Feat/revive

assist/server/adk_orchestrator.py

@@ -0,0 +1,251 @@
+"""
+ADK (Agent Development Kit) orchestrator for conversation processing
+"""
+
+import asyncio
+import logging
+from typing import List, Dict, Any, Optional
+from datetime import datetime
+import json
+
+logger = logging.getLogger(__name__)
+
+class ADKOrchestrator:
+    def __init__(self, memory_store):
+        self.memory_store = memory_store
+        self.active_sessions: Dict[str, Dict] = {}
+        self.processing_queue = asyncio.Queue()
+        self.is_running = False
+
+        # Initialize processing tasks
+        self.tasks = {
+            'transcriber': self._transcriber_task,
+            'summarizer': self._summarizer_task,
+            'action_planner': self._action_planner_task,
+            'relationship_miner': self._relationship_miner_task,
+            'memory_writer': self._memory_writer_task
+        }
+
+    async def start(self):
+        """Start the ADK orchestrator"""
+        if self.is_running:
+            return
+
+        self.is_running = True
+
+        # Start all processing tasks
+        for task_name, task_func in self.tasks.items():
+            asyncio.create_task(self._run_task(task_name, task_func))
+
+        logger.info("ADK orchestrator started")
+
+    async def stop(self):
+        """Stop the ADK orchestrator"""
+        self.is_running = False
+        logger.info("ADK orchestrator stopped")
+
+    async def _run_task(self, task_name: str, task_func):
+        """Run a specific processing task"""
+        while self.is_running:
+            try:
+                # Get next item from queue
+                item = await asyncio.wait_for(self.processing_queue.get(), timeout=1.0)
+
+                # Process with specific task
+                await task_func(item)
+
+            except asyncio.TimeoutError:
+                continue
+            except Exception as e:
+                logger.error(f"Error in {task_name} task: {e}")
+
+    async def process_audio_chunk(self, audio_frames: List[float], connection_id: str):
+        """Process audio chunk through the pipeline"""
+        try:
+            # Create processing item
+            item = {
+                'type': 'audio_chunk',
+                'data': audio_frames,
+                'connection_id': connection_id,
+                'timestamp': datetime.utcnow().isoformat()
+            }
+
+            # Add to processing queue
+            await self.processing_queue.put(item)
+
+        except Exception as e:
+            logger.error(f"Failed to process audio chunk: {e}")
+
+    async def process_video_frame(self, frame_data: bytes, connection_id: str):
+        """Process video frame through the pipeline"""
+        try:
+            # Create processing item
+            item = {
+                'type': 'video_frame',
+                'data': frame_data,
+                'connection_id': connection_id,
+                'timestamp': datetime.utcnow().isoformat()
+            }
+
+            # Add to processing queue
+            await self.processing_queue.put(item)
+
+        except Exception as e:
+            logger.error(f"Failed to process video frame: {e}")
+
+    async def _transcriber_task(self, item: Dict[str, Any]):
+        """Transcriber task - converts audio to text"""
+        try:
+            if item['type'] == 'audio_chunk':
+                # In a real implementation, this would use speech recognition
+                # For now, we'll simulate transcription
+                transcript = await self._simulate_transcription(item['data'])
+
+                if transcript:
+                    # Store transcript
+                    await self.memory_store.store_utterance({
+                        'text': transcript,
+                        'timestamp': item['timestamp'],
+                        'connection_id': item['connection_id'],
+                        'type': 'transcript'
+                    })
+
+                    logger.info(f"Transcribed: {transcript[:50]}...")
+
+        except Exception as e:
+            logger.error(f"Transcriber task error: {e}")
+
+    async def _summarizer_task(self, item: Dict[str, Any]):
+        """Summarizer task - creates conversation summaries"""
+        try:
+            if item['type'] == 'audio_chunk':
+                # Get recent utterances for summarization
+                recent_utterances = await self.memory_store.get_recent_utterances(limit=5)
+
+                if len(recent_utterances) >= 3:  # Summarize when we have enough content
+                    summary = await self._generate_summary(recent_utterances)
+
+                    if summary:
+                        await self.memory_store.store_memory({
+                            'type': 'summary',
+                            'content': summary,
+                            'timestamp': item['timestamp'],
+                            'connection_id': item['connection_id']
+                        })
+
+                        logger.info(f"Generated summary: {summary[:50]}...")
+
+        except Exception as e:
+            logger.error(f"Summarizer task error: {e}")
+
+    async def _action_planner_task(self, item: Dict[str, Any]):
+        """Action planner task - extracts action items"""
+        try:
+            if item['type'] == 'audio_chunk':
+                # Get recent utterances for action extraction
+                recent_utterances = await self.memory_store.get_recent_utterances(limit=10)
+
+                if recent_utterances:
+                    actions = await self._extract_actions(recent_utterances)
+
+                    for action in actions:
+                        await self.memory_store.store_action({
+                            'description': action['description'],
+                            'owner': action.get('owner', 'unknown'),
+                            'due_hint': action.get('due_hint'),
+                            'status': 'pending',
+                            'timestamp': item['timestamp'],
+                            'connection_id': item['connection_id']
+                        })
+
+                        logger.info(f"Extracted action: {action['description']}")
+
+        except Exception as e:
+            logger.error(f"Action planner task error: {e}")
+
+    async def _relationship_miner_task(self, item: Dict[str, Any]):
+        """Relationship miner task - identifies relationships between people"""
+        try:
+            if item['type'] == 'audio_chunk':
+                # Get recent utterances for relationship mining
+                recent_utterances = await self.memory_store.get_recent_utterances(limit=20)
+
+                if recent_utterances:
+                    relationships = await self._extract_relationships(recent_utterances)
+
+                    for relationship in relationships:
+                        await self.memory_store.store_relationship({
+                            'person1': relationship['person1'],
+                            'person2': relationship['person2'],
+                            'relationship_type': relationship['type'],
+                            'evidence': relationship['evidence'],
+                            'timestamp': item['timestamp'],
+                            'connection_id': item['connection_id']
+                        })
+
+                        logger.info(f"Extracted relationship: {relationship['person1']} - {relationship['person2']}")
+
+        except Exception as e:
+            logger.error(f"Relationship miner task error: {e}")
+
+    async def _memory_writer_task(self, item: Dict[str, Any]):
+        """Memory writer task - persists memories to long-term storage"""
+        try:
+            # This task handles the final persistence of processed data
+            # It ensures all memories are properly stored and indexed
+
+            if item['type'] == 'audio_chunk':
+                # Update session information
+                session_id = item['connection_id']
+                if session_id not in self.active_sessions:
+                    self.active_sessions[session_id] = {
+                        'start_time': item['timestamp'],
+                        'utterance_count': 0,
+                        'last_activity': item['timestamp']
+                    }
+
+                self.active_sessions[session_id]['utterance_count'] += 1
+                self.active_sessions[session_id]['last_activity'] = item['timestamp']
+
+                # Persist session data
+                await self.memory_store.update_session(session_id, self.active_sessions[session_id])
+
+        except Exception as e:
+            logger.error(f"Memory writer task error: {e}")
+
+    async def _simulate_transcription(self, audio_frames: List[float]) -> Optional[str]:
+        """Simulate audio transcription"""
+        # In a real implementation, this would use speech recognition
+        # For now, return a placeholder
+        return "This is a simulated transcription of the audio content."
+
+    async def _generate_summary(self, utterances: List[Dict]) -> Optional[str]:
+        """Generate conversation summary"""
+        # In a real implementation, this would use AI summarization
+        # For now, return a placeholder
+        return "This is a simulated summary of the conversation."
+
+    async def _extract_actions(self, utterances: List[Dict]) -> List[Dict]:
+        """Extract action items from utterances"""
+        # In a real implementation, this would use AI to extract actions
+        # For now, return placeholder actions
+        return [
+            {
+                'description': 'Follow up on the discussed topic',
+                'owner': 'user',
+                'due_hint': 'next week'
+            }
+        ]
+
+    async def _extract_relationships(self, utterances: List[Dict]) -> List[Dict]:
+        """Extract relationships from utterances"""
+        # In a real implementation, this would use AI to extract relationships
+        # For now, return placeholder relationships
+        return [
+            {
+                'person1': 'Alice',
+                'person2': 'Bob',
+                'type': 'colleague',
+                'evidence': 'mentioned working together'
+            }
+        ]