Google Cloud Platform

Integrate BroxiAI with Google Cloud Platform services for scalable AI applications

Learn how to integrate BroxiAI workflows with Google Cloud Platform (GCP) services for enhanced AI capabilities, global scale, and enterprise-grade infrastructure.

Overview

BroxiAI integrates seamlessly with GCP services to provide:

Advanced AI and machine learning capabilities
Global infrastructure and edge computing
Enterprise security and compliance
Cost-effective scaling and resource management
Data analytics and intelligence platforms

Core GCP Services Integration

Vertex AI

Connect BroxiAI to Google's unified AI platform for enhanced machine learning capabilities.

Configuration

{
  "provider": "vertex_ai",
  "project_id": "your-gcp-project",
  "location": "us-central1",
  "credentials_path": "${GOOGLE_APPLICATION_CREDENTIALS}",
  "models": {
    "text": "text-bison@001",
    "chat": "chat-bison@001",
    "code": "code-bison@001",
    "embeddings": "textembedding-gecko@001"
  }
}

Supported Models

PaLM 2 for Text: text-bison, text-bison-32k
PaLM 2 for Chat: chat-bison, chat-bison-32k
Codey for Code: code-bison, codechat-bison
Embedding Models: textembedding-gecko
Imagen: imagen-editor, imagen-captioner

Vertex AI Integration Example

from google.cloud import aiplatform
from google.auth import default

class VertexAIIntegration:
    def __init__(self, project_id, location="us-central1"):
        self.project_id = project_id
        self.location = location
        
        # Initialize Vertex AI
        credentials, _ = default()
        aiplatform.init(
            project=project_id,
            location=location,
            credentials=credentials
        )
    
    def generate_text(self, prompt, model="text-bison@001", **parameters):
        """Generate text using Vertex AI"""
        
        from vertexai.language_models import TextGenerationModel
        
        model = TextGenerationModel.from_pretrained(model)
        
        response = model.predict(
            prompt,
            temperature=parameters.get('temperature', 0.2),
            max_output_tokens=parameters.get('max_tokens', 1024),
            top_k=parameters.get('top_k', 40),
            top_p=parameters.get('top_p', 0.8)
        )
        
        return {
            "text": response.text,
            "safety_attributes": response.safety_attributes,
            "token_count": len(response.text.split())
        }
    
    def generate_embeddings(self, texts, model="textembedding-gecko@001"):
        """Generate embeddings using Vertex AI"""
        
        from vertexai.language_models import TextEmbeddingModel
        
        model = TextEmbeddingModel.from_pretrained(model)
        embeddings = model.get_embeddings(texts)
        
        return [
            {
                "text": text,
                "embedding": embedding.values,
                "dimension": len(embedding.values)
            }
            for text, embedding in zip(texts, embeddings)
        ]
    
    def create_custom_model(self, training_data_uri, model_display_name):
        """Create custom model with Vertex AI"""
        
        from google.cloud import aiplatform
        
        job = aiplatform.CustomTrainingJob(
            display_name=f"broxi-custom-{model_display_name}",
            script_path="training_script.py",
            container_uri="gcr.io/cloud-aiplatform/training/tf-enterprise-2.8-gpu:latest",
            requirements=["transformers", "datasets"],
            model_serving_container_image_uri="gcr.io/cloud-aiplatform/prediction/tf2-gpu.2-8:latest"
        )
        
        model = job.run(
            dataset=training_data_uri,
            replica_count=1,
            machine_type="n1-standard-4",
            accelerator_type="NVIDIA_TESLA_K80",
            accelerator_count=1
        )
        
        return model

Cloud Storage

Store and process documents, datasets, and model artifacts.

Use Cases

Document storage for RAG applications
Training data management
Model artifact storage
Backup and archival
Data lake implementation

Integration Example

from google.cloud import storage
import io

class GCSIntegration:
    def __init__(self, project_id):
        self.client = storage.Client(project=project_id)
    
    def upload_document(self, bucket_name, file_path, destination_blob_name):
        """Upload document to Google Cloud Storage"""
        
        bucket = self.client.bucket(bucket_name)
        blob = bucket.blob(destination_blob_name)
        
        # Upload file
        blob.upload_from_filename(file_path)
        
        # Set metadata
        blob.metadata = {
            "uploaded_by": "broxi_ai",
            "upload_timestamp": datetime.utcnow().isoformat(),
            "content_type": blob.content_type
        }
        blob.patch()
        
        return {
            "bucket": bucket_name,
            "blob_name": destination_blob_name,
            "size": blob.size,
            "public_url": blob.public_url,
            "metadata": blob.metadata
        }
    
    def process_documents_batch(self, bucket_name, prefix="documents/"):
        """Batch process documents from GCS"""
        
        bucket = self.client.bucket(bucket_name)
        blobs = bucket.list_blobs(prefix=prefix)
        
        processed_docs = []
        
        for blob in blobs:
            if blob.name.endswith(('.pdf', '.docx', '.txt')):
                # Download document content
                content = blob.download_as_text()
                
                # Process with BroxiAI
                processed_content = self.process_with_broxi(content)
                
                # Store processed version
                processed_blob_name = f"processed/{blob.name}"
                processed_blob = bucket.blob(processed_blob_name)
                processed_blob.upload_from_string(
                    processed_content,
                    content_type="text/plain"
                )
                
                processed_docs.append({
                    "original": blob.name,
                    "processed": processed_blob_name,
                    "size": len(processed_content)
                })
        
        return processed_docs
    
    def setup_lifecycle_management(self, bucket_name):
        """Set up lifecycle management for cost optimization"""
        
        bucket = self.client.bucket(bucket_name)
        
        # Define lifecycle rules
        lifecycle_rule = {
            "action": {"type": "SetStorageClass", "storageClass": "COLDLINE"},
            "condition": {"age": 30}  # Move to coldline after 30 days
        }
        
        archive_rule = {
            "action": {"type": "SetStorageClass", "storageClass": "ARCHIVE"},
            "condition": {"age": 365}  # Move to archive after 1 year
        }
        
        delete_rule = {
            "action": {"type": "Delete"},
            "condition": {"age": 2555}  # Delete after 7 years
        }
        
        bucket.lifecycle_rules = [lifecycle_rule, archive_rule, delete_rule]
        bucket.patch()
        
        return "Lifecycle management configured"

Cloud Firestore

NoSQL database for real-time applications and session management.

Configuration

from google.cloud import firestore
from google.auth import default

class FirestoreIntegration:
    def __init__(self, project_id):
        credentials, _ = default()
        self.db = firestore.Client(project=project_id, credentials=credentials)
    
    def store_conversation(self, user_id, session_id, messages):
        """Store conversation history in Firestore"""
        
        doc_ref = self.db.collection('conversations').document(f"{user_id}_{session_id}")
        
        conversation_data = {
            "user_id": user_id,
            "session_id": session_id,
            "messages": messages,
            "created_at": firestore.SERVER_TIMESTAMP,
            "updated_at": firestore.SERVER_TIMESTAMP,
            "message_count": len(messages)
        }
        
        doc_ref.set(conversation_data, merge=True)
        
        return doc_ref.id
    
    def get_conversation_history(self, user_id, limit=10):
        """Retrieve conversation history for user"""
        
        conversations_ref = self.db.collection('conversations')
        query = conversations_ref.where('user_id', '==', user_id)\
                                .order_by('updated_at', direction=firestore.Query.DESCENDING)\
                                .limit(limit)
        
        conversations = []
        for doc in query.stream():
            data = doc.to_dict()
            data['id'] = doc.id
            conversations.append(data)
        
        return conversations
    
    def store_user_preferences(self, user_id, preferences):
        """Store user preferences and settings"""
        
        doc_ref = self.db.collection('user_preferences').document(user_id)
        
        preference_data = {
            "user_id": user_id,
            "preferences": preferences,
            "updated_at": firestore.SERVER_TIMESTAMP
        }
        
        doc_ref.set(preference_data, merge=True)
        
        return "Preferences stored successfully"
    
    def real_time_listener(self, collection_name, callback):
        """Set up real-time listener for collection changes"""
        
        def on_snapshot(collection_snapshot, changes, read_time):
            for change in changes:
                if change.type.name == 'ADDED':
                    callback('added', change.document.to_dict())
                elif change.type.name == 'MODIFIED':
                    callback('modified', change.document.to_dict())
                elif change.type.name == 'REMOVED':
                    callback('removed', change.document.to_dict())
        
        collection_ref = self.db.collection(collection_name)
        collection_watch = collection_ref.on_snapshot(on_snapshot)
        
        return collection_watch

Cloud Functions

Serverless computing for event-driven BroxiAI integrations.

Function Configuration

# main.py for Cloud Function
import functions_framework
import requests
import json
import os
from google.cloud import firestore

@functions_framework.http
def broxi_webhook_handler(request):
    """Cloud Function to handle BroxiAI webhooks"""
    
    # Verify webhook signature
    if not verify_webhook_signature(request):
        return {"error": "Invalid signature"}, 401
    
    # Parse webhook data
    webhook_data = request.get_json()
    event_type = webhook_data.get('event')
    
    try:
        if event_type == 'workflow.completed':
            handle_workflow_completion(webhook_data)
        elif event_type == 'user.message.received':
            handle_user_message(webhook_data)
        else:
            print(f"Unhandled event type: {event_type}")
        
        return {"status": "success"}, 200
        
    except Exception as e:
        print(f"Error processing webhook: {e}")
        return {"error": "Processing failed"}, 500

def handle_workflow_completion(data):
    """Handle workflow completion event"""
    
    # Store results in Firestore
    db = firestore.Client()
    
    doc_ref = db.collection('workflow_results').document()
    doc_ref.set({
        "workflow_id": data['data']['workflow_id'],
        "run_id": data['data']['run_id'],
        "output": data['data']['output'],
        "execution_time": data['data']['execution_time'],
        "timestamp": firestore.SERVER_TIMESTAMP
    })
    
    # Trigger follow-up actions
    if data['data']['workflow_id'] == 'document_analysis':
        trigger_report_generation(data['data']['output'])

@functions_framework.cloud_event
def process_gcs_upload(cloud_event):
    """Process file uploads to Cloud Storage"""
    
    # Get file information
    bucket_name = cloud_event.data['bucket']
    file_name = cloud_event.data['name']
    
    if file_name.endswith(('.pdf', '.docx', '.txt')):
        # Trigger BroxiAI document processing
        response = requests.post(
            "https://api.broxi.ai/v1/flows/document-processor/run",
            headers={
                "Authorization": f"Bearer {os.environ['BROXI_API_TOKEN']}",
                "Content-Type": "application/json"
            },
            json={
                "input": f"Process document from gs://{bucket_name}/{file_name}",
                "variables": {
                    "bucket": bucket_name,
                    "file_path": file_name
                }
            }
        )
        
        if response.status_code == 200:
            print(f"Document processing initiated for {file_name}")
        else:
            print(f"Failed to process document: {response.text}")

# requirements.txt
"""
functions-framework>=3.0.0
google-cloud-firestore>=2.0.0
google-cloud-storage>=2.0.0
requests>=2.25.0
"""

# Deploy command
"""
gcloud functions deploy broxi-webhook-handler \
    --runtime python311 \
    --trigger-http \
    --allow-unauthenticated \
    --set-env-vars BROXI_API_TOKEN=your_token
"""

BigQuery

Data warehouse and analytics platform for workflow insights.

BigQuery Integration

from google.cloud import bigquery
from google.auth import default
import pandas as pd

class BigQueryIntegration:
    def __init__(self, project_id):
        credentials, _ = default()
        self.client = bigquery.Client(project=project_id, credentials=credentials)
        self.project_id = project_id
    
    def create_workflow_analytics_dataset(self):
        """Create dataset for workflow analytics"""
        
        dataset_id = f"{self.project_id}.broxi_analytics"
        dataset = bigquery.Dataset(dataset_id)
        dataset.location = "US"
        dataset.description = "BroxiAI workflow analytics and metrics"
        
        # Create dataset
        dataset = self.client.create_dataset(dataset, exists_ok=True)
        
        # Create tables
        self.create_workflow_executions_table()
        self.create_user_interactions_table()
        self.create_performance_metrics_table()
        
        return f"Dataset {dataset_id} created successfully"
    
    def create_workflow_executions_table(self):
        """Create table for workflow execution data"""
        
        table_id = f"{self.project_id}.broxi_analytics.workflow_executions"
        
        schema = [
            bigquery.SchemaField("execution_id", "STRING", mode="REQUIRED"),
            bigquery.SchemaField("workflow_id", "STRING", mode="REQUIRED"),
            bigquery.SchemaField("user_id", "STRING", mode="REQUIRED"),
            bigquery.SchemaField("input_text", "STRING", mode="NULLABLE"),
            bigquery.SchemaField("output_text", "STRING", mode="NULLABLE"),
            bigquery.SchemaField("execution_time", "FLOAT", mode="REQUIRED"),
            bigquery.SchemaField("token_usage", "INTEGER", mode="NULLABLE"),
            bigquery.SchemaField("cost", "FLOAT", mode="NULLABLE"),
            bigquery.SchemaField("status", "STRING", mode="REQUIRED"),
            bigquery.SchemaField("error_message", "STRING", mode="NULLABLE"),
            bigquery.SchemaField("timestamp", "TIMESTAMP", mode="REQUIRED"),
            bigquery.SchemaField("metadata", "JSON", mode="NULLABLE")
        ]
        
        table = bigquery.Table(table_id, schema=schema)
        table.time_partitioning = bigquery.TimePartitioning(
            type_=bigquery.TimePartitioningType.DAY,
            field="timestamp"
        )
        
        table = self.client.create_table(table, exists_ok=True)
        
        return f"Table {table_id} created successfully"
    
    def insert_workflow_execution(self, execution_data):
        """Insert workflow execution data"""
        
        table_id = f"{self.project_id}.broxi_analytics.workflow_executions"
        table = self.client.get_table(table_id)
        
        rows_to_insert = [execution_data]
        errors = self.client.insert_rows_json(table, rows_to_insert)
        
        if errors:
            print(f"Errors inserting data: {errors}")
            return False
        
        return True
    
    def analyze_workflow_performance(self, start_date, end_date):
        """Analyze workflow performance over time"""
        
        query = f"""
        SELECT 
            workflow_id,
            COUNT(*) as total_executions,
            AVG(execution_time) as avg_execution_time,
            PERCENTILE_CONT(execution_time, 0.5) OVER() as median_execution_time,
            PERCENTILE_CONT(execution_time, 0.95) OVER() as p95_execution_time,
            SUM(token_usage) as total_tokens,
            SUM(cost) as total_cost,
            COUNT(CASE WHEN status = 'completed' THEN 1 END) as successful_executions,
            COUNT(CASE WHEN status = 'failed' THEN 1 END) as failed_executions
        FROM `{self.project_id}.broxi_analytics.workflow_executions`
        WHERE timestamp BETWEEN '{start_date}' AND '{end_date}'
        GROUP BY workflow_id
        ORDER BY total_executions DESC
        """
        
        query_job = self.client.query(query)
        results = query_job.result()
        
        return [dict(row) for row in results]
    
    def create_usage_dashboard(self):
        """Create usage analytics dashboard queries"""
        
        queries = {
            "daily_usage": f"""
            SELECT 
                DATE(timestamp) as date,
                COUNT(*) as executions,
                SUM(token_usage) as tokens,
                SUM(cost) as cost,
                AVG(execution_time) as avg_response_time
            FROM `{self.project_id}.broxi_analytics.workflow_executions`
            WHERE timestamp >= TIMESTAMP_SUB(CURRENT_TIMESTAMP(), INTERVAL 30 DAY)
            GROUP BY DATE(timestamp)
            ORDER BY date DESC
            """,
            
            "top_users": f"""
            SELECT 
                user_id,
                COUNT(*) as total_requests,
                SUM(cost) as total_cost,
                AVG(execution_time) as avg_response_time
            FROM `{self.project_id}.broxi_analytics.workflow_executions`
            WHERE timestamp >= TIMESTAMP_SUB(CURRENT_TIMESTAMP(), INTERVAL 7 DAY)
            GROUP BY user_id
            ORDER BY total_requests DESC
            LIMIT 10
            """,
            
            "error_analysis": f"""
            SELECT 
                workflow_id,
                error_message,
                COUNT(*) as error_count,
                MAX(timestamp) as last_occurrence
            FROM `{self.project_id}.broxi_analytics.workflow_executions`
            WHERE status = 'failed' 
            AND timestamp >= TIMESTAMP_SUB(CURRENT_TIMESTAMP(), INTERVAL 1 DAY)
            GROUP BY workflow_id, error_message
            ORDER BY error_count DESC
            """
        }
        
        return queries

Pub/Sub

Message queuing and event streaming for scalable architectures.

Pub/Sub Integration

from google.cloud import pubsub_v1
from concurrent.futures import ThreadPoolExecutor
import json

class PubSubIntegration:
    def __init__(self, project_id):
        self.project_id = project_id
        self.publisher = pubsub_v1.PublisherClient()
        self.subscriber = pubsub_v1.SubscriberClient()
    
    def create_broxi_topics(self):
        """Create Pub/Sub topics for BroxiAI events"""
        
        topics = [
            "broxi-workflow-requests",
            "broxi-workflow-results", 
            "broxi-user-events",
            "broxi-system-alerts"
        ]
        
        created_topics = []
        
        for topic_name in topics:
            topic_path = self.publisher.topic_path(self.project_id, topic_name)
            
            try:
                topic = self.publisher.create_topic(request={"name": topic_path})
                created_topics.append(topic.name)
            except Exception as e:
                if "already exists" in str(e).lower():
                    created_topics.append(topic_path)
                else:
                    print(f"Error creating topic {topic_name}: {e}")
        
        return created_topics
    
    def publish_workflow_request(self, workflow_id, input_data, user_id):
        """Publish workflow request to Pub/Sub"""
        
        topic_path = self.publisher.topic_path(self.project_id, "broxi-workflow-requests")
        
        message_data = {
            "workflow_id": workflow_id,
            "input": input_data,
            "user_id": user_id,
            "timestamp": datetime.utcnow().isoformat(),
            "request_id": str(uuid.uuid4())
        }
        
        # Publish message
        data = json.dumps(message_data).encode("utf-8")
        future = self.publisher.publish(topic_path, data)
        
        return future.result()
    
    def setup_workflow_processor(self):
        """Set up subscriber to process workflow requests"""
        
        subscription_path = self.subscriber.subscription_path(
            self.project_id, "broxi-workflow-processor"
        )
        
        def callback(message):
            try:
                # Parse message
                message_data = json.loads(message.data.decode("utf-8"))
                
                # Process workflow request
                result = self.process_workflow_request(message_data)
                
                # Publish result
                self.publish_workflow_result(message_data["request_id"], result)
                
                # Acknowledge message
                message.ack()
                
            except Exception as e:
                print(f"Error processing message: {e}")
                message.nack()
        
        # Start subscriber
        streaming_pull_future = self.subscriber.subscribe(
            subscription_path, 
            callback=callback,
            flow_control=pubsub_v1.types.FlowControl(max_messages=100)
        )
        
        return streaming_pull_future
    
    def process_workflow_request(self, request_data):
        """Process workflow request via BroxiAI API"""
        
        response = requests.post(
            f"https://api.broxi.ai/v1/flows/{request_data['workflow_id']}/run",
            headers={
                "Authorization": f"Bearer {os.environ['BROXI_API_TOKEN']}",
                "Content-Type": "application/json"
            },
            json={
                "input": request_data["input"],
                "variables": {
                    "user_id": request_data["user_id"],
                    "request_id": request_data["request_id"]
                }
            }
        )
        
        if response.status_code == 200:
            return response.json()
        else:
            raise Exception(f"Workflow execution failed: {response.text}")
    
    def publish_workflow_result(self, request_id, result):
        """Publish workflow result"""
        
        topic_path = self.publisher.topic_path(self.project_id, "broxi-workflow-results")
        
        result_data = {
            "request_id": request_id,
            "result": result,
            "timestamp": datetime.utcnow().isoformat()
        }
        
        data = json.dumps(result_data).encode("utf-8")
        future = self.publisher.publish(topic_path, data)
        
        return future.result()

Advanced GCP Integrations

Cloud Run

Deploy BroxiAI integration services as containerized applications.

Cloud Run Service

# app.py
from flask import Flask, request, jsonify
import os
from google.cloud import firestore, storage
import requests

app = Flask(__name__)

# Initialize GCP clients
db = firestore.Client()
storage_client = storage.Client()

@app.route('/process-document', methods=['POST'])
def process_document():
    """Process document with BroxiAI and store results"""
    
    try:
        # Get request data
        data = request.get_json()
        document_url = data.get('document_url')
        user_id = data.get('user_id')
        
        # Download document from GCS
        bucket_name, blob_name = parse_gcs_url(document_url)
        bucket = storage_client.bucket(bucket_name)
        blob = bucket.blob(blob_name)
        document_content = blob.download_as_text()
        
        # Process with BroxiAI
        broxi_response = requests.post(
            "https://api.broxi.ai/v1/flows/document-analyzer/run",
            headers={
                "Authorization": f"Bearer {os.environ['BROXI_API_TOKEN']}",
                "Content-Type": "application/json"
            },
            json={
                "input": document_content,
                "variables": {
                    "document_url": document_url,
                    "user_id": user_id
                }
            }
        )
        
        if broxi_response.status_code == 200:
            result = broxi_response.json()
            
            # Store result in Firestore
            doc_ref = db.collection('document_analysis').document()
            doc_ref.set({
                "user_id": user_id,
                "document_url": document_url,
                "analysis_result": result["output"],
                "processing_time": result["execution_time"],
                "timestamp": firestore.SERVER_TIMESTAMP
            })
            
            return jsonify({
                "status": "success",
                "analysis_id": doc_ref.id,
                "result": result["output"]
            })
        else:
            return jsonify({
                "status": "error",
                "message": "BroxiAI processing failed"
            }), 500
            
    except Exception as e:
        return jsonify({
            "status": "error",
            "message": str(e)
        }), 500

if __name__ == '__main__':
    app.run(host='0.0.0.0', port=int(os.environ.get('PORT', 8080)))

Dockerfile

FROM python:3.11-slim

WORKDIR /app

COPY requirements.txt .
RUN pip install -r requirements.txt

COPY . .

EXPOSE 8080

CMD ["python", "app.py"]

Deploy to Cloud Run

# Build and deploy
gcloud run deploy broxi-document-processor \
    --source . \
    --platform managed \
    --region us-central1 \
    --allow-unauthenticated \
    --set-env-vars BROXI_API_TOKEN=your_token

AI Platform Notebooks

Interactive development environment for BroxiAI integration.

Notebook Setup

# Install required packages
!pip install google-cloud-aiplatform
!pip install google-cloud-storage
!pip install requests

import requests
import json
from google.cloud import aiplatform, storage
from google.auth import default
import pandas as pd
import matplotlib.pyplot as plt

# Configure authentication
credentials, project_id = default()
print(f"Project ID: {project_id}")

# Initialize clients
aiplatform.init(project=project_id, location="us-central1")
storage_client = storage.Client(project=project_id)

# BroxiAI API configuration
BROXI_API_TOKEN = "your_api_token"
BROXI_BASE_URL = "https://api.broxi.ai/v1"

def call_broxi_workflow(workflow_id, input_text, variables=None):
    """Call BroxiAI workflow from notebook"""
    
    response = requests.post(
        f"{BROXI_BASE_URL}/flows/{workflow_id}/run",
        headers={
            "Authorization": f"Bearer {BROXI_API_TOKEN}",
            "Content-Type": "application/json"
        },
        json={
            "input": input_text,
            "variables": variables or {}
        }
    )
    
    return response.json()

# Example: Batch processing documents
def batch_process_documents(bucket_name, prefix="documents/"):
    """Batch process documents with BroxiAI"""
    
    bucket = storage_client.bucket(bucket_name)
    blobs = list(bucket.list_blobs(prefix=prefix))
    
    results = []
    
    for blob in blobs[:10]:  # Process first 10 documents
        if blob.name.endswith(('.txt', '.md')):
            # Download content
            content = blob.download_as_text()
            
            # Process with BroxiAI
            result = call_broxi_workflow(
                "document-summarizer",
                content,
                {"filename": blob.name}
            )
            
            results.append({
                "filename": blob.name,
                "original_length": len(content),
                "summary": result.get("output", ""),
                "processing_time": result.get("execution_time", 0)
            })
    
    return pd.DataFrame(results)

# Run batch processing
df_results = batch_process_documents("your-documents-bucket")
print(df_results.head())

# Visualize results
plt.figure(figsize=(12, 6))
plt.subplot(1, 2, 1)
plt.scatter(df_results['original_length'], df_results['processing_time'])
plt.xlabel('Document Length (characters)')
plt.ylabel('Processing Time (seconds)')
plt.title('Processing Time vs Document Length')

plt.subplot(1, 2, 2)
plt.hist(df_results['processing_time'], bins=10)
plt.xlabel('Processing Time (seconds)')
plt.ylabel('Frequency')
plt.title('Processing Time Distribution')

plt.tight_layout()
plt.show()

Cloud Monitoring

Comprehensive monitoring and alerting for BroxiAI integrations.

Custom Metrics

from google.cloud import monitoring_v3
import time

class GCPMonitoringIntegration:
    def __init__(self, project_id):
        self.project_id = project_id
        self.client = monitoring_v3.MetricServiceClient()
        self.project_name = f"projects/{project_id}"
    
    def create_custom_metrics(self):
        """Create custom metrics for BroxiAI monitoring"""
        
        metrics = [
            {
                "type": "custom.googleapis.com/broxi/workflow_executions",
                "metric_kind": monitoring_v3.MetricDescriptor.MetricKind.GAUGE,
                "value_type": monitoring_v3.MetricDescriptor.ValueType.INT64,
                "description": "Number of BroxiAI workflow executions"
            },
            {
                "type": "custom.googleapis.com/broxi/response_time", 
                "metric_kind": monitoring_v3.MetricDescriptor.MetricKind.GAUGE,
                "value_type": monitoring_v3.MetricDescriptor.ValueType.DOUBLE,
                "description": "BroxiAI workflow response time in seconds"
            },
            {
                "type": "custom.googleapis.com/broxi/token_usage",
                "metric_kind": monitoring_v3.MetricDescriptor.MetricKind.CUMULATIVE,
                "value_type": monitoring_v3.MetricDescriptor.ValueType.INT64,
                "description": "Total tokens used by BroxiAI workflows"
            }
        ]
        
        for metric_config in metrics:
            descriptor = monitoring_v3.MetricDescriptor(
                type=metric_config["type"],
                metric_kind=metric_config["metric_kind"],
                value_type=metric_config["value_type"],
                description=metric_config["description"]
            )
            
            try:
                self.client.create_metric_descriptor(
                    name=self.project_name,
                    metric_descriptor=descriptor
                )
                print(f"Created metric: {metric_config['type']}")
            except Exception as e:
                print(f"Error creating metric {metric_config['type']}: {e}")
    
    def record_workflow_execution(self, workflow_id, response_time, token_usage):
        """Record workflow execution metrics"""
        
        # Record execution count
        self.write_time_series(
            "custom.googleapis.com/broxi/workflow_executions",
            1,
            {"workflow_id": workflow_id}
        )
        
        # Record response time
        self.write_time_series(
            "custom.googleapis.com/broxi/response_time",
            response_time,
            {"workflow_id": workflow_id}
        )
        
        # Record token usage
        self.write_time_series(
            "custom.googleapis.com/broxi/token_usage",
            token_usage,
            {"workflow_id": workflow_id}
        )
    
    def write_time_series(self, metric_type, value, labels=None):
        """Write time series data to Cloud Monitoring"""
        
        series = monitoring_v3.TimeSeries()
        series.metric.type = metric_type
        
        if labels:
            for key, val in labels.items():
                series.metric.labels[key] = val
        
        series.resource.type = "global"
        
        now = time.time()
        seconds = int(now)
        nanos = int((now - seconds) * 10**9)
        interval = monitoring_v3.TimeInterval(
            {"end_time": {"seconds": seconds, "nanos": nanos}}
        )
        
        point = monitoring_v3.Point({
            "interval": interval,
            "value": {"double_value": value} if isinstance(value, float) else {"int64_value": value}
        })
        
        series.points = [point]
        
        self.client.create_time_series(
            name=self.project_name,
            time_series=[series]
        )

Security and Identity

Identity and Access Management (IAM)

Service Account Setup

# Service account for BroxiAI integration
gcloud iam service-accounts create broxi-integration \
    --display-name="BroxiAI Integration Service Account"

# Grant necessary permissions
gcloud projects add-iam-policy-binding PROJECT_ID \
    --member="serviceAccount:broxi-integration@PROJECT_ID.iam.gserviceaccount.com" \
    --role="roles/storage.objectAdmin"

gcloud projects add-iam-policy-binding PROJECT_ID \
    --member="serviceAccount:broxi-integration@PROJECT_ID.iam.gserviceaccount.com" \
    --role="roles/datastore.user"

gcloud projects add-iam-policy-binding PROJECT_ID \
    --member="serviceAccount:broxi-integration@PROJECT_ID.iam.gserviceaccount.com" \
    --role="roles/pubsub.editor"

# Create and download service account key
gcloud iam service-accounts keys create broxi-key.json \
    --iam-account=broxi-integration@PROJECT_ID.iam.gserviceaccount.com

IAM Policy Configuration

from google.cloud import resource_manager
from google.cloud.iam_v1 import Policy, Binding

class IAMManager:
    def __init__(self, project_id):
        self.project_id = project_id
        self.client = resource_manager.Client()
    
    def setup_broxi_permissions(self, service_account_email):
        """Set up IAM permissions for BroxiAI integration"""
        
        project = self.client.project(self.project_id)
        policy = project.get_iam_policy()
        
        # Define required roles
        required_roles = [
            "roles/storage.objectAdmin",
            "roles/datastore.user", 
            "roles/pubsub.editor",
            "roles/cloudfunctions.invoker",
            "roles/run.invoker",
            "roles/aiplatform.user"
        ]
        
        # Add service account to roles
        for role in required_roles:
            binding = Binding(
                role=role,
                members=[f"serviceAccount:{service_account_email}"]
            )
            
            # Check if binding exists
            existing_binding = next(
                (b for b in policy.bindings if b.role == role), None
            )
            
            if existing_binding:
                if f"serviceAccount:{service_account_email}" not in existing_binding.members:
                    existing_binding.members.append(f"serviceAccount:{service_account_email}")
            else:
                policy.bindings.append(binding)
        
        # Update policy
        project.set_iam_policy(policy)
        
        return "IAM permissions configured successfully"

Cost Optimization

Resource Management

Cost Optimization Strategies

class GCPCostOptimizer:
    def __init__(self, project_id):
        self.project_id = project_id
        self.storage_client = storage.Client()
        self.compute_client = compute_v1.InstancesClient()
    
    def optimize_storage_costs(self, bucket_name):
        """Optimize Cloud Storage costs"""
        
        bucket = self.storage_client.bucket(bucket_name)
        
        # Set up lifecycle management
        lifecycle_rules = [
            {
                "action": {"type": "SetStorageClass", "storageClass": "NEARLINE"},
                "condition": {"age": 30, "matchesStorageClass": ["STANDARD"]}
            },
            {
                "action": {"type": "SetStorageClass", "storageClass": "COLDLINE"},
                "condition": {"age": 90, "matchesStorageClass": ["NEARLINE"]}
            },
            {
                "action": {"type": "SetStorageClass", "storageClass": "ARCHIVE"},
                "condition": {"age": 365, "matchesStorageClass": ["COLDLINE"]}
            },
            {
                "action": {"type": "Delete"},
                "condition": {"age": 2555}  # 7 years
            }
        ]
        
        bucket.lifecycle_rules = lifecycle_rules
        bucket.patch()
        
        return "Storage lifecycle rules configured"
    
    def schedule_compute_instances(self):
        """Schedule compute instances for cost optimization"""
        
        # This would integrate with Cloud Scheduler
        # to start/stop instances based on usage patterns
        
        scheduler_config = {
            "development_instances": {
                "start_schedule": "0 9 * * 1-5",  # 9 AM weekdays
                "stop_schedule": "0 18 * * 1-5"   # 6 PM weekdays
            },
            "staging_instances": {
                "start_schedule": "0 8 * * 1-5",  # 8 AM weekdays  
                "stop_schedule": "0 20 * * 1-5"   # 8 PM weekdays
            }
        }
        
        return scheduler_config
    
    def monitor_costs(self):
        """Monitor and alert on costs"""
        
        # Integration with Cloud Billing API
        billing_config = {
            "budget_alerts": [
                {
                    "threshold": 80,
                    "action": "email_notification"
                },
                {
                    "threshold": 95,
                    "action": "disable_billing"
                }
            ],
            "cost_tracking": {
                "by_service": True,
                "by_project": True,
                "by_label": True
            }
        }
        
        return billing_config

Best Practices

Performance Optimization

GCP Best Practices

Use regional resources for better latency
Implement caching with Memorystore
Use Cloud CDN for global content delivery
Optimize data transfer costs
Use preemptible instances for batch processing

Security

Security Best Practices

Use IAM service accounts with minimal permissions
Enable audit logging for all services
Use VPC for network isolation
Implement secret management with Secret Manager
Regular security scanning and updates

Monitoring

Comprehensive Monitoring

Set up Cloud Monitoring dashboards
Configure alerting policies
Use Cloud Logging for centralized logs
Implement distributed tracing
Monitor costs and usage patterns

Next Steps

After GCP integration:

Monitor Performance: Set up comprehensive monitoring
Optimize Costs: Implement cost optimization strategies
Scale Infrastructure: Plan for growth and scaling
Security Review: Conduct security assessments
Disaster Recovery: Implement backup and recovery procedures

AWS Integration: Multi-cloud strategies
Azure Integration: Alternative cloud platform
Security: Security best practices

GCP integration provides powerful AI capabilities, global infrastructure, and cost-effective scaling for BroxiAI applications. Leverage Google's AI expertise and infrastructure for optimal results.

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Overview

Core GCP Services Integration

Vertex AI

Cloud Storage

Cloud Firestore

Cloud Functions

BigQuery

Pub/Sub

Advanced GCP Integrations

Cloud Run

AI Platform Notebooks

Cloud Monitoring

Security and Identity

Identity and Access Management (IAM)

Cost Optimization

Resource Management

Best Practices

Performance Optimization

Security

Monitoring

Next Steps

Related Guides