feat(predictive): implement NOVA architecture with Kafka messaging

Add proof of concept for NOVA (Neural Oscillation Virtual Architecture) using
Kafka as message broker. Implements three processing layers:

- Reactive layer (50-300ms): Fast responses
- Responsive layer (300-1000ms): Context-aware processing
- Reflective layer (background): Learning and adaptation

Key features:
- Asynchronous message processing across layers
- Kafka producer/consumer setup with proper configs
- Simulated processing times for each layer
- JSON message format for inter-layer communication

Technical notes:
- Requires Kafka + Zookeeper containers
- Uses confluent-kafka-python client
- Async/await pattern for parallel processing

predictive_coding/03_kafka_nova_poc.py

@@ -0,0 +1,155 @@
+from confluent_kafka import Producer, Consumer
+import json
+import time
+from typing import Dict, Any
+import asyncio
+
+
+class NOVALayer:
+    def __init__(self, kafka_config: Dict[str, Any]):
+        # Producer config should exclude consumer-specific settings
+        producer_config = {"bootstrap.servers": kafka_config["bootstrap.servers"]}
+
+        # Consumer config can keep all settings
+        consumer_config = kafka_config.copy()
+
+        self.producer = Producer(producer_config)
+        self.consumer = Consumer(consumer_config)
+
+    def publish(self, topic: str, message: Dict[str, Any]):
+        """Publish message to a topic"""
+        try:
+            self.producer.produce(
+                topic,
+                json.dumps(message).encode("utf-8"),
+                callback=self.delivery_report,
+            )
+            self.producer.flush()
+        except Exception as e:
+            print(f"Error producing message: {e}")
+
+    def delivery_report(self, err, msg):
+        """Callback for message delivery reports"""
+        if err is not None:
+            print(f"Message delivery failed: {err}")
+        else:
+            print(f"Message delivered to {msg.topic()}")
+
+
+class ReactiveLayer(NOVALayer):
+    """Fast response layer (50-300ms)"""
+
+    def process(self, message: Dict[str, Any]) -> Dict[str, Any]:
+        """Quick processing of immediate responses"""
+        # Simulate processing time
+        time.sleep(0.1)  # 100ms
+
+        # Simple response generation
+        response = {
+            "type": "reactive_response",
+            "content": f"Quick acknowledgment: {message.get('content', '')}",
+            "timestamp": time.time(),
+        }
+
+        self.publish("nova.reactive.output", response)
+        return response
+
+
+class ResponsiveLayer(NOVALayer):
+    """Context-aware layer (300-1000ms)"""
+
+    def process(self, message: Dict[str, Any]) -> Dict[str, Any]:
+        """Process with context awareness"""
+        # Simulate processing time
+        time.sleep(0.3)  # 300ms
+
+        # Context-aware response
+        response = {
+            "type": "responsive_response",
+            "content": f"Thoughtful response to: {message.get('content', '')}",
+            "context": "user_interaction",
+            "timestamp": time.time(),
+        }
+
+        self.publish("nova.responsive.output", response)
+        return response
+
+
+class ReflectiveLayer(NOVALayer):
+    """Learning and adaptation layer (background)"""
+
+    def process(self, message: Dict[str, Any]) -> Dict[str, Any]:
+        """Process for long-term learning"""
+        # Simulate processing time
+        time.sleep(0.5)  # 500ms
+
+        # Pattern learning and adaptation
+        response = {
+            "type": "reflective_update",
+            "pattern": "user_interaction_pattern",
+            "learning": f"Learned from: {message.get('content', '')}",
+            "timestamp": time.time(),
+        }
+
+        self.publish("nova.reflective.output", response)
+        return response
+
+
+class NOVA:
+    def __init__(self, kafka_config: Dict[str, Any]):
+        self.reactive = ReactiveLayer(kafka_config)
+        self.responsive = ResponsiveLayer(kafka_config)
+        self.reflective = ReflectiveLayer(kafka_config)
+
+    async def process_message(self, message: Dict[str, Any]):
+        """Process message through all layers asynchronously"""
+        # Create tasks for each layer
+        reactive_task = asyncio.create_task(
+            asyncio.to_thread(self.reactive.process, message)
+        )
+        responsive_task = asyncio.create_task(
+            asyncio.to_thread(self.responsive.process, message)
+        )
+        reflective_task = asyncio.create_task(
+            asyncio.to_thread(self.reflective.process, message)
+        )
+
+        # Wait for all tasks to complete
+        results = await asyncio.gather(reactive_task, responsive_task, reflective_task)
+
+        return {
+            "reactive": results[0],
+            "responsive": results[1],
+            "reflective": results[2],
+        }
+
+
+# Example usage
+async def main():
+    # Kafka configuration
+    kafka_config = {
+        "bootstrap.servers": "localhost:9092",
+        "group.id": "nova_group",
+        "auto.offset.reset": "earliest",
+    }
+
+    # Initialize NOVA
+    nova = NOVA(kafka_config)
+
+    # Example message
+    message = {
+        "type": "user_input",
+        "content": "Hello, how are you?",
+        "timestamp": time.time(),
+    }
+
+    # Process message
+    results = nova.process_message(message)
+    print("\nProcessing Results:")
+    print("Reactive:\n", results["reactive"])
+    print("Responsive:\n", results["responsive"])
+    print("Reflective:\n", results["reflective"])
+
+
+if __name__ == "__main__":
+    asyncio.run(main())

requirements.txt

@@ -1,2 +1,6 @@
 numpy
 matplotlib
+python-dotenv
+
+# Kafka
+confluent-kafka==2.3.0