Brane: The AI Brain for Next-Gen Data Intelligence

# Advanced Causal Inference with Redis await redis_async.json().set(f"brane:insight:{insight_id}", "$", { "causal_relationship": { "cause": "marketing_campaign_A", "effect": "conversion_rate_increase", "confidence": 0.89, "statistical_significance": "p < 0.01" }, "intervention_simulation": { "predicted_outcome": "+23% conversions", "confidence_interval": [18, 28] } }) 

# Store complex multimodal data in RedisJSON redis_client.json().set(f"brane:data:{data_id}", "$", { "user_id": "analyst_123", "data_type": "multimodal", "content": { "text_analysis": "Customer satisfaction trending positive", "image_metadata": {"faces_detected": 3, "sentiment": "happy"}, "audio_transcript": "Great product, will recommend!", "sensor_data": {"temperature": 22.5, "humidity": 45} }, "ai_insights": { "cross_modal_correlation": 0.94, "confidence_score": 0.87 } }) 

# Background AI processing pipeline async def process_ai_insights(): while True: # Read from Redis Stream  messages = await redis_async.xread( {"brane:ai_queue": "$"}, block=1000 ) for stream, msgs in messages: for msg_id, fields in msgs: # AI analysis happens here  insights = await generate_causal_insights(fields) # Store results back to Redis  await redis_async.json().set( f"brane:insights:{fields['user_id']}", "$", insights ) # Notify users via Pub/Sub  await redis_async.publish( f"user:{fields['user_id']}:insights", json.dumps(insights) ) 

# Primary database storage (not caching!) user_profile = { "user_id": "data_scientist_001", "preferences": {"analysis_type": "causal", "confidence_threshold": 0.8}, "projects": [ { "name": "Customer Churn Analysis", "status": "active", "insights_count": 47, "last_updated": "2025-08-11T10:30:00Z" } ], "ai_models": { "preferred": "CausalNet-v2", "accuracy_history": [0.89, 0.91, 0.88, 0.93] } } redis_client.json().set(f"brane:user:{user_id}", "$", user_profile) 

# Create sophisticated search index redis_client.execute_command( "FT.CREATE", "brane_insights_idx", "ON", "JSON", "PREFIX", "1", "brane:insight:", "SCHEMA", "$.content.title", "AS", "title", "TEXT", "WEIGHT", "2.0", "$.confidence_score", "AS", "confidence", "NUMERIC", "SORTABLE", "$.tags", "AS", "tags", "TAG", "SEPARATOR", ",", "$.created_at", "AS", "date", "NUMERIC", "SORTABLE" ) # Complex search queries results = redis_client.execute_command( "FT.SEARCH", "brane_insights_idx", "causal AND @confidence:[0.8 +inf]", "SORTBY", "date", "DESC", "LIMIT", "0", "10" ) 

# Multi-consumer data processing redis_client.xgroup_create("brane:data_stream", "ai_processors", id="0") redis_client.xgroup_create("brane:data_stream", "analytics_team", id="0") # Add data to stream await redis_async.xadd("brane:data_stream", { "type": "sensor_data", "user_id": user_id, "data": json.dumps(sensor_readings), "priority": "high", "processing_required": "causal_analysis,prediction" }) # Consumer group processing async def stream_processor(): while True: messages = await redis_async.xreadgroup( "ai_processors", "processor_1", {"brane:data_stream": ">"}, count=10, block=1000 ) for stream, msgs in messages: for msg_id, fields in msgs: await process_ai_analysis(fields) await redis_async.xack("brane:data_stream", "ai_processors", msg_id) 

# WebSocket integration with Pub/Sub class WebSocketManager: def __init__(self): self.connections = {} self.redis_sub = redis.Redis().pubsub() async def handle_redis_messages(self): async for message in self.redis_sub.listen(): if message['type'] == 'message': user_id = message['channel'].decode().split(':')[1] if user_id in self.connections: await self.connections[user_id].send_text( message['data'].decode() ) # Publishing insights await redis_async.publish(f"user:{user_id}:insights", json.dumps({ "type": "causal_discovery", "insight": "Marketing spend drives 15% revenue increase", "confidence": 0.92, "recommended_action": "Increase Q4 marketing budget by 20%" })) 

# Create time-series for different metrics metrics = [ "user_engagement", "model_accuracy", "processing_time", "insight_confidence", "data_volume" ] for metric in metrics: redis_client.execute_command( "TS.CREATE", f"brane:ts:{user_id}:{metric}", "RETENTION", "2592000000", # 30 days retention  "DUPLICATE_POLICY", "LAST", "LABELS", "user_id", user_id, "metric_type", metric, "environment", "production" ) # Advanced time-series queries def get_trend_analysis(user_id: str, metric: str, hours: int = 24): end_time = int(time.time() * 1000) start_time = end_time - (hours * 3600 * 1000) # Get raw data  data_points = redis_client.execute_command( "TS.RANGE", f"brane:ts:{user_id}:{metric}", start_time, end_time, "AGGREGATION", "avg", 3600000 # 1-hour buckets  ) # Calculate trend  if len(data_points) >= 2: slope = (data_points[-1][1] - data_points[0][1]) / len(data_points) trend = "increasing" if slope > 0 else "decreasing" return {"data": data_points, "trend": trend, "slope": slope} 

# Clone the repository git clone https://github.com/AberTheCreator/Brane.git cd Brane # Install dependencies pip install -r requirements.txt # Configure Redis (create .env file) REDIS_HOST=your-redis-host.redis-cloud.com REDIS_PORT=19369 REDIS_PASSWORD=your-password # Launch the application python run_backend.py 

# Automated hypothesis testing insights = await brane_ai.analyze_experiment({ "experiment_id": "ab_test_checkout", "treatment_group": "new_ui", "control_group": "old_ui", "metric": "conversion_rate" }) # Result: "New UI causes 18% increase in conversions # with 95% statistical significance" 

# Real-time anomaly detection sensor_data = { "temperature": 85.2, # Above normal threshold  "vibration": 2.1, "pressure": 45.8 } anomaly = await brane_ai.detect_anomaly(sensor_data) if anomaly.severity == "critical": await send_maintenance_alert(anomaly.root_cause) 

# Executive dashboard insights business_metrics = await brane_ai.get_causal_insights({ "user_id": "ceo", "metrics": ["revenue", "customer_satisfaction", "market_share"], "time_range": "last_quarter" }) # Automated insights: "Customer satisfaction improvements # drive 12% revenue growth with 2-week lag time"