVIDER=openai +OPENAI_API_KEY=your-openai-api-key-here +OPENAI_MODEL=gpt-4o-mini + +# Option 2: Local Ollama +LLM_PROVIDER=ollama +OLLAMA_BASE_URL=http://ollama:11434 +``` + +**Transcription Services (Choose One):** +```bash +# Option 1: Deepgram (Recommended for best transcription quality) +TRANSCRIPTION_PROVIDER=deepgram +DEEPGRAM_API_KEY=your-deepgram-api-key-here + +# Option 2: Mistral (Voxtral models for transcription) +TRANSCRIPTION_PROVIDER=mistral +MISTRAL_API_KEY=your-mistral-api-key-here +MISTRAL_MODEL=voxtral-mini-2507 + +# Option 3: Local ASR service +PARAKEET_ASR_URL=http://host.docker.internal:8080 +``` + +**Important Notes:** +- **OpenAI is strongly recommended** for LLM processing as it provides much better memory extraction and eliminates JSON parsing errors +- **TRANSCRIPTION_PROVIDER** determines which service to use: + - `deepgram`: Uses Deepgram's Nova-3 model for high-quality transcription + - `mistral`: Uses Mistral's Voxtral models for transcription + - If not set, system falls back to offline ASR service +- The system requires either online API keys or offline ASR service configuration + +### Testing Your Setup (Optional) + +After configuration, verify everything works with the integration test suite: +```bash +./run-test.sh + +# Alternative: Manual test with detailed logging +source .env && export DEEPGRAM_API_KEY OPENAI_API_KEY && \ + uv run pytest tests/test_integration.py -vv -s --log-cli-level=INFO +``` +This end-to-end test validates the complete audio processing pipeline. + +## Using the System + +### Web Dashboard + +1. Open `http://localhost:5173` +2. **Login** using the sidebar: + - **Admin**: `admin@example.com` / `your-admin-password` + - **Create new users** via admin interface + +### Dashboard Features + +- **Conversations**: View audio recordings, transcripts, and cropped audio +- **Memories**: Advanced memory search with semantic search, relevance threshold filtering, and memory count display +- **Live Recording**: Real-time audio recording with WebSocket streaming (HTTPS required) +- **User Management**: Create/delete users and their data +- **Client Management**: View active connections and close conversations +- **System Monitoring**: Debug tools and system health monitoring + +### Audio Client Connection + +Connect audio clients via WebSocket with authentication: + +**WebSocket URLs:** +```javascript +// Opus audio stream +ws://your-server-ip:8000/ws?token=YOUR_JWT_TOKEN&device_name=YOUR_DEVICE_NAME + +// PCM audio stream +ws://your-server-ip:8000/ws_pcm?token=YOUR_JWT_TOKEN&device_name=YOUR_DEVICE_NAME +``` + +**Authentication Methods:** +The system uses email-based authentication with JWT tokens: + +```bash +# Login with email +curl -X POST "http://localhost:8000/auth/jwt/login" \ + -H "Content-Type: application/x-www-form-urlencoded" \ + -d "username=admin@example.com&password=your-admin-password" + +# Response: {"access_token": "eyJhbGciOiJIUzI1NiIs...", "token_type": "bearer"} +``` + +**Authentication Flow:** +1. **User Registration**: Admin creates users via API or dashboard +2. **Login**: Users authenticate with email and password +3. **Token Usage**: Include JWT token in API calls and WebSocket connections +4. **Data Access**: Users can only access their own data (admins see all) + +For detailed authentication documentation, see [`auth.md`](./auth.md). + +**Create User Account:** +```bash +export ADMIN_TOKEN="your-admin-token" + +# Create user +curl -X POST "http://localhost:8000/api/create_user" \ + -H "Authorization: Bearer $ADMIN_TOKEN" \ + -H "Content-Type: application/json" \ + -d '{"email": "user@example.com", "password": "userpass", "display_name": "John Doe"}' + +# Response includes the user_id (MongoDB ObjectId) +# {"message": "User user@example.com created successfully", "user": {"id": "507f1f77bcf86cd799439011", ...}} +``` + +**Client ID Format:** +The system automatically generates client IDs using the last 6 characters of the MongoDB ObjectId plus device name (e.g., `439011-phone`, `439011-desktop`). This ensures proper user-client association and data isolation. + +## Add Existing Data + +### Audio File Upload & Processing + +The system supports processing existing audio files through the file upload API. This allows you to import and process pre-recorded conversations without requiring a live WebSocket connection. + +**Upload and Process WAV Files:** +```bash +export USER_TOKEN="your-jwt-token" + +# Upload single WAV file +curl -X POST "http://localhost:8000/api/process-audio-files" \ + -H "Authorization: Bearer $USER_TOKEN" \ + -F "files=@/path/to/audio.wav" \ + -F "device_name=file_upload" + +# Upload multiple WAV files +curl -X POST "http://localhost:8000/api/process-audio-files" \ + -H "Authorization: Bearer $USER_TOKEN" \ + -F "files=@/path/to/recording1.wav" \ + -F "files=@/path/to/recording2.wav" \ + -F "device_name=import_batch" +``` + +**Response Example:** +```json +{ + "message": "Successfully processed 2 audio files", + "processed_files": [ + { + "filename": "recording1.wav", + "sample_rate": 16000, + "channels": 1, + "duration_seconds": 120.5, + "size_bytes": 3856000 + }, + { + "filename": "recording2.wav", + "sample_rate": 44100, + "channels": 2, + "duration_seconds": 85.2, + "size_bytes": 7532800 + } + ], + "client_id": "user01-import_batch" +} +``` + +## System Features + +### Audio Processing +- **Real-time streaming**: WebSocket audio ingestion +- **Multiple formats**: Opus and PCM audio support +- **Per-client processing**: Isolated conversation management +- **Speech detection**: Automatic silence removal +- **Audio cropping**: Extract only speech segments + +**Implementation**: See `src/advanced_omi_backend/main.py` for WebSocket endpoints and `src/advanced_omi_backend/processors.py` for audio processing pipeline. + +### Transcription Options +- **Deepgram API**: Cloud-based batch processing, high accuracy (recommended) +- **Mistral API**: Voxtral models for transcription with REST API processing +- **Self-hosted ASR**: Local Wyoming protocol services with real-time processing +- **Collection timeout**: 1.5 minute collection for optimal online processing quality + +### Conversation Management +- **Automatic chunking**: 60-second audio segments +- **Conversation timeouts**: Auto-close after 1.5 minutes of silence +- **Speaker identification**: Track multiple speakers per conversation +- **Manual controls**: Close conversations via API or dashboard + +### Memory & Intelligence + +#### Pluggable Memory System +- **Two memory providers**: Choose between Friend-Lite native or OpenMemory MCP +- **Friend-Lite Provider**: Full control with custom extraction, individual fact storage, smart deduplication +- **OpenMemory MCP Provider**: Cross-client compatibility (Claude Desktop, Cursor, Windsurf), professional processing + +#### Enhanced Memory Processing +- **Individual fact storage**: No more generic transcript fallbacks +- **Smart memory updates**: LLM-driven ADD/UPDATE/DELETE actions +- **Enhanced prompts**: Improved fact extraction with granular, specific memories +- **User-centric storage**: All memories keyed by database user_id +- **Semantic search**: Vector-based memory retrieval with embeddings +- **Configurable extraction**: YAML-based configuration for memory extraction +- **Debug tracking**: SQLite-based tracking of transcript → memory conversion +- **Client metadata**: Device information preserved for debugging and reference +- **User isolation**: All data scoped to individual users with multi-device support + +**Implementation**: +- **Memory System**: `src/advanced_omi_backend/memory/memory_service.py` + `src/advanced_omi_backend/controllers/memory_controller.py` +- **Configuration**: `memory_config.yaml` + `src/advanced_omi_backend/memory_config_loader.py` + +### Authentication & Security +- **Email Authentication**: Login with email and password +- **JWT tokens**: Secure API and WebSocket authentication with 1-hour expiration +- **Role-based access**: Admin vs regular user permissions +- **Data isolation**: Users can only access their own data +- **Client ID Management**: Automatic client-user association via `objectid_suffix-device_name` format +- **Multi-device support**: Single user can connect multiple devices +- **Security headers**: Proper CORS, cookie security, and token validation + +**Implementation**: See `src/advanced_omi_backend/auth.py` for authentication logic, `src/advanced_omi_backend/users.py` for user management, and [`auth.md`](./auth.md) for comprehensive documentation. + +## Verification + +```bash +# System health check +curl http://localhost:8000/health + +# Web dashboard +open http://localhost:3000 + +# View active clients (requires auth token) +curl -H "Authorization: Bearer your-token" http://localhost:8000/api/clients/active +``` + +## HAVPE Relay Configuration + +For ESP32 audio streaming using the HAVPE relay (`extras/havpe-relay/`): + +```bash +# Environment variables for HAVPE relay +export AUTH_USERNAME="user@example.com" # Email address +export AUTH_PASSWORD="your-password" +export DEVICE_NAME="havpe" # Device identifier + +# Run the relay +cd extras/havpe-relay +python main.py --backend-url http://your-server:8000 --backend-ws-url ws://your-server:8000 +``` + +The relay will automatically: +- Authenticate using `AUTH_USERNAME` (email address) +- Generate client ID as `objectid_suffix-havpe` +- Forward ESP32 audio to the backend with proper authentication +- Handle token refresh and reconnection + +## Development tip +uv sync --group (whatever group you want to sync) +(for example, deepgram, etc.) + +## Troubleshooting + +**Service Issues:** +- Check logs: `docker compose logs friend-backend` +- Restart services: `docker compose restart` +- View all services: `docker compose ps` + +**Authentication Issues:** +- Verify `AUTH_SECRET_KEY` is set and long enough (minimum 32 characters) +- Check admin credentials match `.env` file +- Ensure user email/password combinations are correct + +**Transcription Issues:** +- **Deepgram**: Verify API key is valid and `TRANSCRIPTION_PROVIDER=deepgram` +- **Mistral**: Verify API key is valid and `TRANSCRIPTION_PROVIDER=mistral` +- **Self-hosted**: Ensure ASR service is running on port 8765 +- Check transcription service connection in health endpoint + +**Memory Issues:** +- Ensure Ollama is running and model is pulled +- Check Qdrant connection in health endpoint +- Memory processing happens at conversation end + +**Connection Issues:** +- Use server's IP address, not localhost for mobile clients +- Ensure WebSocket connections include authentication token +- Check firewall/port settings for remote connections + +## Distributed Deployment + +### Single Machine vs Distributed Setup + +**Single Machine (Default):** +```bash +# Everything on one machine +docker compose up --build -d +``` + +**Distributed Setup (GPU + Backend separation):** + +#### GPU Machine Setup +```bash +# Start GPU-accelerated services +cd extras/asr-services +docker compose up moonshine -d + +cd extras/speaker-recognition +docker compose up --build -d + +# Ollama with GPU support +docker run -d --gpus=all -p 11434:11434 \ + -v ollama:/root/.ollama \ + ollama/ollama:latest +``` + +#### Backend Machine Configuration +```bash +# .env configuration for distributed services +OLLAMA_BASE_URL=http://[gpu-machine-tailscale-ip]:11434 +SPEAKER_SERVICE_URL=http://[gpu-machine-tailscale-ip]:8085 +PARAKEET_ASR_URL=http://[gpu-machine-tailscale-ip]:8080 + +# Start lightweight backend services +docker compose up --build -d +``` + +#### Tailscale Networking +```bash +# Install on each machine +curl -fsSL https://tailscale.com/install.sh | sh +sudo tailscale up + +# Find machine IPs +tailscale ip -4 +``` + +**Benefits of Distributed Setup:** +- GPU services on dedicated hardware +- Lightweight backend on VPS/Raspberry Pi +- Automatic Tailscale IP support (100.x.x.x) - no CORS configuration needed +- Encrypted inter-service communication + +**Service Examples:** +- GPU machine: LLM inference, ASR, speaker recognition +- Backend machine: FastAPI, WebUI, databases +- Database machine: MongoDB, Qdrant (optional separation) + +## Data Architecture + +The friend-lite backend uses a **user-centric data architecture**: + +- **All memories are keyed by database user_id** (not client_id) +- **Client information is stored in metadata** for reference and debugging +- **User email is included** for easy identification in admin interfaces +- **Multi-device support**: Users can access their data from any registered device + +For detailed information, see [User Data Architecture](user-data-architecture.md). + +## Memory Provider Selection + +### Choosing a Memory Provider + +Friend-Lite offers two memory backends: + +#### 1. Friend-Lite Native +```bash +# In your .env file +MEMORY_PROVIDER=friend_lite +LLM_PROVIDER=openai +OPENAI_API_KEY=your-openai-key-here +``` + +**Benefits:** +- Full control over memory processing +- Individual fact storage with no fallbacks +- Custom prompts and extraction logic +- Smart deduplication algorithms +- LLM-driven memory updates (ADD/UPDATE/DELETE) +- No external dependencies + +#### 2. OpenMemory MCP +```bash +# First, start the external server +cd extras/openmemory-mcp +docker compose up -d + +# Then configure Friend-Lite +MEMORY_PROVIDER=openmemory_mcp +OPENMEMORY_MCP_URL=http://host.docker.internal:8765 +``` + +**Benefits:** +- Cross-client compatibility (works with Claude Desktop, Cursor, etc.) +- Professional memory processing +- Web UI at http://localhost:8765 +- Battle-tested deduplication + +**Use OpenMemory MCP when:** +- You want cross-client memory sharing +- You're already using OpenMemory in other tools +- You prefer external expertise over custom logic + +**See [MEMORY_PROVIDERS.md](../MEMORY_PROVIDERS.md) for detailed comparison** + +## Memory & Action Item Configuration + +> 🎯 **New to memory configuration?** Read our [Memory Configuration Guide](./memory-configuration-guide.md) for a step-by-step setup guide with examples. + +The system uses **centralized configuration** via `memory_config.yaml` for all memory extraction settings. All hardcoded values have been removed from the code to ensure consistent, configurable behavior. + +### Configuration File Location +- **Path**: `backends/advanced-backend/memory_config.yaml` +- **Hot-reload**: Changes are applied on next processing cycle (no restart required) +- **Fallback**: If file is missing, system uses safe defaults with environment variables + +### LLM Provider & Model Configuration + +⭐ **OpenAI is STRONGLY RECOMMENDED** for optimal memory extraction performance. + +The system supports **multiple LLM providers** - configure via environment variables: + +```bash +# In your .env file +LLM_PROVIDER=openai # RECOMMENDED: Use "openai" for best results +OPENAI_API_KEY=your-openai-api-key +OPENAI_MODEL=gpt-4o-mini # RECOMMENDED: "gpt-5-mini" for better memory extraction + +# Alternative: Local Ollama (may have reduced memory quality) +LLM_PROVIDER=ollama +OLLAMA_BASE_URL=http://ollama:11434 +OLLAMA_MODEL=gemma3n:e4b # Fallback if YAML config fails to load +``` + +**Why OpenAI is recommended:** +- **Enhanced memory extraction**: Creates multiple granular memories instead of fallback transcripts +- **Better fact extraction**: More reliable JSON parsing and structured output +- **No more "fallback memories"**: Eliminates generic transcript-based memory entries +- **Improved conversation understanding**: Better context awareness and detail extraction + +**YAML Configuration** (provider-specific models): +```yaml +memory_extraction: + enabled: true + prompt: | + Extract anything relevant about this conversation that would be valuable to remember. + Focus on key topics, people, decisions, dates, and emotional context. + llm_settings: + # Model selection based on LLM_PROVIDER: + # - Ollama: "gemma3n:e4b", "llama3.1:latest", "llama3.2:latest", etc. + # - OpenAI: "gpt-5-mini" (recommended for JSON reliability), "gpt-5-mini", "gpt-3.5-turbo", etc. + model: "gemma3n:e4b" + temperature: 0.1 + +fact_extraction: + enabled: false # Disabled to avoid JSON parsing issues + # RECOMMENDATION: Enable with OpenAI GPT-4o for better JSON reliability + llm_settings: + model: "gemma3n:e4b" # Auto-switches based on LLM_PROVIDER + temperature: 0.0 # Lower for factual accuracy +``` + +**Provider-Specific Behavior:** +- **Ollama**: Uses local models with Ollama embeddings (nomic-embed-text) +- **OpenAI**: Uses OpenAI models with OpenAI embeddings (text-embedding-3-small) +- **Embeddings**: Automatically selected based on provider (768 dims for Ollama, 1536 for OpenAI) + +#### Fixing JSON Parsing Errors + +If you experience JSON parsing errors in fact extraction: + +1. **Switch to OpenAI GPT-4o** (recommended solution): + ```bash + # In your .env file + LLM_PROVIDER=openai + OPENAI_API_KEY=your-openai-api-key + OPENAI_MODEL=gpt-4o-mini + ``` + +2. **Enable fact extraction** with reliable JSON output: + ```yaml + # In memory_config.yaml + fact_extraction: + enabled: true # Safe to enable with GPT-4o + ``` + +3. **Monitor logs** for JSON parsing success: + ```bash + # Check for JSON parsing errors + docker logs advanced-backend | grep "JSONDecodeError" + + # Verify OpenAI usage + docker logs advanced-backend | grep "OpenAI response" + ``` + +**Why GPT-4o helps with JSON errors:** +- More consistent JSON formatting +- Better instruction following for structured output +- Reduced malformed JSON responses +- Built-in JSON mode for reliable parsing + +#### Testing OpenAI Configuration + +To verify your OpenAI setup is working: + +1. **Check logs for OpenAI usage**: + ```bash + # Start the backend and check logs + docker logs advanced-backend | grep -i "openai" + + # You should see: + # "Using OpenAI provider with model: gpt-5-mini" + ``` + +2. **Test memory extraction** with a conversation: + ```bash + # The health endpoint includes LLM provider info + curl http://localhost:8000/health + + # Response should include: "llm_provider": "openai" + ``` + +3. **Monitor memory processing**: + ```bash + # After a conversation ends, check for successful processing + docker logs advanced-backend | grep "memory processing" + ``` + +If you see errors about missing API keys or models, verify your `.env` file has: +```bash +LLM_PROVIDER=openai +OPENAI_API_KEY=sk-your-actual-api-key-here +OPENAI_MODEL=gpt-4o-mini +``` + +### Quality Control Settings +```yaml +quality_control: + min_conversation_length: 50 # Skip very short conversations + max_conversation_length: 50000 # Skip extremely long conversations + skip_low_content: true # Skip conversations with mostly filler words + min_content_ratio: 0.3 # Minimum meaningful content ratio + skip_patterns: # Regex patterns to skip + - "^(um|uh|hmm|yeah|ok|okay)\\s*$" + - "^test\\s*$" + - "^testing\\s*$" +``` + +### Processing & Performance +```yaml +processing: + parallel_processing: true # Enable concurrent processing + max_concurrent_tasks: 3 # Limit concurrent LLM requests + processing_timeout: 300 # Timeout for memory extraction (seconds) + retry_failed: true # Retry failed extractions + max_retries: 2 # Maximum retry attempts + retry_delay: 5 # Delay between retries (seconds) +``` + +### Debug & Monitoring +```yaml +debug: + enabled: true + db_path: "/app/debug/memory_debug.db" + log_level: "INFO" # DEBUG, INFO, WARNING, ERROR + log_full_conversations: false # Privacy consideration + log_extracted_memories: true # Log successful extractions +``` + +### Configuration Validation +The system validates configuration on startup and provides detailed error messages for invalid settings. Use the debug API to verify your configuration: + +```bash +# Check current configuration +curl -H "Authorization: Bearer $ADMIN_TOKEN" \ + http://localhost:8000/api/debug/memory/config +``` + +### API Endpoints for Debugging +- `GET /api/debug/memory/stats` - Processing statistics +- `GET /api/debug/memory/sessions` - Recent memory sessions +- `GET /api/debug/memory/session/{audio_uuid}` - Detailed session info +- `GET /api/debug/memory/config` - Current configuration +- `GET /api/debug/memory/pipeline/{audio_uuid}` - Pipeline trace + +**Implementation**: See `src/advanced_omi_backend/routers/modules/system_routes.py` for debug endpoints and system utilities. + +## Next Steps + +- **Configure Google OAuth** for easy user login +- **Set up Ollama** for local memory processing +- **Deploy ASR service** for self-hosted transcription +- **Connect audio clients** using the WebSocket API +- **Explore the dashboard** to manage conversations and users +- **Review the user data architecture** for understanding data organization +- **Customize memory extraction** by editing `memory_config.yaml` +- **Monitor processing performance** using debug API endpoints \ No newline at end of file diff --git a/Docs/init-system.md b/Docs/init-system.md index fbcbcbe9..c4bcf0d1 100644 --- a/Docs/init-system.md +++ b/Docs/init-system.md @@ -12,7 +12,7 @@ Friend-Lite uses a unified initialization system with clean separation of concerns: -- **Configuration** (`init.py`) - Set up service configurations, API keys, and .env files +- **Configuration** (`wizard.py`) - Set up service configurations, API keys, and .env files - **Service Management** (`services.py`) - Start, stop, and manage running services The root orchestrator handles service selection and delegates configuration to individual service scripts. In general, setup scripts only configure and do not start services automatically. Exceptions: `extras/asr-services` and `extras/openmemory-mcp` are startup scripts. This prevents unnecessary resource usage and gives you control over when services actually run. @@ -22,7 +22,7 @@ The root orchestrator handles service selection and delegates configuration to i ## Architecture ### Root Orchestrator -- **Location**: `/init.py` +- **Location**: `/wizard.py` - **Purpose**: Service selection and delegation only - **Does NOT**: Handle service-specific configuration or duplicate setup logic @@ -39,7 +39,7 @@ Set up multiple services together with automatic URL coordination: ```bash # From project root -uv run --with-requirements setup-requirements.txt python init.py +uv run --with-requirements setup-requirements.txt python wizard.py ``` The orchestrator will: diff --git a/backends/advanced/Caddyfile.template b/backends/advanced/Caddyfile.template index e0ffb2f6..21caf0ee 100644 --- a/backends/advanced/Caddyfile.template +++ b/backends/advanced/Caddyfile.template @@ -13,7 +13,7 @@ # For production, replace 'localhost' with your domain name and Caddy # will automatically obtain Let's Encrypt certificates. -localhost { +localhost TAILSCALE_IP { # Enable automatic HTTPS tls internal diff --git a/backends/advanced/init.py b/backends/advanced/init.py index 35776430..383c24ed 100644 --- a/backends/advanced/init.py +++ b/backends/advanced/init.py @@ -13,9 +13,9 @@ import sys from datetime import datetime from pathlib import Path -from typing import Any, Dict, Optional +from typing import Any, Dict -from rich import print as rprint +from dotenv import set_key from rich.console import Console from rich.panel import Panel from rich.prompt import Confirm, Prompt @@ -336,67 +336,60 @@ def setup_https(self): self.console.print(f"[yellow][WARNING][/yellow] nginx.conf generation failed: {e}") else: self.console.print("[yellow][WARNING][/yellow] nginx.conf.template not found") + + # Generate Caddyfile from template + self.console.print("[blue][INFO][/blue] Creating Caddyfile configuration...") + caddyfile_template = script_dir / "Caddyfile.template" + if caddyfile_template.exists(): + try: + with open(caddyfile_template, 'r') as f: + caddyfile_content = f.read() + + # Replace TAILSCALE_IP with server_ip + caddyfile_content = caddyfile_content.replace('TAILSCALE_IP', server_ip) + + with open('Caddyfile', 'w') as f: + f.write(caddyfile_content) + + self.console.print(f"[green][SUCCESS][/green] Caddyfile created for: {server_ip}") + + except Exception as e: + self.console.print(f"[yellow][WARNING][/yellow] Caddyfile generation failed: {e}") + else: + self.console.print("[yellow][WARNING][/yellow] Caddyfile.template not found") else: self.config["HTTPS_ENABLED"] = "false" def generate_env_file(self): - """Generate the .env file from configuration""" - env_content = f"""# ============================================================================= -# Friend-Lite Advanced Backend Configuration -# Generated on {datetime.now().strftime('%Y-%m-%d %H:%M:%S')} -# ============================================================================= - -# Authentication -AUTH_SECRET_KEY={self.config.get('AUTH_SECRET_KEY', '')} -ADMIN_EMAIL={self.config.get('ADMIN_EMAIL', '')} -ADMIN_PASSWORD={self.config.get('ADMIN_PASSWORD', '')} - -# Transcription Provider -TRANSCRIPTION_PROVIDER={self.config.get('TRANSCRIPTION_PROVIDER', '')} -DEEPGRAM_API_KEY={self.config.get('DEEPGRAM_API_KEY', '')} -MISTRAL_API_KEY={self.config.get('MISTRAL_API_KEY', '')} -MISTRAL_MODEL={self.config.get('MISTRAL_MODEL', '')} - -# LLM Provider -LLM_PROVIDER={self.config.get('LLM_PROVIDER', '')} -OPENAI_API_KEY={self.config.get('OPENAI_API_KEY', '')} -OPENAI_MODEL={self.config.get('OPENAI_MODEL', '')} -OPENAI_BASE_URL={self.config.get('OPENAI_BASE_URL', '')} -OLLAMA_BASE_URL={self.config.get('OLLAMA_BASE_URL', '')} -OLLAMA_MODEL={self.config.get('OLLAMA_MODEL', '')} -# Memory Provider -MEMORY_PROVIDER={self.config.get('MEMORY_PROVIDER', 'friend_lite')} -QDRANT_BASE_URL={self.config.get('QDRANT_BASE_URL', 'qdrant')} -OPENMEMORY_MCP_URL={self.config.get('OPENMEMORY_MCP_URL', '')} -OPENMEMORY_CLIENT_NAME={self.config.get('OPENMEMORY_CLIENT_NAME', '')} -OPENMEMORY_USER_ID={self.config.get('OPENMEMORY_USER_ID', '')} - -# Optional Services -SPEAKER_SERVICE_URL={self.config.get('SPEAKER_SERVICE_URL', '')} -PARAKEET_ASR_URL={self.config.get('PARAKEET_ASR_URL', '')} - -# Network Configuration -BACKEND_PUBLIC_PORT={self.config.get('BACKEND_PUBLIC_PORT', '8000')} -WEBUI_PORT={self.config.get('WEBUI_PORT', '5173')} - -# Database -MONGODB_URI=mongodb://mongo:27017 -DATABASE_NAME=friend_db - -# CORS (supports Tailscale IPs automatically) -CORS_ORIGINS=http://localhost:3000,http://localhost:5173 - -# Logging -LOG_LEVEL=INFO -""" - - # Create .env file with secure permissions (owner read/write only) - env_path = ".env" - fd = os.open(env_path, os.O_WRONLY | os.O_CREAT | os.O_TRUNC, mode=0o600) - with os.fdopen(fd, 'w') as f: - f.write(env_content) - - self.console.print("[green][SUCCESS][/green] .env file created successfully with secure permissions") + """Generate .env file from template and update with configuration""" + env_path = Path(".env") + env_template = Path(".env.template") + + # Backup existing .env if it exists + if env_path.exists(): + timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") + backup_path = env_path.with_suffix(f'.backup.{timestamp}') + shutil.copy2(env_path, backup_path) + self.console.print(f"[blue][INFO][/blue] Backed up existing .env to {backup_path.name}") + + # Copy template to .env + if env_template.exists(): + shutil.copy2(env_template, env_path) + self.console.print("[blue][INFO][/blue] Copied .env.template to .env") + else: + self.console.print("[yellow][WARNING][/yellow] .env.template not found, creating new .env") + env_path.touch(mode=0o600) + + # Update configured values using set_key + env_path_str = str(env_path) + for key, value in self.config.items(): + if value: # Only set non-empty values + set_key(env_path_str, key, value) + + # Ensure secure permissions + os.chmod(env_path, 0o600) + + self.console.print("[green][SUCCESS][/green] .env file configured successfully with secure permissions") def copy_config_templates(self): """Copy other configuration files""" diff --git a/backends/advanced/nginx.conf b/backends/advanced/nginx.conf deleted file mode 100644 index 12c811e7..00000000 --- a/backends/advanced/nginx.conf +++ /dev/null @@ -1,221 +0,0 @@ -worker_processes 1; - -events { - worker_connections 1024; -} - -http { - # Basic settings - sendfile on; - tcp_nopush on; - tcp_nodelay on; - keepalive_timeout 65; - types_hash_max_size 2048; - client_max_body_size 100M; - - # MIME types - include /etc/nginx/mime.types; - default_type application/octet-stream; - - # Logging - access_log /var/log/nginx/access.log; - error_log /var/log/nginx/error.log; - - # Gzip compression - gzip on; - gzip_vary on; - gzip_min_length 10240; - gzip_proxied expired no-cache no-store private auth; - gzip_types - text/plain - text/css - text/xml - text/javascript - application/x-javascript - application/xml+rss - application/javascript - application/json; - - # WebSocket proxy settings - map $http_upgrade $connection_upgrade { - default upgrade; - '' close; - } - - # Upstream services - upstream friend_backend { - server friend-backend:8000; - } - - upstream friend_webui { - server webui:5173; - } - - # HTTPS Server - server { - listen 443 ssl http2; - server_name localhost 100.83.66.30; - - # SSL Configuration - ssl_certificate /etc/nginx/ssl/server.crt; - ssl_certificate_key /etc/nginx/ssl/server.key; - ssl_protocols TLSv1.2 TLSv1.3; - ssl_ciphers ECDHE-ECDSA-AES256-GCM-SHA384:ECDHE-RSA-AES256-GCM-SHA384; - ssl_prefer_server_ciphers off; - - # Security headers - add_header X-Frame-Options DENY; - add_header X-Content-Type-Options nosniff; - add_header X-XSS-Protection "1; mode=block"; - - # Backend API endpoints - location /api/ { - proxy_pass http://friend_backend/api/; - proxy_set_header Host $host; - proxy_set_header X-Real-IP $remote_addr; - proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for; - proxy_set_header X-Forwarded-Proto $scheme; - proxy_redirect off; - } - - # Authentication endpoints - location /auth/ { - proxy_pass http://friend_backend/auth/; - proxy_set_header Host $host; - proxy_set_header X-Real-IP $remote_addr; - proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for; - proxy_set_header X-Forwarded-Proto $scheme; - proxy_redirect off; - } - - # Users endpoints - location /users/ { - proxy_pass http://friend_backend/users/; - proxy_set_header Host $host; - proxy_set_header X-Real-IP $remote_addr; - proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for; - proxy_set_header X-Forwarded-Proto $scheme; - proxy_redirect off; - } - - # WebSocket endpoints for audio streaming - location /ws_pcm { - proxy_pass http://friend_backend/ws_pcm; - proxy_http_version 1.1; - proxy_set_header Upgrade $http_upgrade; - proxy_set_header Connection $connection_upgrade; - proxy_set_header Host $host; - proxy_set_header X-Real-IP $remote_addr; - proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for; - proxy_set_header X-Forwarded-Proto $scheme; - proxy_cache_bypass $http_upgrade; - proxy_read_timeout 86400; - proxy_send_timeout 86400; - proxy_connect_timeout 60s; - proxy_buffering off; - proxy_request_buffering off; - } - - location /ws_omi { - proxy_pass http://friend_backend/ws_omi; - proxy_http_version 1.1; - proxy_set_header Upgrade $http_upgrade; - proxy_set_header Connection $connection_upgrade; - proxy_set_header Host $host; - proxy_set_header X-Real-IP $remote_addr; - proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for; - proxy_set_header X-Forwarded-Proto $scheme; - proxy_cache_bypass $http_upgrade; - proxy_read_timeout 86400; - proxy_send_timeout 86400; - proxy_connect_timeout 60s; - proxy_buffering off; - proxy_request_buffering off; - } - - # Legacy WebSocket endpoint - location /ws { - proxy_pass http://friend_backend/ws; - proxy_http_version 1.1; - proxy_set_header Upgrade $http_upgrade; - proxy_set_header Connection $connection_upgrade; - proxy_set_header Host $host; - proxy_set_header X-Real-IP $remote_addr; - proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for; - proxy_set_header X-Forwarded-Proto $scheme; - proxy_cache_bypass $http_upgrade; - proxy_read_timeout 86400; - proxy_send_timeout 86400; - proxy_connect_timeout 60s; - proxy_buffering off; - proxy_request_buffering off; - } - - # Health check endpoints - location /health { - proxy_pass http://friend_backend/health; - proxy_set_header Host $host; - proxy_set_header X-Real-IP $remote_addr; - proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for; - proxy_set_header X-Forwarded-Proto $scheme; - } - - # Readiness check endpoint - location /readiness { - proxy_pass http://friend_backend/readiness; - proxy_set_header Host $host; - proxy_set_header X-Real-IP $remote_addr; - proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for; - proxy_set_header X-Forwarded-Proto $scheme; - } - - # Audio file serving - location /audio/ { - proxy_pass http://friend_backend/audio/; - proxy_set_header Host $host; - proxy_set_header X-Real-IP $remote_addr; - proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for; - proxy_set_header X-Forwarded-Proto $scheme; - proxy_redirect off; - - # Add headers for audio file serving - proxy_set_header Accept-Ranges bytes; - proxy_cache_bypass $http_range; - } - - # Vite HMR WebSocket (specific path) - location /@vite/client { - proxy_pass http://friend_webui/@vite/client; - proxy_set_header Host $host; - proxy_set_header X-Real-IP $remote_addr; - proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for; - proxy_set_header X-Forwarded-Proto $scheme; - proxy_http_version 1.1; - proxy_set_header Upgrade $http_upgrade; - proxy_set_header Connection $connection_upgrade; - proxy_cache_bypass $http_upgrade; - } - - # Frontend Vite dev server (with HMR support) - location / { - proxy_pass http://friend_webui/; - proxy_set_header Host $host; - proxy_set_header X-Real-IP $remote_addr; - proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for; - proxy_set_header X-Forwarded-Proto $scheme; - proxy_redirect off; - - # Handle WebSocket upgrade for Vite HMR - proxy_http_version 1.1; - proxy_set_header Upgrade $http_upgrade; - proxy_set_header Connection $connection_upgrade; - } - } - - # HTTP redirect to HTTPS - server { - listen 80; - server_name localhost 100.83.66.30; - return 301 https://$host$request_uri; - } -} \ No newline at end of file diff --git a/backends/advanced/setup-requirements.txt b/backends/advanced/setup-requirements.txt index 89d23f8d..fe569286 100644 --- a/backends/advanced/setup-requirements.txt +++ b/backends/advanced/setup-requirements.txt @@ -1,3 +1,4 @@ # Dependencies for interactive setup script rich>=13.0.0 -pyyaml>=6.0.0 \ No newline at end of file +pyyaml>=6.0.0 +python-dotenv \ No newline at end of file diff --git a/backends/advanced/src/advanced_omi_backend/client.py b/backends/advanced/src/advanced_omi_backend/client.py index 3c43a43a..30b3cc62 100644 --- a/backends/advanced/src/advanced_omi_backend/client.py +++ b/backends/advanced/src/advanced_omi_backend/client.py @@ -12,7 +12,6 @@ from pathlib import Path from typing import Dict, List, Optional, Tuple -from advanced_omi_backend.conversation_manager import get_conversation_manager from advanced_omi_backend.database import AudioChunksRepository from advanced_omi_backend.task_manager import get_task_manager from wyoming.audio import AudioChunk @@ -133,33 +132,19 @@ async def close_current_conversation(self): audio_logger.info(f"🔒 No active conversation to close for client {self.client_id}") return - # Debug logging for memory processing investigation - audio_logger.info(f"🔍 ClientState close_current_conversation debug for {self.client_id}:") - audio_logger.info(f" - current_audio_uuid: {self.current_audio_uuid}") - audio_logger.info(f" - user_id: {self.user_id}") - audio_logger.info(f" - user_email: {self.user_email}") - audio_logger.info(f" - client_id: {self.client_id}") - - # Use ConversationManager for clean separation of concerns - conversation_manager = get_conversation_manager() - success = await conversation_manager.close_conversation( - client_id=self.client_id, - audio_uuid=self.current_audio_uuid, - user_id=self.user_id, - user_email=self.user_email, - conversation_start_time=self.conversation_start_time, - speech_segments=self.speech_segments, - chunk_dir=self.chunk_dir, - ) + # NOTE: ClientState is legacy V1 code. In V2 architecture, conversation closure + # is handled by the websocket controllers using RQ jobs directly. + # This method is kept minimal for backward compatibility. - if success: - # Clean up speech segments for this conversation - if self.current_audio_uuid in self.speech_segments: - del self.speech_segments[self.current_audio_uuid] - if self.current_audio_uuid in self.current_speech_start: - del self.current_speech_start[self.current_audio_uuid] - else: - audio_logger.warning(f"⚠️ Conversation closure had issues for {self.current_audio_uuid}") + audio_logger.info(f"🔒 Closing conversation for client {self.client_id}, audio_uuid: {self.current_audio_uuid}") + + # Clean up speech segments for this conversation + if self.current_audio_uuid in self.speech_segments: + del self.speech_segments[self.current_audio_uuid] + if self.current_audio_uuid in self.current_speech_start: + del self.current_speech_start[self.current_audio_uuid] + + audio_logger.info(f"✅ Cleaned up state for {self.current_audio_uuid}") async def start_new_conversation(self): """Start a new conversation by closing current and resetting state.""" diff --git a/backends/advanced/src/advanced_omi_backend/controllers/audio_controller.py b/backends/advanced/src/advanced_omi_backend/controllers/audio_controller.py index a805a6f0..b8f60fb8 100644 --- a/backends/advanced/src/advanced_omi_backend/controllers/audio_controller.py +++ b/backends/advanced/src/advanced_omi_backend/controllers/audio_controller.py @@ -12,7 +12,7 @@ from fastapi import UploadFile from fastapi.responses import JSONResponse -from advanced_omi_backend.audio_utils import AudioValidationError, write_audio_file +from advanced_omi_backend.utils.audio_utils import AudioValidationError, write_audio_file from advanced_omi_backend.models.job import JobPriority from advanced_omi_backend.models.user import User from advanced_omi_backend.models.conversation import create_conversation diff --git a/backends/advanced/src/advanced_omi_backend/controllers/conversation_controller.py b/backends/advanced/src/advanced_omi_backend/controllers/conversation_controller.py index c9233dc7..04c4139c 100644 --- a/backends/advanced/src/advanced_omi_backend/controllers/conversation_controller.py +++ b/backends/advanced/src/advanced_omi_backend/controllers/conversation_controller.py @@ -10,7 +10,7 @@ from pathlib import Path from typing import Optional -from advanced_omi_backend.audio_utils import ( +from advanced_omi_backend.utils.audio_utils import ( _process_audio_cropping_with_relative_timestamps, ) from advanced_omi_backend.client_manager import ( diff --git a/backends/advanced/src/advanced_omi_backend/controllers/websocket_controller.py b/backends/advanced/src/advanced_omi_backend/controllers/websocket_controller.py index 919daa1b..cb5e27f6 100644 --- a/backends/advanced/src/advanced_omi_backend/controllers/websocket_controller.py +++ b/backends/advanced/src/advanced_omi_backend/controllers/websocket_controller.py @@ -21,7 +21,7 @@ from advanced_omi_backend.auth import websocket_auth from advanced_omi_backend.client_manager import generate_client_id, get_client_manager from advanced_omi_backend.constants import OMI_CHANNELS, OMI_SAMPLE_RATE, OMI_SAMPLE_WIDTH -from advanced_omi_backend.audio_utils import process_audio_chunk +from advanced_omi_backend.utils.audio_utils import process_audio_chunk from advanced_omi_backend.services.audio_stream import AudioStreamProducer from advanced_omi_backend.services.audio_stream.producer import get_audio_stream_producer @@ -128,7 +128,84 @@ async def create_client_state(client_id: str, user, device_name: Optional[str] = async def cleanup_client_state(client_id: str): - """Clean up and remove client state.""" + """Clean up and remove client state, including cancelling speech detection job and marking session complete.""" + # Cancel the speech detection job for this client + from advanced_omi_backend.controllers.queue_controller import redis_conn + from rq.job import Job + import redis.asyncio as redis + + try: + job_id_key = f"speech_detection_job:{client_id}" + job_id_bytes = redis_conn.get(job_id_key) + + if job_id_bytes: + job_id = job_id_bytes.decode() + logger.info(f"🛑 Cancelling speech detection job {job_id} for client {client_id}") + + try: + # Fetch and cancel the job + job = Job.fetch(job_id, connection=redis_conn) + job.cancel() + logger.info(f"✅ Successfully cancelled speech detection job {job_id}") + except Exception as job_error: + logger.warning(f"⚠️ Failed to cancel job {job_id}: {job_error}") + + # Clean up the tracking key + redis_conn.delete(job_id_key) + logger.info(f"🧹 Cleaned up job tracking key for client {client_id}") + else: + logger.debug(f"No speech detection job found for client {client_id}") + except Exception as e: + logger.warning(f"⚠️ Error during job cancellation for client {client_id}: {e}") + + # Mark all active sessions for this client as complete AND delete Redis streams + try: + # Get async Redis client + redis_url = os.getenv("REDIS_URL", "redis://localhost:6379/0") + async_redis = redis.from_url(redis_url, decode_responses=False) + + # Find all session keys for this client and mark them complete + pattern = f"audio:session:*" + cursor = 0 + sessions_closed = 0 + + while True: + cursor, keys = await async_redis.scan(cursor, match=pattern, count=100) + + for key in keys: + # Check if this session belongs to this client + client_id_bytes = await async_redis.hget(key, "client_id") + if client_id_bytes and client_id_bytes.decode() == client_id: + # Mark session as complete (WebSocket disconnected) + await async_redis.hset(key, mapping={ + "status": "complete", + "completed_at": str(time.time()), + "completion_reason": "websocket_disconnect" + }) + session_id = key.decode().replace("audio:session:", "") + logger.info(f"📊 Marked session {session_id[:12]} as complete (WebSocket disconnect)") + sessions_closed += 1 + + if cursor == 0: + break + + if sessions_closed > 0: + logger.info(f"✅ Closed {sessions_closed} active session(s) for client {client_id}") + + # Delete Redis Streams for this client + stream_pattern = f"audio:stream:{client_id}" + stream_key = await async_redis.exists(stream_pattern) + if stream_key: + await async_redis.delete(stream_pattern) + logger.info(f"🧹 Deleted Redis stream: {stream_pattern}") + else: + logger.debug(f"No Redis stream found for client {client_id}") + + await async_redis.close() + + except Exception as session_error: + logger.warning(f"⚠️ Error marking sessions complete for client {client_id}: {session_error}") + # Use ClientManager for atomic client removal with cleanup client_manager = get_client_manager() removed = await client_manager.remove_client_with_cleanup(client_id) @@ -251,7 +328,6 @@ async def _initialize_streaming_session( job_ids = start_streaming_jobs( session_id=client_state.stream_session_id, user_id=user_id, - user_email=user_email, client_id=client_id ) @@ -620,7 +696,7 @@ async def _process_batch_audio_complete( return try: - from advanced_omi_backend.audio_utils import write_audio_file + from advanced_omi_backend.utils.audio_utils import write_audio_file from advanced_omi_backend.models.conversation import create_conversation # Combine all chunks @@ -664,14 +740,12 @@ async def _process_batch_audio_complete( application_logger.info(f"📝 Batch mode: Created conversation {conversation_id}") - # Enqueue complete batch processing job chain - from advanced_omi_backend.controllers.queue_controller import start_batch_processing_jobs + # Enqueue post-conversation processing job chain + from advanced_omi_backend.controllers.queue_controller import start_post_conversation_jobs - job_ids = start_batch_processing_jobs( + job_ids = start_post_conversation_jobs( conversation_id=conversation_id, audio_uuid=audio_uuid, - user_id=user_id, - user_email=user_email, audio_file_path=file_path ) diff --git a/backends/advanced/src/advanced_omi_backend/conversation_manager.py b/backends/advanced/src/advanced_omi_backend/conversation_manager.py deleted file mode 100644 index a240dd99..00000000 --- a/backends/advanced/src/advanced_omi_backend/conversation_manager.py +++ /dev/null @@ -1,106 +0,0 @@ -"""Conversation Manager for handling conversation lifecycle and processing coordination. - -This module separates conversation management concerns from ClientState to follow -the Single Responsibility Principle. It handles conversation closure, memory processing -queuing, and audio cropping coordination. -""" - -import logging -from typing import Optional - -audio_logger = logging.getLogger("audio") - - -class ConversationManager: - """Manages conversation lifecycle and processing coordination. - - This class handles the responsibilities previously mixed into ClientState, - providing a clean separation of concerns for conversation management. - - V2 Architecture: Uses RQ jobs for all transcription and memory processing. - """ - - def __init__(self): - audio_logger.info("ConversationManager initialized") - - async def close_conversation( - self, - client_id: str, - audio_uuid: str, - user_id: str, - user_email: Optional[str], - conversation_start_time: float, - speech_segments: dict, - chunk_dir, # Can be Path or str - ) -> bool: - """Close a conversation and coordinate all necessary processing. - - Args: - client_id: Client identifier - audio_uuid: Unique audio conversation identifier - user_id: User identifier - user_email: User email - db_helper: Database helper instance - conversation_start_time: When conversation started - speech_segments: Speech segments for cropping - chunk_dir: Directory for audio chunks - - Returns: - True if conversation was closed successfully - """ - audio_logger.info(f"🔒 Closing conversation {audio_uuid} for client {client_id}") - - try: - # V2 Architecture: All processing handled by RQ jobs - # Step 1: Enqueue final high-quality transcription via RQ - # This will add a new transcript version and trigger memory processing - from advanced_omi_backend.database import AudioChunksRepository - - repo = AudioChunksRepository() - audio_session = await repo.get_chunk(audio_uuid) - - if audio_session and audio_session.get("conversation_id"): - # Only enqueue if conversation was created (speech detected) - import uuid - from advanced_omi_backend.workers.transcription_jobs import transcribe_full_audio_job - from advanced_omi_backend.controllers.queue_controller import transcription_queue, JOB_RESULT_TTL - - conversation_id = audio_session["conversation_id"] - version_id = str(uuid.uuid4()) # Generate new version ID for final transcription - audio_logger.info(f"📤 Enqueuing final transcription job for conversation {conversation_id}") - - job = transcription_queue.enqueue( - transcribe_full_audio_job, - conversation_id, - audio_uuid, - audio_session["audio_file_path"], - version_id, - user_id, - job_timeout=300, - result_ttl=JOB_RESULT_TTL, - job_id=f"transcript-reprocess_{conversation_id[:12]}", - description=f"Final transcription for conversation {conversation_id[:12]} (conversation close)" - ) - audio_logger.info(f"✅ Enqueued final transcription job {job.id} for conversation {conversation_id}") - else: - audio_logger.info(f"⏭️ No conversation created for {audio_uuid} (no speech detected), skipping final transcription") - - audio_logger.info(f"✅ Successfully closed conversation {audio_uuid}") - return True - - except Exception as e: - audio_logger.error(f"❌ Error closing conversation {audio_uuid}: {e}", exc_info=True) - return False - - - -# Global singleton instance -_conversation_manager: Optional[ConversationManager] = None - - -def get_conversation_manager() -> ConversationManager: - """Get the global ConversationManager instance.""" - global _conversation_manager - if _conversation_manager is None: - _conversation_manager = ConversationManager() - return _conversation_manager diff --git a/backends/advanced/src/advanced_omi_backend/models/conversation.py b/backends/advanced/src/advanced_omi_backend/models/conversation.py index 864c68e2..0833bef8 100644 --- a/backends/advanced/src/advanced_omi_backend/models/conversation.py +++ b/backends/advanced/src/advanced_omi_backend/models/conversation.py @@ -30,6 +30,12 @@ class MemoryProvider(str, Enum): FRIEND_LITE = "friend_lite" OPENMEMORY_MCP = "openmemory_mcp" + class ConversationStatus(str, Enum): + """Conversation processing status.""" + ACTIVE = "active" # Has running jobs or open websocket + COMPLETED = "completed" # All jobs succeeded + FAILED = "failed" # One or more jobs failed + # Nested Models class SpeakerSegment(BaseModel): """Individual speaker segment in a transcript.""" @@ -67,6 +73,10 @@ class MemoryVersion(BaseModel): user_id: Indexed(str) = Field(description="User who owns this conversation") client_id: Indexed(str) = Field(description="Client device identifier") + # Audio file reference + audio_path: Optional[str] = Field(None, description="Path to audio file (relative to CHUNK_DIR)") + cropped_audio_path: Optional[str] = Field(None, description="Path to cropped audio file (relative to CHUNK_DIR)") + # Creation metadata created_at: Indexed(datetime) = Field(default_factory=datetime.utcnow, description="When the conversation was created") diff --git a/backends/advanced/src/advanced_omi_backend/routers/modules/queue_routes.py b/backends/advanced/src/advanced_omi_backend/routers/modules/queue_routes.py index 89679dba..adc2a99e 100644 --- a/backends/advanced/src/advanced_omi_backend/routers/modules/queue_routes.py +++ b/backends/advanced/src/advanced_omi_backend/routers/modules/queue_routes.py @@ -4,7 +4,7 @@ """ import logging -from fastapi import APIRouter, Depends, Query, HTTPException +from fastapi import APIRouter, Depends, Query, HTTPException, Request from pydantic import BaseModel from typing import List, Optional @@ -95,6 +95,46 @@ async def get_job( raise HTTPException(status_code=404, detail="Job not found") +@router.delete("/jobs/{job_id}") +async def cancel_job( + job_id: str, + current_user: User = Depends(current_active_user) +): + """Cancel or delete a job.""" + try: + job = Job.fetch(job_id, connection=redis_conn) + + # Check user permission (non-admins can only cancel their own jobs) + if not current_user.is_superuser: + job_user_id = job.kwargs.get("user_id") if job.kwargs else None + if job_user_id != str(current_user.user_id): + raise HTTPException(status_code=403, detail="Access forbidden") + + # Cancel if queued or processing, delete if completed/failed + if job.is_queued or job.is_started: + # Cancel the job + job.cancel() + logger.info(f"Cancelled job {job_id}") + return { + "job_id": job_id, + "action": "cancelled", + "message": f"Job {job_id} has been cancelled" + } + else: + # Delete completed/failed jobs + job.delete() + logger.info(f"Deleted job {job_id}") + return { + "job_id": job_id, + "action": "deleted", + "message": f"Job {job_id} has been deleted" + } + + except Exception as e: + logger.error(f"Failed to cancel/delete job {job_id}: {e}") + raise HTTPException(status_code=404, detail=f"Job not found or could not be cancelled: {str(e)}") + + @router.get("/jobs/by-session/{session_id}") async def get_jobs_by_session( session_id: str, @@ -113,7 +153,7 @@ async def get_jobs_by_session( all_jobs = [] processed_job_ids = set() # Track which jobs we've already processed - queues = ["default", "transcription", "memory"] + queues = ["default", "transcription", "memory", "audio"] def get_job_status(job, registries_map): """Determine job status from registries.""" @@ -410,7 +450,7 @@ async def flush_jobs( total_removed = 0 # Get all queues - queues = ["default", "transcription", "memory"] + queues = ["default", "transcription", "memory", "audio"] for queue_name in queues: queue = get_queue(queue_name) @@ -484,32 +524,57 @@ async def flush_all_jobs( from advanced_omi_backend.controllers.queue_controller import get_queue total_removed = 0 - queues = ["default", "transcription", "memory"] + queues = ["default", "transcription", "memory", "audio"] for queue_name in queues: queue = get_queue(queue_name) + # First, empty the queue itself (removes queued jobs) + queued_count = len(queue) + queue.empty() + total_removed += queued_count + logger.info(f"Emptied {queued_count} queued jobs from {queue_name}") + # Remove from all registries registries = [ - FinishedJobRegistry(queue=queue), - FailedJobRegistry(queue=queue), - CanceledJobRegistry(queue=queue), - StartedJobRegistry(queue=queue), - DeferredJobRegistry(queue=queue), - ScheduledJobRegistry(queue=queue) + ("finished", FinishedJobRegistry(queue=queue)), + ("failed", FailedJobRegistry(queue=queue)), + ("canceled", CanceledJobRegistry(queue=queue)), + ("started", StartedJobRegistry(queue=queue)), + ("deferred", DeferredJobRegistry(queue=queue)), + ("scheduled", ScheduledJobRegistry(queue=queue)) ] - for registry in registries: - for job_id in registry.get_job_ids(): + for registry_name, registry in registries: + job_ids = list(registry.get_job_ids()) # Convert to list to avoid iterator issues + logger.info(f"Flushing {len(job_ids)} jobs from {queue_name}/{registry_name}") + + for job_id in job_ids: try: + # Try to fetch and delete the job job = Job.fetch(job_id, connection=redis_conn) + + # Cancel if running, then delete + if job.is_started: + try: + job.cancel() + logger.info(f"Cancelled running job {job_id}") + except Exception as cancel_error: + logger.warning(f"Could not cancel job {job_id}: {cancel_error}") + job.delete() total_removed += 1 + except Exception as e: - logger.error(f"Error deleting job {job_id}: {e}") + # Job might already be deleted or not exist - try to remove from registry anyway + logger.warning(f"Error deleting job {job_id}: {e}") + try: + registry.remove(job_id) + logger.info(f"Removed stale job reference {job_id} from {registry_name} registry") + except Exception as reg_error: + logger.error(f"Could not remove {job_id} from registry: {reg_error}") - # Also empty the queue itself - queue.empty() + logger.info(f"Flushed {total_removed} jobs from all queues") return { "total_removed": total_removed, @@ -549,6 +614,12 @@ async def get_redis_sessions( session_data = await redis_client.hgetall(key) if session_data: session_id = key.decode().replace("audio:session:", "") + + # Get conversation count for this session + conversation_count_key = f"session:conversation_count:{session_id}" + conversation_count_bytes = await redis_client.get(conversation_count_key) + conversation_count = int(conversation_count_bytes.decode()) if conversation_count_bytes else 0 + sessions.append({ "session_id": session_id, "user_id": session_data.get(b"user_id", b"").decode(), @@ -559,7 +630,8 @@ async def get_redis_sessions( "status": session_data.get(b"status", b"").decode(), "started_at": session_data.get(b"started_at", b"").decode(), "chunks_published": int(session_data.get(b"chunks_published", b"0").decode() or 0), - "last_chunk_at": session_data.get(b"last_chunk_at", b"").decode() + "last_chunk_at": session_data.get(b"last_chunk_at", b"").decode(), + "conversation_count": conversation_count }) except Exception as e: logger.error(f"Error getting session info for {key}: {e}") @@ -626,4 +698,252 @@ async def clear_old_sessions( except Exception as e: logger.error(f"Failed to clear sessions: {e}", exc_info=True) - raise HTTPException(status_code=500, detail=f"Failed to clear sessions: {str(e)}") \ No newline at end of file + raise HTTPException(status_code=500, detail=f"Failed to clear sessions: {str(e)}") + + +@router.get("/dashboard") +async def get_dashboard_data( + request: Request, + expanded_sessions: str = Query(default="", description="Comma-separated list of session IDs to fetch jobs for"), + current_user: User = Depends(current_active_user) +): + """Get all data needed for the Queue dashboard in a single API call. + + Returns: + - Jobs grouped by status (queued, processing, completed, failed) + - Queue statistics + - Streaming status + - Session jobs for expanded sessions + """ + try: + from advanced_omi_backend.controllers import system_controller + from rq.registry import FinishedJobRegistry, FailedJobRegistry, StartedJobRegistry + from advanced_omi_backend.controllers.queue_controller import get_queue + + # Parse expanded sessions list + expanded_session_ids = [s.strip() for s in expanded_sessions.split(",") if s.strip()] if expanded_sessions else [] + + # Fetch all data in parallel + import asyncio + + async def fetch_jobs_by_status(status_name: str, limit: int = 100): + """Fetch jobs by status using existing registry logic.""" + try: + queues = ["default", "transcription", "memory", "audio"] + all_jobs = [] + + for queue_name in queues: + queue = get_queue(queue_name) + + # Get job IDs based on status + if status_name == "queued": + job_ids = queue.job_ids[:limit] + elif status_name == "processing": + job_ids = list(StartedJobRegistry(queue=queue).get_job_ids())[:limit] + elif status_name == "completed": + job_ids = list(FinishedJobRegistry(queue=queue).get_job_ids())[:limit] + elif status_name == "failed": + job_ids = list(FailedJobRegistry(queue=queue).get_job_ids())[:limit] + else: + continue + + # Fetch job details + for job_id in job_ids: + try: + job = Job.fetch(job_id, connection=redis_conn) + + # Check user permission + if not current_user.is_superuser: + job_user_id = job.kwargs.get("user_id") if job.kwargs else None + if job_user_id != str(current_user.user_id): + continue + + # Add job with metadata + all_jobs.append({ + "job_id": job.id, + "job_type": job.func_name.split('.')[-1] if job.func_name else "unknown", + "user_id": job.kwargs.get("user_id") if job.kwargs else None, + "status": status_name, + "priority": "normal", # RQ doesn't have priority concept + "data": {"description": job.description or ""}, + "result": job.result, + "meta": job.meta if job.meta else {}, + "kwargs": job.kwargs if job.kwargs else {}, + "error_message": str(job.exc_info) if job.exc_info else None, + "created_at": job.created_at.isoformat() if job.created_at else None, + "started_at": job.started_at.isoformat() if job.started_at else None, + "ended_at": job.ended_at.isoformat() if job.ended_at else None, + "retry_count": 0, # RQ doesn't track this by default + "max_retries": 0, + "progress_percent": 0, + "progress_message": "", + "queue": queue_name + }) + except Exception as e: + logger.debug(f"Error fetching job {job_id}: {e}") + continue + + return all_jobs + except Exception as e: + logger.error(f"Error fetching {status_name} jobs: {e}") + return [] + + async def fetch_stats(): + """Fetch queue stats.""" + try: + return get_job_stats() + except Exception as e: + logger.error(f"Error fetching stats: {e}") + return {"total_jobs": 0, "queued_jobs": 0, "processing_jobs": 0, "completed_jobs": 0, "failed_jobs": 0} + + async def fetch_streaming_status(): + """Fetch streaming status.""" + try: + # Use the actual request object from the parent function + return await system_controller.get_streaming_status(request) + except Exception as e: + logger.error(f"Error fetching streaming status: {e}") + return {"active_sessions": [], "stream_health": {}, "rq_queues": {}} + + async def fetch_session_jobs(session_id: str): + """Fetch jobs for a specific session.""" + try: + # Reuse the existing logic from get_jobs_by_session endpoint + from advanced_omi_backend.models.conversation import Conversation + + # Get conversation IDs for this session + conversations = await Conversation.find(Conversation.audio_uuid == session_id).to_list() + conversation_ids = {conv.conversation_id for conv in conversations} + + all_jobs = [] + processed_job_ids = set() + queues = ["default", "transcription", "memory", "audio"] + + def get_job_status(job): + if job.is_queued: + return "queued" + elif job.is_started: + return "processing" + elif job.is_finished: + return "completed" + elif job.is_failed: + return "failed" + elif job.is_deferred: + return "deferred" + else: + return "unknown" + + # Find all jobs for this session + for queue_name in queues: + queue = get_queue(queue_name) + + # Check all registries + from rq.registry import ( + FinishedJobRegistry, FailedJobRegistry, StartedJobRegistry, + CanceledJobRegistry, DeferredJobRegistry, ScheduledJobRegistry + ) + + registries = [ + ("queued", queue.job_ids), + ("processing", StartedJobRegistry(queue=queue).get_job_ids()), + ("completed", FinishedJobRegistry(queue=queue).get_job_ids()), + ("failed", FailedJobRegistry(queue=queue).get_job_ids()) + ] + + for status_name, job_ids in registries: + for job_id in job_ids: + if job_id in processed_job_ids: + continue + + try: + job = Job.fetch(job_id, connection=redis_conn) + + # Check if job belongs to this session + matches_session = False + if job.meta and 'audio_uuid' in job.meta and job.meta['audio_uuid'] == session_id: + matches_session = True + elif job.args and len(job.args) > 0 and job.args[0] == session_id: + matches_session = True + + if not matches_session: + continue + + # Check user permission + if not current_user.is_superuser: + job_user_id = job.kwargs.get("user_id") if job.kwargs else None + if job_user_id != str(current_user.user_id): + continue + + processed_job_ids.add(job_id) + all_jobs.append({ + "job_id": job.id, + "job_type": job.func_name.split('.')[-1] if job.func_name else "unknown", + "queue": queue_name, + "status": get_job_status(job), + "created_at": job.created_at.isoformat() if job.created_at else None, + "started_at": job.started_at.isoformat() if job.started_at else None, + "ended_at": job.ended_at.isoformat() if job.ended_at else None, + "description": job.description or "", + "result": job.result, + "meta": job.meta if job.meta else {}, + "error_message": str(job.exc_info) if job.exc_info else None + }) + except Exception as e: + logger.debug(f"Error fetching job {job_id}: {e}") + continue + + return {"session_id": session_id, "jobs": all_jobs} + except Exception as e: + logger.error(f"Error fetching jobs for session {session_id}: {e}") + return {"session_id": session_id, "jobs": []} + + # Execute all fetches in parallel + queued_jobs_task = fetch_jobs_by_status("queued", limit=100) + processing_jobs_task = fetch_jobs_by_status("processing", limit=100) + completed_jobs_task = fetch_jobs_by_status("completed", limit=50) + failed_jobs_task = fetch_jobs_by_status("failed", limit=50) + stats_task = fetch_stats() + streaming_status_task = fetch_streaming_status() + session_jobs_tasks = [fetch_session_jobs(sid) for sid in expanded_session_ids] + + results = await asyncio.gather( + queued_jobs_task, + processing_jobs_task, + completed_jobs_task, + failed_jobs_task, + stats_task, + streaming_status_task, + *session_jobs_tasks, + return_exceptions=True + ) + + queued_jobs = results[0] if not isinstance(results[0], Exception) else [] + processing_jobs = results[1] if not isinstance(results[1], Exception) else [] + completed_jobs = results[2] if not isinstance(results[2], Exception) else [] + failed_jobs = results[3] if not isinstance(results[3], Exception) else [] + stats = results[4] if not isinstance(results[4], Exception) else {"total_jobs": 0} + streaming_status = results[5] if not isinstance(results[5], Exception) else {"active_sessions": []} + session_jobs_results = results[6:] if len(results) > 6 else [] + + # Convert session jobs list to dict + session_jobs = {} + for result in session_jobs_results: + if not isinstance(result, Exception) and result: + session_jobs[result["session_id"]] = result["jobs"] + + return { + "jobs": { + "queued": queued_jobs, + "processing": processing_jobs, + "completed": completed_jobs, + "failed": failed_jobs + }, + "stats": stats, + "streaming_status": streaming_status, + "session_jobs": session_jobs, + "timestamp": asyncio.get_event_loop().time() + } + + except Exception as e: + logger.error(f"Failed to get dashboard data: {e}", exc_info=True) + raise HTTPException(status_code=500, detail=f"Failed to get dashboard data: {str(e)}") \ No newline at end of file diff --git a/backends/advanced/src/advanced_omi_backend/services/audio_stream/aggregator.py b/backends/advanced/src/advanced_omi_backend/services/audio_stream/aggregator.py index 9b82aabf..26b985ab 100644 --- a/backends/advanced/src/advanced_omi_backend/services/audio_stream/aggregator.py +++ b/backends/advanced/src/advanced_omi_backend/services/audio_stream/aggregator.py @@ -68,7 +68,7 @@ async def get_session_results(self, session_id: str) -> list[dict]: # Log detailed result info chunk_ids = [r["chunk_id"] for r in results] total_text_length = sum(len(r["text"]) for r in results) - logger.info( + logger.debug( f"🔄 Retrieved {len(results)} results for session {session_id}: " f"chunks={chunk_ids}, total_text={total_text_length} chars" ) @@ -143,7 +143,7 @@ async def get_combined_results(self, session_id: str) -> dict: "provider": provider } - logger.info( + logger.debug( f"📦 Combined {len(results)} chunks for session {session_id}: " f"{len(full_text)} chars, {len(all_words)} words, {len(all_segments)} segments" ) diff --git a/backends/advanced/src/advanced_omi_backend/services/audio_stream/consumer.py b/backends/advanced/src/advanced_omi_backend/services/audio_stream/consumer.py index ea770253..c36ee188 100644 --- a/backends/advanced/src/advanced_omi_backend/services/audio_stream/consumer.py +++ b/backends/advanced/src/advanced_omi_backend/services/audio_stream/consumer.py @@ -577,7 +577,7 @@ async def store_result( approximate=True ) - logger.info( + logger.debug( f"➡️ Stored result {chunk_id} in {session_results_stream}: " f"text_len={len(text)}, msg_id={message_id.decode()}" ) diff --git a/backends/advanced/src/advanced_omi_backend/services/audio_stream/producer.py b/backends/advanced/src/advanced_omi_backend/services/audio_stream/producer.py index 98e93cfc..95bf25e1 100644 --- a/backends/advanced/src/advanced_omi_backend/services/audio_stream/producer.py +++ b/backends/advanced/src/advanced_omi_backend/services/audio_stream/producer.py @@ -248,7 +248,7 @@ async def add_audio_chunk( # Log every 10th chunk to avoid spam if session_buffer["chunk_count"] % 10 == 0 or session_buffer["chunk_count"] <= 5: - logger.info( + logger.debug( f"📤 Added fixed-size chunk {chunk_id_formatted} to {stream_name} " f"({len(chunk_audio)} bytes = {len(chunk_audio)/bytes_per_second:.3f}s, " f"buffer remaining: {len(session_buffer['buffer'])} bytes)" diff --git a/backends/advanced/src/advanced_omi_backend/services/transcription/deepgram.py b/backends/advanced/src/advanced_omi_backend/services/transcription/deepgram.py index 89b80de1..27840d8f 100644 --- a/backends/advanced/src/advanced_omi_backend/services/transcription/deepgram.py +++ b/backends/advanced/src/advanced_omi_backend/services/transcription/deepgram.py @@ -53,7 +53,7 @@ async def transcribe(self, audio_data: bytes, sample_rate: int, diarize: bool = headers = {"Authorization": f"Token {self.api_key}", "Content-Type": "audio/raw"} - logger.info(f"Sending {len(audio_data)} bytes to Deepgram API") + logger.debug(f"Sending {len(audio_data)} bytes to Deepgram API") # Calculate dynamic timeout based on audio file size estimated_duration = len(audio_data) / (sample_rate * 2 * 1) # 16-bit mono @@ -128,7 +128,7 @@ async def transcribe(self, audio_data: bytes, sample_rate: int, diarize: bool = segments.append(current_segment) else: transcript = alternative.get("transcript", "").strip() - logger.info( + logger.debug( f"Deepgram basic transcription successful: {len(transcript)} characters" ) @@ -264,7 +264,7 @@ async def start_stream(self, client_id: str, sample_rate: int = 16000, diarize: "stream_id": str(uuid.uuid4()) } - logger.info(f"Deepgram WebSocket connected for client {client_id}") + logger.debug(f"Deepgram WebSocket connected for client {client_id}") except Exception as e: logger.error(f"Failed to start Deepgram streaming for {client_id}: {e}") diff --git a/backends/advanced/src/advanced_omi_backend/speaker_recognition_client.py b/backends/advanced/src/advanced_omi_backend/speaker_recognition_client.py index b66b6f08..8905f059 100644 --- a/backends/advanced/src/advanced_omi_backend/speaker_recognition_client.py +++ b/backends/advanced/src/advanced_omi_backend/speaker_recognition_client.py @@ -475,7 +475,7 @@ async def check_if_enrolled_speaker_present( import uuid from pathlib import Path from advanced_omi_backend.utils.audio_extraction import extract_audio_for_results - from advanced_omi_backend.audio_utils import write_pcm_to_wav + from advanced_omi_backend.utils.audio_utils import write_pcm_to_wav logger.info(f"🎤 [SPEAKER CHECK] Starting speaker check for session {session_id}") logger.info(f"🎤 [SPEAKER CHECK] Client: {client_id}, User: {user_id}") diff --git a/backends/advanced/src/advanced_omi_backend/audio_utils.py b/backends/advanced/src/advanced_omi_backend/utils/audio_utils.py similarity index 87% rename from backends/advanced/src/advanced_omi_backend/audio_utils.py rename to backends/advanced/src/advanced_omi_backend/utils/audio_utils.py index 302d068e..2a4aeaf9 100644 --- a/backends/advanced/src/advanced_omi_backend/audio_utils.py +++ b/backends/advanced/src/advanced_omi_backend/utils/audio_utils.py @@ -267,68 +267,50 @@ async def _process_audio_cropping_with_relative_timestamps( chunk_repo: Optional['AudioChunksRepository'] = None, ) -> bool: """ - Process audio cropping with automatic relative timestamp conversion. - This function handles both live processing and reprocessing scenarios. + Process audio cropping with speech segments already in relative format. + + The segments are expected to be in relative format (seconds from audio start), + as provided by Deepgram transcription. No timestamp conversion is needed. """ try: - # Convert absolute timestamps to relative timestamps - # Extract file start time from filename: timestamp_client_uuid.wav - filename = original_path.split("/")[-1] - logger.info(f"🕐 Parsing filename: {filename}") - filename_parts = filename.split("_") - if len(filename_parts) < 3: - logger.error( - f"Invalid filename format: {filename}. Expected format: timestamp_client_id_audio_uuid.wav" - ) - return False - - try: - file_start_timestamp = float(filename_parts[0]) - except ValueError as e: - logger.error(f"Cannot parse timestamp from filename {filename}: {e}") - return False - - # Convert speech segments to relative timestamps - relative_segments = [] - for start_abs, end_abs in speech_segments: + # Validate input segments + validated_segments = [] + for start_rel, end_rel in speech_segments: # Validate input timestamps - if start_abs >= end_abs: + if start_rel >= end_rel: logger.warning( - f"⚠️ Invalid speech segment: start={start_abs} >= end={end_abs}, skipping" + f"⚠️ Invalid speech segment: start={start_rel} >= end={end_rel}, skipping" ) continue - start_rel = start_abs - file_start_timestamp - end_rel = end_abs - file_start_timestamp - - # Ensure relative timestamps are positive (sanity check) + # Ensure timestamps are positive (sanity check) if start_rel < 0: logger.warning( - f"⚠️ Negative start timestamp: {start_rel} (absolute: {start_abs}, file_start: {file_start_timestamp}), clamping to 0.0" + f"⚠️ Negative start timestamp: {start_rel}, clamping to 0.0" ) start_rel = 0.0 if end_rel < 0: logger.warning( - f"⚠️ Negative end timestamp: {end_rel} (absolute: {end_abs}, file_start: {file_start_timestamp}), skipping segment" + f"⚠️ Negative end timestamp: {end_rel}, skipping segment" ) continue - relative_segments.append((start_rel, end_rel)) + validated_segments.append((start_rel, end_rel)) - logger.info(f"🕐 Converting timestamps for {audio_uuid}: file_start={file_start_timestamp}") - logger.info(f"🕐 Absolute segments: {speech_segments}") - logger.info(f"🕐 Relative segments: {relative_segments}") + logger.info(f"🕐 Processing cropping for {audio_uuid}") + logger.info(f"🕐 Input segments (relative timestamps): {speech_segments}") + logger.info(f"🕐 Validated segments: {validated_segments}") - # Validate that we have valid relative segments after conversion - if not relative_segments: + # Validate that we have valid segments + if not validated_segments: logger.warning( - f"No valid relative segments after timestamp conversion for {audio_uuid}" + f"No valid segments for cropping {audio_uuid}" ) return False - success = await _crop_audio_with_ffmpeg(original_path, relative_segments, output_path) + success = await _crop_audio_with_ffmpeg(original_path, validated_segments, output_path) if success: - # Update database with cropped file info (keep original absolute timestamps for reference) + # Update database with cropped file info cropped_filename = output_path.split("/")[-1] if chunk_repo is not None: await chunk_repo.update_cropped_audio(audio_uuid, cropped_filename, speech_segments) diff --git a/backends/advanced/src/advanced_omi_backend/utils/conversation_utils.py b/backends/advanced/src/advanced_omi_backend/utils/conversation_utils.py index c6cfa06e..ef83f3ba 100644 --- a/backends/advanced/src/advanced_omi_backend/utils/conversation_utils.py +++ b/backends/advanced/src/advanced_omi_backend/utils/conversation_utils.py @@ -13,6 +13,39 @@ logger = logging.getLogger(__name__) +def is_meaningful_speech(combined_results: dict) -> bool: + """ + Convenience wrapper to check if combined transcription results contain meaningful speech. + + This is a shared helper used by both speech detection and conversation timeout logic. + + Args: + combined_results: Combined results from TranscriptionResultsAggregator with: + - "text": str - Full transcript text + - "words": list - Word-level data with confidence and timing + - "segments": list - Speaker segments + - "chunk_count": int - Number of chunks processed + + Returns: + bool: True if meaningful speech detected, False otherwise + + Example: + >>> combined = await aggregator.get_combined_results(session_id) + >>> if is_meaningful_speech(combined): + >>> print("Meaningful speech detected!") + """ + if not combined_results.get("text"): + return False + + transcript_data = { + "text": combined_results["text"], + "words": combined_results.get("words", []) + } + + speech_analysis = analyze_speech(transcript_data) + return speech_analysis["has_speech"] + + def analyze_speech(transcript_data: dict) -> dict: """ Analyze transcript for meaningful speech to determine if conversation should be created. diff --git a/backends/advanced/src/advanced_omi_backend/workers/audio_jobs.py b/backends/advanced/src/advanced_omi_backend/workers/audio_jobs.py index 0cd84a63..f9a08c90 100644 --- a/backends/advanced/src/advanced_omi_backend/workers/audio_jobs.py +++ b/backends/advanced/src/advanced_omi_backend/workers/audio_jobs.py @@ -145,7 +145,7 @@ async def process(): if result.get("success") and result.get("conversation_id"): from .transcription_jobs import transcribe_full_audio_job, recognise_speakers_job from .memory_jobs import process_memory_job - from advanced_omi_backend.controllers.queue_controller import transcription_queue, memory_queue, JOB_RESULT_TTL + from advanced_omi_backend.controllers.queue_controller import transcription_queue, memory_queue, default_queue, JOB_RESULT_TTL conversation_id = result["conversation_id"] @@ -162,7 +162,7 @@ async def process(): result_ttl=JOB_RESULT_TTL, job_id=f"upload_{conversation_id[:8]}", description=f"Transcribe audio for {conversation_id[:8]}", - meta={'audio_uuid': result["audio_uuid"]} + meta={'audio_uuid': result["audio_uuid"], 'conversation_id': conversation_id} ) logger.info(f"📥 RQ: Enqueued transcription job {transcript_job.id}") @@ -180,28 +180,44 @@ async def process(): result_ttl=JOB_RESULT_TTL, job_id=f"speaker_{conversation_id[:8]}", description=f"Recognize speakers for {conversation_id[:8]}", - meta={'audio_uuid': result["audio_uuid"]} + meta={'audio_uuid': result["audio_uuid"], 'conversation_id': conversation_id} ) logger.info(f"📥 RQ: Enqueued speaker recognition job {speaker_job.id} (depends on {transcript_job.id})") - # Job 3: Extract memories (depends on speaker recognition) + # Job 3: Audio cropping (depends on speaker recognition) + cropping_job = default_queue.enqueue( + process_cropping_job, + conversation_id, + result["file_path"], + user_id, + depends_on=speaker_job, + job_timeout=300, + result_ttl=JOB_RESULT_TTL, + job_id=f"crop_{conversation_id[:8]}", + description=f"Crop audio for {conversation_id[:8]}", + meta={'audio_uuid': result["audio_uuid"], 'conversation_id': conversation_id} + ) + logger.info(f"📥 RQ: Enqueued audio cropping job {cropping_job.id} (depends on {speaker_job.id})") + + # Job 4: Extract memories (depends on cropping) memory_job = memory_queue.enqueue( process_memory_job, None, # client_id - will be read from conversation in DB user_id, "", # user_email - will be read from user in DB conversation_id, - depends_on=speaker_job, + depends_on=cropping_job, job_timeout=1800, result_ttl=JOB_RESULT_TTL, job_id=f"memory_{conversation_id[:8]}", description=f"Extract memories for {conversation_id[:8]}", - meta={'audio_uuid': result["audio_uuid"]} + meta={'audio_uuid': result["audio_uuid"], 'conversation_id': conversation_id} ) - logger.info(f"📥 RQ: Enqueued memory job {memory_job.id} (depends on {speaker_job.id})") + logger.info(f"📥 RQ: Enqueued memory job {memory_job.id} (depends on {cropping_job.id})") result["transcript_job_id"] = transcript_job.id result["speaker_job_id"] = speaker_job.id + result["cropping_job_id"] = cropping_job.id result["memory_job_id"] = memory_job.id return result @@ -214,64 +230,117 @@ async def process(): raise -def process_cropping_job( - client_id: str, - user_id: str, - audio_uuid: str, - original_path: str, - speech_segments: list, - output_path: str +@async_job(redis=True, beanie=True) +async def process_cropping_job( + conversation_id: str, + audio_path: str, + redis_client=None ) -> Dict[str, Any]: """ - RQ job function for audio cropping. + RQ job function for audio cropping - removes silent segments from audio. - This function is executed by RQ workers and can survive server restarts. + This job: + 1. Reads transcript segments from conversation + 2. Extracts speech timestamps + 3. Creates cropped audio file with only speech segments + 4. Updates audio_chunks collection with cropped file path + + Args: + conversation_id: Conversation ID + audio_path: Path to original audio file + redis_client: Redis client (injected by decorator) + + Returns: + Dict with processing results """ - import asyncio - from advanced_omi_backend.audio_utils import _process_audio_cropping_with_relative_timestamps + from pathlib import Path + from advanced_omi_backend.utils.audio_utils import _process_audio_cropping_with_relative_timestamps from advanced_omi_backend.database import get_collections, AudioChunksRepository + from advanced_omi_backend.models.conversation import Conversation + from advanced_omi_backend.config import CHUNK_DIR try: - logger.info(f"🔄 RQ: Starting audio cropping for audio {audio_uuid}") - - loop = asyncio.new_event_loop() - asyncio.set_event_loop(loop) - - try: - async def process(): - # Get repository - collections = get_collections() - repository = AudioChunksRepository(collections["chunks_col"]) - - # Convert list of lists to list of tuples - segments_tuples = [tuple(seg) for seg in speech_segments] - - # Process cropping - await _process_audio_cropping_with_relative_timestamps( - original_path, - segments_tuples, - output_path, - audio_uuid, - repository - ) - - logger.info(f"✅ RQ: Completed audio cropping for audio {audio_uuid}") - - return { - "success": True, - "audio_uuid": audio_uuid, - "output_path": output_path, - "segments": len(speech_segments) - } - - result = loop.run_until_complete(process()) - return result + logger.info(f"🔄 RQ: Starting audio cropping for conversation {conversation_id}") + + # Get conversation to access segments + conversation = await Conversation.find_one(Conversation.conversation_id == conversation_id) + if not conversation: + raise ValueError(f"Conversation {conversation_id} not found") + + # Extract speech segments from transcript + segments = conversation.segments + if not segments or len(segments) == 0: + logger.warning(f"⚠️ No segments found for conversation {conversation_id}, skipping cropping") + return { + "success": False, + "conversation_id": conversation_id, + "reason": "no_segments" + } + + # Convert segments to (start, end) tuples + speech_segments = [(seg.start, seg.end) for seg in segments] + logger.info(f"Found {len(speech_segments)} speech segments for cropping") + + # Generate output path for cropped audio + audio_uuid = conversation.audio_uuid + original_path = Path(audio_path) + cropped_filename = f"cropped_{original_path.name}" + output_path = CHUNK_DIR / cropped_filename + + # Get repository for database updates + collections = get_collections() + repository = AudioChunksRepository(collections["chunks_col"]) + + # Process cropping + success = await _process_audio_cropping_with_relative_timestamps( + str(original_path), + speech_segments, + str(output_path), + audio_uuid, + repository + ) - finally: - loop.close() + if not success: + logger.error(f"❌ RQ: Audio cropping failed for conversation {conversation_id}") + return { + "success": False, + "conversation_id": conversation_id, + "reason": "cropping_failed" + } + + # Calculate cropped duration + cropped_duration_seconds = sum(end - start for start, end in speech_segments) + + # Update conversation with cropped audio path + conversation.cropped_audio_path = cropped_filename + await conversation.save() + logger.info(f"💾 Updated conversation {conversation_id[:12]} with cropped_audio_path: {cropped_filename}") + + logger.info(f"✅ RQ: Completed audio cropping for conversation {conversation_id} ({cropped_duration_seconds:.1f}s)") + + # Update job metadata with cropped duration + from rq import get_current_job + current_job = get_current_job() + if current_job: + if not current_job.meta: + current_job.meta = {} + current_job.meta['cropped_duration_seconds'] = round(cropped_duration_seconds, 1) + current_job.meta['segments_cropped'] = len(speech_segments) + current_job.save_meta() + + return { + "success": True, + "conversation_id": conversation_id, + "audio_uuid": audio_uuid, + "original_path": str(original_path), + "cropped_path": str(output_path), + "cropped_filename": cropped_filename, + "segments_count": len(speech_segments), + "cropped_duration_seconds": cropped_duration_seconds + } except Exception as e: - logger.error(f"❌ RQ: Audio cropping failed for audio {audio_uuid}: {e}") + logger.error(f"❌ RQ: Audio cropping failed for conversation {conversation_id}: {e}") raise @@ -279,24 +348,27 @@ async def process(): async def audio_streaming_persistence_job( session_id: str, user_id: str, - user_email: str, client_id: str, redis_client=None ) -> Dict[str, Any]: """ - Long-running RQ job that collects audio chunks from Redis stream and writes to disk progressively. + Long-running RQ job that progressively writes audio chunks to disk as they arrive. + + Opens a WAV file immediately and appends chunks in real-time, making the file + available for playback in the UI before the session completes. - Runs in parallel with transcription processing to reduce memory pressure on WebSocket. + Runs in parallel with transcription processing to reduce memory pressure. Args: session_id: Stream session ID user_id: User ID - user_email: User email client_id: Client ID redis_client: Redis client (injected by decorator) Returns: Dict with audio_file_path, chunk_count, total_bytes, duration_seconds + + Note: user_email is fetched from the database when needed. """ logger.info(f"🎵 Starting audio persistence for session {session_id}") @@ -323,20 +395,141 @@ async def audio_streaming_persistence_job( max_runtime = 3540 # 59 minutes start_time = time.time() - # Audio collection - audio_chunks = [] - chunk_count = 0 + from advanced_omi_backend.config import CHUNK_DIR + from easy_audio_interfaces.filesystem.filesystem_interfaces import LocalFileSink + from wyoming.audio import AudioChunk + + # Ensure directory exists + CHUNK_DIR.mkdir(parents=True, exist_ok=True) + + # File rotation state + current_conversation_id = None + file_sink = None + file_path = None + wav_filename = None + conversation_chunk_count = 0 + conversation_start_time = None + + # Audio collection stats (across all conversations in this session) + total_chunk_count = 0 total_bytes = 0 end_signal_received = False consecutive_empty_reads = 0 max_empty_reads = 3 # Exit after 3 consecutive empty reads (deterministic check) + conversation_count = 0 while True: # Check timeout if time.time() - start_time > max_runtime: logger.warning(f"⏱️ Timeout reached for audio persistence {session_id}") + # Close current file if open + if file_sink: + await file_sink.close() + logger.info(f"✅ Closed file on timeout: {wav_filename}") break + # Check if session is finalizing (user stopped recording or WebSocket disconnected) + session_status = await redis_client.hget(session_key, "status") + if session_status and session_status.decode() in ["finalizing", "complete"]: + logger.info(f"🛑 Session finalizing detected, writing final chunks...") + # Give a brief moment for any in-flight chunks to arrive + await asyncio.sleep(0.5) + # Do one final read to write remaining chunks to current file + if file_sink: + try: + final_messages = await redis_client.xreadgroup( + audio_group_name, + audio_consumer_name, + {audio_stream_name: ">"}, + count=50, + block=500 + ) + if final_messages: + for stream_name, msgs in final_messages: + for message_id, fields in msgs: + audio_data = fields.get(b"audio_data", b"") + chunk_id = fields.get(b"chunk_id", b"").decode() + if chunk_id != "END" and len(audio_data) > 0: + chunk = AudioChunk( + rate=16000, + width=2, + channels=1, + audio=audio_data + ) + await file_sink.write(chunk) + conversation_chunk_count += 1 + total_chunk_count += 1 + total_bytes += len(audio_data) + await redis_client.xack(audio_stream_name, audio_group_name, message_id) + logger.info(f"📦 Final read wrote {len(final_messages[0][1]) if final_messages else 0} more chunks") + except Exception as e: + logger.debug(f"Final audio read error (non-fatal): {e}") + + # Close final file + await file_sink.close() + logger.info(f"✅ Closed final file: {wav_filename} ({conversation_chunk_count} chunks)") + break + + # Check for conversation change (file rotation signal) + conversation_key = f"conversation:current:{session_id}" + new_conversation_id = await redis_client.get(conversation_key) + + if new_conversation_id: + new_conversation_id = new_conversation_id.decode() + + # Conversation changed - rotate to new file + if new_conversation_id != current_conversation_id: + # Close previous file if exists + if file_sink: + await file_sink.close() + duration = (time.time() - conversation_start_time) if conversation_start_time else 0 + logger.info( + f"✅ Closed conversation {current_conversation_id[:12]} file: {wav_filename} " + f"({conversation_chunk_count} chunks, {duration:.1f}s)" + ) + + # Open new file for new conversation + current_conversation_id = new_conversation_id + conversation_count += 1 + conversation_chunk_count = 0 + conversation_start_time = time.time() + + timestamp = int(time.time() * 1000) + wav_filename = f"{timestamp}_{client_id}_{current_conversation_id}.wav" + file_path = CHUNK_DIR / wav_filename + + file_sink = LocalFileSink( + file_path=str(file_path), + sample_rate=16000, + channels=1, + sample_width=2 + ) + await file_sink.open() + logger.info( + f"📁 Opened new file for conversation #{conversation_count} ({current_conversation_id[:12]}): {file_path}" + ) + + # Store file path in Redis (keyed by conversation_id, not session_id) + audio_file_key = f"audio:file:{current_conversation_id}" + await redis_client.set(audio_file_key, str(file_path), ex=3600) + logger.info(f"💾 Stored audio file path in Redis: {audio_file_key}") + else: + # Key deleted - conversation ended, close current file + if file_sink and current_conversation_id: + await file_sink.close() + duration = (time.time() - conversation_start_time) if conversation_start_time else 0 + logger.info( + f"✅ Closed conversation {current_conversation_id[:12]} file after conversation ended: {wav_filename} " + f"({conversation_chunk_count} chunks, {duration:.1f}s)" + ) + file_sink = None # Clear sink to prevent writing to closed file + current_conversation_id = None + + # If no file open yet, wait for conversation to be created + if not file_sink: + await asyncio.sleep(0.5) + continue + # Read audio chunks from stream (non-blocking) try: audio_messages = await redis_client.xreadgroup( @@ -362,13 +555,24 @@ async def audio_streaming_persistence_job( logger.info(f"📡 Received END signal in audio persistence") end_signal_received = True elif len(audio_data) > 0: - audio_chunks.append(audio_data) - chunk_count += 1 + # Write chunk immediately to file + chunk = AudioChunk( + rate=16000, + width=2, + channels=1, + audio=audio_data + ) + await file_sink.write(chunk) + conversation_chunk_count += 1 + total_chunk_count += 1 total_bytes += len(audio_data) # Log every 40 chunks to avoid spam - if chunk_count % 40 == 0: - logger.info(f"📦 Collected {chunk_count} audio chunks ({total_bytes / 1024 / 1024:.2f} MB)") + if total_chunk_count % 40 == 0: + logger.info( + f"📦 Session {session_id[:12]}: {total_chunk_count} total chunks " + f"(conversation {current_conversation_id[:12]}: {conversation_chunk_count} chunks)" + ) # ACK the message await redis_client.xack(audio_stream_name, audio_group_name, message_id) @@ -388,47 +592,37 @@ async def audio_streaming_persistence_job( await asyncio.sleep(0.1) # Check every 100ms for responsiveness - # Write complete audio file - if audio_chunks: - from advanced_omi_backend.audio_utils import write_audio_file - - complete_audio = b''.join(audio_chunks) - timestamp = int(time.time() * 1000) + # Job complete - calculate final stats + runtime_seconds = time.time() - start_time - logger.info(f"💾 Writing {len(audio_chunks)} chunks ({total_bytes / 1024 / 1024:.2f} MB) to disk") - - wav_filename, file_path, duration = await write_audio_file( - raw_audio_data=complete_audio, - audio_uuid=session_id, - client_id=client_id, - user_id=user_id, - user_email=user_email, - timestamp=timestamp, - validate=False - ) - logger.info(f"✅ Wrote audio file: {wav_filename} ({duration:.1f}s, {chunk_count} chunks)") - - # Store file path in Redis for finalize job to find - audio_file_key = f"audio:file:{session_id}" - await redis_client.set(audio_file_key, file_path, ex=3600) - logger.info(f"💾 Stored audio file path in Redis: {audio_file_key}") + # Calculate duration (16kHz, 16-bit mono = 32000 bytes/second) + if total_bytes > 0: + duration = total_bytes / (16000 * 2 * 1) # sample_rate * sample_width * channels else: - logger.warning(f"⚠️ No audio chunks collected for session {session_id}") - file_path = None + logger.warning(f"⚠️ No audio chunks written for session {session_id}") duration = 0.0 - # Clean up Redis tracking key + logger.info( + f"🎵 Audio persistence job complete for session {session_id}: " + f"{conversation_count} conversations, {total_chunk_count} total chunks, " + f"{total_bytes / 1024 / 1024:.2f} MB, {runtime_seconds:.1f}s runtime" + ) + + # Clean up Redis tracking keys audio_job_key = f"audio_persistence:session:{session_id}" await redis_client.delete(audio_job_key) - logger.info(f"🧹 Cleaned up tracking key {audio_job_key}") + conversation_key = f"conversation:current:{session_id}" + await redis_client.delete(conversation_key) + logger.info(f"🧹 Cleaned up tracking keys for session {session_id}") return { "session_id": session_id, - "audio_file_path": file_path, - "chunk_count": chunk_count, + "conversation_count": conversation_count, + "last_audio_file_path": str(file_path) if file_path else None, + "total_chunk_count": total_chunk_count, "total_bytes": total_bytes, "duration_seconds": duration, - "runtime_seconds": time.time() - start_time + "runtime_seconds": runtime_seconds } @@ -481,18 +675,22 @@ def enqueue_audio_processing( def enqueue_cropping( - client_id: str, + conversation_id: str, + audio_path: str, user_id: str, - audio_uuid: str, - original_path: str, - speech_segments: list, - output_path: str, priority: JobPriority = JobPriority.NORMAL ): """ Enqueue an audio cropping job. - Returns RQ Job object for tracking. + Args: + conversation_id: Conversation ID + audio_path: Path to audio file + user_id: User ID + priority: Job priority level + + Returns: + RQ Job object for tracking. """ timeout_mapping = { JobPriority.URGENT: 300, # 5 minutes @@ -503,18 +701,15 @@ def enqueue_cropping( job = default_queue.enqueue( process_cropping_job, - client_id, + conversation_id, + audio_path, user_id, - audio_uuid, - original_path, - speech_segments, - output_path, job_timeout=timeout_mapping.get(priority, 180), result_ttl=JOB_RESULT_TTL, - job_id=f"cropping_{audio_uuid[:8]}", - description=f"Crop audio for {audio_uuid[:8]}", - meta={'audio_uuid': audio_uuid} + job_id=f"crop_{conversation_id[:12]}", + description=f"Crop audio for conversation {conversation_id[:12]}", + meta={'conversation_id': conversation_id} ) - logger.info(f"📥 RQ: Enqueued cropping job {job.id} for audio {audio_uuid}") + logger.info(f"📥 RQ: Enqueued cropping job {job.id} for conversation {conversation_id}") return job diff --git a/backends/advanced/src/advanced_omi_backend/workers/conversation_jobs.py b/backends/advanced/src/advanced_omi_backend/workers/conversation_jobs.py index 2e44e034..7f717160 100644 --- a/backends/advanced/src/advanced_omi_backend/workers/conversation_jobs.py +++ b/backends/advanced/src/advanced_omi_backend/workers/conversation_jobs.py @@ -10,10 +10,6 @@ from typing import Dict, Any from advanced_omi_backend.models.job import async_job -from advanced_omi_backend.controllers.queue_controller import ( - transcription_queue, - REDIS_URL, -) logger = logging.getLogger(__name__) @@ -22,9 +18,9 @@ async def open_conversation_job( session_id: str, user_id: str, - user_email: str, client_id: str, speech_detected_at: float, + speech_job_id: str = None, redis_client=None ) -> Dict[str, Any]: """ @@ -35,22 +31,28 @@ async def open_conversation_job( Args: session_id: Stream session ID user_id: User ID - user_email: User email client_id: Client ID speech_detected_at: Timestamp when speech was first detected + speech_job_id: Optional speech detection job ID to update with conversation_id redis_client: Redis client (injected by decorator) Returns: Dict with conversation_id, final_result_count, runtime_seconds + + Note: user_email is fetched from the database when needed. """ from advanced_omi_backend.services.audio_stream import TranscriptionResultsAggregator from advanced_omi_backend.models.conversation import Conversation import uuid from advanced_omi_backend.models.conversation import create_conversation + from rq import get_current_job logger.info(f"📝 Creating and opening conversation for session {session_id} (speech detected at {speech_detected_at})") + # Get current job for meta storage + current_job = get_current_job() + # Create minimal streaming conversation conversation_id = str(uuid.uuid4()) conversation = create_conversation( @@ -66,11 +68,32 @@ async def open_conversation_job( await conversation.insert() logger.info(f"✅ Created streaming conversation {conversation_id} for session {session_id}") + # Update speech detection job metadata with conversation_id + if speech_job_id: + try: + from rq.job import Job + from advanced_omi_backend.controllers.queue_controller import redis_conn + + speech_job = Job.fetch(speech_job_id, connection=redis_conn) + if speech_job and speech_job.meta: + speech_job.meta['conversation_id'] = conversation_id + # Remove session_level flag - now linked to conversation + speech_job.meta.pop('session_level', None) + speech_job.save_meta() + logger.info(f"🔗 Updated speech job {speech_job_id[:12]} with conversation_id") + except Exception as e: + logger.warning(f"⚠️ Failed to update speech job metadata: {e}") + # Store conversation_id in Redis for finalize job to find conversation_key = f"conversation:session:{session_id}" await redis_client.set(conversation_key, conversation_id, ex=3600) logger.info(f"💾 Stored conversation ID in Redis: {conversation_key}") + # Signal audio persistence job to rotate to this conversation's file + current_conversation_key = f"conversation:current:{session_id}" + await redis_client.set(current_conversation_key, conversation_id, ex=3600) + logger.info(f"🔄 Signaled audio persistence to rotate file for conversation {conversation_id[:12]}") + # Use redis_client parameter aggregator = TranscriptionResultsAggregator(redis_client) @@ -82,6 +105,17 @@ async def open_conversation_job( last_result_count = 0 finalize_received = False + # Inactivity timeout configuration + import os + inactivity_timeout_seconds = float(os.getenv("SPEECH_INACTIVITY_THRESHOLD_SECONDS", "60")) + inactivity_timeout_minutes = inactivity_timeout_seconds / 60 + last_meaningful_speech_time = time.time() # Initialize with conversation start + timeout_triggered = False # Track if closure was due to timeout + last_inactivity_log_time = time.time() # Track when we last logged inactivity + last_word_count = 0 # Track word count to detect actual new speech + + logger.info(f"📊 Conversation timeout configured: {inactivity_timeout_minutes} minutes ({inactivity_timeout_seconds}s)") + while True: # Check if session is finalizing (set by producer when recording stops) if not finalize_received: @@ -91,15 +125,96 @@ async def open_conversation_job( logger.info(f"🛑 Session finalizing, waiting for audio persistence job to complete...") break # Exit immediately when finalize signal received - # Check timeout + # Check max runtime timeout if time.time() - start_time > max_runtime: - logger.warning(f"⏱️ Timeout reached for {conversation_id}") + logger.warning(f"⏱️ Max runtime reached for {conversation_id}") break # Get combined results from aggregator combined = await aggregator.get_combined_results(session_id) current_count = combined["chunk_count"] + # Analyze speech content using detailed analysis + from advanced_omi_backend.utils.conversation_utils import analyze_speech + + transcript_data = { + "text": combined["text"], + "words": combined.get("words", []) + } + speech_analysis = analyze_speech(transcript_data) + + # Extract speaker information from segments + speakers = [] + segments = combined.get("segments", []) + if segments: + for seg in segments: + speaker = seg.get("speaker", "Unknown") + if speaker and speaker != "Unknown" and speaker not in speakers: + speakers.append(speaker) + + # Check if NEW speech arrived (word count increased) + # Track word count instead of chunk count to avoid resetting on noise/silence chunks + current_word_count = speech_analysis.get("word_count", 0) + if current_word_count > last_word_count: + last_meaningful_speech_time = time.time() + last_word_count = current_word_count + # Store timestamp in Redis for visibility/debugging + await redis_client.set( + f"conversation:last_speech:{conversation_id}", + last_meaningful_speech_time, + ex=3600 # 1 hour TTL + ) + logger.debug(f"🗣️ New speech detected (word count: {current_word_count}), updated last_speech timestamp") + + # Update job meta with current state + if current_job: + if not current_job.meta: + current_job.meta = {} + + from datetime import datetime + + # Set created_at only once (first time we update metadata) + if 'created_at' not in current_job.meta: + current_job.meta['created_at'] = datetime.now().isoformat() + + current_job.meta.update({ + "conversation_id": conversation_id, + "audio_uuid": session_id, # Link to session for job grouping + "client_id": client_id, # Ensure client_id is always present + "transcript": combined["text"][:500] + "..." if len(combined["text"]) > 500 else combined["text"], # First 500 chars + "transcript_length": len(combined["text"]), + "speakers": speakers, + "word_count": speech_analysis.get("word_count", 0), + "duration_seconds": speech_analysis.get("duration", 0), + "has_speech": speech_analysis.get("has_speech", False), + "last_update": datetime.now().isoformat(), + "inactivity_seconds": time.time() - last_meaningful_speech_time, + "chunks_processed": current_count + }) + current_job.save_meta() + + # Check inactivity timeout and log every 10 seconds + inactivity_duration = time.time() - last_meaningful_speech_time + current_time = time.time() + + # Log inactivity every 10 seconds + if current_time - last_inactivity_log_time >= 10: + logger.info(f"⏱️ Time since last speech: {inactivity_duration:.1f}s (timeout: {inactivity_timeout_seconds:.0f}s)") + last_inactivity_log_time = current_time + + if inactivity_duration > inactivity_timeout_seconds: + logger.info( + f"🕐 Conversation {conversation_id} inactive for " + f"{inactivity_duration/60:.1f} minutes (threshold: {inactivity_timeout_minutes} min), " + f"auto-closing conversation (session remains active for next conversation)..." + ) + # DON'T set session to finalizing - just close this conversation + # Session remains "active" so new conversations can be created + # Only user manual stop or WebSocket disconnect should finalize the session + timeout_triggered = True + finalize_received = True + break + # Update conversation if new results arrived if current_count > last_result_count: # Update conversation in MongoDB @@ -126,8 +241,8 @@ async def open_conversation_job( logger.info(f"✅ Conversation {conversation_id} updates complete, waiting for audio file to be ready...") # Wait for audio_streaming_persistence_job to complete and write the file path - # Poll for the audio file key - this is deterministic, not a timeout-based grace period - audio_file_key = f"audio:file:{session_id}" + # Audio persistence now writes files per-conversation, so key uses conversation_id + audio_file_key = f"audio:file:{conversation_id}" file_path_bytes = None max_wait_audio = 30 # Maximum 30 seconds to wait for audio file wait_start = time.time() @@ -142,48 +257,58 @@ async def open_conversation_job( # Check if still within reasonable time elapsed = time.time() - wait_start if elapsed % 5 == 0: # Log every 5 seconds - logger.info(f"⏳ Waiting for audio file... ({elapsed:.0f}s elapsed)") + logger.info(f"⏳ Waiting for audio file (conversation {conversation_id[:12]})... ({elapsed:.0f}s elapsed)") await asyncio.sleep(0.5) # Check every 500ms if not file_path_bytes: - logger.error(f"❌ Audio file path not found in Redis after {max_wait_audio}s") - logger.warning(f"⚠️ Audio persistence job may have failed or is still running - cannot enqueue batch transcription") + logger.error(f"❌ Audio file path not found in Redis after {max_wait_audio}s (key: {audio_file_key})") + logger.warning(f"⚠️ Audio persistence job may not have rotated file yet - cannot enqueue batch transcription") else: file_path = file_path_bytes.decode() logger.info(f"📁 Retrieved audio file path: {file_path}") - # Enqueue complete batch processing job chain - from advanced_omi_backend.controllers.queue_controller import start_batch_processing_jobs - - job_ids = start_batch_processing_jobs( + # Update conversation with audio file path + conversation = await Conversation.find_one( + Conversation.conversation_id == conversation_id + ) + if conversation: + # Store just the filename (relative to CHUNK_DIR) + from pathlib import Path + audio_filename = Path(file_path).name + conversation.audio_path = audio_filename + await conversation.save() + logger.info(f"💾 Updated conversation {conversation_id[:12]} with audio_path: {audio_filename}") + else: + logger.warning(f"⚠️ Conversation {conversation_id} not found for audio_path update") + + # Enqueue post-conversation processing pipeline + from advanced_omi_backend.controllers.queue_controller import start_post_conversation_jobs + + job_ids = start_post_conversation_jobs( conversation_id=conversation_id, audio_uuid=session_id, - user_id=user_id, - user_email=user_email, audio_file_path=file_path ) logger.info( - f"📥 RQ: Enqueued batch processing chain: " - f"{job_ids['transcription']} → {job_ids['speaker_recognition']} → {job_ids['memory']}" + f"📥 Pipeline: transcribe({job_ids['transcription']}) → " + f"speaker({job_ids['speaker_recognition']}) → " + f"crop({job_ids['cropping']}) → " + f"memory({job_ids['memory']})" ) # Wait a moment to ensure jobs are registered in RQ await asyncio.sleep(0.5) - # DON'T mark session as complete yet - dependent jobs are still processing - # Session remains in "finalizing" status until process_memory_job completes - logger.info(f"⏳ Session {session_id} remains in 'finalizing' status while batch jobs process") - # Clean up Redis streams to prevent memory leaks try: - # Delete the audio input stream - audio_stream_key = f"audio:stream:{client_id}" - await redis_client.delete(audio_stream_key) - logger.info(f"🧹 Deleted audio stream: {audio_stream_key}") + # NOTE: Do NOT delete audio:stream:{client_id} here! + # The audio stream is per-client (WebSocket connection), not per-conversation. + # It's still actively receiving audio and will be reused by the next conversation. + # Only delete it on WebSocket disconnect (handled in websocket_controller.py) - # Delete the transcription results stream + # Delete the transcription results stream (per-session/conversation) results_stream_key = f"transcription:results:{session_id}" await redis_client.delete(results_stream_key) logger.info(f"🧹 Deleted results stream: {results_stream_key}") @@ -194,13 +319,56 @@ async def open_conversation_job( except Exception as cleanup_error: logger.warning(f"⚠️ Error during stream cleanup: {cleanup_error}") - # Clean up Redis tracking key so new speech detection jobs can start + # Clean up Redis tracking keys so speech detection job knows conversation is complete open_job_key = f"open_conversation:session:{session_id}" await redis_client.delete(open_job_key) logger.info(f"🧹 Cleaned up tracking key {open_job_key}") + # Delete the conversation:current signal so audio persistence knows conversation ended + current_conversation_key = f"conversation:current:{session_id}" + await redis_client.delete(current_conversation_key) + logger.info(f"🧹 Deleted conversation:current signal for session {session_id[:12]}") + + # Increment conversation count for this session + conversation_count_key = f"session:conversation_count:{session_id}" + conversation_count = await redis_client.incr(conversation_count_key) + await redis_client.expire(conversation_count_key, 3600) # 1 hour TTL + logger.info(f"📊 Conversation count for session {session_id}: {conversation_count}") + + # Check if session is still active (user still recording) and restart listening jobs + session_status = await redis_client.hget(session_key, "status") + if session_status: + status_str = session_status.decode() if isinstance(session_status, bytes) else session_status + + if status_str == "active": + # Session still active - enqueue new speech detection for next conversation + logger.info(f"🔄 Enqueueing new speech detection (conversation #{conversation_count + 1})") + + from advanced_omi_backend.controllers.queue_controller import transcription_queue + from advanced_omi_backend.workers.transcription_jobs import stream_speech_detection_job + + speech_job = transcription_queue.enqueue( + stream_speech_detection_job, + session_id, + user_id, + client_id, + job_timeout=3600, + result_ttl=600, + job_id=f"speech-detect_{session_id[:12]}_{conversation_count}", + description=f"Speech detection for conversation #{conversation_count + 1}", + meta={'audio_uuid': session_id, 'client_id': client_id, 'session_level': True} + ) + + logger.info(f"✅ Enqueued speech detection job {speech_job.id}") + else: + logger.info(f"Session {session_id} status={status_str}, not restarting (user stopped recording)") + else: + logger.info(f"Session {session_id} not found, not restarting (session ended)") + return { "conversation_id": conversation_id, + "conversation_count": conversation_count, "final_result_count": last_result_count, - "runtime_seconds": time.time() - start_time + "runtime_seconds": time.time() - start_time, + "timeout_triggered": timeout_triggered } diff --git a/backends/advanced/src/advanced_omi_backend/workers/memory_jobs.py b/backends/advanced/src/advanced_omi_backend/workers/memory_jobs.py index a838ee67..c1a6dfc0 100644 --- a/backends/advanced/src/advanced_omi_backend/workers/memory_jobs.py +++ b/backends/advanced/src/advanced_omi_backend/workers/memory_jobs.py @@ -20,19 +20,23 @@ @async_job(redis=True, beanie=True) async def process_memory_job( - client_id: str, - user_id: str, - user_email: str, conversation_id: str, redis_client=None ) -> Dict[str, Any]: """ RQ job function for memory extraction and processing from conversations. + V2 Architecture: + 1. Extracts memories from conversation transcript + 2. Checks primary speakers filter if configured + 3. Uses configured memory provider (friend_lite or openmemory_mcp) + 4. Stores memory references in conversation document + + Note: Listening jobs are restarted by open_conversation_job (not here). + This allows users to resume talking immediately after conversation closes, + without waiting for memory processing to complete. + Args: - client_id: Client identifier - user_id: User ID - user_email: User email conversation_id: Conversation ID to process redis_client: Redis client (injected by decorator) @@ -52,20 +56,18 @@ async def process_memory_job( logger.warning(f"No conversation found for {conversation_id}") return {"success": False, "error": "Conversation not found"} - # Read client_id and user_email from conversation/user if not provided - # (Parameters may be empty if called via job dependency) - actual_client_id = client_id or conversation_model.client_id - actual_user_email = user_email + # Get client_id, user_id, and user_email from conversation/user + client_id = conversation_model.client_id + user_id = conversation_model.user_id - if not actual_user_email: - user = await get_user_by_id(user_id) - if user: - actual_user_email = user.email - else: - logger.warning(f"Could not find user {user_id}") - actual_user_email = "" + user = await get_user_by_id(user_id) + if user: + user_email = user.email + else: + logger.warning(f"Could not find user {user_id}") + user_email = "" - logger.info(f"🔄 Processing memory for conversation {conversation_id}, client={actual_client_id}, user={user_id}") + logger.info(f"🔄 Processing memory for conversation {conversation_id}, client={client_id}, user={user_id}") # Extract conversation text from transcript segments full_conversation = "" @@ -116,10 +118,10 @@ async def process_memory_job( memory_service = get_memory_service() memory_result = await memory_service.add_memory( full_conversation, - actual_client_id, + client_id, conversation_id, user_id, - actual_user_email, + user_email, allow_update=True, ) @@ -140,17 +142,38 @@ async def process_memory_job( processing_time = time.time() - start_time logger.info(f"✅ Completed memory processing for conversation {conversation_id} - created {len(created_memory_ids)} memories in {processing_time:.2f}s") - # Mark session as complete in Redis (this is the last job in the chain) - if conversation_model and conversation_model.audio_uuid: - session_key = f"audio:session:{conversation_model.audio_uuid}" + # Update job metadata with memory information + from rq import get_current_job + current_job = get_current_job() + if current_job: + if not current_job.meta: + current_job.meta = {} + + # Fetch memory details to display in UI + memory_details = [] try: - await redis_client.hset(session_key, mapping={ - "status": "complete", - "completed_at": str(time.time()) - }) - logger.info(f"✅ Marked session {conversation_model.audio_uuid} as complete (all jobs finished)") + for memory_id in created_memory_ids[:5]: # Limit to first 5 for display + memory_entry = await memory_service.get_memory(memory_id, user_id) + if memory_entry: + memory_details.append({ + "memory_id": memory_id, + "text": memory_entry.get("text", "")[:200] # First 200 chars + }) except Exception as e: - logger.warning(f"⚠️ Could not mark session as complete: {e}") + logger.warning(f"Failed to fetch memory details for UI: {e}") + + current_job.meta.update({ + "conversation_id": conversation_id, + "memories_created": len(created_memory_ids), + "memory_ids": created_memory_ids[:5], # Store first 5 IDs + "memory_details": memory_details, + "processing_time": processing_time + }) + current_job.save_meta() + + # NOTE: Listening jobs are restarted by open_conversation_job (not here) + # This allows users to resume talking immediately after conversation closes, + # without waiting for memory processing to complete. return { "success": True, @@ -158,18 +181,7 @@ async def process_memory_job( "processing_time": processing_time } else: - # Mark session as complete even if no memories created - if conversation_model and conversation_model.audio_uuid: - session_key = f"audio:session:{conversation_model.audio_uuid}" - try: - await redis_client.hset(session_key, mapping={ - "status": "complete", - "completed_at": str(time.time()) - }) - logger.info(f"✅ Marked session {conversation_model.audio_uuid} as complete (no memories)") - except Exception as e: - logger.warning(f"⚠️ Could not mark session as complete: {e}") - + # No memories created - still successful return {"success": True, "memories_created": 0, "skipped": True} else: return {"success": False, "error": "Memory service returned False"} diff --git a/backends/advanced/src/advanced_omi_backend/workers/rq_worker_entry.py b/backends/advanced/src/advanced_omi_backend/workers/rq_worker_entry.py new file mode 100644 index 00000000..c402005f --- /dev/null +++ b/backends/advanced/src/advanced_omi_backend/workers/rq_worker_entry.py @@ -0,0 +1,54 @@ +#!/usr/bin/env python3 +""" +RQ Worker Entry Point with Logging Configuration. + +This script configures Python logging before starting RQ workers, +ensuring that application-level logs from job functions are visible. +""" + +import logging +import os +import sys + +# Configure logging BEFORE importing any application modules +logging.basicConfig( + level=logging.INFO, + format="%(asctime)s [%(levelname)s] %(name)s: %(message)s", + stream=sys.stdout +) + +logger = logging.getLogger(__name__) + + +def main(): + """Start RQ worker with proper logging configuration.""" + from rq import Worker + from redis import Redis + + # Get Redis URL from environment + redis_url = os.getenv('REDIS_URL', 'redis://localhost:6379/0') + + # Get queue names from command line arguments + queue_names = sys.argv[1:] if len(sys.argv) > 1 else ['transcription', 'memory', 'default'] + + logger.info(f"🚀 Starting RQ worker for queues: {', '.join(queue_names)}") + logger.info(f"📡 Redis URL: {redis_url}") + + # Create Redis connection + redis_conn = Redis.from_url(redis_url) + + # Create and start worker + worker = Worker( + queue_names, + connection=redis_conn, + log_job_description=True + ) + + logger.info("✅ RQ worker ready") + + # This blocks until worker is stopped + worker.work(logging_level='INFO') + + +if __name__ == "__main__": + main() diff --git a/backends/advanced/src/advanced_omi_backend/workers/transcription_jobs.py b/backends/advanced/src/advanced_omi_backend/workers/transcription_jobs.py index c69bc3fa..5417d657 100644 --- a/backends/advanced/src/advanced_omi_backend/workers/transcription_jobs.py +++ b/backends/advanced/src/advanced_omi_backend/workers/transcription_jobs.py @@ -120,7 +120,6 @@ async def transcribe_full_audio_job( audio_uuid: str, audio_path: str, version_id: str, - user_id: str, trigger: str = "reprocess", redis_client=None ) -> Dict[str, Any]: @@ -140,7 +139,6 @@ async def transcribe_full_audio_job( audio_uuid: Audio UUID (unused but kept for compatibility) audio_path: Path to audio file version_id: Version ID for new transcript - user_id: User ID trigger: Trigger source redis_client: Redis client (injected by decorator) @@ -155,6 +153,15 @@ async def transcribe_full_audio_job( start_time = time.time() + # Get the conversation + conversation = await Conversation.find_one(Conversation.conversation_id == conversation_id) + if not conversation: + raise ValueError(f"Conversation {conversation_id} not found") + + # Use the provided audio path + actual_audio_path = audio_path + logger.info(f"📁 Using audio for transcription: {audio_path}") + # Get the transcription provider provider = get_transcription_provider(mode="batch") if not provider: @@ -164,9 +171,9 @@ async def transcribe_full_audio_job( logger.info(f"Using transcription provider: {provider_name}") # Read the audio file - audio_file_path = Path(audio_path) + audio_file_path = Path(actual_audio_path) if not audio_file_path.exists(): - raise FileNotFoundError(f"Audio file not found: {audio_path}") + raise FileNotFoundError(f"Audio file not found: {actual_audio_path}") # Load audio data with open(audio_file_path, 'rb') as f: @@ -189,12 +196,6 @@ async def transcribe_full_audio_job( # Calculate processing time (transcription only) processing_time = time.time() - start_time - # Get the conversation using Beanie - conversation = await Conversation.find_one(Conversation.conversation_id == conversation_id) - if not conversation: - logger.error(f"Conversation {conversation_id} not found") - return {"success": False, "error": "Conversation not found"} - # Convert segments to SpeakerSegment objects speaker_segments = [] for seg in segments: @@ -300,12 +301,27 @@ async def transcribe_full_audio_job( logger.info(f"✅ Transcript processing completed for {conversation_id} in {processing_time:.2f}s") + # Update job metadata with title and summary for UI display + from rq import get_current_job + current_job = get_current_job() + if current_job: + if not current_job.meta: + current_job.meta = {} + current_job.meta.update({ + "conversation_id": conversation_id, + "title": conversation.title, + "summary": conversation.summary, + "transcript_length": len(transcript_text), + "word_count": len(words), + "processing_time": processing_time + }) + current_job.save_meta() + return { "success": True, "conversation_id": conversation_id, "version_id": version_id, "audio_path": str(audio_file_path), - "user_id": user_id, "transcript": transcript_text, "segments": [seg.model_dump() for seg in speaker_segments], "words": words, # Needed by speaker recognition @@ -320,7 +336,6 @@ async def recognise_speakers_job( conversation_id: str, version_id: str, audio_path: str, - user_id: str, transcript_text: str, words: list, redis_client=None @@ -337,7 +352,6 @@ async def recognise_speakers_job( conversation_id: Conversation ID version_id: Transcript version ID to update audio_path: Path to audio file - user_id: User ID transcript_text: Transcript text from transcription job words: Word-level timing data from transcription job redis_client: Redis client (injected by decorator) @@ -358,6 +372,13 @@ async def recognise_speakers_job( logger.error(f"Conversation {conversation_id} not found") return {"success": False, "error": "Conversation not found"} + # Get user_id from conversation + user_id = conversation.user_id + + # Use the provided audio path + actual_audio_path = audio_path + logger.info(f"📁 Using audio for speaker recognition: {audio_path}") + # Find the transcript version to update transcript_version = None for version in conversation.transcript_versions: @@ -410,7 +431,7 @@ async def recognise_speakers_job( } speaker_result = await speaker_client.diarize_identify_match( - audio_path=audio_path, + audio_path=actual_audio_path, # Use cropped audio if available transcript_data=transcript_data, user_id=user_id ) @@ -478,7 +499,6 @@ async def recognise_speakers_job( "success": True, "conversation_id": conversation_id, "version_id": version_id, - "user_id": user_id, "speaker_recognition_enabled": True, "identified_speakers": list(identified_speakers), "segment_count": len(updated_segments), @@ -503,198 +523,178 @@ async def recognise_speakers_job( async def stream_speech_detection_job( session_id: str, user_id: str, - user_email: str, client_id: str, redis_client=None ) -> Dict[str, Any]: """ - Job that monitors transcription stream for speech (STREAMING MODE ONLY). + Listen for meaningful speech, optionally check for enrolled speakers, then start conversation. - Decorated with @async_job to handle setup/teardown automatically. + Simple flow: + 1. Listen for meaningful speech + 2. If speaker filter enabled → check for enrolled speakers + 3. If criteria met → start open_conversation_job and EXIT + 4. Conversation will restart new speech detection when complete - Job lifecycle: - 1. Monitors transcription stream for speech - 2. When speech detected: - - Checks if conversation already open (prevents duplicates) - - If no open conversation: creates conversation + starts open_conversation_job - - Exits (job completes) - 3. New stream_speech_detection_job can be started when conversation closes - - This architecture alternates between "listening for speech" and "actively recording conversation". - - This is part of the V2 architecture using RQ jobs as orchestrators. - - For batch/upload mode, conversations are created upfront and transcribe_full_audio_job is used. + Args: + session_id: Stream session ID + user_id: User ID + client_id: Client ID + redis_client: Redis client (injected by decorator) - Args: - session_id: Stream session ID - user_id: User ID - user_email: User email - client_id: Client ID + Returns: + Dict with session info and conversation_job_id or no_speech_detected - Returns: - Dict with session_id, conversation_id, open_conversation_job_id, detected_speakers, runtime_seconds - """ + Note: user_email is fetched from the database when needed. + """ from advanced_omi_backend.services.audio_stream import TranscriptionResultsAggregator + from advanced_omi_backend.utils.conversation_utils import analyze_speech + from advanced_omi_backend.speaker_recognition_client import SpeakerRecognitionClient from .conversation_jobs import open_conversation_job + from rq import get_current_job - logger.info(f"🔍 RQ: Starting stream speech detection for session {session_id}") + logger.info(f"🔍 Starting speech detection for session {session_id[:12]}") - # Use redis_client from decorator + # Setup aggregator = TranscriptionResultsAggregator(redis_client) - - # Job control + current_job = get_current_job() session_key = f"audio:session:{session_id}" - max_runtime = 3540 # 59 minutes (graceful exit before RQ timeout at 60 min) start_time = time.time() + max_runtime = 3540 # 59 minutes + + # Get conversation count + conversation_count_key = f"session:conversation_count:{session_id}" + conversation_count_bytes = await redis_client.get(conversation_count_key) + conversation_count = int(conversation_count_bytes) if conversation_count_bytes else 0 - conversation_id = None - open_conversation_job_id = None - detected_speakers = [] # Track enrolled speakers detected during speech detection + # Check if speaker filtering is enabled + speaker_filter_enabled = os.getenv("RECORD_ONLY_ENROLLED_SPEAKERS", "false").lower() == "true" + logger.info(f"📊 Conversation #{conversation_count + 1}, Speaker filter: {'enabled' if speaker_filter_enabled else 'disabled'}") + # Main loop: Listen for speech while True: - # Check if session has ended (status = "finalizing" or "complete") - # session_status = await redis_client.hget(session_key, "status") - # if session_status: - # status_str = session_status.decode() if isinstance(session_status, bytes) else session_status - # if status_str in ["finalizing", "complete"]: - # logger.info(f"🛑 Session {status_str}, stopping speech detection") - # break - - # # Check timeout - # if time.time() - start_time > max_runtime: - # logger.warning(f"⏱️ Timeout reached for {session_id}") - # break - - # Get combined transcription results (aggregator does the combining) - combined = await aggregator.get_combined_results(session_id) + # Exit conditions + session_status = await redis_client.hget(session_key, "status") + if session_status and session_status.decode() in ["complete", "closed"]: + logger.info(f"🛑 Session ended, exiting") + break + + if time.time() - start_time > max_runtime: + logger.warning(f"⏱️ Max runtime reached, exiting") + break + # Get transcription results + combined = await aggregator.get_combined_results(session_id) if not combined["text"]: - await asyncio.sleep(2) # Check every 2 seconds + await asyncio.sleep(2) continue - # Analyze for speech using centralized detection from utils - from advanced_omi_backend.utils.conversation_utils import analyze_speech - transcript_data = { - "text": combined["text"], - "words": combined["words"] - } + # Step 1: Check for meaningful speech + transcript_data = {"text": combined["text"], "words": combined.get("words", [])} speech_analysis = analyze_speech(transcript_data) - has_speech = speech_analysis["has_speech"] - print(f"🔍 SPEECH ANALYSIS: session={session_id}, has_speech={has_speech}, conv_id={conversation_id}, words={speech_analysis.get('word_count', 0)}") logger.info( - f"🔍 Speech analysis for {session_id}: has_speech={has_speech}, " - f"conversation_id={conversation_id}, word_count={speech_analysis.get('word_count', 0)}" + f"🔍 {speech_analysis.get('word_count', 0)} words, " + f"{speech_analysis.get('duration', 0):.1f}s, " + f"has_speech: {speech_analysis.get('has_speech', False)}" ) - if has_speech and not conversation_id: - print(f"💬 SPEECH DETECTED! Checking if enrolled speakers present...") - logger.info(f"💬 Speech detected in {session_id}!") - - # Check if we should filter by enrolled speakers (two-stage filter: text first, then speaker) - record_only_enrolled = os.getenv("RECORD_ONLY_ENROLLED_SPEAKERS", "false").lower() == "true" - - if record_only_enrolled: - logger.info(f"🎤 Checking if enrolled speakers are present...") - - from advanced_omi_backend.speaker_recognition_client import SpeakerRecognitionClient + if not speech_analysis.get("has_speech", False): + await asyncio.sleep(2) + continue - # Get raw transcription results (with chunk IDs) - raw_results = await aggregator.get_session_results(session_id) + logger.info(f"💬 Meaningful speech detected!") + + # Step 2: If speaker filter enabled, check for enrolled speakers + identified_speakers = [] + if speaker_filter_enabled: + logger.info(f"🎤 Checking for enrolled speakers...") + speaker_client = SpeakerRecognitionClient() + raw_results = await aggregator.get_session_results(session_id) + + enrolled_present, speaker_result = await speaker_client.check_if_enrolled_speaker_present( + redis_client=redis_client, + client_id=client_id, + session_id=session_id, + user_id=user_id, + transcription_results=raw_results + ) - # Check if enrolled speaker is speaking (also returns speaker recognition results) - speaker_client = SpeakerRecognitionClient() - enrolled_speaker_present, speaker_recognition_result = await speaker_client.check_if_enrolled_speaker_present( - redis_client=redis_client, - client_id=client_id, - session_id=session_id, - user_id=user_id, - transcription_results=raw_results - ) + if not enrolled_present: + logger.info(f"⏭️ No enrolled speakers, continuing to listen...") + await asyncio.sleep(2) + continue + + # Extract identified speakers + if speaker_result and "segments" in speaker_result: + for seg in speaker_result["segments"]: + identified_as = seg.get("identified_as") + if identified_as and identified_as != "Unknown" and identified_as not in identified_speakers: + identified_speakers.append(identified_as) + + logger.info(f"✅ Enrolled speaker(s): {', '.join(identified_speakers) if identified_speakers else 'Unknown'}") + + # Step 3: Start conversation and EXIT + speech_detected_at = time.time() + open_job_key = f"open_conversation:session:{session_id}" + + # Enqueue conversation job with speech detection job ID + from datetime import datetime + + speech_job_id = current_job.id if current_job else None + + open_job = transcription_queue.enqueue( + open_conversation_job, + session_id, + user_id, + client_id, + speech_detected_at, + speech_job_id, # Pass speech detection job ID + job_timeout=3600, + result_ttl=600, + job_id=f"open-conv_{session_id[:12]}_{conversation_count}", + description=f"Conversation #{conversation_count+1} for {session_id[:12]}", + meta={'audio_uuid': session_id, 'client_id': client_id} + ) - if not enrolled_speaker_present: - logger.info(f"⏭️ Meaningful speech detected but not from enrolled speakers, continuing to listen...") - await asyncio.sleep(2) - continue - - # Extract identified speakers from the result - identified_speakers = [] - if speaker_recognition_result and "segments" in speaker_recognition_result: - for seg in speaker_recognition_result["segments"]: - identified_as = seg.get("identified_as") - # Filter out None and "Unknown" values - if identified_as and identified_as != "Unknown" and identified_as not in identified_speakers: - identified_speakers.append(identified_as) - - num_segments = len(speaker_recognition_result["segments"]) - - if identified_speakers: - speakers_str = ", ".join(identified_speakers) - logger.info(f"✅ Enrolled speaker(s) detected: {speakers_str}") - logger.info(f"🎤 Speaker recognition returned {num_segments} segments with {len(identified_speakers)} enrolled speaker(s)") - print(f"✅ ENROLLED SPEAKERS DETECTED: {speakers_str} ({num_segments} segments)") - detected_speakers = identified_speakers # Store for return value - else: - logger.info(f"✅ Enrolled speaker detected! (no identified_as field in segments)") - logger.info(f"🎤 Speaker recognition returned {num_segments} segments during enrollment check") - else: - logger.info(f"✅ Enrolled speaker detected! Proceeding to create conversation...") - - # Check if conversation job already running for this session - open_job_key = f"open_conversation:session:{session_id}" - existing_job = await redis_client.get(open_job_key) - - if existing_job: - # Already have an open conversation job running - open_conversation_job_id = existing_job.decode() - logger.info(f"✅ Conversation job already running: {open_conversation_job_id}") - else: - # No conversation job running - enqueue one - speech_detected_at = time.time() - logger.info(f"📝 Enqueueing open_conversation_job (speech detected at {speech_detected_at})") - - # Start open_conversation_job to create and monitor conversation - open_job = transcription_queue.enqueue( - open_conversation_job, - session_id, - user_id, - user_email, - client_id, - speech_detected_at, - job_timeout=3600, - result_ttl=600, - job_id=f"open-conv_{session_id[:12]}", - description=f"Open conversation for session {session_id[:12]}" - ) - open_conversation_job_id = open_job.id + # Track the job + await redis_client.set(open_job_key, open_job.id, ex=3600) - # Store job tracking (TTL handles cleanup automatically) - await redis_client.set( - open_job_key, - open_job.id, - ex=3600 # Expire after 1 hour - ) + # Store metadata in speech detection job + if current_job: + if not current_job.meta: + current_job.meta = {} - logger.info(f"✅ Enqueued conversation job {open_job.id}") + # Remove session_level flag now that conversation is starting + current_job.meta.pop('session_level', None) - # Exit this job now that conversation job is running - logger.info(f"🏁 Exiting speech detection job - conversation job is now managing session") - break - else: - if not has_speech: - logger.debug(f"⏭️ No speech detected yet (words: {speech_analysis.get('word_count', 0)})") - else: - logger.debug(f"ℹ️ Speech detected but conversation already exists: {conversation_id}") + current_job.meta.update({ + "conversation_job_id": open_job.id, + "detected_speakers": identified_speakers, + "speech_detected_at": datetime.fromtimestamp(speech_detected_at).isoformat(), + "session_id": session_id, + "audio_uuid": session_id, # For job grouping + "client_id": client_id # For job grouping + }) + current_job.save_meta() - await asyncio.sleep(2) # Check every 2 seconds + logger.info(f"✅ Started conversation job {open_job.id}, exiting speech detection") - logger.info(f"✅ Stream speech detection complete for {session_id}") + return { + "session_id": session_id, + "user_id": user_id, + "client_id": client_id, + "conversation_job_id": open_job.id, + "speech_detected_at": datetime.fromtimestamp(speech_detected_at).isoformat(), + "runtime_seconds": time.time() - start_time + } + # Session ended without speech + logger.info(f"✅ Session ended without speech") return { "session_id": session_id, - "open_conversation_job_id": open_conversation_job_id, - "detected_speakers": detected_speakers, + "user_id": user_id, + "client_id": client_id, + "no_speech_detected": 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timestamp.split('T')[1].includes('-') + ? timestamp + : timestamp + 'Z' + return new Date(isoString).toLocaleString() } // If timestamp is 0, return placeholder if (timestamp === 0) { @@ -207,19 +211,19 @@ export default function Conversations() { } } - const toggleTranscriptExpansion = async (audioUuid: string) => { + const toggleTranscriptExpansion = async (conversationId: string) => { // If already expanded, just collapse - if (expandedTranscripts.has(audioUuid)) { + if (expandedTranscripts.has(conversationId)) { setExpandedTranscripts(prev => { const newSet = new Set(prev) - newSet.delete(audioUuid) + newSet.delete(conversationId) return newSet }) return } - // Find the conversation by audio_uuid - const conversation = conversations.find(c => c.audio_uuid === audioUuid) + // Find the conversation by conversation_id + const conversation = conversations.find(c => c.conversation_id === conversationId) if (!conversation || !conversation.conversation_id) { console.error('Cannot expand transcript: conversation_id missing') return @@ -227,7 +231,7 @@ export default function Conversations() { // If segments are already loaded, just expand if (conversation.segments && conversation.segments.length > 0) { - setExpandedTranscripts(prev => new Set(prev).add(audioUuid)) + setExpandedTranscripts(prev => new Set(prev).add(conversationId)) return } @@ -237,12 +241,12 @@ export default function Conversations() { if (response.status === 200 && response.data.conversation) { // Update the conversation in state with full segments and transcript setConversations(prev => prev.map(c => - c.audio_uuid === audioUuid + c.conversation_id === conversationId ? { ...c, segments: response.data.conversation.segments, transcript: response.data.conversation.transcript } : c )) // Expand the transcript - setExpandedTranscripts(prev => new Set(prev).add(audioUuid)) + setExpandedTranscripts(prev => new Set(prev).add(conversationId)) } } catch (err: any) { console.error('Failed to fetch conversation details:', err) @@ -557,7 +561,7 @@ export default function Conversations() { {/* Transcript Header with Expand/Collapse */}
toggleTranscriptExpansion(conversation.audio_uuid)} + onClick={() => conversation.conversation_id && toggleTranscriptExpansion(conversation.conversation_id)} >

Transcript {((conversation.segments && conversation.segments.length > 0) || conversation.segment_count) && ( @@ -567,7 +571,7 @@ export default function Conversations() { )}

- {expandedTranscripts.has(conversation.audio_uuid) ? ( + {conversation.conversation_id && expandedTranscripts.has(conversation.conversation_id) ? ( ) : ( @@ -576,7 +580,7 @@ export default function Conversations() {
{/* Transcript Content - Conditionally Rendered */} - {expandedTranscripts.has(conversation.audio_uuid) && ( + {conversation.conversation_id && expandedTranscripts.has(conversation.conversation_id) && (
{conversation.segments && conversation.segments.length > 0 ? (
diff --git a/backends/advanced/webui/src/pages/Queue.tsx b/backends/advanced/webui/src/pages/Queue.tsx index 4b7f4ed0..f472bcc5 100644 --- a/backends/advanced/webui/src/pages/Queue.tsx +++ b/backends/advanced/webui/src/pages/Queue.tsx @@ -24,28 +24,6 @@ import { } from 'lucide-react'; import { queueApi } from '../services/api'; -interface QueueJob { - job_id: string; - job_type: string; - user_id: string; - status: 'queued' | 'processing' | 'completed' | 'failed' | 'cancelled' | 'deferred' | 'waiting'; - priority: 'low' | 'normal' | 'high'; - data: { - description?: string; - [key: string]: any; - }; - result?: any; - error_message?: string; - created_at: string; - started_at?: string; - completed_at?: string; - ended_at?: string; // API returns this field instead of completed_at - retry_count: number; - max_retries: number; - progress_percent: number; - progress_message: string; -} - interface QueueStats { total_jobs: number; queued_jobs: number; @@ -109,10 +87,12 @@ interface CompletedSession { } interface StreamingStatus { - active_sessions: StreamingSession[]; + active_sessions: StreamingSession[]; // Kept for backward compatibility completed_sessions: CompletedSession[]; stream_health: { - [provider: string]: StreamHealth; + [streamKey: string]: StreamHealth & { + stream_age_seconds?: number; + }; }; rq_queues: { [queue: string]: { @@ -124,7 +104,6 @@ interface StreamingStatus { } const Queue: React.FC = () => { - const [jobs, setJobs] = useState([]); const [stats, setStats] = useState(null); const [streamingStatus, setStreamingStatus] = useState(null); const [loading, setLoading] = useState(true); @@ -150,6 +129,7 @@ const Queue: React.FC = () => { }); const [flushing, setFlushing] = useState(false); const [expandedSessions, setExpandedSessions] = useState>(new Set()); + const [expandedJobs, setExpandedJobs] = useState>(new Set()); const [sessionJobs, setSessionJobs] = useState<{[sessionId: string]: any[]}>({}); const [lastUpdate, setLastUpdate] = useState(Date.now()); const [autoRefreshEnabled, setAutoRefreshEnabled] = useState(() => { @@ -176,49 +156,7 @@ const Queue: React.FC = () => { refreshingRef.current = refreshing; }, [refreshing]); - // Refresh jobs for all expanded, active, and completed sessions - const refreshSessionJobs = useCallback(async () => { - const currentExpanded = expandedSessionsRef.current; - const currentStreamingStatus = streamingStatusRef.current; - - // Get all active session IDs - const activeSessionIds = currentStreamingStatus?.active_sessions - ?.filter(s => s.status !== 'complete') - .map(s => s.session_id) || []; - - // Get all completed session IDs - const completedSessionIds = currentStreamingStatus?.completed_sessions - ?.map(s => s.session_id) || []; - - // Get all session IDs that should have jobs loaded (expanded, active, or completed) - const sessionIdsToRefresh = new Set([...currentExpanded, ...activeSessionIds, ...completedSessionIds]); - - if (sessionIdsToRefresh.size === 0) return; - - // Fetch jobs for all sessions in parallel - const fetchPromises = Array.from(sessionIdsToRefresh).map(async (sessionId) => { - try { - const response = await queueApi.getJobsBySession(sessionId); - return { sessionId, jobs: response.data.jobs }; - } catch (error) { - console.error(`❌ Failed to refresh jobs for session ${sessionId}:`, error); - return { sessionId, jobs: [] }; - } - }); - - const results = await Promise.all(fetchPromises); - - // Update session jobs state with all results - setSessionJobs(prev => { - const updated = { ...prev }; - results.forEach(({ sessionId, jobs }) => { - updated[sessionId] = jobs; - }); - return updated; - }); - }, []); - - // Main data fetch function + // Main data fetch function - uses consolidated dashboard endpoint const fetchData = useCallback(async () => { if (refreshingRef.current) { return; @@ -227,20 +165,108 @@ const Queue: React.FC = () => { setRefreshing(true); try { - // Fetch all main data in parallel - await Promise.all([fetchJobs(), fetchStats(), fetchStreamingStatus()]); + const currentExpanded = expandedSessionsRef.current; + const expandedSessionIds = Array.from(currentExpanded); + + // Single API call to get all dashboard data + const response = await queueApi.getDashboard(expandedSessionIds); + const dashboardData = response.data; + + // Extract jobs from response + const queuedJobs = dashboardData.jobs.queued || []; + const processingJobs = dashboardData.jobs.processing || []; + const completedJobs = dashboardData.jobs.completed || []; + const failedJobs = dashboardData.jobs.failed || []; + + // Combine all jobs + const allFetchedJobs = [...queuedJobs, ...processingJobs, ...completedJobs, ...failedJobs]; + + console.log(`📊 Fetched ${allFetchedJobs.length} total jobs via consolidated endpoint`); + console.log(` - Queued: ${queuedJobs.length}`); + console.log(` - Processing: ${processingJobs.length}`); + console.log(` - Completed: ${completedJobs.length}`); + console.log(` - Failed: ${failedJobs.length}`); + + // Debug: Log open_conversation_job details + const openConvJobs = allFetchedJobs.filter(j => j?.job_type === 'open_conversation_job'); + console.log(`🔍 Found ${openConvJobs.length} open_conversation_job(s):`); + openConvJobs.forEach(job => { + console.log(` Job ID: ${job.job_id}`); + console.log(` Status: ${job.status}`); + console.log(` meta.audio_uuid: ${job.meta?.audio_uuid}`); + console.log(` meta.conversation_id: ${job.meta?.conversation_id}`); + }); + + // Group jobs by session_id (use audio_uuid from metadata) + const jobsBySession: {[sessionId: string]: any[]} = {}; + + allFetchedJobs.forEach(job => { + if (!job || !job.job_id) return; // Skip invalid jobs + + // Extract session_id from meta.audio_uuid + const sessionId = job.meta?.audio_uuid; + if (sessionId) { + if (!jobsBySession[sessionId]) { + jobsBySession[sessionId] = []; + } + jobsBySession[sessionId].push(job); + + // Debug logging for grouping + if (job.job_type === 'open_conversation_job') { + console.log(`✅ Grouped open_conversation_job ${job.job_id} under session ${sessionId}`); + } + } else { + // Log jobs that couldn't be grouped + console.log(`⚠️ Job ${job.job_id} (${job.job_type}) has no session_id - cannot group`); + } + }); - // Then refresh session jobs - await refreshSessionJobs(); + // Merge session jobs from dashboard response + if (dashboardData.session_jobs) { + Object.entries(dashboardData.session_jobs).forEach(([sessionId, jobs]: [string, any]) => { + // Merge with existing jobs and deduplicate by job_id + const existingJobs = jobsBySession[sessionId] || []; + const existingJobIds = new Set(existingJobs.map((j: any) => j.job_id)); + const newJobs = jobs.filter((j: any) => !existingJobIds.has(j.job_id)); + jobsBySession[sessionId] = [...existingJobs, ...newJobs]; + }); + } + // Update state + setSessionJobs(jobsBySession); + setStats(dashboardData.stats); + setStreamingStatus(dashboardData.streaming_status); setLastUpdate(Date.now()); + + // Auto-expand active conversations (those with open_conversation_job in progress) + const newExpanded = new Set(expandedSessions); + let expandedCount = 0; + + // Find all conversations with active open_conversation_job + Object.entries(jobsBySession).forEach(([_sessionId, jobs]) => { + const openConvJob = jobs.find((j: any) => j.job_type === 'open_conversation_job'); + if (openConvJob && openConvJob.status === 'started') { + const conversationId = openConvJob.meta?.conversation_id; + if (conversationId && !expandedSessions.has(conversationId)) { + newExpanded.add(conversationId); + expandedCount++; + console.log(`🔓 Auto-expanding active conversation: ${conversationId}`); + } + } + }); + + // Update expanded sessions if any new active conversations found + if (expandedCount > 0) { + console.log(`📂 Auto-expanded ${expandedCount} active conversation(s)`); + setExpandedSessions(newExpanded); + } } catch (error) { - console.error('❌ Error fetching queue data:', error); + console.error('❌ Error fetching dashboard data:', error); } finally { setLoading(false); setRefreshing(false); } - }, [refreshSessionJobs]); + }, []); // Save auto-refresh preference to localStorage useEffect(() => { @@ -267,70 +293,6 @@ const Queue: React.FC = () => { fetchData(); }, [filters, pagination.offset, fetchData]); - const fetchJobs = async () => { - try { - const params = new URLSearchParams({ - limit: pagination.limit.toString(), - offset: pagination.offset.toString(), - sort: 'created_at', - order: 'desc' - }); - - if (filters.status) params.append('status', filters.status); - if (filters.job_type) params.append('job_type', filters.job_type); - if (filters.priority) params.append('priority', filters.priority); - - const response = await queueApi.getJobs(params); - const data = response.data; - setJobs(data.jobs); - setPagination(prev => ({ - ...prev, - total: data.pagination.total, - has_more: data.pagination.has_more - })); - } catch (error) { - console.error('❌ Error fetching jobs:', error); - } - }; - - const fetchStats = async () => { - try { - const response = await queueApi.getStats(); - const data = response.data; - setStats(data); - } catch (error) { - console.error('❌ Error fetching stats:', error); - } - }; - - const fetchStreamingStatus = async () => { - try { - const response = await queueApi.getStreamingStatus(); - const data = response.data; - setStreamingStatus(data); - - // Auto-expand active sessions - if (data.active_sessions && data.active_sessions.length > 0) { - setExpandedSessions(prev => { - const newExpanded = new Set(prev); - let hasChanges = false; - - data.active_sessions.filter((s: StreamingSession) => s.status !== 'complete').forEach((session: StreamingSession) => { - if (!newExpanded.has(session.session_id)) { - newExpanded.add(session.session_id); - hasChanges = true; - } - }); - - return hasChanges ? newExpanded : prev; - }); - } - } catch (error) { - console.error('❌ Error fetching streaming status:', error); - // Don't fail the whole page if streaming status fails - setStreamingStatus(null); - } - }; const viewJobDetails = async (jobId: string) => { setLoadingJobDetails(true); @@ -345,25 +307,42 @@ const Queue: React.FC = () => { } }; - const retryJob = async (jobId: string) => { - try { - await queueApi.retryJob(jobId, false); - fetchJobs(); - } catch (error) { - console.error('Error retrying job:', error); - } - }; + // ESC key handler for modals + useEffect(() => { + const handleEscape = (e: KeyboardEvent) => { + if (e.key === 'Escape') { + if (selectedJob) { + setSelectedJob(null); + } else if (showFlushModal) { + setShowFlushModal(false); + } + } + }; - const cancelJob = async (jobId: string) => { - if (!confirm('Are you sure you want to cancel this job?')) return; - - try { - await queueApi.cancelJob(jobId); - fetchJobs(); - } catch (error) { - console.error('Error cancelling job:', error); - } - }; + document.addEventListener('keydown', handleEscape); + return () => document.removeEventListener('keydown', handleEscape); + }, [selectedJob, showFlushModal]); + + // Commented out - keeping for future use + // const retryJob = async (jobId: string) => { + // try { + // await queueApi.retryJob(jobId, false); + // fetchData(); + // } catch (error) { + // console.error('Error retrying job:', error); + // } + // }; + + // const cancelJob = async (jobId: string) => { + // if (!confirm('Are you sure you want to cancel this job?')) return; + + // try { + // await queueApi.cancelJob(jobId); + // fetchData(); + // } catch (error) { + // console.error('Error cancelling job:', error); + // } + // }; const cleanupStuckWorkers = async () => { if (!confirm('This will clean up all stuck workers and pending messages. Continue?')) return; @@ -380,8 +359,8 @@ const Queue: React.FC = () => { ).join('\n') }`); - // Refresh streaming status to show updated counts - fetchStreamingStatus(); + // Refresh data to show updated counts + fetchData(); } catch (error: any) { console.error('❌ Error during cleanup:', error); alert(`Failed to cleanup workers: ${error.response?.data?.error || error.message}`); @@ -399,8 +378,8 @@ const Queue: React.FC = () => { alert(`✅ Cleanup complete!\n\nRemoved ${data.cleaned_count} old session(s)`); - // Refresh streaming status to show updated counts - fetchStreamingStatus(); + // Refresh data to show updated counts + fetchData(); } catch (error: any) { console.error('❌ Error during cleanup:', error); alert(`Failed to cleanup sessions: ${error.response?.data?.error || error.message}`); @@ -409,7 +388,7 @@ const Queue: React.FC = () => { const applyFilters = () => { setPagination(prev => ({ ...prev, offset: 0 })); - fetchJobs(); + fetchData(); }; const clearFilters = () => { @@ -417,20 +396,6 @@ const Queue: React.FC = () => { setPagination(prev => ({ ...prev, offset: 0 })); }; - const nextPage = () => { - if (pagination.has_more) { - setPagination(prev => ({ ...prev, offset: prev.offset + prev.limit })); - } - }; - - const prevPage = () => { - if (pagination.offset > 0) { - setPagination(prev => ({ - ...prev, - offset: Math.max(0, prev.offset - prev.limit) - })); - } - }; const getStatusIcon = (status: string) => { switch (status) { @@ -458,16 +423,6 @@ const Queue: React.FC = () => { } }; - const getJobTypeShort = (type: string) => { - const typeMap: { [key: string]: string } = { - 'process_audio_files': 'Process', - 'process_single_audio_file': 'Process', - 'reprocess_transcript': 'Reprocess', - 'reprocess_memory': 'Memory' - }; - return typeMap[type] || type; - }; - const getJobTypeIcon = (type: string) => { const iconClass = "w-3.5 h-3.5"; switch (type) { @@ -489,6 +444,11 @@ const Queue: React.FC = () => { }; const getJobTypeColor = (type: string, status: string) => { + // Safety check for undefined/null values + if (!type || !status) { + return { bgColor: 'bg-gray-400', borderColor: 'border-gray-500' }; + } + // Base colors by job type let bgColor = 'bg-gray-400'; let borderColor = 'border-gray-500'; @@ -542,213 +502,6 @@ const Queue: React.FC = () => { return { bgColor, borderColor }; }; - const renderJobTimeline = (jobs: any[], session: StreamingSession | CompletedSession) => { - if (!jobs || jobs.length === 0) return null; - - // Sort jobs by created_at first - const sortedJobs = [...jobs].sort((a, b) => - new Date(a.created_at).getTime() - new Date(b.created_at).getTime() - ); - - // Calculate timeline boundaries - // For active sessions, use session timestamps - // For completed sessions without started_at, use earliest job timestamp - let sessionStart: number; - let sessionEnd: number; - - if ('started_at' in session) { - // Active session - use session.started_at - sessionStart = session.started_at * 1000; - } else { - // Completed session - calculate from jobs - // Use the earliest job timestamp (created_at or started_at) - const earliestTime = Math.min(...sortedJobs.map(j => { - const created = new Date(j.created_at).getTime(); - const started = j.started_at ? new Date(j.started_at).getTime() : created; - return Math.min(created, started); - })); - sessionStart = earliestTime; - } - - if ('completed_at' in session) { - // Completed session - use the latest job end time (not session.completed_at) - // This handles batch jobs that run after the session is marked complete - const latestJobEnd = Math.max(...sortedJobs.map(j => { - const completed = j.completed_at ? new Date(j.completed_at).getTime() : 0; - const ended = j.ended_at ? new Date(j.ended_at).getTime() : 0; - const started = j.started_at ? new Date(j.started_at).getTime() : 0; - return Math.max(completed, ended, started); - })); - // Use the later of: session completion or latest job end - sessionEnd = Math.max(session.completed_at * 1000, latestJobEnd); - } else { - // Active session - use current time - sessionEnd = Date.now(); - } - - const totalDuration = sessionEnd - sessionStart; - - if (totalDuration <= 0) return null; - - // Smart row assignment - place jobs in rows to avoid overlaps - const rows: any[][] = []; - sortedJobs.forEach(job => { - const jobStart = job.started_at ? new Date(job.started_at).getTime() : new Date(job.created_at).getTime(); - - // Find first row where this job doesn't overlap - let assignedRow = -1; - for (let rowIndex = 0; rowIndex < rows.length; rowIndex++) { - const row = rows[rowIndex]; - const lastJobInRow = row[row.length - 1]; - // Calculate when the last job in this row ends (use Date.now() for active jobs) - const lastJobEnd = lastJobInRow.completed_at || lastJobInRow.ended_at - ? new Date((lastJobInRow.completed_at || lastJobInRow.ended_at)!).getTime() - : (lastJobInRow.status === 'processing' ? Date.now() : new Date(lastJobInRow.started_at || lastJobInRow.created_at).getTime()); - - // If this job starts after the last job in this row ends, we can use this row - if (jobStart >= lastJobEnd) { - assignedRow = rowIndex; - break; - } - } - - // If no suitable row found, create a new one - if (assignedRow === -1) { - assignedRow = rows.length; - rows.push([]); - } - - rows[assignedRow].push(job); - job._assignedRow = assignedRow; - }); - - // Calculate height based on number of rows needed - const rowCount = rows.length; - const timelineHeight = Math.max(4, rowCount * 2); // At least 4rem, 2rem per row - - return ( -
-
Timeline:
-
- {/* Timeline grid lines */} -
- {[0, 25, 50, 75, 100].map(percent => ( -
- ))} -
- - {/* Job bars */} - {sortedJobs.map((job) => { - const jobStart = job.started_at ? new Date(job.started_at).getTime() : new Date(job.created_at).getTime(); - const jobEnd = job.completed_at || job.ended_at - ? new Date((job.completed_at || job.ended_at)!).getTime() - : (job.status === 'processing' ? Date.now() : jobStart); - - const startPercent = Math.max(0, ((jobStart - sessionStart) / totalDuration) * 100); - const duration = jobEnd - jobStart; - const widthPercent = Math.max(1, (duration / totalDuration) * 100); - - // Color based on job type - const { bgColor, borderColor } = getJobTypeColor(job.job_type, job.status); - - // Calculate position in assigned row - const rowIndex = job._assignedRow; - const rowHeight = 100 / rowCount; - const barHeight = Math.min(25, rowHeight * 0.6); // 60% of row height, max 25% - const topPercent = (rowIndex * rowHeight) + (rowHeight - barHeight) / 2; - - // Format duration for display - const durationMs = jobEnd - jobStart; - let durationStr = ''; - if (durationMs < 1000) durationStr = `${durationMs}ms`; - else if (durationMs < 60000) durationStr = `${(durationMs / 1000).toFixed(1)}s`; - else durationStr = `${Math.floor(durationMs / 60000)}m ${Math.floor((durationMs % 60000) / 1000)}s`; - - return ( -
-
-
- {getJobTypeIcon(job.job_type)} -
-
- - {/* Tooltip on hover - smart positioning to avoid viewport overflow */} -
80 ? 'auto' : '50%', - right: startPercent > 80 ? '0' : 'auto', - transform: startPercent >= 20 && startPercent <= 80 ? 'translateX(-50%)' : 'none' - }} - > -
{job.job_type}
-
{job.status} • {durationStr}
-
-
- ); - })} -
- - {/* Timeline labels */} -
- 0s - {(totalDuration / 1000).toFixed(0)}s -
-
- ); - }; - - const getJobResult = (job: QueueJob) => { - if (job.status !== 'completed' || !job.result) { - return -; - } - - const result = job.result; - - // Show different results based on job type - if (job.job_type === 'reprocess_transcript') { - const segments = result.transcript_segments || 0; - const speakers = result.speakers_identified || 0; - - return ( -
-
{segments} segments
- {speakers > 0 && ( -
{speakers} speakers identified
- )} -
- ); - } - - if (job.job_type === 'reprocess_memory') { - const memories = result.memory_count || 0; - return ( -
- {memories} memories -
- ); - } - - return ( -
- ✓ Success -
- ); - }; const flushJobs = async () => { setFlushing(true); @@ -812,7 +565,7 @@ const Queue: React.FC = () => { return `${Math.floor(durationMs / 3600000)}h ${Math.floor((durationMs % 3600000) / 60000)}m`; }; - const toggleSessionExpansion = async (sessionId: string) => { + const toggleSessionExpansion = (sessionId: string) => { const newExpanded = new Set(expandedSessions); if (newExpanded.has(sessionId)) { @@ -820,23 +573,27 @@ const Queue: React.FC = () => { newExpanded.delete(sessionId); setExpandedSessions(newExpanded); } else { - // Expand and fetch jobs if not already loaded + // Expand and trigger refresh to fetch jobs via dashboard endpoint newExpanded.add(sessionId); setExpandedSessions(newExpanded); + // Trigger a refresh if jobs not already loaded if (!sessionJobs[sessionId]) { - try { - const response = await queueApi.getJobsBySession(sessionId); - const data = response.data; - setSessionJobs(prev => ({ ...prev, [sessionId]: data.jobs })); - } catch (error) { - console.error(`❌ Failed to fetch jobs for session ${sessionId}:`, error); - setSessionJobs(prev => ({ ...prev, [sessionId]: [] })); - } + fetchData(); } } }; + const toggleJobExpansion = (jobId: string) => { + const newExpanded = new Set(expandedJobs); + if (newExpanded.has(jobId)) { + newExpanded.delete(jobId); + } else { + newExpanded.add(jobId); + } + setExpandedJobs(newExpanded); + }; + if (loading) { return (
@@ -977,7 +734,7 @@ const Queue: React.FC = () => { {streamingStatus && (
-

Audio Streaming Status

+

Audio Streaming & Conversations

- {streamingStatus?.active_sessions && streamingStatus.active_sessions.length > 0 && ( + {streamingStatus?.stream_health && Object.keys(streamingStatus.stream_health).length > 0 && ( )}
- {/* Active and Completed Sessions Grid */} + {/* Stream Workers Section - Shows audio streams + listen jobs */} +
+

Stream Workers (Client Sessions)

+
+ {streamingStatus?.stream_health && Object.entries(streamingStatus.stream_health).map(([streamKey, health]) => { + // Extract client_id from stream key (format: audio:stream:{client_id}) + const clientId = streamKey.replace('audio:stream:', ''); + + // Find all listen jobs for this client with deduplication + const allJobsRaw = Object.values(sessionJobs).flat().filter(job => job != null); + + // Deduplicate by job_id + const jobMap = new Map(); + allJobsRaw.forEach((job: any) => { + if (job && job.job_id) { + jobMap.set(job.job_id, job); + } + }); + const allJobs = Array.from(jobMap.values()); + + // Debug logging for listen job filtering + console.log(`🔍 Stream ${streamKey}:`); + console.log(` - clientId extracted: ${clientId}`); + console.log(` - Total jobs available: ${allJobs.length}`); + const speechDetectionJobs = allJobs.filter((job: any) => job && job.job_type === 'stream_speech_detection_job'); + console.log(` - Speech detection jobs: ${speechDetectionJobs.length}`, speechDetectionJobs.map((j: any) => ({ job_id: j.job_id, meta_client_id: j.meta?.client_id }))); + + // Get all listen jobs for this client (only active/queued/processing, not completed) + const allListenJobs = allJobs.filter((job: any) => + job && job.job_type === 'stream_speech_detection_job' && + job.meta?.client_id === clientId && + job.status !== 'completed' && + job.status !== 'failed' + ); + + // Show only the LATEST active speech detection job (most recent created_at) + // Completed ones have already exited and shouldn't be shown here + const listenJobs = allListenJobs.length > 0 + ? [allListenJobs.sort((a, b) => + new Date(b.created_at).getTime() - new Date(a.created_at).getTime() + )[0]] + : []; + + console.log(` - All listen jobs (active): ${allListenJobs.length}, showing latest: ${listenJobs.length}`); + + return ( +
+
+ {streamKey} + Active +
+ +
+
+ Stream Length: + {health.stream_length} +
+
+ Age: + {(health.stream_age_seconds || 0).toFixed(0)}s +
+
+ Pending: + 0 ? 'text-yellow-600' : 'text-green-600'}`}> + {health.total_pending} + +
+ {health.consumer_groups && health.consumer_groups.map((group) => ( +
+
{group.name}:
+ {group.consumers.map((consumer) => ( +
+ {consumer.name} + 0 ? 'text-yellow-600' : 'text-green-600'}> + {consumer.pending} pending + +
+ ))} +
+ ))} + + {/* Current Speech Detection Job */} + {listenJobs.length > 0 && ( +
+
Current Speech Detection:
+ {listenJobs.map((job) => { + const runtime = job.started_at + ? Math.floor((Date.now() - new Date(job.started_at).getTime()) / 1000) + : 0; + const minutes = Math.floor(runtime / 60); + const seconds = runtime % 60; + + return ( +
+
+
+ {getStatusIcon(job.status)} + {job.job_type} + + {job.status} + +
+ +
+ + {/* Job metadata */} +
+
+ Job ID: + {job.job_id.substring(0, 12)}... +
+ {job.started_at && ( +
+ Runtime: + {minutes}m {seconds}s +
+ )} + {job.created_at && ( +
+ Created: + {new Date(job.created_at).toLocaleTimeString()} +
+ )} + {job.meta?.speech_detected_at && ( +
+ Speech Detected: + {new Date(job.meta.speech_detected_at).toLocaleString()} +
+ )} +
+
+ ); + })} +
+ )} +
+
+ ); + })} +
+
+ + {/* Active and Completed Conversations Grid */}
- {/* Active Sessions */} + {/* Active Conversations - Grouped by conversation_id */}
-

Active Streaming Sessions

- {streamingStatus?.active_sessions && streamingStatus.active_sessions.filter(s => s.status !== 'complete').length > 0 ? ( -
- {streamingStatus.active_sessions.filter(s => s.status !== 'complete').map((session) => { - const isExpanded = expandedSessions.has(session.session_id); - const jobs = sessionJobs[session.session_id] || []; - - return ( -
-
toggleSessionExpansion(session.session_id)} - > -
-
- {isExpanded ? ( - - ) : ( - +

Active Conversations

+ {(() => { + // Group all jobs by conversation_id with deduplication + const allJobsRaw = Object.values(sessionJobs).flat().filter(job => job != null); + + // Deduplicate by job_id + const jobMap = new Map(); + allJobsRaw.forEach((job: any) => { + if (job && job.job_id) { + jobMap.set(job.job_id, job); + } + }); + const allJobs = Array.from(jobMap.values()); + + // Group ALL jobs by conversation_id (regardless of status) + // Also link jobs by audio_uuid so persistence jobs get grouped with conversation + const allConversationJobs = new Map(); + const audioUuidToConversationId = new Map(); + + // First pass: collect conversation_id to audio_uuid mappings + allJobs.forEach(job => { + if (!job) return; + const conversationId = job.meta?.conversation_id; + const audioUuid = job.meta?.audio_uuid; + + if (conversationId && audioUuid) { + audioUuidToConversationId.set(audioUuid, conversationId); + } + }); + + // Second pass: group jobs by conversation_id or audio_uuid + // EXCLUDE session-level jobs (like audio persistence) + allJobs.forEach(job => { + if (!job) return; + + // Skip session-level jobs (they run for entire session, not per conversation) + // Also skip audio persistence jobs by job_type (for backward compatibility with old jobs) + if (job.meta?.session_level === true || job.job_type === 'audio_streaming_persistence_job') { + return; + } + + const conversationId = job.meta?.conversation_id; + const audioUuid = job.meta?.audio_uuid; + + // Determine the grouping key + let groupKey = conversationId; + if (!groupKey && audioUuid) { + // Try to find conversation_id via audio_uuid mapping + groupKey = audioUuidToConversationId.get(audioUuid); + } + + if (groupKey) { + if (!allConversationJobs.has(groupKey)) { + allConversationJobs.set(groupKey, []); + } + allConversationJobs.get(groupKey)!.push(job); + } + }); + + // Filter to only show conversations where at least one job is NOT completed + const conversationMap = new Map(); + allConversationJobs.forEach((jobs, conversationId) => { + const hasActiveJob = jobs.some(j => j.status !== 'completed' && j.status !== 'failed'); + if (hasActiveJob) { + conversationMap.set(conversationId, jobs); + } + }); + + if (conversationMap.size === 0) { + return ( +
+ No active conversations +
+ ); + } + + return ( +
+ {Array.from(conversationMap.entries()).map(([conversationId, jobs]) => { + const isExpanded = expandedSessions.has(conversationId); + + // Find the open_conversation_job for metadata + const openConvJob = jobs.find(j => j.job_type === 'open_conversation_job'); + const meta = openConvJob?.meta || {}; + + // Extract conversation info + const clientId = meta.client_id || 'Unknown'; + const transcript = meta.transcript || ''; + const speakers = meta.speakers || []; + const wordCount = meta.word_count || 0; + const lastUpdate = meta.last_update || ''; + const createdAt = openConvJob?.created_at || null; + + return ( +
+
toggleSessionExpansion(conversationId)} + > +
+
+ {isExpanded ? ( + + ) : ( + + )} + + {clientId} + Active + {speakers.length > 0 && ( + + {speakers.length} speaker{speakers.length > 1 ? 's' : ''} + + )} +
+
+ Conversation: {conversationId.substring(0, 8)}... • + {createdAt && `Started: ${new Date(createdAt).toLocaleTimeString()} • `} + Words: {wordCount} + {lastUpdate && ` • Updated: ${new Date(lastUpdate).toLocaleTimeString()}`} +
+ {transcript && ( +
+ "{transcript.substring(0, 100)}{transcript.length > 100 ? '...' : ''}" +
)} - - {session.client_id} - {session.provider} - {session.status} -
-
- Session: {session.session_id.substring(0, 8)}... • - Chunks: {session.chunks_published} • - Duration: {Math.floor(session.age_seconds)}s • - Idle: {session.idle_seconds.toFixed(1)}s
-
{/* Expanded Jobs Section */} {isExpanded && ( -
- {/* Timeline Visualization */} - {renderJobTimeline(jobs, session)} +
+ {/* Pipeline Timeline */} +
+
Pipeline Timeline:
+ {(() => { + // Helper function to get display name from job type + const getJobDisplayName = (jobType: string) => { + const nameMap: { [key: string]: string } = { + 'stream_speech_detection_job': 'Speech', + 'open_conversation_job': 'Open', + 'transcribe_full_audio_job': 'Transcript', + 'recognise_speakers_job': 'Speakers', + 'process_memory_job': 'Memory' + }; + return nameMap[jobType] || jobType.replace(/_/g, ' ').replace(/\b\w/g, l => l.toUpperCase()); + }; + + // Helper function to get icon for job type + const getJobIcon = (jobType: string) => { + if (jobType.includes('speech') || jobType.includes('detect')) return Brain; + if (jobType.includes('conversation') || jobType.includes('open')) return Brain; + if (jobType.includes('transcribe')) return FileText; + if (jobType.includes('speaker') || jobType.includes('recognise')) return Brain; + if (jobType.includes('memory')) return Brain; + return Brain; // Default icon + }; + + // Build dynamic pipeline from actual jobs with timing data + // Sort by start time to show chronological order + const jobsWithTiming = jobs + .filter(j => j && j.started_at) + .map(job => { + const startTime = new Date(job.started_at!).getTime(); + const endTime = job.completed_at || job.ended_at + ? new Date((job.completed_at || job.ended_at)!).getTime() + : (job.status === 'processing' ? Date.now() : startTime); + + return { + job, + startTime, + endTime, + duration: (endTime - startTime) / 1000, + name: getJobDisplayName(job.job_type), + icon: getJobIcon(job.job_type) + }; + }) + .sort((a, b) => a.startTime - b.startTime); + + const jobTimes = jobsWithTiming; + + // Find earliest start and latest end + const validTimes = jobTimes.filter(t => t !== null); + if (validTimes.length === 0) { + return ( +
No job timing data available
+ ); + } + + const earliestStart = Math.min(...validTimes.map(t => t!.startTime)); + const latestEnd = Math.max(...validTimes.map(t => t!.endTime)); + const totalDuration = (latestEnd - earliestStart) / 1000; // in seconds + + // Format duration for display + const formatDuration = (seconds: number) => { + if (seconds < 1) return `${(seconds * 1000).toFixed(0)}ms`; + if (seconds < 60) return `${seconds.toFixed(1)}s`; + const mins = Math.floor(seconds / 60); + const secs = Math.floor(seconds % 60); + return `${mins}m ${secs}s`; + }; + + // Generate time axis markers (0%, 25%, 50%, 75%, 100%) + const timeMarkers = [0, 0.25, 0.5, 0.75, 1].map(pct => ({ + percent: pct * 100, + time: formatDuration(totalDuration * pct) + })); + + return ( +
+ {/* Time axis */} +
+ {timeMarkers.map((marker, idx) => ( +
+
+
+ {marker.time} +
+
+ ))} +
-
Jobs for this session:
- {jobs.length > 0 ? ( + {/* Job timeline bars */} +
+ {jobTimes.map((jobTime) => { + const { job, startTime, endTime, duration, name, icon: Icon } = jobTime; + + // Calculate position and width as percentage of total timeline + const startPercent = ((startTime - earliestStart) / (latestEnd - earliestStart)) * 100; + const widthPercent = ((endTime - startTime) / (latestEnd - earliestStart)) * 100; + + // Use job type colors + const jobColors = getJobTypeColor(job.job_type, job.status); + const barColor = jobColors.bgColor; + const borderColor = jobColors.borderColor; + + return ( +
+ {/* Stage Icon */} +
+ +
+ + {/* Stage Name */} + {name} + + {/* Timeline Container */} +
+ {/* Job Bar */} +
+ + {formatDuration(duration)} + +
+
+
+ ); + })} +
+ + {/* Total Duration */} +
+ Total: {formatDuration(totalDuration)} +
+
+ ); + })()} +
+ +
Conversation Jobs:
+ {jobs.filter(j => j != null && j.job_id).length > 0 ? (
- {jobs.map((job, index) => ( -
-
-
- #{index + 1} - {getJobTypeIcon(job.job_type)} - {getStatusIcon(job.status)} - {job.job_type} - - {job.status} - - {job.queue} + {jobs + .filter(j => j != null && j.job_id) + .sort((a, b) => new Date(a.created_at).getTime() - new Date(b.created_at).getTime()) + .map((job, index) => ( +
+
toggleJobExpansion(job.job_id)} + > +
+
+ #{index + 1} + {expandedJobs.has(job.job_id) ? ( + + ) : ( + + )} + {getJobTypeIcon(job.job_type)} + {getStatusIcon(job.status)} + {job.job_type} + + {job.status} + + {job.queue} + {/* Show memory count badge on collapsed card */} + {!expandedJobs.has(job.job_id) && job.job_type === 'process_memory_job' && job.result?.memories_created !== undefined && ( + + {job.result.memories_created} memories + + )} +
-
- {job.started_at && ( - Started: {new Date(job.started_at).toLocaleTimeString()} - )} - {job.started_at && ( - • Duration: {formatDuration(job)} +
+ + {/* Collapsible metadata section */} + {expandedJobs.has(job.job_id) && ( +
+
+ {job.started_at && ( + Started: {new Date(job.started_at).toLocaleTimeString()} + )} + {job.started_at && ( + • Duration: {formatDuration(job)} + )} +
+ + {/* Show job-specific metadata */} + {job.meta && ( +
+ {/* open_conversation_job metadata */} + {job.job_type === 'open_conversation_job' && ( + <> + {job.meta.word_count !== undefined && ( +
Words: {job.meta.word_count}
+ )} + {job.meta.speakers && job.meta.speakers.length > 0 && ( +
Speakers: {job.meta.speakers.join(', ')}
+ )} + {job.meta.inactivity_seconds !== undefined && ( +
Idle: {Math.floor(job.meta.inactivity_seconds)}s
+ )} + {job.meta.transcript && ( +
+ "{job.meta.transcript.substring(0, 80)}..." +
+ )} + + )} + + {/* transcribe_full_audio_job metadata */} + {job.job_type === 'transcribe_full_audio_job' && job.result && ( + <> + {job.result.transcript && ( +
Transcript: {job.result.transcript.length} chars
+ )} + {job.result.processing_time_seconds && ( +
Processing: {job.result.processing_time_seconds.toFixed(1)}s
+ )} + + )} + + {/* recognise_speakers_job metadata */} + {job.job_type === 'recognise_speakers_job' && job.result && ( + <> + {job.result.identified_speakers && job.result.identified_speakers.length > 0 && ( +
Identified: {job.result.identified_speakers.join(', ')}
+ )} + {job.result.segment_count && ( +
Segments: {job.result.segment_count}
+ )} + + )} + + {/* process_memory_job metadata */} + {job.job_type === 'process_memory_job' && job.meta && ( + <> + {job.meta.memories_created !== undefined && ( +
Memories: {job.meta.memories_created} created
+ )} + {job.meta.processing_time && ( +
Processing: {job.meta.processing_time.toFixed(1)}s
+ )} + {job.meta.memory_details && job.meta.memory_details.length > 0 && ( +
+
Memories Created:
+ {job.meta.memory_details.map((memory: any, idx: number) => ( +
+ "{memory.text}" +
+ ))} +
+ )} + + )} + + {/* Show conversation_id if present */} + {job.meta.conversation_id && ( +
+ Conv: {job.meta.conversation_id.substring(0, 8)}... +
+ )} +
)}
-
+ )}
) : ( -
No jobs found for this session
+
No jobs found for this conversation
)}
)}
- ); - })} -
- ) : ( -
- No active sessions -
- )} + ); + })} +
+ ); + })()}
- {/* Completed Sessions */} + {/* Completed Conversations - Grouped by conversation_id */}
-

Completed Sessions (Last Hour)

- {streamingStatus?.completed_sessions && streamingStatus.completed_sessions.length > 0 ? ( -
- {streamingStatus.completed_sessions.map((session) => { - const isExpanded = expandedSessions.has(session.session_id); - const jobs = sessionJobs[session.session_id] || []; - - return ( -
-
toggleSessionExpansion(session.session_id)} - > -
-
- {isExpanded ? ( - - ) : ( - - )} - {session.has_conversation ? ( - - ) : ( - - )} - {session.client_id} - {session.has_conversation ? ( - Conversation +

Completed Conversations (Last Hour)

+ {(() => { + // Group all jobs by conversation_id for completed conversations with deduplication + const allJobsRaw = Object.values(sessionJobs).flat().filter(job => job != null); + + // Deduplicate by job_id + const jobMap = new Map(); + allJobsRaw.forEach((job: any) => { + if (job && job.job_id) { + jobMap.set(job.job_id, job); + } + }); + const allJobs = Array.from(jobMap.values()); + + // Group ALL jobs by conversation_id (regardless of status) + // Also link jobs by audio_uuid so persistence jobs get grouped with conversation + const allConversationJobs = new Map(); + const audioUuidToConversationId = new Map(); + + // First pass: collect conversation_id to audio_uuid mappings + allJobs.forEach(job => { + if (!job) return; + const conversationId = job.meta?.conversation_id; + const audioUuid = job.meta?.audio_uuid; + + if (conversationId && audioUuid) { + audioUuidToConversationId.set(audioUuid, conversationId); + } + }); + + // Second pass: group jobs by conversation_id or audio_uuid + // EXCLUDE session-level jobs (like audio persistence) + allJobs.forEach(job => { + if (!job) return; + + // Skip session-level jobs (they run for entire session, not per conversation) + // Also skip audio persistence jobs by job_type (for backward compatibility with old jobs) + if (job.meta?.session_level === true || job.job_type === 'audio_streaming_persistence_job') { + return; + } + + const conversationId = job.meta?.conversation_id; + const audioUuid = job.meta?.audio_uuid; + + // Determine the grouping key + let groupKey = conversationId; + if (!groupKey && audioUuid) { + // Try to find conversation_id via audio_uuid mapping + groupKey = audioUuidToConversationId.get(audioUuid); + } + + if (groupKey) { + if (!allConversationJobs.has(groupKey)) { + allConversationJobs.set(groupKey, []); + } + allConversationJobs.get(groupKey)!.push(job); + } + }); + + // Filter to only show conversations where ALL jobs are completed or failed + const conversationMap = new Map(); + allConversationJobs.forEach((jobs, conversationId) => { + const allJobsComplete = jobs.every(j => j.status === 'completed' || j.status === 'failed'); + if (allJobsComplete) { + conversationMap.set(conversationId, jobs); + } + }); + + if (conversationMap.size === 0) { + return ( +
+ No completed conversations +
+ ); + } + + return ( +
+ {Array.from(conversationMap.entries()).map(([conversationId, jobs]) => { + const isExpanded = expandedSessions.has(conversationId); + + // Find the open_conversation_job for metadata + const openConvJob = jobs.find(j => j.job_type === 'open_conversation_job'); + const meta = openConvJob?.meta || {}; + + // Find transcription job for title/summary + const transcriptionJob = jobs.find(j => j.job_type === 'transcribe_full_audio_job'); + const transcriptionMeta = transcriptionJob?.meta || {}; + + // Extract conversation info from metadata + const clientId = meta.client_id || 'Unknown'; + const transcript = meta.transcript || ''; + const speakers = meta.speakers || []; + const wordCount = meta.word_count || 0; + const createdAt = openConvJob?.created_at || null; + const title = transcriptionMeta.title || null; + const summary = transcriptionMeta.summary || null; + + // Check if all jobs are complete + const allComplete = jobs.every(j => j.status === 'completed'); + + return ( +
+
toggleSessionExpansion(conversationId)} + > +
+
+ {isExpanded ? ( + + ) : ( + + )} + {allComplete ? ( + + ) : ( + + )} + {clientId} + + {allComplete ? 'Complete' : 'Processing'} + + {speakers.length > 0 && ( + + {speakers.length} speaker{speakers.length > 1 ? 's' : ''} + + )} +
+
+ Conversation: {conversationId.substring(0, 8)}... • + Words: {wordCount} + {createdAt && ( + <> • Created: {new Date(createdAt).toLocaleString()} + )} +
+ {/* Show title/summary for completed, or transcript for in-progress or when no title exists */} + {allComplete ? ( + <> + {title ? ( +
+ {title} +
+ ) : transcript ? ( +
+ "{transcript.substring(0, 100)}{transcript.length > 100 ? '...' : ''}" +
+ ) : null} + {summary && ( +
+ {summary} +
+ )} + ) : ( - {session.reason || 'No speech'} + transcript && ( +
+ "{transcript.substring(0, 100)}{transcript.length > 100 ? '...' : ''}" +
+ ) )}
-
- Session: {session.session_id.substring(0, 8)}... • - {new Date(session.completed_at * 1000).toLocaleTimeString()} -
-
- {/* Expanded Jobs Section */} - {isExpanded && ( -
- {/* Timeline Visualization */} - {renderJobTimeline(jobs, session)} - -
Jobs for this session:
- {jobs.length > 0 ? ( -
- {jobs.map((job, index) => ( -
-
-
- #{index + 1} - {getJobTypeIcon(job.job_type)} - {getStatusIcon(job.status)} - {job.job_type} - - {job.status} - - {job.queue} + {/* Expanded Jobs Section */} + {isExpanded && ( +
+ {/* Pipeline Timeline */} +
+
Pipeline Timeline:
+ {(() => { + // Helper function to get display name from job type + const getJobDisplayName = (jobType: string) => { + const nameMap: { [key: string]: string } = { + 'stream_speech_detection_job': 'Speech', + 'open_conversation_job': 'Open', + 'transcribe_full_audio_job': 'Transcript', + 'recognise_speakers_job': 'Speakers', + 'process_memory_job': 'Memory' + }; + return nameMap[jobType] || jobType.replace(/_/g, ' ').replace(/\b\w/g, l => l.toUpperCase()); + }; + + // Helper function to get icon for job type + const getJobIcon = (jobType: string) => { + if (jobType.includes('speech') || jobType.includes('detect')) return Brain; + if (jobType.includes('conversation') || jobType.includes('open')) return Brain; + if (jobType.includes('transcribe')) return FileText; + if (jobType.includes('speaker') || jobType.includes('recognise')) return Brain; + if (jobType.includes('memory')) return Brain; + return Brain; // Default icon + }; + + // Build dynamic pipeline from actual jobs with timing data + // Sort by start time to show chronological order + const jobsWithTiming = jobs + .filter(j => j && j.started_at) + .map(job => { + const startTime = new Date(job.started_at!).getTime(); + const endTime = job.completed_at || job.ended_at + ? new Date((job.completed_at || job.ended_at)!).getTime() + : (job.status === 'processing' ? Date.now() : startTime); + + return { + job, + startTime, + endTime, + duration: (endTime - startTime) / 1000, + name: getJobDisplayName(job.job_type), + icon: getJobIcon(job.job_type) + }; + }) + .sort((a, b) => a.startTime - b.startTime); + + const jobTimes = jobsWithTiming; + + // Find earliest start and latest end + const validTimes = jobTimes.filter(t => t !== null); + if (validTimes.length === 0) { + return ( +
No job timing data available
+ ); + } + + const earliestStart = Math.min(...validTimes.map(t => t!.startTime)); + const latestEnd = Math.max(...validTimes.map(t => t!.endTime)); + const totalDuration = (latestEnd - earliestStart) / 1000; // in seconds + + // Format duration for display + const formatDuration = (seconds: number) => { + if (seconds < 1) return `${(seconds * 1000).toFixed(0)}ms`; + if (seconds < 60) return `${seconds.toFixed(1)}s`; + const mins = Math.floor(seconds / 60); + const secs = Math.floor(seconds % 60); + return `${mins}m ${secs}s`; + }; + + // Generate time axis markers (0%, 25%, 50%, 75%, 100%) + const timeMarkers = [0, 0.25, 0.5, 0.75, 1].map(pct => ({ + percent: pct * 100, + time: formatDuration(totalDuration * pct) + })); + + return ( +
+ {/* Time axis */} +
+ {timeMarkers.map((marker, idx) => ( +
+
+
+ {marker.time} +
+
+ ))}
-
- {job.started_at && ( - Started: {new Date(job.started_at).toLocaleTimeString()} - )} - {job.started_at && ( - • Duration: {formatDuration(job)} - )} + + {/* Job timeline bars */} +
+ {jobTimes.map((jobTime) => { + const { job, startTime, endTime, duration, name, icon: Icon } = jobTime; + + // Calculate position and width as percentage of total timeline + const startPercent = ((startTime - earliestStart) / (latestEnd - earliestStart)) * 100; + const widthPercent = ((endTime - startTime) / (latestEnd - earliestStart)) * 100; + + // Use job type colors + const jobColors = getJobTypeColor(job.job_type, job.status); + const barColor = jobColors.bgColor; + const borderColor = jobColors.borderColor; + + return ( +
+ {/* Stage Icon */} +
+ +
+ + {/* Stage Name */} + {name} + + {/* Timeline Container */} +
+ {/* Job Bar */} +
+ + {formatDuration(duration)} + +
+
+
+ ); + })} +
+ + {/* Total Duration */} +
+ Total: {formatDuration(totalDuration)}
- -
- ))} + ); + })()}
- ) : ( -
No jobs found for this session
- )} -
- )} -
- ); - })} -
- ) : ( -
- No completed sessions -
- )} -
-
- {/* Stream Health */} -
-

Stream Workers

-
- {streamingStatus?.stream_health && Object.entries(streamingStatus.stream_health).map(([provider, health]) => ( -
-
- {provider} - {health.error ? ( - Inactive - ) : ( - Active - )} -
+
Conversation Jobs:
+ {jobs.filter(j => j != null && j.job_id).length > 0 ? ( +
+ {jobs + .filter(j => j != null && j.job_id) + .sort((a, b) => new Date(a.created_at).getTime() - new Date(b.created_at).getTime()) + .map((job, index) => ( +
+
+
toggleJobExpansion(job.job_id)} + > + {expandedJobs.has(job.job_id) ? ( + + ) : ( + + )} + #{index + 1} + {getJobTypeIcon(job.job_type)} + {getStatusIcon(job.status)} + {job.job_type} + + {job.status} + + {job.queue || job.data?.queue || 'unknown'} + {/* Show memory count badge on collapsed card */} + {!expandedJobs.has(job.job_id) && job.job_type === 'process_memory_job' && job.result?.memories_created !== undefined && ( + + {job.result.memories_created} memories + + )} +
+ +
- {health.error ? ( -

{health.error}

- ) : ( -
-
- Stream Length: - {health.stream_length} -
-
- Pending: - 0 ? 'text-yellow-600' : 'text-green-600'}`}> - {health.total_pending} - -
- {health.consumer_groups && health.consumer_groups.map((group) => ( -
-
Consumers:
- {group.consumers.map((consumer) => ( -
- {consumer.name} - 0 ? 'text-yellow-600' : 'text-green-600'}> - {consumer.pending} pending - + {/* Collapsible metadata section */} + {expandedJobs.has(job.job_id) && ( +
+
+ {job.started_at && ( + Started: {new Date(job.started_at).toLocaleTimeString()} + )} + {job.started_at && ( + • Duration: {formatDuration(job)} + )} +
+ + {/* Show job-specific metadata */} + {job.meta && ( +
+ {/* open_conversation_job metadata */} + {job.job_type === 'open_conversation_job' && ( + <> + {job.meta.word_count !== undefined && ( +
Words: {job.meta.word_count}
+ )} + {job.meta.speakers && job.meta.speakers.length > 0 && ( +
Speakers: {job.meta.speakers.join(', ')}
+ )} + {job.meta.inactivity_seconds !== undefined && ( +
Idle: {Math.floor(job.meta.inactivity_seconds)}s
+ )} + {job.meta.transcript && ( +
+ "{job.meta.transcript.substring(0, 80)}..." +
+ )} + + )} + + {/* transcribe_full_audio_job metadata */} + {job.job_type === 'transcribe_full_audio_job' && job.result && ( + <> + {job.result.transcript && ( +
Transcript: {job.result.transcript.length} chars
+ )} + {job.result.processing_time_seconds && ( +
Processing: {job.result.processing_time_seconds.toFixed(1)}s
+ )} + + )} + + {/* recognise_speakers_job metadata */} + {job.job_type === 'recognise_speakers_job' && job.result && ( + <> + {job.result.identified_speakers && job.result.identified_speakers.length > 0 && ( +
Identified: {job.result.identified_speakers.join(', ')}
+ )} + {job.result.segment_count && ( +
Segments: {job.result.segment_count}
+ )} + + )} + + {/* process_memory_job metadata */} + {job.job_type === 'process_memory_job' && job.result && ( + <> + {job.result.memories_created !== undefined && ( +
Memories: {job.result.memories_created} created
+ )} + {job.result.processing_time_seconds && ( +
Processing: {job.result.processing_time_seconds.toFixed(1)}s
+ )} + + )} + + {/* Show conversation_id if present */} + {job.meta.conversation_id && ( +
+ Conv: {job.meta.conversation_id.substring(0, 8)}... +
+ )} +
+ )} +
+ )} +
+ ))} +
+ ) : ( +
No jobs found for this conversation
+ )}
- ))} + )}
- ))} -
- )} -
- ))} + ); + })} +
+ ); + })()}
@@ -1352,109 +1906,7 @@ const Queue: React.FC = () => {
- {/* Jobs Table */} -
-
-

Jobs

-
- -
- - - - - - - - - - - - - - {jobs.map((job) => ( - - - - - - - - - - ))} - -
DateJob IDTypeStatusDurationResultActions
- {new Date(job.created_at).toLocaleString('en-US', { month: 'short', day: 'numeric', hour: '2-digit', minute: '2-digit' })} - -
- {job.job_id} -
- 		-
{getJobTypeShort(job.job_type)}
- 		- - {getStatusIcon(job.status)} - {job.status.charAt(0).toUpperCase() + job.status.slice(1)} - - -
- {formatDuration(job)} -
- 		- {getJobResult(job)} - - {job.status === 'failed' && ( - - )} - - {(job.status === 'queued' || job.status === 'processing') && ( - - )} -
-
- - {/* Pagination */} - {pagination.total > pagination.limit && ( -
-
- Showing {pagination.offset + 1} to {Math.min(pagination.offset + pagination.limit, pagination.total)} of {pagination.total} results -
-
- - -
-
- )} -
+ {/* Old Jobs Table and Pagination - Removed in favor of session-based view above */} {/* Job Details Modal */} {selectedJob && ( @@ -1546,6 +1998,159 @@ const Queue: React.FC = () => {
)} + + {/* Formatted Job Metadata - Job-specific displays */} + {selectedJob.meta && Object.keys(selectedJob.meta).length > 0 && ( +
+ + + {/* open_conversation_job formatted metadata */} + {selectedJob.func_name?.includes('open_conversation_job') && ( +
+ {selectedJob.meta.word_count !== undefined && ( +
+ Word Count: {selectedJob.meta.word_count} +
+ )} + {selectedJob.meta.speakers && selectedJob.meta.speakers.length > 0 && ( +
+ Speakers: {selectedJob.meta.speakers.join(', ')} +
+ )} + {selectedJob.meta.transcript_length !== undefined && ( +
+ Transcript Length: {selectedJob.meta.transcript_length} chars +
+ )} + {selectedJob.meta.duration_seconds !== undefined && ( +
+ Duration: {selectedJob.meta.duration_seconds.toFixed(1)}s +
+ )} + {selectedJob.meta.inactivity_seconds !== undefined && ( +
+ Idle Time: {Math.floor(selectedJob.meta.inactivity_seconds)}s +
+ )} + {selectedJob.meta.chunks_processed !== undefined && ( +
+ Chunks Processed: {selectedJob.meta.chunks_processed} +
+ )} + {selectedJob.meta.transcript && ( +
+
Transcript:
+
+ "{selectedJob.meta.transcript}" +
+
+ )} +
+ )} + + {/* process_memory_job formatted metadata */} + {selectedJob.func_name?.includes('process_memory_job') && selectedJob.meta.memory_details && selectedJob.meta.memory_details.length > 0 && ( +
+
+ Memories Created: {selectedJob.meta.memories_created || selectedJob.meta.memory_details.length} +
+ {selectedJob.meta.processing_time !== undefined && ( +
+ Processing Time: {selectedJob.meta.processing_time.toFixed(1)}s +
+ )} +
+
Memory Details:
+
+ {selectedJob.meta.memory_details.map((mem: any, idx: number) => ( +
+ {mem.text} +
+ ))} +
+
+
+ )} + + {/* stream_speech_detection_job formatted metadata */} + {selectedJob.func_name?.includes('stream_speech_detection_job') && ( +
+ {selectedJob.meta.speech_detected_at && ( +
+ Speech Detected At: {new Date(selectedJob.meta.speech_detected_at).toLocaleString()} +
+ )} + {selectedJob.meta.detected_speakers && selectedJob.meta.detected_speakers.length > 0 && ( +
+ Detected Speakers: {selectedJob.meta.detected_speakers.join(', ')} +
+ )} + {selectedJob.meta.conversation_job_id && ( +
+ Conversation Job: {selectedJob.meta.conversation_job_id} +
+ )} +
+ )} + + {/* transcribe_full_audio_job formatted metadata */} + {selectedJob.func_name?.includes('transcribe_full_audio_job') && (selectedJob.meta.title || selectedJob.meta.summary) && ( +
+ {selectedJob.meta.title && ( +
+ Title: {selectedJob.meta.title} +
+ )} + {selectedJob.meta.summary && ( +
+ Summary: {selectedJob.meta.summary} +
+ )} + {selectedJob.meta.transcript_length !== undefined && ( +
+ Transcript Length: {selectedJob.meta.transcript_length} chars +
+ )} + {selectedJob.meta.word_count !== undefined && ( +
+ Word Count: {selectedJob.meta.word_count} +
+ )} + {selectedJob.meta.processing_time !== undefined && ( +
+ Processing Time: {selectedJob.meta.processing_time.toFixed(1)}s +
+ )} +
+ )} + + {/* process_cropping_job formatted metadata */} + {selectedJob.func_name?.includes('process_cropping_job') && ( +
+ {selectedJob.meta.cropped_duration_seconds !== undefined && ( +
+ Cropped Duration: {selectedJob.meta.cropped_duration_seconds.toFixed(1)}s +
+ )} + {selectedJob.meta.segments_cropped !== undefined && ( +
+ Segments Cropped: {selectedJob.meta.segments_cropped} +
+ )} +
+ )} + + {/* Raw JSON metadata (collapsible) */} +
+ + Raw Metadata JSON + + 
+                        {JSON.stringify(selectedJob.meta, null, 2)}
+
+
+
+ )}
)}
diff --git a/backends/advanced/webui/src/services/api.ts b/backends/advanced/webui/src/services/api.ts index 41a1810d..abe7678c 100644 --- a/backends/advanced/webui/src/services/api.ts +++ b/backends/advanced/webui/src/services/api.ts @@ -142,16 +142,26 @@ export const systemApi = { } export const queueApi = { - getJobs: (params: URLSearchParams) => api.get(`/api/queue/jobs?${params}`), + // Consolidated dashboard endpoint - replaces individual getJobs, getStats, getStreamingStatus calls + getDashboard: (expandedSessions: string[] = []) => api.get('/api/queue/dashboard', { + params: { expanded_sessions: expandedSessions.join(',') } + }), + + // Individual endpoints (kept for debugging and specific use cases) getJob: (jobId: string) => api.get(`/api/queue/jobs/${jobId}`), - getJobsBySession: (sessionId: string) => api.get(`/api/queue/jobs/by-session/${sessionId}`), - getStats: () => api.get('/api/queue/stats'), - getStreamingStatus: () => api.get('/api/streaming/status'), - cleanupStuckWorkers: () => api.post('/api/streaming/cleanup'), - cleanupOldSessions: (maxAgeSeconds: number = 3600) => api.post(`/api/streaming/cleanup-sessions?max_age_seconds=${maxAgeSeconds}`), retryJob: (jobId: string, force: boolean = false) => api.post(`/api/queue/jobs/${jobId}/retry`, { force }), cancelJob: (jobId: string) => api.delete(`/api/queue/jobs/${jobId}`), + + // Cleanup operations + cleanupStuckWorkers: () => api.post('/api/streaming/cleanup'), + cleanupOldSessions: (maxAgeSeconds: number = 3600) => api.post(`/api/streaming/cleanup-sessions?max_age_seconds=${maxAgeSeconds}`), + + // Legacy endpoints - kept for backward compatibility but not used in Queue page + // getJobs: (params: URLSearchParams) => api.get(`/api/queue/jobs?${params}`), + // getJobsBySession: (sessionId: string) => api.get(`/api/queue/jobs/by-session/${sessionId}`), + // getStats: () => api.get('/api/queue/stats'), + // getStreamingStatus: () => api.get('/api/streaming/status'), } export const uploadApi = { diff --git a/extras/local-omi-bt/README.md b/extras/local-omi-bt/README.md new file mode 100644 index 00000000..dfa3d566 --- /dev/null +++ b/extras/local-omi-bt/README.md @@ -0,0 +1,3 @@ +# Usage +Run using `uv run --with-requirements requirements.txt python connect-laptop.py` +from this directory. \ No newline at end of file diff --git a/extras/local-omi-bt/connect-omi.py b/extras/local-omi-bt/connect-omi.py new file mode 100644 index 00000000..6f11b289 --- /dev/null +++ b/extras/local-omi-bt/connect-omi.py @@ -0,0 +1,163 @@ +import asyncio +import logging +import os +import sys +from asyncio import Queue +from typing import Any, AsyncGenerator + +import asyncstdlib as asyncstd +from bleak import BleakClient, BleakScanner +from bleak.backends.device import BLEDevice +from dotenv import load_dotenv, set_key +from easy_audio_interfaces.filesystem import RollingFileSink +from friend_lite.bluetooth import listen_to_omi, print_devices +from friend_lite.decoder import OmiOpusDecoder +from wyoming.audio import AudioChunk + +# Setup logging +logging.basicConfig( + format="%(asctime)s %(levelname)s %(name)s: %(message)s", + level=logging.INFO +) +logger = logging.getLogger(__name__) + +env_path = ".env" +load_dotenv(env_path) + +sys.path.append(os.path.dirname(__file__)) +from send_to_adv import stream_to_backend + +OMI_MAC = os.getenv("OMI_MAC") +if not OMI_MAC: + logger.info("OMI_MAC not found in .env. Will try to find and set.") + +# Standard Omi audio characteristic UUID +OMI_CHAR_UUID = "19B10001-E8F2-537E-4F6C-D104768A1214" + +async def source_bytes(audio_queue: Queue[bytes]) -> AsyncGenerator[bytes, None]: + """Single source iterator from the queue.""" + while True: + chunk = await audio_queue.get() + try: + yield chunk + finally: + audio_queue.task_done() + + +async def as_audio_chunks(it) -> AsyncGenerator[AudioChunk, None]: + """Convert bytes iterator to AudioChunk iterator.""" + async for data in it: + yield AudioChunk(audio=data, rate=16000, width=2, channels=1) + +# Add this to friend-lite sdk +async def list_devices(prefix: str = "OMI") -> list[BLEDevice]: + devices = await BleakScanner.discover() + filtered_devices = [] + for d in devices: + if d.name: + if prefix.casefold() in d.name.casefold(): + filtered_devices.append(d) + return filtered_devices + + +def main() -> None: + # api_key: str | None = os.getenv("DEEPGRAM_API_KEY") + # if not api_key: + # print("Set your Deepgram API Key in the DEEPGRAM_API_KEY environment variable.") + # return + + audio_queue: Queue[bytes] = Queue() + decoder = OmiOpusDecoder() + + def handle_ble_data(sender: Any, data: bytes) -> None: + decoded_pcm: bytes = decoder.decode_packet(data) + if decoded_pcm: + try: + audio_queue.put_nowait(decoded_pcm) + except Exception as e: + logger.error("Queue Error: %s", e) + + + async def find_and_set_omi_mac() -> None: + devices = await list_devices() + assert len(devices) == 1, "Expected 1 Omi device, got %d" % len(devices) + OMI_MAC = devices[0].address + set_key(env_path, "OMI_MAC", OMI_MAC) + logger.info("OMI_MAC set to %s and saved to .env" % OMI_MAC) + + async def run() -> None: + logger.info("Starting OMI Bluetooth connection and audio streaming") + if not OMI_MAC: + await find_and_set_omi_mac() + else: + logger.info("using OMI_MAC from .env: %s" % OMI_MAC) + + # First, verify device is available by attempting to connect + logger.info("Checking if device is available...") + try: + async with BleakClient(OMI_MAC) as test_client: + logger.info(f"Successfully connected to device {OMI_MAC}") + except Exception as e: + logger.error(f"Failed to connect to device {OMI_MAC}: {e}") + logger.error("Exiting without creating audio sink or backend connection") + return + + # Device is available, now setup audio sink and backend connection + logger.info("Device found and connected, setting up audio pipeline...") + + # Create file sink now that we know device is available + file_sink = RollingFileSink( + directory="./audio_chunks", + prefix="omi_audio", + segment_duration_seconds=30, + sample_rate=16000, + channels=1, + sample_width=2, + ) + + # Queue for backend streaming + backend_queue = asyncio.Queue() + + async def process_audio(): + """Process audio chunks from BLE queue and duplicate to file + backend""" + async for chunk_bytes in source_bytes(audio_queue): + # Create AudioChunk for both destinations + chunk = AudioChunk(audio=chunk_bytes, rate=16000, width=2, channels=1) + + # Write to file sink + await file_sink.write(chunk) + + # Send to backend queue (non-blocking) + await backend_queue.put(chunk) + + async def backend_stream_wrapper(): + """Wrapper to stream from backend_queue to backend""" + async def queue_to_stream(): + while True: + chunk = await backend_queue.get() + if chunk is None: # Sentinel value to stop + break + yield chunk + + try: + await stream_to_backend(queue_to_stream()) + except Exception as e: + logger.error(f"Backend streaming error: {e}", exc_info=True) + + async with file_sink: + try: + await asyncio.gather( + listen_to_omi(OMI_MAC, OMI_CHAR_UUID, handle_ble_data), + process_audio(), + backend_stream_wrapper(), + ) + except Exception as e: + logger.error(f"Error in audio processing: {e}", exc_info=True) + finally: + # Signal backend stream to stop + await backend_queue.put(None) + + asyncio.run(run()) + +if __name__ == '__main__': + main() \ No newline at end of file diff --git a/extras/local-omi-bt/requirements.txt b/extras/local-omi-bt/requirements.txt new file mode 100644 index 00000000..5d0f167f --- /dev/null +++ b/extras/local-omi-bt/requirements.txt @@ -0,0 +1,10 @@ +bleak==0.22.3 +numpy>=1.26.4 +scipy>=1.12.0 +opuslib>=3.0.1 +friend-lite-sdk +easy_audio_interfaces +python-dotenv +asyncstdlib +httpx +websockets \ No newline at end of file diff --git a/extras/local-omi-bt/send_to_adv.py b/extras/local-omi-bt/send_to_adv.py new file mode 100644 index 00000000..1705a355 --- /dev/null +++ b/extras/local-omi-bt/send_to_adv.py @@ -0,0 +1,143 @@ +import json +import logging +import os +from typing import AsyncGenerator, Optional + +import httpx +import websockets +from dotenv import load_dotenv +from wyoming.audio import AudioChunk + +# Configuration +websocket_uri = "ws://100.83.66.30:8000/ws_pcm" +backend_url = "http://100.83.66.30:8000" +env_path = ".env" +load_dotenv(env_path) + +ADMIN_PASSWORD = os.getenv("ADMIN_PASSWORD") +ADMIN_EMAIL = os.getenv("ADMIN_EMAIL") +DEVICE_NAME = "omi-bt" # Device name for client identification + +logger = logging.getLogger(__name__) + + +async def get_jwt_token(username: str, password: str) -> Optional[str]: + """ + Get JWT token from backend using username and password. + + Args: + username: User email/username + password: User password + + Returns: + JWT token string or None if authentication failed + """ + try: + logger.info(f"Authenticating with backend as: {username}") + + async with httpx.AsyncClient(timeout=10.0) as client: + response = await client.post( + f"{backend_url}/auth/jwt/login", + data={'username': username, 'password': password}, + headers={'Content-Type': 'application/x-www-form-urlencoded'} + ) + + if response.status_code == 200: + auth_data = response.json() + token = auth_data.get('access_token') + + if token: + logger.info("JWT authentication successful") + return token + else: + logger.error("No access token in response") + return None + else: + error_msg = "Invalid credentials" + try: + error_data = response.json() + error_msg = error_data.get('detail', error_msg) + except: + pass + logger.error(f"Authentication failed: {error_msg}") + return None + + except httpx.TimeoutException: + logger.error("Authentication request timed out") + return None + except httpx.RequestError as e: + logger.error(f"Authentication request failed: {e}") + return None + except Exception as e: + logger.error(f"Unexpected authentication error: {e}") + return None + + +async def stream_to_backend(stream: AsyncGenerator[AudioChunk, None]): + """ + Stream audio to backend using Wyoming protocol with JWT authentication. + + Args: + stream: AsyncGenerator yielding AudioChunk objects + """ + # Get JWT token for authentication + token = await get_jwt_token(ADMIN_EMAIL, ADMIN_PASSWORD) + if not token: + logger.error("Failed to get JWT token, cannot stream audio") + return + + # Connect with JWT token as query parameter + uri_with_token = f"{websocket_uri}?token={token}&device_name={DEVICE_NAME}" + + logger.info(f"Connecting to WebSocket: {websocket_uri}") + async with websockets.connect(uri_with_token) as websocket: + # Wait for ready message from backend + ready_msg = await websocket.recv() + logger.info(f"Backend ready: {ready_msg}") + + # Send audio-start event (Wyoming protocol) + audio_start = { + "type": "audio-start", + "data": { + "rate": 16000, + "width": 2, + "channels": 1, + "mode": "streaming" # or "batch" + }, + "payload_length": None + } + await websocket.send(json.dumps(audio_start) + '\n') + logger.info("Sent audio-start event") + + # Stream audio chunks + chunk_count = 0 + async for chunk in stream: + chunk_count += 1 + audio_data = chunk.audio # Extract bytes from AudioChunk + + # Send audio-chunk header (Wyoming protocol) + audio_chunk_header = { + "type": "audio-chunk", + "data": { + "rate": chunk.rate, + "width": chunk.width, + "channels": chunk.channels + }, + "payload_length": len(audio_data) + } + await websocket.send(json.dumps(audio_chunk_header) + '\n') + + # Send audio data as binary + await websocket.send(audio_data) + + if chunk_count % 100 == 0: + logger.info(f"Sent {chunk_count} chunks") + + # Send audio-stop event + audio_stop = { + "type": "audio-stop", + "data": {}, + "payload_length": None + } + await websocket.send(json.dumps(audio_stop) + '\n') + logger.info(f"Sent audio-stop event. Total chunks: {chunk_count}") diff --git a/extras/local-omi-bt/start.sh b/extras/local-omi-bt/start.sh new file mode 100755 index 00000000..a597e326 --- /dev/null +++ b/extras/local-omi-bt/start.sh @@ -0,0 +1 @@ +uv run --with-requirements requirements.txt python connect-laptop.py diff --git a/extras/speaker-recognition/Dockerfile b/extras/speaker-recognition/Dockerfile index 66c80c98..40fccc5d 100644 --- a/extras/speaker-recognition/Dockerfile +++ b/extras/speaker-recognition/Dockerfile @@ -2,6 +2,9 @@ FROM python:3.12-bookworm ARG CUDA_VERSION +# Capture build arg as environment variable for runtime +ENV CUDA_VERSION=${CUDA_VERSION} + # Install system dependencies including PortAudio for pyaudio RUN apt-get update && apt-get install -y \ build-essential \ @@ -38,4 +41,5 @@ ENV PYTHONPATH=/app EXPOSE 8085 # Run the service -CMD ["uv", "run", "--extra", "${CUDA_VERSION}", "--no-dev", "simple-speaker-service"] \ No newline at end of file +# Use shell form to allow environment variable expansion +CMD uv run --extra ${CUDA_VERSION} --no-dev simple-speaker-service \ No newline at end of file diff --git a/extras/speaker-recognition/docker-compose.yml b/extras/speaker-recognition/docker-compose.yml index 213700e7..820d9667 100644 --- a/extras/speaker-recognition/docker-compose.yml +++ b/extras/speaker-recognition/docker-compose.yml @@ -3,9 +3,12 @@ services: speaker-service: &base-speaker-service profiles: ["cpu"] + platform: linux/amd64 build: context: . dockerfile: Dockerfile + args: + CUDA_VERSION: ${CUDA_VERSION:-cpu} image: speaker-recognition:latest env_file: - .env @@ -54,6 +57,7 @@ services: # React Web UI web-ui: + platform: linux/amd64 build: context: webui dockerfile: Dockerfile diff --git a/extras/speaker-recognition/init.py b/extras/speaker-recognition/init.py new file mode 100755 index 00000000..3f4b5e8c --- /dev/null +++ b/extras/speaker-recognition/init.py @@ -0,0 +1,358 @@ +#!/usr/bin/env python3 +""" +Friend-Lite Speaker Recognition Setup Script +Interactive configuration for speaker recognition service +""" + +import argparse +import getpass +import os +import shutil +import subprocess +import sys +from datetime import datetime +from pathlib import Path +from typing import Any, Dict + +from dotenv import set_key +from rich import print as rprint +from rich.console import Console +from rich.panel import Panel +from rich.prompt import Confirm, Prompt +from rich.text import Text + + +class SpeakerRecognitionSetup: + def __init__(self, args=None): + self.console = Console() + self.config: Dict[str, Any] = {} + self.args = args or argparse.Namespace() + + def print_header(self, title: str): + """Print a colorful header""" + self.console.print() + panel = Panel( + Text(title, style="cyan bold"), + style="cyan", + expand=False + ) + self.console.print(panel) + self.console.print() + + def print_section(self, title: str): + """Print a section header""" + self.console.print() + self.console.print(f"[magenta]► {title}[/magenta]") + self.console.print("[magenta]" + "─" * len(f"► {title}") + "[/magenta]") + + def prompt_value(self, prompt: str, default: str = "") -> str: + """Prompt for a value with optional default""" + try: + return Prompt.ask(prompt, default=default) + except EOFError: + self.console.print(f"Using default: {default}") + return default + + def prompt_password(self, prompt: str) -> str: + """Prompt for password (hidden input)""" + while True: + try: + password = getpass.getpass(f"{prompt}: ") + if password: # Just need non-empty + return password + self.console.print("[yellow][WARNING][/yellow] Token is required") + except (EOFError, KeyboardInterrupt): + self.console.print("[red][ERROR][/red] Token is required for speaker recognition") + sys.exit(1) + + def prompt_choice(self, prompt: str, choices: Dict[str, str], default: str = "1") -> str: + """Prompt for a choice from options""" + self.console.print(prompt) + for key, desc in choices.items(): + self.console.print(f" {key}) {desc}") + self.console.print() + + while True: + try: + choice = Prompt.ask("Enter choice", default=default) + if choice in choices: + return choice + self.console.print(f"[red]Invalid choice. Please select from {list(choices.keys())}[/red]") + except EOFError: + self.console.print(f"Using default choice: {default}") + return default + + def backup_existing_env(self): + """Backup existing .env file""" + env_path = Path(".env") + if env_path.exists(): + timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") + backup_path = f".env.backup.{timestamp}" + shutil.copy2(env_path, backup_path) + self.console.print(f"[blue][INFO][/blue] Backed up existing .env file to {backup_path}") + + def setup_hf_token(self): + """Configure Hugging Face token""" + self.print_section("Hugging Face Token Setup") + self.console.print("Required for pyannote speaker recognition models") + self.console.print("Get yours from: https://huggingface.co/settings/tokens") + self.console.print() + + # Check if provided via command line + if hasattr(self.args, 'hf_token') and self.args.hf_token: + self.config["HF_TOKEN"] = self.args.hf_token + self.console.print("[green][SUCCESS][/green] HF Token configured from command line") + else: + hf_token = self.prompt_password("HF Token") + self.config["HF_TOKEN"] = hf_token + self.console.print("[green][SUCCESS][/green] HF Token configured") + + def setup_compute_mode(self): + """Configure compute mode (CPU/GPU)""" + self.print_section("Compute Mode Configuration") + + # Check if provided via command line + if hasattr(self.args, 'compute_mode') and self.args.compute_mode: + compute_mode = self.args.compute_mode + self.console.print(f"[green][SUCCESS][/green] Compute mode configured from command line: {compute_mode}") + else: + choices = { + "1": "CPU-only (works everywhere)", + "2": "GPU acceleration (requires NVIDIA+CUDA)" + } + choice = self.prompt_choice("Choose compute mode:", choices, "1") + compute_mode = "gpu" if choice == "2" else "cpu" + + self.config["COMPUTE_MODE"] = compute_mode + + # Set CUDA_VERSION for Docker build + if compute_mode == "cpu": + self.config["CUDA_VERSION"] = "cpu" + else: + # Default to cu121 for GPU mode (can be overridden in .env) + self.config["CUDA_VERSION"] = "cu121" + + self.console.print(f"[blue][INFO][/blue] Using {compute_mode.upper()} mode") + + def setup_deepgram(self): + """Configure Deepgram API key if provided""" + # Only set if provided via command line + if hasattr(self.args, 'deepgram_api_key') and self.args.deepgram_api_key: + self.config["DEEPGRAM_API_KEY"] = self.args.deepgram_api_key + self.console.print("[green][SUCCESS][/green] Deepgram API key configured from command line") + + def setup_https(self): + """Configure HTTPS settings""" + # Check if HTTPS configuration provided via command line + if hasattr(self.args, 'enable_https') and self.args.enable_https: + enable_https = True + server_ip = getattr(self.args, 'server_ip', 'localhost') + self.console.print(f"[green][SUCCESS][/green] HTTPS configured via command line: {server_ip}") + else: + # Interactive configuration + self.print_section("HTTPS Configuration (Optional)") + self.console.print("HTTPS is required for microphone access in browsers") + self.console.print() + + choices = { + "1": "HTTP mode (development, localhost only)", + "2": "HTTPS mode with SSL (production, remote access, microphone access)" + } + choice = self.prompt_choice("Choose connection mode:", choices, "1") + enable_https = (choice == "2") + + if enable_https: + self.console.print() + self.console.print("[blue][INFO][/blue] For distributed deployments, use your Tailscale IP") + self.console.print("[blue][INFO][/blue] For local-only access, use 'localhost'") + self.console.print("Examples: localhost, 100.64.1.2, your-domain.com") + server_ip = self.prompt_value("Server IP/Domain for SSL certificate", "localhost") + + if enable_https: + self.config["REACT_UI_HTTPS"] = "true" + self.config["REACT_UI_PORT"] = "5175" + + # Generate SSL certificates + self.console.print("[blue][INFO][/blue] Generating SSL certificates...") + ssl_script = Path("ssl/generate-ssl.sh") + if ssl_script.exists(): + try: + subprocess.run([str(ssl_script), server_ip], check=True, timeout=180) + self.console.print("[green][SUCCESS][/green] SSL certificates generated") + except subprocess.TimeoutExpired: + self.console.print("[yellow][WARNING][/yellow] SSL certificate generation timed out") + except subprocess.CalledProcessError: + self.console.print("[yellow][WARNING][/yellow] SSL certificate generation failed") + else: + self.console.print(f"[yellow][WARNING][/yellow] SSL script not found at {ssl_script}") + + # Generate nginx.conf from template + self.console.print("[blue][INFO][/blue] Creating nginx configuration...") + nginx_template = Path("nginx.conf.template") + if nginx_template.exists(): + try: + with open(nginx_template, 'r') as f: + nginx_content = f.read() + + # Replace TAILSCALE_IP with server_ip + nginx_content = nginx_content.replace('TAILSCALE_IP', server_ip) + + with open('nginx.conf', 'w') as f: + f.write(nginx_content) + + self.console.print(f"[green][SUCCESS][/green] nginx.conf created for: {server_ip}") + except Exception as e: + self.console.print(f"[yellow][WARNING][/yellow] nginx.conf generation failed: {e}") + else: + self.console.print("[yellow][WARNING][/yellow] nginx.conf.template not found") + + self.console.print() + self.console.print("📋 [bold]HTTPS Mode URLs:[/bold]") + self.console.print(f" 🌐 HTTPS Access: https://localhost:8444/") + self.console.print(f" 🌐 HTTP Redirect: http://localhost:8081/ → HTTPS") + self.console.print(f" 📱 Service API: https://localhost:8444/api/") + self.console.print(f" 💡 Accept SSL certificate in browser") + else: + self.config["REACT_UI_HTTPS"] = "false" + self.config["REACT_UI_PORT"] = "5174" + + self.console.print() + self.console.print("📋 [bold]HTTP Mode URLs:[/bold]") + self.console.print(" 📱 Service API: http://localhost:8085") + self.console.print(" 📱 Web Interface: http://localhost:5174") + self.console.print(" ⚠️ Note: Microphone access may not work over HTTP on remote connections") + + def generate_env_file(self): + """Generate .env file from template and update with configuration""" + env_path = Path(".env") + env_template = Path(".env.template") + + # Backup existing .env if it exists + if env_path.exists(): + timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") + backup_path = env_path.with_suffix(f'.backup.{timestamp}') + shutil.copy2(env_path, backup_path) + self.console.print(f"[blue][INFO][/blue] Backed up existing .env to {backup_path.name}") + + # Copy template to .env + if env_template.exists(): + shutil.copy2(env_template, env_path) + self.console.print("[blue][INFO][/blue] Copied .env.template to .env") + else: + self.console.print("[yellow][WARNING][/yellow] .env.template not found, creating new .env") + env_path.touch(mode=0o600) + + # Update configured values using set_key + env_path_str = str(env_path) + for key, value in self.config.items(): + if value: # Only set non-empty values + set_key(env_path_str, key, value) + + # Ensure secure permissions + os.chmod(env_path, 0o600) + + self.console.print("[green][SUCCESS][/green] .env file configured successfully with secure permissions") + + def show_summary(self): + """Show configuration summary""" + self.print_section("Configuration Summary") + self.console.print() + + self.console.print(f"✅ HF Token: {'Configured' if self.config.get('HF_TOKEN') else 'Not configured'}") + self.console.print(f"✅ Compute Mode: {self.config.get('COMPUTE_MODE', 'Not configured')}") + self.console.print(f"✅ HTTPS Enabled: {self.config.get('REACT_UI_HTTPS', 'false')}") + if self.config.get('DEEPGRAM_API_KEY'): + self.console.print(f"✅ Deepgram API Key: Configured") + + def show_model_agreement_links(self): + """Show required Hugging Face model agreement links""" + self.print_section("⚠️ Required: Accept Model Agreements") + self.console.print() + + self.console.print("Before using speaker recognition, you must accept agreements for these gated models:") + self.console.print() + + model_links = [ + ("Speaker Diarization", "https://huggingface.co/pyannote/speaker-diarization-community-1"), + ("Segmentation Model", "https://huggingface.co/pyannote/segmentation-3.0"), + ("Segmentation Model2", "https://huggingface.co/pyannote/segmentation-3.1"), + ("Embedding Model", "https://huggingface.co/pyannote/wespeaker-voxceleb-resnet34-LM") + ] + + for idx, (name, url) in enumerate(model_links, 1): + self.console.print(f" {idx}. [cyan]{name}[/cyan]") + self.console.print(f" {url}") + self.console.print() + + self.console.print("[yellow]→[/yellow] Open each link above and click 'Agree and access repository'") + self.console.print("[yellow]→[/yellow] You must be logged into Hugging Face with the same account used for HF_TOKEN") + self.console.print() + + def show_next_steps(self): + """Show next steps""" + self.print_section("Next Steps") + self.console.print() + + self.console.print("1. Start the speaker recognition service:") + if self.config.get('REACT_UI_HTTPS') == 'true': + self.console.print(" [cyan]docker compose up --build -d[/cyan]") + else: + self.console.print(" [cyan]docker compose up --build -d speaker-service web-ui[/cyan]") + + def run(self): + """Run the complete setup process""" + self.print_header("🗣️ Speaker Recognition Setup") + self.console.print("Configure speaker identification and enrollment service") + self.console.print() + + try: + # Run setup steps + self.setup_hf_token() + self.setup_compute_mode() + self.setup_deepgram() + self.setup_https() + + # Generate files + self.print_header("Configuration Complete!") + self.generate_env_file() + + # Show results + self.show_summary() + self.show_model_agreement_links() + self.show_next_steps() + + self.console.print() + self.console.print("[green][SUCCESS][/green] Speaker Recognition setup complete! 🎉") + + except KeyboardInterrupt: + self.console.print() + self.console.print("[yellow]Setup cancelled by user[/yellow]") + sys.exit(0) + except Exception as e: + self.console.print(f"[red][ERROR][/red] Setup failed: {e}") + sys.exit(1) + + +def main(): + """Main entry point""" + parser = argparse.ArgumentParser(description="Speaker Recognition Service Setup") + parser.add_argument("--hf-token", + help="Hugging Face token (default: prompt user)") + parser.add_argument("--compute-mode", + choices=["cpu", "gpu"], + help="Compute mode: cpu or gpu (default: prompt user)") + parser.add_argument("--deepgram-api-key", + help="Deepgram API key (optional)") + parser.add_argument("--enable-https", action="store_true", + help="Enable HTTPS configuration (default: prompt user)") + parser.add_argument("--server-ip", + help="Server IP/domain for SSL certificate (default: prompt user)") + + args = parser.parse_args() + + setup = SpeakerRecognitionSetup(args) + setup.run() + + +if __name__ == "__main__": + main() diff --git a/extras/speaker-recognition/pyproject.toml b/extras/speaker-recognition/pyproject.toml index fa7987ad..673551d5 100644 --- a/extras/speaker-recognition/pyproject.toml +++ b/extras/speaker-recognition/pyproject.toml @@ -38,6 +38,11 @@ local-audio = [ "easy-audio-interfaces[local-audio]>=0.7.1", ] +cpu = [ + "torch>=2.0.0", + "torchaudio>=2.0.0", +] + cu121 = [ "torch>=2.0.0", "torchaudio>=2.0.0", @@ -56,6 +61,7 @@ cu128 = [ [tool.uv] conflicts = [ [ + { extra = "cpu" }, { extra = "cu121" }, { extra = "cu126" }, { extra = "cu128" }, @@ -64,16 +70,23 @@ conflicts = [ [tool.uv.sources] torch = [ + { index = "pytorch-cpu", extra = "cpu" }, { index = "pytorch-cu121", extra = "cu121" }, { index = "pytorch-cu126", extra = "cu126" }, { index = "pytorch-cu128", extra = "cu128" }, ] torchaudio = [ + { index = "pytorch-cpu", extra = "cpu" }, { index = "pytorch-cu121", extra = "cu121" }, { index = "pytorch-cu126", extra = "cu126" }, { index = "pytorch-cu128", extra = "cu128" }, ] +[[tool.uv.index]] +name = "pytorch-cpu" +url = "https://download.pytorch.org/whl/cpu" +explicit = true + [[tool.uv.index]] name = "pytorch-cu121" url = "https://download.pytorch.org/whl/cu121" diff --git a/extras/speaker-recognition/setup-requirements.txt b/extras/speaker-recognition/setup-requirements.txt new file mode 100644 index 00000000..8ce711ba --- /dev/null +++ b/extras/speaker-recognition/setup-requirements.txt @@ -0,0 +1,3 @@ +# Dependencies for speaker-recognition setup script +rich>=13.0.0 +python-dotenv>=1.0.0 diff --git a/extras/speaker-recognition/uv.lock b/extras/speaker-recognition/uv.lock index 2568a236..40c9b4a0 100644 --- a/extras/speaker-recognition/uv.lock +++ b/extras/speaker-recognition/uv.lock @@ -2,19 +2,57 @@ version = 1 revision = 2 requires-python = ">=3.10" resolution-markers = [ - "python_full_version >= '3.13'", - "python_full_version == '3.12.*'", - "python_full_version == '3.11.*'", - "python_full_version < '3.11'", -] + "(python_full_version >= '3.13' and platform_machine != 'aarch64' and extra != 'extra-26-simple-speaker-recognition-cpu' and extra != 'extra-26-simple-speaker-recognition-cu121' and extra != 'extra-26-simple-speaker-recognition-cu126' and extra == 'extra-26-simple-speaker-recognition-cu128') or (python_full_version >= '3.13' and sys_platform != 'linux' and extra != 'extra-26-simple-speaker-recognition-cpu' and extra != 'extra-26-simple-speaker-recognition-cu121' and extra != 'extra-26-simple-speaker-recognition-cu126' and extra == 'extra-26-simple-speaker-recognition-cu128')", + "python_full_version >= '3.13' and platform_machine == 'aarch64' and sys_platform == 'linux' and extra != 'extra-26-simple-speaker-recognition-cpu' and extra != 'extra-26-simple-speaker-recognition-cu121' and extra != 'extra-26-simple-speaker-recognition-cu126' and extra == 'extra-26-simple-speaker-recognition-cu128'", + "(python_full_version == '3.12.*' and platform_machine != 'aarch64' and extra != 'extra-26-simple-speaker-recognition-cpu' and extra != 'extra-26-simple-speaker-recognition-cu121' and extra != 'extra-26-simple-speaker-recognition-cu126' and extra == 'extra-26-simple-speaker-recognition-cu128') or (python_full_version == '3.12.*' and sys_platform != 'linux' and extra != 'extra-26-simple-speaker-recognition-cpu' and extra != 'extra-26-simple-speaker-recognition-cu121' and extra != 'extra-26-simple-speaker-recognition-cu126' and extra == 'extra-26-simple-speaker-recognition-cu128')", + "python_full_version == '3.12.*' and platform_machine == 'aarch64' and sys_platform == 'linux' and extra != 'extra-26-simple-speaker-recognition-cpu' and extra != 'extra-26-simple-speaker-recognition-cu121' and extra != 'extra-26-simple-speaker-recognition-cu126' and extra == 'extra-26-simple-speaker-recognition-cu128'", + "(python_full_version == '3.11.*' and platform_machine != 'aarch64' and extra != 'extra-26-simple-speaker-recognition-cpu' and extra != 'extra-26-simple-speaker-recognition-cu121' and extra != 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sys_platform != 'linux' and extra != 'extra-26-simple-speaker-recognition-cpu' and extra != 'extra-26-simple-speaker-recognition-cu121' and extra == 'extra-26-simple-speaker-recognition-cu126' and extra != 'extra-26-simple-speaker-recognition-cu128')", + "python_full_version >= '3.13' and platform_machine == 'aarch64' and sys_platform == 'linux' and extra != 'extra-26-simple-speaker-recognition-cpu' and extra != 'extra-26-simple-speaker-recognition-cu121' and extra == 'extra-26-simple-speaker-recognition-cu126' and extra != 'extra-26-simple-speaker-recognition-cu128'", + "(python_full_version == '3.12.*' and platform_machine != 'aarch64' and extra != 'extra-26-simple-speaker-recognition-cpu' and extra != 'extra-26-simple-speaker-recognition-cu121' and extra == 'extra-26-simple-speaker-recognition-cu126' and extra != 'extra-26-simple-speaker-recognition-cu128') or (python_full_version == '3.12.*' and sys_platform != 'linux' and extra != 'extra-26-simple-speaker-recognition-cpu' and extra != 'extra-26-simple-speaker-recognition-cu121' and extra == 'extra-26-simple-speaker-recognition-cu126' and extra != 'extra-26-simple-speaker-recognition-cu128')", + "python_full_version == '3.12.*' and platform_machine == 'aarch64' and sys_platform == 'linux' and extra != 'extra-26-simple-speaker-recognition-cpu' and extra != 'extra-26-simple-speaker-recognition-cu121' and extra == 'extra-26-simple-speaker-recognition-cu126' and extra != 'extra-26-simple-speaker-recognition-cu128'", + "(python_full_version == '3.11.*' and platform_machine != 'aarch64' and extra != 'extra-26-simple-speaker-recognition-cpu' and extra != 'extra-26-simple-speaker-recognition-cu121' and extra == 'extra-26-simple-speaker-recognition-cu126' and extra != 'extra-26-simple-speaker-recognition-cu128') or (python_full_version == '3.11.*' and sys_platform != 'linux' and extra != 'extra-26-simple-speaker-recognition-cpu' and extra != 'extra-26-simple-speaker-recognition-cu121' and extra == 'extra-26-simple-speaker-recognition-cu126' and extra != 'extra-26-simple-speaker-recognition-cu128')", + "python_full_version == '3.11.*' and platform_machine == 'aarch64' and sys_platform == 'linux' and extra != 'extra-26-simple-speaker-recognition-cpu' and extra != 'extra-26-simple-speaker-recognition-cu121' and extra == 'extra-26-simple-speaker-recognition-cu126' and extra != 'extra-26-simple-speaker-recognition-cu128'", + "(python_full_version < '3.11' and platform_machine != 'aarch64' and extra != 'extra-26-simple-speaker-recognition-cpu' and extra != 'extra-26-simple-speaker-recognition-cu121' and extra == 'extra-26-simple-speaker-recognition-cu126' and extra != 'extra-26-simple-speaker-recognition-cu128') or (python_full_version < '3.11' and sys_platform != 'linux' and extra != 'extra-26-simple-speaker-recognition-cpu' and extra != 'extra-26-simple-speaker-recognition-cu121' and extra == 'extra-26-simple-speaker-recognition-cu126' and extra != 'extra-26-simple-speaker-recognition-cu128')", + "python_full_version < '3.11' and platform_machine == 'aarch64' and sys_platform == 'linux' and extra != 'extra-26-simple-speaker-recognition-cpu' and extra != 'extra-26-simple-speaker-recognition-cu121' and extra == 'extra-26-simple-speaker-recognition-cu126' and extra != 'extra-26-simple-speaker-recognition-cu128'", + "python_full_version >= '3.13' and extra != 'extra-26-simple-speaker-recognition-cpu' and extra == 'extra-26-simple-speaker-recognition-cu121' and extra != 'extra-26-simple-speaker-recognition-cu126' and extra != 'extra-26-simple-speaker-recognition-cu128'", + "python_full_version == '3.12.*' and extra != 'extra-26-simple-speaker-recognition-cpu' and extra == 'extra-26-simple-speaker-recognition-cu121' and extra != 'extra-26-simple-speaker-recognition-cu126' and extra != 'extra-26-simple-speaker-recognition-cu128'", + "python_full_version == '3.11.*' and extra != 'extra-26-simple-speaker-recognition-cpu' and extra == 'extra-26-simple-speaker-recognition-cu121' and extra != 'extra-26-simple-speaker-recognition-cu126' and extra != 'extra-26-simple-speaker-recognition-cu128'", + "python_full_version < '3.11' and extra != 'extra-26-simple-speaker-recognition-cpu' and extra == 'extra-26-simple-speaker-recognition-cu121' and extra != 'extra-26-simple-speaker-recognition-cu126' and extra != 'extra-26-simple-speaker-recognition-cu128'", + "(python_full_version >= '3.13' and platform_machine != 'aarch64' and sys_platform == 'linux' and extra == 'extra-26-simple-speaker-recognition-cpu' and extra != 'extra-26-simple-speaker-recognition-cu121' and extra != 'extra-26-simple-speaker-recognition-cu126' and extra != 'extra-26-simple-speaker-recognition-cu128') or (python_full_version >= '3.13' and sys_platform != 'darwin' and sys_platform != 'linux' and extra == 'extra-26-simple-speaker-recognition-cpu' and extra != 'extra-26-simple-speaker-recognition-cu121' and extra != 'extra-26-simple-speaker-recognition-cu126' and extra != 'extra-26-simple-speaker-recognition-cu128')", + "python_full_version >= '3.13' and platform_machine == 'aarch64' and sys_platform == 'linux' and extra == 'extra-26-simple-speaker-recognition-cpu' and extra != 'extra-26-simple-speaker-recognition-cu121' and extra != 'extra-26-simple-speaker-recognition-cu126' and extra != 'extra-26-simple-speaker-recognition-cu128'", + "python_full_version >= '3.13' and sys_platform == 'darwin' and extra == 'extra-26-simple-speaker-recognition-cpu' and extra != 'extra-26-simple-speaker-recognition-cu121' and extra != 'extra-26-simple-speaker-recognition-cu126' and extra != 'extra-26-simple-speaker-recognition-cu128'", + "(python_full_version == '3.12.*' and platform_machine != 'aarch64' and sys_platform == 'linux' and extra == 'extra-26-simple-speaker-recognition-cpu' and extra != 'extra-26-simple-speaker-recognition-cu121' and extra != 'extra-26-simple-speaker-recognition-cu126' and extra != 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docker-compose.yml # Start development server CMD ["npm", "run", "dev", "--", "--host", "0.0.0.0"] \ No newline at end of file diff --git a/quickstart.md b/quickstart.md index e4fdef90..bcadc72a 100644 --- a/quickstart.md +++ b/quickstart.md @@ -20,10 +20,20 @@ Think of it like having Siri/Alexa, but it's **your own AI** running on **your h - **Tailscale** - Connects securely to your home computer - **Friend-Lite Mobile App** - Interface for your OMI device and conversations -### AI Services (You'll Get API Keys) -- **Deepgram** - Converts speech to text (free credits available) -- **OpenAI** - Powers memory extraction and insights (pay per use) -- **Optional: Hugging Face** - For speaker recognition (free) +### AI Services (Choose Your Path) + +**Option A: Cloud Services (Easiest - Recommended for Beginners)** +- **Deepgram** - Speech-to-text ($200 free credits, then pay per use) +- **OpenAI** - Memory extraction (~$1-5/month typical usage) +- Best quality, minimal setup, small monthly cost + +**Option B: Local Services (Free but More Complex)** +- **Parakeet ASR** - Offline speech-to-text (runs on your computer) +- **Ollama** - Local AI models (runs on your computer) +- Completely free and private, requires more powerful hardware + +**Optional Add-ons (Both Paths)** +- **Hugging Face** - Speaker recognition (free API key) ## Step 1: Install Required Software @@ -59,28 +69,38 @@ sudo tailscale up *Log in with the same account you used on your computer* -## Step 2: Get Your AI API Keys +## Step 2: Choose Your AI Services Path -You need accounts with AI services (most have free tiers): +### Path A: Cloud Services (Recommended for Beginners) -### Deepgram (Speech-to-Text) - Required +**Deepgram (Speech-to-Text)** 1. Go to [console.deepgram.com](https://console.deepgram.com) 2. Sign up for free account (get $200 free credits) 3. Go to "API Keys" → Create new key 4. **Copy the key** - you'll need it in setup -### OpenAI (AI Brain) - Required +**OpenAI (AI Brain)** 1. Go to [platform.openai.com](https://platform.openai.com) 2. Create account and add payment method (typically costs $1-5/month) 3. Go to "API Keys" → Create new key 4. **Copy the key** - you'll need it in setup -### Hugging Face (Speaker Recognition) - Optional +**Optional: Hugging Face (Speaker Recognition)** 1. Go to [huggingface.co](https://huggingface.co/join) 2. Create free account 3. Go to Settings → Access Tokens → Create new token 4. **Copy the token** - for identifying different speakers +### Path B: Local Services (Free & Private) + +**No API keys needed!** Everything runs on your computer. + +The setup wizard will automatically download and configure: +- **Parakeet ASR** - Local speech-to-text service +- **Ollama** - Local AI model runner + +*Note: First-time setup will download AI models (this can take time and storage space)* + ## Step 3: Download and Setup Friend-Lite ### On Your Home Computer @@ -93,7 +113,7 @@ cd friend-lite **Run the setup wizard:** ```bash -uv run --with-requirements setup-requirements.txt python init.py +uv run --with-requirements setup-requirements.txt python wizard.py ``` ### What the Setup Wizard Will Ask You @@ -103,20 +123,37 @@ The wizard will ask questions - here's what to answer: **"Admin email"**: Your email (for logging into web dashboard) **"Admin password"**: Password for web dashboard (8+ characters) -**"Choose transcription provider"**: Choose 1 (Deepgram) +#### For Cloud Services (Path A): + +**"Choose transcription provider"**: Choose `deepgram` **"Deepgram API key"**: Paste the key you got from Deepgram -**"Choose LLM provider"**: Choose 1 (OpenAI) +**"Choose LLM provider"**: Choose `openai` **"OpenAI API key"**: Paste the key you got from OpenAI **"OpenAI model"**: Keep default (gpt-4o-mini) +#### For Local Services (Path B): + +**"Choose transcription provider"**: Choose `parakeet` +- The wizard will configure local Parakeet ASR service +- No API key needed + +**"Choose LLM provider"**: Choose `ollama` +- The wizard will configure local Ollama +- No API key needed +- Default model: llama3.2 (will be downloaded automatically) + +#### Optional (Both Paths): + **"Enable Speaker Recognition"**: Say Yes if you got Hugging Face token **"Hugging Face token"**: Paste your token (if you got one) +#### HTTPS Setup (Required for Both): + **"Enable HTTPS"**: **Say Yes** (needed for phone connection) -**"Server IP for SSL certificate"**: +**"Server IP for SSL certificate"**: - Run `tailscale ip` in another terminal -- Copy the IP that starts with `100.` (like `100.64.1.5`) +- Copy the IP that starts with `100.` (like `100.64.1.5`) - Paste that IP here *The wizard creates all the configuration files automatically* @@ -211,11 +248,19 @@ Before connecting your phone, make sure everything works: - **Certificate warnings**: Click "Advanced" → "Proceed" in browser - **Test connection fails**: Verify you're using `https://` and correct Tailscale IP -### API Issues +### Service Issues + +**Cloud Services (Deepgram/OpenAI):** - **Transcription not working**: Check Deepgram API key is correct - **No memories created**: Check OpenAI API key and account has credits - **High costs**: Switch to `gpt-4o-mini` model for cheaper processing +**Local Services (Parakeet/Ollama):** +- **Parakeet not starting**: Check `docker compose ps` - Parakeet container should be running +- **Slow transcription**: Local ASR is slower than cloud services, this is normal +- **Ollama model download stuck**: Check internet connection, models can be large (5-20GB) +- **Out of memory errors**: Local services need sufficient RAM, try smaller Ollama models + ## Need Help? - **Full Documentation**: [CLAUDE.md](CLAUDE.md) - Complete technical reference diff --git a/setup-requirements.txt b/setup-requirements.txt index 869e79fc..de5610c1 100644 --- a/setup-requirements.txt +++ b/setup-requirements.txt @@ -1,3 +1,3 @@ # Dependencies for Friend-Lite root setup orchestrator rich>=13.0.0 -dotenv \ No newline at end of file +python-dotenv \ No newline at end of file diff --git a/init.py b/wizard.py similarity index 91% rename from init.py rename to wizard.py index ee28cb5e..4d37fadd 100755 --- a/init.py +++ b/wizard.py @@ -9,6 +9,7 @@ from datetime import datetime from pathlib import Path +from dotenv import get_key from rich import print as rprint from rich.console import Console from rich.prompt import Confirm @@ -16,28 +17,15 @@ console = Console() def read_env_value(env_file_path, key): - """Read a value from an .env file""" - try: - env_path = Path(env_file_path) - if not env_path.exists(): - return None - - with open(env_path, 'r') as f: - for line in f: - line = line.strip() - if line.startswith(f'{key}=') and not line.startswith('#'): - # Extract value after = sign - value = line.split('=', 1)[1] - # Remove quotes if present - if value.startswith('"') and value.endswith('"'): - value = value[1:-1] - elif value.startswith("'") and value.endswith("'"): - value = value[1:-1] - return value if value else None - return None - except Exception: + """Read a value from an .env file using python-dotenv""" + env_path = Path(env_file_path) + if not env_path.exists(): return None + value = get_key(str(env_path), key) + # get_key returns None if key doesn't exist or value is empty + return value if value else None + SERVICES = { 'backend': { 'advanced': { @@ -50,17 +38,17 @@ def read_env_value(env_file_path, key): 'extras': { 'speaker-recognition': { 'path': 'extras/speaker-recognition', - 'cmd': ['./setup.sh'], + 'cmd': ['uv', 'run', '--with-requirements', 'setup-requirements.txt', 'python', 'init.py'], 'description': 'Speaker identification and enrollment' }, 'asr-services': { - 'path': 'extras/asr-services', + 'path': 'extras/asr-services', 'cmd': ['./setup.sh'], 'description': 'Offline speech-to-text (Parakeet)' }, 'openmemory-mcp': { 'path': 'extras/openmemory-mcp', - 'cmd': ['./setup.sh'], + 'cmd': ['./setup.sh'], 'description': 'OpenMemory MCP server' } } @@ -71,18 +59,18 @@ def check_service_exists(service_name, service_config): service_path = Path(service_config['path']) if not service_path.exists(): return False, f"Directory {service_path} does not exist" - - # For advanced backend, check if init.py exists - if service_name == 'advanced': + + # For services with Python init scripts, check if init.py exists + if service_name in ['advanced', 'speaker-recognition']: script_path = service_path / 'init.py' if not script_path.exists(): return False, f"Script {script_path} does not exist" else: - # For extras, check if setup.sh exists + # For other extras, check if setup.sh exists script_path = service_path / 'setup.sh' if not script_path.exists(): return False, f"Script {script_path} does not exist (will be created in Phase 2)" - + return True, "OK" def select_services(): @@ -161,19 +149,24 @@ def run_service_setup(service_name, selected_services, https_enabled=False, serv if service_name == 'speaker-recognition' and https_enabled and server_ip: cmd.extend(['--enable-https', '--server-ip', server_ip]) - # For speaker-recognition, try to pass API keys if available + # For speaker-recognition, try to pass API keys and config if available if service_name == 'speaker-recognition': # Pass Deepgram API key from backend if available backend_env_path = 'backends/advanced/.env' deepgram_key = read_env_value(backend_env_path, 'DEEPGRAM_API_KEY') if deepgram_key and deepgram_key != 'your_deepgram_api_key_here': cmd.extend(['--deepgram-api-key', deepgram_key]) - + # Pass HF Token from existing speaker recognition .env if available speaker_env_path = 'extras/speaker-recognition/.env' hf_token = read_env_value(speaker_env_path, 'HF_TOKEN') if hf_token and hf_token != 'your_huggingface_token_here': cmd.extend(['--hf-token', hf_token]) + + # Pass compute mode from existing .env if available + compute_mode = read_env_value(speaker_env_path, 'COMPUTE_MODE') + if compute_mode in ['cpu', 'gpu']: + cmd.extend(['--compute-mode', compute_mode]) # For openmemory-mcp, try to pass OpenAI API key from backend if available if service_name == 'openmemory-mcp': @@ -248,7 +241,7 @@ def main(): server_ip = None # Check if we have services that benefit from HTTPS - https_services = {'advanced', 'speaker-recognition'} + https_services = {'advanced', 'speaker-recognition'} # advanced will always need https then needs_https = bool(https_services.intersection(selected_services)) if needs_https: @@ -336,6 +329,7 @@ def main(): # Show individual service usage console.print(f"\n💡 [dim]Tip: You can also setup services individually:[/dim]") console.print(f"[dim] cd backends/advanced && uv run --with-requirements setup-requirements.txt python init.py[/dim]") + console.print(f"[dim] cd extras/speaker-recognition && uv run --with-requirements setup-requirements.txt python init.py[/dim]") if __name__ == "__main__": main() \ No newline at end of file