The Future of Generative AI: Trends to Watch

# AWS Implementation
import boto3
import json
import base64

def aws_multimodal_processing(image_path, prompt):
    # Initialize Bedrock client
    bedrock_runtime = boto3.client(
        service_name='bedrock-runtime',
        region_name='us-east-1'
    )
    
    # Read and encode image
    with open(image_path, "rb") as image_file:
        image_bytes = image_file.read()
        base64_image = base64.b64encode(image_bytes).decode('utf-8')
    
    # Create request payload for Claude 3 Sonnet (multimodal model)
    request_body = {
        "anthropic_version": "bedrock-2023-05-31",
        "max_tokens": 1000,
        "messages": [
            {
                "role": "user",
                "content": [
                    {
                        "type": "image",
                        "source": {
                            "type": "base64",
                            "media_type": "image/jpeg",
                            "data": base64_image
                        }
                    },
                    {
                        "type": "text",
                        "text": prompt
                    }
                ]
            }
        ]
    }
    
    # Invoke the model
    response = bedrock_runtime.invoke_model(
        modelId='anthropic.claude-3-sonnet-20240229-v1:0',
        body=json.dumps(request_body)
    )
    
    # Parse and return the response
    response_body = json.loads(response['body'].read())
    return response_body['content'][0]['text']

# GCP Implementation
from google.cloud import aiplatform
from vertexai.preview.multimodal_model import MultimodalModel

def gcp_multimodal_processing(image_path, prompt):
    # Initialize Vertex AI
    aiplatform.init(project='your-gcp-project-id', location='us-central1')
    
    # Load Gemini multimodal model
    multimodal_model = MultimodalModel.from_pretrained("gemini-pro-vision")
    
    # Process image and text
    image = aiplatform.Image.load_from_file(image_path)
    response = multimodal_model.generate_content(
        [image, prompt],
        generation_config={
            "max_output_tokens": 1024,
            "temperature": 0.4,
            "top_p": 0.8,
            "top_k": 40
        }
    )
    
    return response.text

# Azure Implementation
import os
from openai import AzureOpenAI

def azure_multimodal_processing(image_path, prompt):
    # Initialize Azure OpenAI client
    client = AzureOpenAI(
        api_key=os.getenv("AZURE_OPENAI_API_KEY"),
        api_version="2023-12-01-preview",
        azure_endpoint=os.getenv("AZURE_OPENAI_ENDPOINT")
    )
    
    # Read image file
    with open(image_path, "rb") as image_file:
        image_data = image_file.read()
    
    # Create message with both image and text
    messages = [
        {
            "role": "user",
            "content": [
                {
                    "type": "text",
                    "text": prompt
                },
                {
                    "type": "image_url",
                    "image_url": {
                        "url": f"data:image/jpeg;base64,{base64.b64encode(image_data).decode('utf-8')}"
                    }
                }
            ]
        }
    ]
    
    # Get completion from the model
    response = client.chat.completions.create(
        model="gpt-4-vision-preview",
        messages=messages,
        max_tokens=1000
    )
    
    return response.choices[0].message.content

# Cloud-Independent Implementation (using Ollama)
import requests
import base64

def ollama_multimodal_processing(image_path, prompt):
    # Read and encode image
    with open(image_path, "rb") as image_file:
        image_data = base64.b64encode(image_file.read()).decode('utf-8')
    
    # Prepare the request
    url = "http://localhost:11434/api/generate"
    payload = {
        "model": "llava",
        "prompt": prompt,
        "images": [image_data]
    }
    
    # Make the request
    response = requests.post(url, json=payload)
    result = response.json()
    
    return result["response"]

# AWS SageMaker Fine-Tuning Example
import boto3
import sagemaker
from sagemaker.huggingface import HuggingFace

def aws_fine_tuning():
    # Initialize SageMaker session
    session = sagemaker.Session()
    role = sagemaker.get_execution_role()
    
    # Define hyperparameters
    hyperparameters = {
        'model_id': 'meta-llama/Llama-2-7b',
        'epochs': 3,
        'per_device_train_batch_size': 4,
        'per_device_eval_batch_size': 4,
        'gradient_accumulation_steps': 8,
        'learning_rate': 2e-5,
        'warmup_steps': 100
    }
    
    # Define training job configuration
    huggingface_estimator = HuggingFace(
        entry_point='train.py',
        source_dir='./scripts',
        instance_type='ml.g5.2xlarge',
        instance_count=1,
        role=role,
        transformers_version='4.28.1',
        pytorch_version='2.0.0',
        py_version='py310',
        hyperparameters=hyperparameters
    )
    
    # Start training
    huggingface_estimator.fit({
        'train': 's3://your-bucket/training-data',
        'validation': 's3://your-bucket/validation-data'
    })
    
    # Deploy model
    predictor = huggingface_estimator.deploy(
        initial_instance_count=1,
        instance_type='ml.g5.xlarge'
    )
    
    return predictor.endpoint_name

# GCP Vertex AI Fine-Tuning Example
from google.cloud import aiplatform
from google.cloud.aiplatform import vertex_ai_tuning

def gcp_fine_tuning():
    # Initialize Vertex AI
    aiplatform.init(project='your-project-id', location='us-central1')
    
    # Create a custom training job
    job = aiplatform.CustomTrainingJob(
        display_name="llama-tuning-job",
        script_path="train.py",
        container_uri="us-docker.pkg.dev/vertex-ai/training/pytorch-gpu.1-13:latest",
        requirements=["transformers==4.28.1", "datasets==2.12.0", "accelerate==0.19.0"],
        model_serving_container_image_uri="us-docker.pkg.dev/vertex-ai/prediction/pytorch-gpu.1-13:latest"
    )
    
    # Start the training job
    model = job.run(
        machine_type="n1-standard-8",
        accelerator_type="NVIDIA_TESLA_V100",
        accelerator_count=1,
        replica_count=1,
        dataset_uri="gs://your-bucket/training-data",
        args=[
            "--model_name_or_path=meta-llama/Llama-2-7b",
            "--output_dir=./model",
            "--num_train_epochs=3",
            "--per_device_train_batch_size=4"
        ]
    )
    
    # Deploy the model
    endpoint = model.deploy(
        machine_type="n1-standard-4",
        accelerator_type="NVIDIA_TESLA_T4",
        accelerator_count=1
    )
    
    return endpoint.resource_name

# Azure OpenAI Fine-Tuning Example
import os
from openai import AzureOpenAI

def azure_fine_tuning():
    # Initialize Azure OpenAI client
    client = AzureOpenAI(
        api_key=os.getenv("AZURE_OPENAI_API_KEY"),
        api_version="2023-12-01-preview",
        azure_endpoint=os.getenv("AZURE_OPENAI_ENDPOINT")
    )
    
    # Create fine-tuning job
    response = client.fine_tuning.jobs.create(
        model="gpt-35-turbo",
        training_file="file-abc123",  # File ID of uploaded training data
        hyperparameters={
            "n_epochs": 3,
            "batch_size": 4,
            "learning_rate_multiplier": 0.1
        },
        suffix="customer-support-specialized"
    )
    
    job_id = response.id
    
    # Monitor job progress
    job_status = client.fine_tuning.jobs.retrieve(job_id)
    
    # When complete, the fine-tuned model can be accessed using the model name
    fine_tuned_model = f"{job_status.fine_tuned_model}"
    
    return fine_tuned_model

# Cloud-Independent Implementation (using HuggingFace Transformers)
from transformers import (
    AutoModelForCausalLM, 
    AutoTokenizer,
    TrainingArguments, 
    Trainer, 
    DataCollatorForLanguageModeling
)
from datasets import load_dataset

def huggingface_fine_tuning():
    # Load base model and tokenizer
    model_name = "meta-llama/Llama-2-7b"
    model = AutoModelForCausalLM.from_pretrained(model_name)
    tokenizer = AutoTokenizer.from_pretrained(model_name)
    
    # Prepare training data
    dataset = load_dataset("your-dataset")
    
    def tokenize_function(examples):
        return tokenizer(examples["text"], truncation=True, padding="max_length", max_length=512)
    
    tokenized_dataset = dataset.map(tokenize_function, batched=True)
    
    # Define training arguments
    training_args = TrainingArguments(
        output_dir="./results",
        per_device_train_batch_size=4,
        gradient_accumulation_steps=8,
        learning_rate=2e-5,
        num_train_epochs=3,
        weight_decay=0.01,
        save_strategy="epoch",
        evaluation_strategy="epoch",
        fp16=True,  # Mixed precision training
    )
    
    # Initialize trainer
    data_collator = DataCollatorForLanguageModeling(
        tokenizer=tokenizer, 
        mlm=False
    )
    
    trainer = Trainer(
        model=model,
        args=training_args,
        train_dataset=tokenized_dataset["train"],
        eval_dataset=tokenized_dataset["validation"],
        data_collator=data_collator,
    )
    
    # Start training
    trainer.train()
    
    # Save the fine-tuned model
    model.save_pretrained("./fine-tuned-model")
    tokenizer.save_pretrained("./fine-tuned-model")
    
    return "./fine-tuned-model"

# AWS RAG Implementation
import boto3
import json
from opensearchpy import OpenSearch, RequestsHttpConnection
from requests_aws4auth import AWS4Auth

def aws_rag_implementation(query, index_name="documents"):
    # Initialize clients
    region = "us-east-1"
    bedrock_runtime = boto3.client(service_name="bedrock-runtime", region_name=region)
    
    # Initialize OpenSearch client
    host = "your-opensearch-endpoint.us-east-1.es.amazonaws.com"
    service = "es"
    credentials = boto3.Session().get_credentials()
    awsauth = AWS4Auth(credentials.access_key, credentials.secret_key,
                       region, service, session_token=credentials.token)
    
    opensearch_client = OpenSearch(
        hosts=[{"host": host, "port": 443}],
        http_auth=awsauth,
        use_ssl=True,
        verify_certs=True,
        connection_class=RequestsHttpConnection
    )
    
    # Step 1: Convert user query to embedding using the embeddings model
    embedding_response = bedrock_runtime.invoke_model(
        modelId="amazon.titan-embed-text-v1",
        contentType="application/json",
        accept="application/json",
        body=json.dumps({"inputText": query})
    )
    
    embedding_response_body = json.loads(embedding_response.get("body").read())
    embedding = embedding_response_body.get("embedding")
    
    # Step 2: Search OpenSearch index with the embedding
    search_query = {
        "size": 5,
        "query": {
            "knn": {
                "embedding_vector": {
                    "vector": embedding,
                    "k": 5
                }
            }
        }
    }
    
    search_response = opensearch_client.search(
        body=search_query,
        index=index_name
    )
    
    # Step 3: Extract relevant context from search results
    contexts = []
    for hit in search_response["hits"]["hits"]:
        contexts.append(hit["_source"]["text"])
    
    context_text = "\n\n".join(contexts)
    
    # Step 4: Generate response with context using Claude model
    prompt = f"""You are an AI assistant helping with information retrieval.
    Use the following context to answer the user's question. If the answer is not 
    in the context, say that you don't know based on available information.
    
    Context:
    {context_text}
    
    User Question: {query}
    """
    
    response = bedrock_runtime.invoke_model(
        modelId="anthropic.claude-3-sonnet-20240229-v1:0",
        contentType="application/json",
        accept="application/json",
        body=json.dumps({
            "anthropic_version": "bedrock-2023-05-31",
            "max_tokens": 1000,
            "messages": [
                {
                    "role": "user",
                    "content": prompt
                }
            ]
        })
    )
    
    response_body = json.loads(response.get("body").read())
    answer = response_body["content"][0]["text"]
    
    return {
        "query": query,
        "context": context_text,
        "answer": answer
    }

# GCP RAG Implementation
from google.cloud import aiplatform
from vertexai.language_models import TextEmbeddingModel, TextGenerationModel
import vertexai
import numpy as np

def gcp_rag_implementation(query, project_id="your-project-id", location="us-central1"):
    # Initialize Vertex AI
    vertexai.init(project=project_id, location=location)
    
    # Step 1: Generate embeddings for the query
    embedding_model = TextEmbeddingModel.from_pretrained("textembedding-gecko@latest")
    query_embeddings = embedding_model.get_embeddings([query])[0].values
    
    # Step 2: Search Vector Store (using Vertex AI Vector Search)
    # Assuming you have already created an index and populated it
    index_endpoint = aiplatform.MatchingEngineIndexEndpoint(
        index_endpoint_name="your-index-endpoint"
    )
    
    matched_neighbors = index_endpoint.find_neighbors(
        deployed_index_id="your-deployed-index-id",
        queries=[query_embeddings],
        num_neighbors=5
    )
    
    # Step 3: Retrieve content from the matched documents
    # This assumes you've stored document IDs in the index and can retrieve content
    contexts = []
    for neighbor in matched_neighbors[0]:
        # Retrieve document content based on neighbor.id
        # This is placeholder code - in practice, you'd lookup the content
        doc_content = retrieve_document_by_id(neighbor.id)
        contexts.append(doc_content)
    
    context_text = "\n\n".join(contexts)
    
    # Step 4: Generate response with LLM
    generation_model = TextGenerationModel.from_pretrained("gemini-pro")
    prompt = f"""You are an AI assistant helping with information retrieval.
    Use the following context to answer the user's question. If the answer is not 
    in the context, say that you don't know based on available information.
    
    Context:
    {context_text}
    
    User Question: {query}
    """
    
    response = generation_model.predict(
        prompt=prompt,
        temperature=0.2,
        max_output_tokens=1024,
    )
    
    return {
        "query": query,
        "context": context_text,
        "answer": response.text
    }

def retrieve_document_by_id(doc_id):
    # This is a placeholder function
    # In a real application, you would retrieve the document from a database
    return f"Content for document {doc_id}"

# Azure RAG Implementation
import os
from openai import AzureOpenAI
from azure.search.documents import SearchClient
from azure.core.credentials import AzureKeyCredential

def azure_rag_implementation(query):
    # Initialize clients
    search_service_endpoint = os.getenv("AZURE_SEARCH_SERVICE_ENDPOINT")
    search_api_key = os.getenv("AZURE_SEARCH_API_KEY")
    index_name = "documents"
    
    openai_client = AzureOpenAI(
        api_key=os.getenv("AZURE_OPENAI_API_KEY"),
        api_version="2023-12-01-preview",
        azure_endpoint=os.getenv("AZURE_OPENAI_ENDPOINT")
    )
    
    search_client = SearchClient(
        endpoint=search_service_endpoint,
        index_name=index_name,
        credential=AzureKeyCredential(search_api_key)
    )
    
    # Step 1: Generate embeddings for the query
    embedding_response = openai_client.embeddings.create(
        input=query,
        model="text-embedding-ada-002"
    )
    query_embedding = embedding_response.data[0].embedding
    
    # Step 2: Search Azure AI Search using vector search
    search_results = search_client.search(
        search_text=None,  # Not using keyword search
        vector=query_embedding,
        vector_fields=["embedding"],
        top=5
    )
    
    # Step 3: Extract context from search results
    contexts = []
    for result in search_results:
        contexts.append(result["content"])
    
    context_text = "\n\n".join(contexts)
    
    # Step 4: Generate response with Azure OpenAI
    prompt = f"""You are an AI assistant helping with information retrieval.
    Use the following context to answer the user's question. If the answer is not 
    in the context, say that you don't know based on available information.
    
    Context:
    {context_text}
    
    User Question: {query}
    """
    
    response = openai_client.chat.completions.create(
        model="gpt-4",
        messages=[
            {"role": "system", "content": "You are an AI assistant helping with information retrieval."},
            {"role": "user", "content": prompt}
        ],
        temperature=0.3,
        max_tokens=800
    )
    
    return {
        "query": query,
        "context": context_text,
        "answer": response.choices[0].message.content
    }

# Cloud-Independent RAG Implementation (using open-source tools)
from langchain.vectorstores import Chroma
from langchain.embeddings import HuggingFaceEmbeddings
from langchain.llms import Ollama
from langchain.prompts import PromptTemplate
from langchain.chains import RetrievalQA

def open_source_rag_implementation(query, db_directory="./chroma_db"):
    # Step 1: Initialize embedding model
    embedding_model = HuggingFaceEmbeddings(
        model_name="sentence-transformers/all-MiniLM-L6-v2"
    )
    
    # Step 2: Load vector database
    # Note: Assumes you've already populated the database
    vector_db = Chroma(
        persist_directory=db_directory,
        embedding_function=embedding_model
    )
    
    # Step 3: Initialize retriever
    retriever = vector_db.as_retriever(search_kwargs={"k": 5})
    
    # Step 4: Initialize LLM
    llm = Ollama(model="llama2")
    
    # Step 5: Create prompt template
    prompt_template = """You are an AI assistant helping with information retrieval.
    Use the following context to answer the user's question. If the answer is not 
    in the context, say that you don't know based on available information.
    
    Context:
    {context}
    
    User Question: {question}
    """
    
    prompt = PromptTemplate(
        template=prompt_template,
        input_variables=["context", "question"]
    )
    
    # Step 6: Create RAG chain
    qa_chain = RetrievalQA.from_chain_type(
        llm=llm,
        chain_type="stuff",
        retriever=retriever,
        chain_type_kwargs={"prompt": prompt}
    )
    
    # Step 7: Run query
    result = qa_chain.invoke({"query": query})
    
    return {
        "query": query,
        "answer": result["result"]
    }

# AWS - Implementing AI Governance with SageMaker Model Monitor and Clarify
import boto3
import json
import numpy as np
import pandas as pd
from sagemaker.clarify import DataConfig, BiasConfig, ModelConfig, SHAPConfig
from sagemaker.model_monitor import ModelMonitor, DataCaptureConfig

def aws_ai_governance():
    # Initialize SageMaker client
    sagemaker_client = boto3.client('sagemaker')
    
    # Step 1: Configure bias detection with SageMaker Clarify
    bias_analysis_config = {
        "bias_config": {
            "label": "target",
            "facet": [{"name_or_index": "sensitive_attribute"}],
            "label_values_or_threshold": [1],
            "group_variable": "sensitive_attribute"
        },
        "methods": {
            "pre_training_bias": {
                "methods": ["DPPL", "DI", "DCR"]
            },
            "post_training_bias": {
                "methods": ["DPPL", "DI", "DCR", "DCA", "DCR"]
            }
        },
        "report": {
            "name": "bias_report",
            "title": "Bias Report"
        }
    }
    
    # Step 2: Set up model monitoring
    model_monitor = ModelMonitor(
        role="arn:aws:iam::123456789012:role/SageMakerMonitoringRole",
        instance_count=1,
        instance_type="ml.m5.xlarge",
        volume_size_in_gb=20,
        max_runtime_in_seconds=1800
    )
    
    # Configure data capture for the endpoint
    data_capture_config = DataCaptureConfig(
        enable_capture=True,
        sampling_percentage=100,
        destination_s3_uri="s3://your-bucket/data-capture"
    )
    
    # Step 3: Create model explainability configuration with SHAP
    explainability_config = {
        "shap_config": {
            "baseline": "s3://your-bucket/baseline.csv",
            "num_samples": 100,
            "agg_method": "mean_abs",
            "use_logit": False
        },
        "report": {
            "name": "explainability_report",
            "title": "Model Explainability Report"
        }
    }
    
    # Step 4: Set up CloudWatch alerting
    cloudwatch_client = boto3.client('cloudwatch')
    
    # Create alarm for model drift
    response = cloudwatch_client.put_metric_alarm(
        AlarmName="ModelDriftAlarm",
        ComparisonOperator="GreaterThanThreshold",
        EvaluationPeriods=1,
        MetricName="feature_drift_score",
        Namespace="AWS/SageMaker",
        Period=3600,
        Statistic="Maximum",
        Threshold=0.7,
        ActionsEnabled=True,
        AlarmActions=["arn:aws:sns:us-east-1:123456789012:ModelMonitoringTopic"],
        AlarmDescription="Alarm when model feature drift exceeds threshold",
        Dimensions=[
            {
                "Name": "EndpointName",
                "Value": "your-endpoint-name"
            }
        ]
    )
    
    # Step 5: Implement model governance logging
    def log_model_governance_event(event_type, event_details):
        logs_client = boto3.client('logs')
        logs_client.put_log_events(
            logGroupName="/aws/sagemaker/model-governance",
            logStreamName="model-events",
            logEvents=[
                {
                    'timestamp': int(time.time() * 1000),
                    'message': json.dumps({
                        'event_type': event_type,
                        'details': event_details
                    })
                }
            ]
        )
    
    return {
        "bias_config": bias_analysis_config,
        "monitoring_config": data_capture_config,
        "explainability_config": explainability_config
    }

# GCP - Implementing AI Governance with Vertex AI
from google.cloud import aiplatform
from google.cloud.aiplatform.metadata import metadata_store
from google.cloud.aiplatform_v1 import ModelMonitoringServiceClient
from google.cloud.aiplatform_v1.types import (
    ModelMonitoringAlertConfig,
    ModelMonitoringObjectiveConfig,
    ThresholdConfig
)
import datetime

def gcp_ai_governance(project_id="your-project-id", location="us-central1"):
    # Initialize Vertex AI
    aiplatform.init(project=project_id, location=location)
    
    # Step 1: Set up Vertex ML Metadata tracking
    metadata_store_client = metadata_store.MetadataStore()
    
    # Create a metadata schema for model governance
    schema_title = "model_governance_schema"
    schema_version = "0.0.1"
    
    schema_metadata = {
        "description": "Schema for tracking model governance metrics",
        "owner": "AI Governance Team",
    }
    
    schema_property_specs = {
        "model_name": {"string_type": {}},
        "model_version": {"string_type": {}},
        "training_dataset": {"string_type": {}},
        "evaluation_metrics": {"struct_type": {}},
        "bias_metrics": {"struct_type": {}},
        "approval_status": {"string_type": {}},
        "approver": {"string_type": {}},
        "approval_date": {"string_type": {}},
    }
    
    # Create the schema
    schema = metadata_store_client.create_metadata_schema(
        schema_title=schema_title,
        schema_version=schema_version,
        schema_metadata=schema_metadata,
        property_specs=schema_property_specs,
    )
    
    # Step 2: Configure Model Monitoring
    monitoring_client = ModelMonitoringServiceClient()
    
    # Configure monitoring objective (for feature drift)
    objective_config = ModelMonitoringObjectiveConfig(
        training_dataset={"gcs_source": {"uris": ["gs://your-bucket/training-data.csv"]}},
        training_prediction_skew_detection_config={
            "skew_thresholds": {
                "feature1": ThresholdConfig(value=0.3),
                "feature2": ThresholdConfig(value=0.2)
            }
        }
    )
    
    # Configure alert policy
    alert_config = ModelMonitoringAlertConfig(
        email_alert_config={
            "user_emails": ["admin@example.com", "data-scientist@example.com"]
        },
        notification_channels=["projects/your-project-id/notificationChannels/123456789"]
    )
    
    # Step 3: Set up explainability for the model
    def create_explainable_model(model_id, instance_type="n1-standard-4"):
        model = aiplatform.Model(model_id)
        
        explanation_metadata = {
            "inputs": {
                "feature1": {"input_tensor_name": "feature1", "modality": "numeric"},
                "feature2": {"input_tensor_name": "feature2", "modality": "numeric"}
            },
            "outputs": {
                "prediction": {"output_tensor_name": "prediction"}
            }
        }
        
        explanation_parameters = {
            "sampled_shapley_attribution": {
                "path_count": 10
            }
        }
        
        endpoint = model.deploy(
            machine_type=instance_type,
            explanation_metadata=explanation_metadata,
            explanation_parameters=explanation_parameters
        )
        
        return endpoint
    
    # Step 4: Create a logging function for governance events
    def log_governance_event(event_type, event_details):
        # Create a metadata entry for this governance event
        metadata_entry = metadata_store_client.create_metadata(
            metadata_schema=schema.name,
            metadata={
                "model_name": event_details.get("model_name", ""),
                "model_version": event_details.get("model_version", ""),
                "training_dataset": event_details.get("training_dataset", ""),
                "evaluation_metrics": event_details.get("evaluation_metrics", {}),
                "bias_metrics": event_details.get("bias_metrics", {}),
                "approval_status": event_details.get("approval_status", "PENDING"),
                "approver": event_details.get("approver", ""),
                "approval_date": datetime.datetime.now().isoformat()
            }
        )
        
        return metadata_entry
    
    return {
        "metadata_schema": schema.name,
        "objective_config": objective_config,
        "alert_config": alert_config
    }

# Azure - Implementing AI Governance
from azure.ai.ml import MLClient
from azure.ai.ml.entities import Model, ModelConfiguration, ModelMonitor
from azure.identity import DefaultAzureCredential
from azure.ai.ml.entities._assets.linked_service import LinkedService
from azure.ai.ml.entities._assets.data_asset import DataAsset
from azure.ai.ml.entities._job.monitor import MonitorData, MonitorTarget, MonitorType

def azure_ai_governance():
    # Initialize Azure ML client
    credential = DefaultAzureCredential()
    ml_client = MLClient(
        credential=credential,
        subscription_id="your-subscription-id",
        resource_group_name="your-resource-group",
        workspace_name="your-workspace"
    )
    
    # Step 1: Register model with responsible AI information
    model = Model(
        path="azureml://jobs/model-training-job/outputs/model",
        name="responsible-ai-model",
        description="Model with responsible AI documentation and monitoring",
        type="custom_model",
    )
    
    registered_model = ml_client.models.create_or_update(model)
    
    # Step 2: Create data drift monitor
    # First, register baseline data
    baseline_data = DataAsset(
        name="model-baseline-data",
        version="1",
        description="Baseline data for drift detection",
        path="azureml://datastores/your-datastore/paths/baseline"
    )
    
    registered_baseline = ml_client.data.create_or_update(baseline_data)
    
    # Create model monitor for data drift
    drift_monitor = ModelMonitor(
        name="data-drift-monitor",
        description="Monitor data drift for our model",
        target=MonitorTarget(
            endpoint_name="your-endpoint-name",
            model_name=registered_model.name,
            model_version=registered_model.version
        ),
        dataset_monitor=MonitorData(
            baseline_data=registered_baseline.id,
            target_data="azureml://datastores/your-datastore/paths/target",
            metrics=["jensen_shannon_distance", "wasserstein_distance"],
            alert_enabled=True,
            alert_threshold=0.7
        ),
        schedule="0 0 * * *",  # Daily at midnight
        log_analytics={
            "workspace_id": "your-log-analytics-workspace-id",
            "primary_key": "your-log-analytics-primary-key"
        }
    )
    
    drift_monitor_job = ml_client.model_monitors.begin_create_or_update(drift_monitor)
    
    # Step 3: Implement explainability for the model
    # Explainability is set during deployment
    def deploy_model_with_explanations():
        # Register the environment for the model
        env = Environment(
            name="interpret-env",
            description="Environment with explainer packages",
            conda_file="path/to/conda.yml"  # Include interpret-ml package here
        )
        
        registered_env = ml_client.environments.create_or_update(env)
        
        # Create inference config with explainer
        inference_config = InferenceConfig(
            entry_script="score.py",
            environment=registered_env
        )
        
        # Include explainer settings in the deployment config
        deployment_config = AksWebservice.deploy_configuration(
            cpu_cores=1, 
            memory_gb=1,
            enable_app_insights=True,
            collect_model_data=True
        )
        
        # Deploy the model with explainability
        service = Model.deploy(
            ml_client,
            service_name="explainable-model-service",
            models=[registered_model],
            inference_config=inference_config,
            deployment_config=deployment_config,
            overwrite=True
        )
        
        return service
    
    # Step 4: Implement model governance auditing
    def log_governance_action(action_type, details):
        from applicationinsights import TelemetryClient
        
        # Initialize Application Insights for logging
        telemetry_client = TelemetryClient("your-app-insights-key")
        
        # Log the governance action
        telemetry_client.track_event(
            action_type,
            properties={
                "model_id": registered_model.id,
                "user": details.get("user", "unknown"),
                "timestamp": datetime.datetime.now().isoformat(),
                "action_details": json.dumps(details)
            }
        )
        
        telemetry_client.flush()
    
    return {
        "registered_model": registered_model.id,
        "drift_monitor": drift_monitor_job.name
    }

# Cloud-Independent Implementation with MLflow and Evidently
import mlflow
import pandas as pd
from evidently.model_profile import Profile
from evidently.model_profile.sections import DataDriftProfileSection
from evidently.pipeline.column_mapping import ColumnMapping
import json
import logging
from datetime import datetime

def open_source_ai_governance():
    # Step 1: Set up MLflow tracking
    mlflow.set_tracking_uri("http://localhost:5000")
    experiment_name = "responsible-ai-governance"
    mlflow.set_experiment(experiment_name)
    
    # Step 2: Register the model with governance metadata
    with mlflow.start_run(run_name="model-governance-setup") as run:
        # Log governance metadata
        mlflow.log_params({
            "governance_framework": "NIST AI Risk Management Framework",
            "intended_use": "Customer churn prediction",
            "model_owner": "Data Science Team",
            "approval_status": "PENDING_REVIEW",
            "risk_level": "MEDIUM",
            "data_sensitivity": "INTERNAL_ONLY"
        })
        
        # Log model performance metrics
        mlflow.log_metrics({
            "accuracy": 0.85,
            "precision": 0.83,
            "recall": 0.79,
            "f1_score": 0.81,
            "auc": 0.88
        })
        
        # Log model with additional governance artifacts
        # This assumes you've already trained the model
        # model_path = "path/to/your/model"
        # mlflow.sklearn.log_model(model, "model")
        
        # Log model card as JSON
        model_card = {
            "model_details": {
                "name": "Customer Churn Predictor",
                "version": "1.0.0",
                "description": "Predicts likelihood of customer churn based on usage patterns and demographics"
            },
            "intended_use": {
                "primary_uses": ["Identify at-risk customers for retention campaigns"],
                "out_of_scope_uses": ["Automated decision-making without human review", "Credit decisions"]
            },
            "factors": {
                "relevant_factors": ["Demographics", "Usage patterns", "Customer history"],
                "evaluation_factors": ["Performance varies by customer tenure", "Sensitive to seasonal patterns"]
            },
            "metrics": {
                "performance_measures": ["Accuracy", "Precision", "Recall", "F1", "AUC"],
                "decision_thresholds": "Default threshold is 0.5, can be adjusted based on business needs"
            },
            "evaluation_data": {
                "datasets": ["Training: Jan-June 2023", "Testing: July-Sept 2023"],
                "motivation": "Temporal split to evaluate model stability over time"
            },
            "training_data": {
                "datasets": ["Customer data from Jan 2021 to June 2023"],
                "preprocessing": ["Missing value imputation", "Feature normalization"]
            },
            "quantitative_analyses": {
                "bias_evaluation": "Evaluated fairness across age groups and geographic regions",
                "performance_results": "See metrics logged with this model version"
            },
            "ethical_considerations": {
                "ethical_risks": ["Potential reinforcement of historical biases", "Privacy concerns"],
                "mitigations": ["Regular fairness audits", "Anonymized feature engineering"]
            }
        }
        
        with open("model_card.json", "w") as f:
            json.dump(model_card, f, indent=2)
        
        mlflow.log_artifact("model_card.json", "governance")
    
    # Step 3: Set up data drift monitoring with Evidently
    def monitor_data_drift(reference_data, current_data, column_mapping=None):
        # Initialize data drift profile
        data_drift_profile = Profile(sections=[DataDriftProfileSection()])
        
        # Calculate drift
        data_drift_profile.calculate(reference_data, current_data, column_mapping=column_mapping)
        
        # Get drift report as JSON
        drift_json = data_drift_profile.json()
        
        # Log drift report to MLflow
        with mlflow.start_run(run_name="data-drift-monitoring") as run:
            # Convert drift report to dictionary
            drift_report = json.loads(drift_json)
            
            # Extract and log key metrics
            drift_share = drift_report["data_drift"]["data"]["metrics"]["share_of_drifted_columns"]
            mlflow.log_metric("drift_share", drift_share)
            
            # Log full report as artifact
            with open("drift_report.json", "w") as f:
                json.dump(drift_report, f)
            
            mlflow.log_artifact("drift_report.json", "monitoring/drift")
            
            # Log alert if drift exceeds threshold
            if drift_share > 0.3:
                logging.warning(f"Data drift detected: {drift_share:.2f} of features have drifted")
                
                # Record governance event
                governance_event = {
                    "event_type": "DATA_DRIFT_ALERT",
                    "timestamp": datetime.now().isoformat(),
                    "details": {
                        "drift_share": drift_share,
                        "drifted_columns": [col for col, drift in 
                                            drift_report["data_drift"]["data"]["metrics"]["column_drift"].items() 
                                            if drift["drift_detected"]],
                        "severity": "HIGH" if drift_share > 0.5 else "MEDIUM"
                    }
                }
                
                with open("drift_alert.json", "w") as f:
                    json.dump(governance_event, f)
                    
                mlflow.log_artifact("drift_alert.json", "governance/alerts")
        
        return drift_report
    
    # Step 4: Create governance approval workflow
    def record_governance_approval(model_version, approver, decision, comments):
        with mlflow.start_run(run_name="governance-approval") as run:
            approval_event = {
                "model_version": model_version,
                "approver": approver,
                "decision": decision,  # "APPROVED", "REJECTED", "NEEDS_CHANGES"
                "timestamp": datetime.now().isoformat(),
                "comments": comments,
                "requirements_met": decision == "APPROVED"
            }
            
            # Log governance event
            with open("approval_record.json", "w") as f:
                json.dump(approval_event, f)
                
            mlflow.log_artifact("approval_record.json", "governance/approvals")
            
            # Update model tags with approval status
            client = mlflow.tracking.MlflowClient()
            client.set_model_version_tag(
                name="customer-churn-model", 
                version=model_version, 
                key="approval_status", 
                value=decision
            )
            
            # Log approval as a metric for tracking
            mlflow.log_param("approval_decision", decision)
            mlflow.log_param("approver", approver)
            
            return approval_event
    
    return {
        "experiment_name": experiment_name,
        "governance_functions": {
            "monitor_data_drift": monitor_data_drift,
            "record_governance_approval": record_governance_approval
        }
    }

# AWS IoT Greengrass Implementation for Edge AI
import json
import awsiot.greengrasscoreipc
import awsiot.greengrasscoreipc.client as client
from awsiot.greengrasscoreipc.model import (
    QOS,
    PublishToIoTCoreRequest
)
import onnxruntime as ort
import numpy as np
from transformers import AutoTokenizer, AutoConfig

class EdgeGenerativeAI:
    def __init__(self, model_path="/greengrass/v2/packages/artifacts/EdgeAI/models/"):
        # Initialize tokenizer
        self.tokenizer = AutoTokenizer.from_pretrained(f"{model_path}/tokenizer")
        
        # Load ONNX optimized model
        self.onnx_session = ort.InferenceSession(
            f"{model_path}/model_quantized.onnx",
            providers=["CPUExecutionProvider"]  # Or use "CUDAExecutionProvider" if GPU available
        )
        
        # Get model config
        self.config = AutoConfig.from_pretrained(f"{model_path}/config")
        
        # Initialize IPC client for AWS IoT Greengrass
        self.ipc_client = awsiot.greengrasscoreipc.connect()
        
    def generate_text(self, prompt, max_length=100):
        # Tokenize input
        inputs = self.tokenizer(prompt, return_tensors="np")
        
        # Get input names for the ONNX model
        input_names = [input.name for input in self.onnx_session.get_inputs()]
        
        # Prepare inputs for the model
        onnx_inputs = {name: inputs[name.split('.')[-1]] for name in input_names if name.split('.')[-1] in inputs}
        
        # Run inference
        outputs = self.onnx_session.run(None, onnx_inputs)
        
        # Process outputs to generate text
        generated_ids = outputs[0]
        generated_text = self.tokenizer.decode(generated_ids[0], skip_special_tokens=True)
        
        return generated_text
    
    def publish_result(self, result, topic="edge/ai/results"):
        # Create request to publish to IoT Core
        request = PublishToIoTCoreRequest()
        request.topic_name = topic
        request.payload = json.dumps({"result": result}).encode()
        request.qos = QOS.AT_LEAST_ONCE
        
        # Publish the message
        operation = self.ipc_client.new_publish_to_iot_core()
        operation.activate(request)
        future = operation.get_response()
        
        # Wait for the response
        future.result(timeout=10)
        
        return True

# Example usage
def run_edge_inference():
    # Initialize the generative AI component
    edge_ai = EdgeGenerativeAI()
    
    # Generate text from a prompt
    prompt = "Summarize the benefits of edge AI in three points:"
    generated_text = edge_ai.generate_text(prompt)
    
    # Publish the result to AWS IoT Core
    edge_ai.publish_result(generated_text)
    
    return generated_text

# Main entry point
if __name__ == "__main__":
    result = run_edge_inference()
    print(f"Generated text: {result}")

# Example component recipe for AWS IoT Greengrass deployment
"""
{
  "RecipeFormatVersion": "2020-01-25",
  "ComponentName": "com.example.EdgeGenerativeAI",
  "ComponentVersion": "1.0.0",
  "ComponentDescription": "Edge AI component for local generative AI inference",
  "ComponentPublisher": "Example Corp",
  "ComponentDependencies": {
    "aws.greengrass.TokenExchangeService": {
      "VersionRequirement": ">=2.0.0",
      "DependencyType": "HARD"
    }
  },
  "Manifests": [
    {
      "Platform": {
        "os": "linux"
      },
      "Lifecycle": {
        "Install": "pip3 install onnxruntime transformers numpy",
        "Run": "python3 {artifacts:decompressedPath}/edge_ai.py"
      },
      "Artifacts": [
        {
          "URI": "s3://your-bucket/edge_ai.py",
          "Unarchive": "NONE"
        },
        {
          "URI": "s3://your-bucket/models/model_quantized.onnx",
          "Unarchive": "NONE"
        },
        {
          "URI": "s3://your-bucket/models/tokenizer/",
          "Unarchive": "ZIP"
        },
        {
          "URI": "s3://your-bucket/models/config/",
          "Unarchive": "ZIP"
        }
      ]
    }
  ]
}
"""

# GCP Edge TPU Implementation
from pycoral.utils import edgetpu
from pycoral.adapters import common
from pycoral.adapters import classify
import tflite_runtime.interpreter as tflite
import numpy as np
import json

class GCPEdgeAI:
    def __init__(self, model_path="models/edge_model_quantized_edgetpu.tflite", vocab_path="models/vocab.txt"):
        # Initialize TensorFlow Lite interpreter with Edge TPU
        self.interpreter = edgetpu.make_interpreter(model_path)
        self.interpreter.allocate_tensors()
        
        # Get input and output details
        self.input_details = self.interpreter.get_input_details()
        self.output_details = self.interpreter.get_output_details()
        
        # Load vocabulary for tokenization/detokenization
        self.vocab = self._load_vocab(vocab_path)
        self.id_to_token = {idx: token for idx, token in enumerate(self.vocab)}
        
    def _load_vocab(self, vocab_path):
        with open(vocab_path, 'r') as f:
            return [line.strip() for line in f]
    
    def generate_text(self, prompt, max_length=50):
        # Basic tokenization (simplified for example)
        tokens = prompt.lower().split()
        input_ids = [self.vocab.index(token) if token in self.vocab else 0 for token in tokens]
        
        # Pad or truncate to expected input size
        expected_shape = self.input_details[0]['shape'][1]
        if len(input_ids) < expected_shape:
            input_ids = input_ids + [0] * (expected_shape - len(input_ids))
        else:
            input_ids = input_ids[:expected_shape]
        
        # Set input tensor
        input_tensor = np.array([input_ids], dtype=np.int32)
        self.interpreter.set_tensor(self.input_details[0]['index'], input_tensor)
        
        # Run inference
        self.interpreter.invoke()
        
        # Get output
        output = self.interpreter.get_tensor(self.output_details[0]['index'])
        
        # Convert output to text (simplified)
        generated_ids = np.argmax(output, axis=-1)[0]
        generated_text = " ".join([self.id_to_token.get(idx, "") for idx in generated_ids])
        
        return generated_text
    
    def publish_result(self, result, project_id="your-project-id", topic="edge-ai-results"):
        from google.cloud import pubsub_v1
        
        # Initialize publisher client
        publisher = pubsub_v1.PublisherClient()
        topic_path = publisher.topic_path(project_id, topic)
        
        # Publish message
        data = json.dumps({"result": result}).encode("utf-8")
        future = publisher.publish(topic_path, data)
        
        return future.result()  # Returns the message ID

# Example usage
def run_gcp_edge_inference():
    # Initialize Edge AI
    edge_ai = GCPEdgeAI()
    
    # Generate text
    prompt = "Summarize the benefits of edge AI in three points:"
    result = edge_ai.generate_text(prompt)
    
    # Publish results to Pub/Sub
    message_id = edge_ai.publish_result(result)
    
    return {
        "result": result,
        "message_id": message_id
    }

# Main entry point
if __name__ == "__main__":
    result = run_gcp_edge_inference()
    print(f"Generated text: {result['result']}")
    print(f"Message ID: {result['message_id']}")

# Example conversion script to prepare model for Edge TPU
"""
# convert_to_edge_tpu.py
import tensorflow as tf

def convert_model_for_edge_tpu(saved_model_dir, output_tflite_file):
    # Load the SavedModel
    model = tf.saved_model.load(saved_model_dir)
    
    # Convert to TensorFlow Lite model
    converter = tf.lite.TFLiteConverter.from_saved_model(saved_model_dir)
    
    # Set optimization flags
    converter.optimizations = [tf.lite.Optimize.DEFAULT]
    converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8]
    converter.inference_input_type = tf.int8
    converter.inference_output_type = tf.int8
    
    # Representative dataset for quantization
    def representative_dataset_gen():
        # Generate representative data for quantization
        # This would typically be a small sample of your input data
        for _ in range(100):
            yield [np.random.randint(0, 1000, size=(1, 128), dtype=np.int32)]
    
    converter.representative_dataset = representative_dataset_gen
    
    # Convert model
    tflite_model = converter.convert()
    
    # Save model
    with open(output_tflite_file, 'wb') as f:
        f.write(tflite_model)
    
    print(f"Model saved to {output_tflite_file}")
    
    # Compile for Edge TPU
    # Note: This requires the Edge TPU compiler to be installed
    # https://coral.ai/docs/edgetpu/compiler/
    import subprocess
    result = subprocess.run(['edgetpu_compiler', output_tflite_file])
    
    if result.returncode == 0:
        print("Model successfully compiled for Edge TPU")
    else:
        print("Error compiling model for Edge TPU")

if __name__ == "__main__":
    import argparse
    
    parser = argparse.ArgumentParser(description='Convert model for Edge TPU')
    parser.add_argument('--model_dir', required=True, help='Path to SavedModel directory')
    parser.add_argument('--output_file', required=True, help='Path to output TFLite file')
    
    args = parser.parse_args()
    
    convert_model_for_edge_tpu(args.model_dir, args.output_file)
"""

# Azure IoT Edge Implementation
import json
import time
import onnxruntime as ort
import numpy as np
from azure.iot.device import IoTHubModuleClient, Message, MethodResponse
from transformers import AutoTokenizer

class AzureEdgeAI:
    def __init__(self, model_path="/app/models/"):
        # Initialize IoT Hub Module Client
        self.client = IoTHubModuleClient.create_from_edge_environment()
        
        # Load ONNX model optimized for edge
        self.onnx_session = ort.InferenceSession(
            f"{model_path}/model_optimized.onnx", 
            providers=["CPUExecutionProvider"]
        )
        
        # Initialize tokenizer
        self.tokenizer = AutoTokenizer.from_pretrained(f"{model_path}/tokenizer")
        
        # Register callback for direct method calls
        self.client.on_method_request_received = self.method_request_handler
        
    def method_request_handler(self, method_request):
        # Handle direct method calls
        if method_request.name == "GenerateText":
            try:
                # Parse payload
                payload = json.loads(method_request.payload)
                prompt = payload.get("prompt", "")
                max_length = payload.get("max_length", 100)
                
                # Generate text
                result = self.generate_text(prompt, max_length)
                
                # Send response
                response_payload = {"result": result}
                response = MethodResponse(method_request.request_id, 200, json.dumps(response_payload))
                self.client.send_method_response(response)
                
                # Also send telemetry
                self.send_telemetry({"prompt": prompt, "result": result})
                
            except Exception as e:
                # Handle errors
                error_payload = {"error": str(e)}
                error_response = MethodResponse(method_request.request_id, 400, json.dumps(error_payload))
                self.client.send_method_response(error_response)
        else:
            # Method not implemented
            not_impl_response = MethodResponse(method_request.request_id, 404, "")
            self.client.send_method_response(not_impl_response)
    
    def generate_text(self, prompt, max_length=100):
        # Tokenize input
        inputs = self.tokenizer(prompt, return_tensors="np")
        
        # Get input names
        input_names = [input.name for input in self.onnx_session.get_inputs()]
        
        # Prepare inputs
        onnx_inputs = {name: inputs[name.split('.')[-1]] for name in input_names if name.split('.')[-1] in inputs}
        
        # Run inference
        outputs = self.onnx_session.run(None, onnx_inputs)
        
        # Process output
        generated_ids = outputs[0]
        generated_text = self.tokenizer.decode(generated_ids[0], skip_special_tokens=True)
        
        return generated_text
    
    def send_telemetry(self, data):
        # Create IoT Hub message
        message = Message(json.dumps(data))
        message.content_type = "application/json"
        message.content_encoding = "utf-8"
        
        # Send telemetry
        self.client.send_message_to_output(message, "telemetryOutput")

# Main entry point for module
def main():
    # Initialize the edge AI module
    edge_ai = AzureEdgeAI()
    
    # Keep the module running to handle method calls
    try:
        print("Edge AI module running. Press Ctrl-C to exit")
        while True:
            time.sleep(1000)
    except KeyboardInterrupt:
        print("Edge AI module stopped")

if __name__ == "__main__":
    main()

# Example client code to invoke the module
"""
# client.py
import asyncio
from azure.iot.device import IoTHubDeviceClient
from azure.iot.device import MethodRequest

async def invoke_edge_module():
    # Create device client
    device_client = IoTHubDeviceClient.create_from_connection_string(
        "your-device-connection-string"
    )
    
    # Connect to IoT Hub
    await device_client.connect()
    
    try:
        # Invoke GenerateText method on the module
        method_params = {
            "methodName": "GenerateText",
            "payload": {
                "prompt": "Summarize the benefits of edge AI in three points:",
                "max_length": 100
            },
            "responseTimeoutInSeconds": 30,
            "connectTimeoutInSeconds": 5
        }
        
        response = await device_client.invoke_method(
            "EdgeGenerativeAI",  # Module ID
            method_params
        )
        
        # Print response
        print(f"Status: {response.status}")
        print(f"Response: {response.payload}")
        
    finally:
        # Disconnect
        await device_client.disconnect()

if __name__ == "__main__":
    asyncio.run(invoke_edge_module())
"""

# Example deployment manifest for Azure IoT Edge
"""
{
  "modulesContent": {
    "$edgeAgent": {
      "properties.desired": {
        "modules": {
          "EdgeGenerativeAI": {
            "type": "docker",
            "status": "running",
            "restartPolicy": "always",
            "settings": {
              "image": "yourcontainerregistry.azurecr.io/edge-ai:1.0",
              "createOptions": {
                "HostConfig": {
                  "Binds": [
                    "/data/models:/app/models"
                  ]
                }
              }
            }
          }
        }
      }
    },
    "$edgeHub": {
      "properties.desired": {
        "routes": {
          "EdgeGenerativeAIToIoTHub": "FROM /messages/modules/EdgeGenerativeAI/outputs/telemetryOutput INTO $upstream"
        }
      }
    },
    "EdgeGenerativeAI": {
      "properties.desired": {
        "ModelSettings": {
          "ModelPath": "/app/models/model_optimized.onnx",
          "TokenizerPath": "/app/models/tokenizer"
        }
      }
    }
  }
}
"""

# Dockerfile for Azure IoT Edge module
"""
FROM python:3.9-slim

WORKDIR /app

# Install dependencies
COPY requirements.txt ./
RUN pip install -r requirements.txt

# Copy module code
COPY . .

# Create models directory
RUN mkdir -p /app/models

# Set the entry point
CMD ["python", "main.py"]
"""

# Example requirements.txt
"""
azure-iot-device==2.12.0
numpy==1.23.5
onnxruntime==1.14.1
transformers==4.28.1
"""

# Self-Hosted Edge AI Implementations

# 1. Python Web Service with ONNX Runtime
import onnxruntime as ort
import numpy as np
from transformers import AutoTokenizer
from flask import Flask, request, jsonify

class ONNXEdgeAI:
    def __init__(self, model_path="models/model_quantized.onnx", tokenizer_path="models/tokenizer"):
        # Load ONNX model
        self.onnx_session = ort.InferenceSession(
            model_path,
            providers=["CPUExecutionProvider"]
        )
        
        # Load tokenizer
        self.tokenizer = AutoTokenizer.from_pretrained(tokenizer_path)
        
    def generate_text(self, prompt, max_length=100):
        # Tokenize input
        inputs = self.tokenizer(prompt, return_tensors="np")
        
        # Get input names
        input_names = [input.name for input in self.onnx_session.get_inputs()]
        
        # Prepare inputs
        onnx_inputs = {name: inputs[name.split('.')[-1]] for name in input_names if name.split('.')[-1] in inputs}
        
        # Run inference
        outputs = self.onnx_session.run(None, onnx_inputs)
        
        # Process output
        generated_ids = outputs[0]
        generated_text = self.tokenizer.decode(generated_ids[0], skip_special_tokens=True)
        
        return generated_text

# Create Flask app for web service
app = Flask(__name__)
edge_ai = ONNXEdgeAI()

@app.route('/generate', methods=['POST'])
def generate():
    data = request.json
    prompt = data.get('prompt', '')
    max_length = data.get('max_length', 100)
    
    try:
        generated_text = edge_ai.generate_text(prompt, max_length)
        return jsonify({'result': generated_text})
    except Exception as e:
        return jsonify({'error': str(e)}), 500

if __name__ == '__main__':
    # Run the web service on the edge device
    app.run(host='0.0.0.0', port=5000)

# Example Docker Compose setup for the web service
"""
version: '3'
services:
  edge-ai:
    build:
      context: .
      dockerfile: Dockerfile
    ports:
      - "5000:5000"
    volumes:
      - ./models:/app/models
    restart: unless-stopped
    deploy:
      resources:
        limits:
          cpus: '2'
          memory: 2G
"""

# Example Dockerfile for the web service
"""
FROM python:3.9-slim

WORKDIR /app

# Install dependencies
COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt

# Copy application code
COPY app.py .

# Create models directory
RUN mkdir -p /app/models

# Expose port
EXPOSE 5000

# Run the application
CMD ["python", "app.py"]
"""

# 2. Android Mobile Implementation (Kotlin)
"""
// MainActivity.kt
package com.example.edgeai

import android.os.Bundle
import android.widget.Button
import android.widget.EditText
import android.widget.TextView
import androidx.appcompat.app.AppCompatActivity
import kotlinx.coroutines.CoroutineScope
import kotlinx.coroutines.Dispatchers
import kotlinx.coroutines.launch
import kotlinx.coroutines.withContext
import org.pytorch.LiteModuleLoader
import org.pytorch.Module
import org.pytorch.Tensor
import java.io.File
import java.io.FileOutputStream
import java.nio.ByteBuffer

class MainActivity : AppCompatActivity() {
    private lateinit var module: Module
    private lateinit var tokenizer: BertTokenizer
    
    override fun onCreate(savedInstanceState: Bundle?) {
        super.onCreate(savedInstanceState)
        setContentView(R.layout.activity_main)
        
        // Initialize model and tokenizer
        CoroutineScope(Dispatchers.IO).launch {
            module = loadModel()
            tokenizer = BertTokenizer(loadVocab())
            
            withContext(Dispatchers.Main) {
                findViewById(R.id.generateButton).isEnabled = true
            }
        }
        
        // Set up UI elements
        val promptEditText = findViewById(R.id.promptEditText)
        val resultTextView = findViewById(R.id.resultTextView)
        val generateButton = findViewById(R.id.generateButton)
        
        // Set up button click listener
        generateButton.setOnClickListener {
            val prompt = promptEditText.text.toString()
            
            CoroutineScope(Dispatchers.IO).launch {
                val generatedText = generateText(prompt)
                
                withContext(Dispatchers.Main) {
                    resultTextView.text = generatedText
                }
            }
        }
    }
    
    private fun loadModel(): Module {
        val modelName = "model_quantized.ptl"
        val modelFile = File(applicationContext.filesDir, modelName)
        
        if (!modelFile.exists()) {
            val assets = applicationContext.assets.open(modelName)
            val buffer = ByteArray(assets.available())
            assets.read(buffer)
            assets.close()
            
            val output = FileOutputStream(modelFile)
            output.write(buffer)
            output.close()
        }
        
        return LiteModuleLoader.load(modelFile.absolutePath)
    }
    
    private fun loadVocab(): List {
        val vocabList = mutableListOf()
        applicationContext.assets.open("vocab.txt").bufferedReader().useLines { lines ->
            lines.forEach { vocabList.add(it) }
        }
        return vocabList
    }
    
    private fun generateText(prompt: String, maxLength: Int = 50): String {
        // Tokenize input
        val encodedInput = tokenizer.encode(prompt)
        val inputIds = encodedInput.ids
        
        // Create input tensor
        val inputBuffer = ByteBuffer.allocate(inputIds.size * 4)
        inputIds.forEach { inputBuffer.putInt(it) }
        inputBuffer.flip()
        
        val inputTensor = Tensor.fromBlob(
            inputBuffer,
            longArrayOf(1, inputIds.size.toLong()),
            Tensor.INT32
        )
        
        // Run inference
        val outputTensor = module.forward(mapOf("input_ids" to inputTensor)).toTensor()
        
        // Process output
        val outputBuffer = outputTensor.getDataAsFloatArray()
        val generatedIds = mutableListOf()
        
        // Simplified output processing - in reality, you'd implement beam search or sampling here
        var idx = 0
        for (i in 0 until maxLength) {
            val maxValue = outputBuffer.slice(idx until idx + tokenizer.vocabSize).maxOrNull() ?: 0f
            val maxIndex = outputBuffer.slice(idx until idx + tokenizer.vocabSize).indexOf(maxValue)
            generatedIds.add(maxIndex)
            idx += tokenizer.vocabSize
        }
        
        // Decode output
        return tokenizer.decode(generatedIds)
    }
}

// BertTokenizer.kt
package com.example.edgeai

class BertTokenizer(private val vocab: List) {
    val vocabSize = vocab.size
    private val vocabMap = vocab.withIndex().associate { it.value to it.index }
    
    fun encode(text: String): TokenizerResult {
        // Simple tokenization by space (in real life, use BPE/WordPiece)
        val tokens = text.lowercase().split(" ")
        val ids = tokens.map { vocabMap[it] ?: vocabMap["[UNK]"] ?: 0 }
        
        return TokenizerResult(ids)
    }
    
    fun decode(ids: List): String {
        return ids.joinToString(" ") { vocab.getOrNull(it) ?: "" }
    }
    
    data class TokenizerResult(val ids: List)
}
"""

# Example layout XML for the Android app
"""




    

    

    

    

    


"""

# 3. Raspberry Pi Edge AI Implementation
"""
# edge_ai_pi.py
import numpy as np
import tflite_runtime.interpreter as tflite
import json
import time
import argparse
import os

class R

# AWS Bedrock Implementation for Synthetic Data Generation
import boto3
import json
import pandas as pd
import numpy as np
import time

def aws_synthetic_data_generation(real_data_sample, num_synthetic_samples=100):
    """
    Generate synthetic tabular data using AWS Bedrock
    
    Args:
        real_data_sample: A small sample of real data to guide the generation
        num_synthetic_samples: Number of synthetic samples to generate
    
    Returns:
        DataFrame of synthetic data
    """
    # Initialize Bedrock client
    bedrock_runtime = boto3.client(
        service_name="bedrock-runtime",
        region_name="us-east-1"
    )
    
    # Convert sample to string representation
    sample_str = real_data_sample.head(5).to_string(index=False)
    
    # Create prompt for synthetic data generation
    prompt = f"""I need to generate synthetic tabular data that resembles the following real data sample:
    
    {sample_str}
    
    Please generate {num_synthetic_samples} rows of synthetic data that:
    1. Maintains the same column names and data types
    2. Preserves the statistical distributions of the original data
    3. Maintains correlations between columns
    4. Ensures the data is realistic and does not contain identifiable information
    
    Output the data in CSV format only, with no additional explanation.
    """
    
    # Create request payload
    request_body = {
        "anthropic_version": "bedrock-2023-05-31",
        "max_tokens": 4000,
        "messages": [
            {
                "role": "user",
                "content": prompt
            }
        ]
    }
    
    # Invoke Claude model
    response = bedrock_runtime.invoke_model(
        modelId="anthropic.claude-3-sonnet-20240229-v1:0",
        body=json.dumps(request_body)
    )
    
    # Parse response
    response_body = json.loads(response.get("body").read())
    synthetic_data_text = response_body["content"][0]["text"]
    
    # Convert text to DataFrame
    # This assumes the response is in CSV format
    from io import StringIO
    synthetic_df = pd.read_csv(StringIO(synthetic_data_text))
    
    # Validate and correct data types to match the original
    for col in real_data_sample.columns:
        if col in synthetic_df.columns:
            synthetic_df[col] = synthetic_df[col].astype(real_data_sample[col].dtype)
    
    return synthetic_df

# AWS SageMaker Data Wrangler for Tabular Data Generation
def aws_tabular_synthetic_data():
    import sagemaker
    from sagemaker import get_execution_role
    from sagemaker.session import Session
    
    # Initialize SageMaker session
    role = get_execution_role()
    session = sagemaker.Session()
    
    # Create processing job for synthetic data generation
    from sagemaker.processing import ProcessingInput, ProcessingOutput, ScriptProcessor
    
    # Create script processor
    script_processor = ScriptProcessor(
        command=['python3'],
        image_uri='your-container-uri',  # Custom container with data generation libraries
        role=role,
        instance_count=1,
        instance_type='ml.m5.xlarge',
        sagemaker_session=session
    )
    
    # Run processing job
    script_processor.run(
        code='generate_synthetic_data.py',
        inputs=[
            ProcessingInput(
                source='s3://your-bucket/real-data.csv',
                destination='/opt/ml/processing/input'
            )
        ],
        outputs=[
            ProcessingOutput(
                source='/opt/ml/processing/output',
                destination='s3://your-bucket/synthetic-data'
            )
        ],
        arguments=[
            '--num-samples', '1000',
            '--output-file', '/opt/ml/processing/output/synthetic_data.csv',
            '--method', 'ctgan'  # Conditional Tabular GAN
        ]
    )
    
    # Example synthetic data generation script
    """
    # generate_synthetic_data.py
    import argparse
    import pandas as pd
    import numpy as np
    from sdv.tabular import CTGAN
    
    def main():
        parser = argparse.ArgumentParser()
        parser.add_argument('--num-samples', type=int, default=1000)
        parser.add_argument('--output-file', type=str, required=True)
        parser.add_argument('--method', type=str, default='ctgan')
        args = parser.parse_args()
        
        # Load real data
        df = pd.read_csv('/opt/ml/processing/input/real-data.csv')
        
        # Train synthetic data model
        if args.method == 'ctgan':
            model = CTGAN()
            model.fit(df)
            
            # Generate synthetic data
            synthetic_data = model.sample(args.num_samples)
            
            # Save to output location
            synthetic_data.to_csv(args.output_file, index=False)
            
        # Add other methods as needed
        
    if __name__ == '__main__':
        main()
    """
    
    return {
        "job_name": script_processor._current_job_name,
        "output_path": "s3://your-bucket/synthetic-data"
    }

# GCP Vertex AI Implementation for Synthetic Data
from google.cloud import aiplatform
from vertexai.preview.generative_models import GenerativeModel
import pandas as pd
import numpy as np
import json

def gcp_synthetic_data_generation(real_data_sample, num_synthetic_samples=100):
    """
    Generate synthetic tabular data using Google Vertex AI
    """
    # Initialize Vertex AI
    aiplatform.init(project="your-project-id", location="us-central1")
    
    # Load Gemini model
    model = GenerativeModel("gemini-pro")
    
    # Convert sample to string representation
    sample_str = real_data_sample.head(5).to_string(index=False)
    
    # Create prompt for synthetic data generation
    prompt = f"""I need to generate synthetic tabular data that resembles the following real data sample:
    
    {sample_str}
    
    Please generate {num_synthetic_samples} rows of synthetic data that:
    1. Maintains the same column names and data types
    2. Preserves the statistical distributions of the original data
    3. Maintains correlations between columns
    4. Ensures the data is realistic and does not contain identifiable information
    
    Output the data in CSV format only, with no additional explanation.
    """
    
    # Generate synthetic data
    response = model.generate_content(prompt)
    synthetic_data_text = response.text
    
    # Convert text to DataFrame
    from io import StringIO
    synthetic_df = pd.read_csv(StringIO(synthetic_data_text))
    
    # Validate and correct data types to match the original
    for col in real_data_sample.columns:
        if col in synthetic_df.columns:
            synthetic_df[col] = synthetic_df[col].astype(real_data_sample[col].dtype)
    
    return synthetic_df

# GCP Dataflow for Large-Scale Synthetic Data Generation
def gcp_dataflow_synthetic_data():
    """
    Create a Dataflow pipeline for large-scale synthetic data generation
    """
    # Example Dataflow pipeline code
    """
    import apache_beam as beam
    from apache_beam.options.pipeline_options import PipelineOptions
    import numpy as np
    import pandas as pd
    import json
    import random
    from typing import Dict, List, Tuple
    
    class GenerateSyntheticDataFn(beam.DoFn):
        def __init__(self, schema, distributions):
            self.schema = schema
            self.distributions = distributions
            
        def process(self, element):
            # Generate one synthetic record based on schema and distributions
            record = {}
            for col, col_info in self.schema.items():
                data_type = col_info['type']
                if data_type == 'categorical':
                    values = self.distributions[col]['values']
                    probabilities = self.distributions[col]['probabilities']
                    record[col] = np.random.choice(values, p=probabilities)
                elif data_type == 'numerical':
                    mean = self.distributions[col]['mean']
                    std = self.distributions[col]['std']
                    record[col] = float(np.random.normal(mean, std))
                elif data_type == 'datetime':
                    start_date = self.distributions[col]['start']
                    end_date = self.distributions[col]['end']
                    # Generate random date between start and end
                    delta = end_date - start_date
                    random_days = random.randint(0, delta.days)
                    record[col] = (start_date + datetime.timedelta(days=random_days)).isoformat()
            
            yield record
    
    def run():
        # Example schema and distributions derived from real data
        schema = {
            'age': {'type': 'numerical'},
            'income': {'type': 'numerical'},
            'category': {'type': 'categorical'},
            'signup_date': {'type': 'datetime'}
        }
        
        distributions = {
            'age': {'mean': 35.2, 'std': 12.5},
            'income': {'mean': 68000, 'std': 25000},
            'category': {
                'values': ['A', 'B', 'C', 'D'],
                'probabilities': [0.3, 0.4, 0.2, 0.1]
            },
            'signup_date': {
                'start': datetime.date(2020, 1, 1),
                'end': datetime.date(2023, 12, 31)
            }
        }
        
        # Define pipeline options
        options = PipelineOptions(
            runner='DataflowRunner',
            project='your-project-id',
            region='us-central1',
            temp_location='gs://your-bucket/temp',
            job_name='synthetic-data-generation'
        )
        
        # Create the pipeline
        with beam.Pipeline(options=options) as p:
            # Generate synthetic records
            synthetic_data = (
                p
                | 'Create Records' >> beam.Create(range(1000000))  # Number of records to generate
                | 'Generate Data' >> beam.ParDo(GenerateSyntheticDataFn(schema, distributions))
                | 'Convert to JSON' >> beam.Map(json.dumps)
                | 'Write to GCS' >> beam.io.WriteToText('gs://your-bucket/synthetic-data/output')
            )
            
    if __name__ == '__main__':
        run()
    """
    
    return "Dataflow Synthetic Data Pipeline (see commented code for implementation)"

# Azure OpenAI Implementation for Synthetic Data
import os
import json
import pandas as pd
import numpy as np
from openai import AzureOpenAI

def azure_synthetic_data_generation(real_data_sample, num_synthetic_samples=100):
    """
    Generate synthetic tabular data using Azure OpenAI
    """
    # Initialize Azure OpenAI client
    client = AzureOpenAI(
        api_key=os.getenv("AZURE_OPENAI_API_KEY"),
        api_version="2023-12-01-preview",
        azure_endpoint=os.getenv("AZURE_OPENAI_ENDPOINT")
    )
    
    # Convert sample to string representation
    sample_str = real_data_sample.head(5).to_string(index=False)
    
    # Create prompt for synthetic data generation
    prompt = f"""I need to generate synthetic tabular data that resembles the following real data sample:
    
    {sample_str}
    
    Please generate {num_synthetic_samples} rows of synthetic data that:
    1. Maintains the same column names and data types
    2. Preserves the statistical distributions of the original data
    3. Maintains correlations between columns
    4. Ensures the data is realistic and does not contain identifiable information
    
    Output the data in CSV format only, with no additional explanation.
    """
    
    # Generate synthetic data using GPT-4
    response = client.chat.completions.create(
        model="gpt-4",
        messages=[
            {"role": "system", "content": "You are a data generation assistant that creates realistic synthetic data based on real data samples."},
            {"role": "user", "content": prompt}
        ],
        temperature=0.7,
        max_tokens=4000
    )
    
    # Extract synthetic data text
    synthetic_data_text = response.choices[0].message.content
    
    # Convert text to DataFrame
    from io import StringIO
    synthetic_df = pd.read_csv(StringIO(synthetic_data_text))
    
    # Validate and correct data types to match the original
    for col in real_data_sample.columns:
        if col in synthetic_df.columns:
            synthetic_df[col] = synthetic_df[col].astype(real_data_sample[col].dtype)
    
    return synthetic_df

# Azure Synapse Analytics for Large-Scale Synthetic Data
def azure_synapse_synthetic_data():
    """
    Create an Azure Synapse Analytics pipeline for large-scale synthetic data generation
    """
    # This would typically be implemented using the Azure SDK or REST API
    # Below is a conceptual implementation
    
    # Example Azure Synapse Notebook (PySpark) code
    """
    from pyspark.sql import SparkSession
    from pyspark.sql.functions import *
    from pyspark.sql.types import *
    import numpy as np
    import random
    
    # Initialize Spark session
    spark = SparkSession.builder.appName("SyntheticDataGeneration").getOrCreate()
    
    # Define schema based on real data
    schema = StructType([
        StructField("customer_id", StringType(), False),
        StructField("age", IntegerType(), True),
        StructField("income", DoubleType(), True),
        StructField("category", StringType(), True),
        StructField("purchase_date", DateType(), True)
    ])
    
    # Create UDF for random data generation
    def generate_random_customer():
        # Generate random customer ID
        customer_id = f"CUST-{random.randint(10000, 99999)}"
        
        # Generate age based on normal distribution
        age = max(18, min(90, int(np.random.normal(35, 10))))
        
        # Generate income based on normal distribution
        income = max(20000, np.random.normal(65000, 25000))
        
        # Generate category based on distribution
        categories = ["A", "B", "C", "D"]
        probabilities = [0.3, 0.4, 0.2, 0.1]
        category = np.random.choice(categories, p=probabilities)
        
        # Generate purchase date
        days_ago = random.randint(0, 365*3)  # Last 3 years
        purchase_date = (current_date() - F.expr(f"INTERVAL {days_ago} days")).cast(DateType())
        
        return (customer_id, age, income, category, purchase_date)
    
    # Create empty DataFrame with schema
    empty_df = spark.createDataFrame([], schema)
    
    # Generate 1 million records
    num_partitions = 100
    records_per_partition = 10000
    
    synthetic_df = (
        spark.range(0, records_per_partition * num_partitions, 1, num_partitions)
        .rdd
        .map(lambda x: generate_random_customer())
        .toDF(schema)
    )
    
    # Write to Azure Data Lake Storage
    synthetic_df.write.mode("overwrite").parquet("abfss://container@account.dfs.core.windows.net/synthetic-data/")
    """
    
    return "Azure Synapse Synthetic Data Pipeline (see commented code for implementation)"

# Self-Hosted/Open Source Synthetic Data Generation
import pandas as pd
import numpy as np
from sdv.tabular import CTGAN, GaussianCopula
from sdv.evaluation.single_table import evaluate_quality

def open_source_synthetic_data_generation(real_data, num_synthetic_samples=1000, method="ctgan"):
    """
    Generate synthetic tabular data using open-source libraries
    
    Args:
        real_data: DataFrame containing real data
        num_synthetic_samples: Number of synthetic samples to generate
        method: Generation method ('ctgan' or 'copula')
    
    Returns:
        DataFrame of synthetic data and quality metrics
    """
    # Select synthetic data generation model
    if method == "ctgan":
        # Conditional Tabular GAN
        model = CTGAN()
    else:
        # Gaussian Copula model
        model = GaussianCopula()
    
    # Fit model to real data
    model.fit(real_data)
    
    # Generate synthetic data
    synthetic_data = model.sample(num_synthetic_samples)
    
    # Evaluate quality of synthetic data
    quality_report = evaluate_quality(
        real_data,
        synthetic_data,
        aggregate=True
    )
    
    return {
        "synthetic_data": synthetic_data,
        "quality_metrics": quality_report
    }

# Example of image generation for training data augmentation
import tensorflow as tf
from tensorflow.keras.preprocessing.image import ImageDataGenerator
import numpy as np
import matplotlib.pyplot as plt

def image_data_augmentation(images, labels, augmentation_factor=5):
    """
    Augment image training data using traditional techniques
    """
    # Create data generator with augmentation
    datagen = ImageDataGenerator(
        rotation_range=20,
        width_shift_range=0.2,
        height_shift_range=0.2,
        shear_range=0.2,
        zoom_range=0.2,
        horizontal_flip=True,
        fill_mode='nearest'
    )
    
    # Prepare augmented dataset containers
    augmented_images = []
    augmented_labels = []
    
    # Generate augmented images
    for i in range(len(images)):
        image = images[i]
        label = labels[i]
        
        # Convert to numpy array with batch dimension
        image = np.expand_dims(image, 0)
        
        # Generate augmented images
        aug_iter = datagen.flow(image, batch_size=1)
        
        # Add original image
        augmented_images.append(image[0])
        augmented_labels.append(label)
        
        # Add augmented versions
        for j in range(augmentation_factor - 1):
            aug_image = aug_iter.next()[0]
            augmented_images.append(aug_image)
            augmented_labels.append(label)
    
    # Convert to numpy arrays
    augmented_images = np.array(augmented_images)
    augmented_labels = np.array(augmented_labels)
    
    return augmented_images, augmented_labels

# Advanced text data augmentation with NLP techniques
import nltk
from nltk.corpus import wordnet
import random
import spacy

def text_data_augmentation(texts, labels, augmentation_factor=3):
    """
    Augment text training data using NLP techniques
    """
    # Download necessary NLTK data
    nltk.download('wordnet')
    nltk.download('punkt')
    
    # Load SpaCy model
    nlp = spacy.load("en_core_web_sm")
    
    augmented_texts = []
    augmented_labels = []
    
    # Add original data
    augmented_texts.extend(texts)
    augmented_labels.extend(labels)
    
    for i, text in enumerate(texts):
        label = labels[i]
        
        # Parse with SpaCy
        doc = nlp(text)
        
        # Create augmented versions
        for _ in range(augmentation_factor - 1):
            # Choose augmentation technique randomly
            technique = random.choice(["synonym", "deletion", "swap"])
            
            if technique == "synonym":
                # Replace some words with synonyms
                new_text = synonym_replacement(text)
            elif technique == "deletion":
                # Randomly delete some words
                new_text = random_deletion(text)
            else:
                # Randomly swap words
                new_text = random_swap(text)
            
            augmented_texts.append(new_text)
            augmented_labels.append(label)
    
    return augmented_texts, augmented_labels

def synonym_replacement(text, n=1):
    """Replace n words in the text with synonyms"""
    words = nltk.word_tokenize(text)
    new_words = words.copy()
    random_word_indexes = random.sample(range(len(words)), min(n, len(words)))
    
    for idx in random_word_indexes:
        word = words[idx]
        synonyms = get_synonyms(word)
        if synonyms:
            new_words[idx] = random.choice(synonyms)
    
    return " ".join(new_words)

def get_synonyms(word):
    """Get synonyms of a word"""
    synonyms = []
    for syn in wordnet.synsets(word):
        for lemma in syn.lemmas():
            synonyms.append(lemma.name())
    return list(set(synonyms))

def random_deletion(text, p=0.1):
    """Randomly delete words from text with probability p"""
    words = nltk.word_tokenize(text)
    if len(words) == 1:
        return text
    
    new_words = []
    for word in words:
        if random.random() > p:
            new_words.append(word)
    
    if len(new_words) == 0:
        return words[0]
    
    return " ".join(new_words)

def random_swap(text, n=1):
    """Randomly swap n pairs of words in the text"""
    words = nltk.word_tokenize(text)
    new_words = words.copy()
    
    for _ in range(min(n, len(words)//2)):
        idx1, idx2 = random.sample(range(len(new_words)), 2)
        new_words[idx1], new_words[idx2] = new_words[idx2], new_words[idx1]
    
    return " ".join(new_words)

# AWS Bedrock Agents Implementation
import boto3
import json
import time

def create_aws_bedrock_agent():
    # Initialize Bedrock client
    bedrock = boto3.client(
        service_name="bedrock-agent",
        region_name="us-east-1"
    )
    
    # Step 1: Create an action group (tool)
    action_group_response = bedrock.create_agent_action_group(
        agentId="your-agent-id",
        actionGroupName="WeatherTool",
        apiSchema={
            "openapi": "3.0.0",
            "info": {
                "title": "Weather API",
                "version": "1.0.0"
            },
            "paths": {
                "/getWeather": {
                    "get": {
                        "operationId": "getWeather",
                        "summary": "Get weather for a location",
                        "parameters": [
                            {
                                "name": "location",
                                "in": "query",
                                "description": "City and state or country",
                                "required": True,
                                "schema": {"type": "string"}
                            }
                        ],
                        "responses": {
                            "200": {
                                "description": "Weather information",
                                "content": {
                                    "application/json": {
                                        "schema": {
                                            "type": "object",
                                            "properties": {
                                                "temperature": {"type": "number"},
                                                "condition": {"type": "string"},
                                                "humidity": {"type": "number"}
                                            }
                                        }
                                    }
                                }
                            }
                        }
                    }
                }
            }
        },
        description="Tool to get weather information for a location",
        actionGroupExecutor={
            "lambda": {
                "lambdaArn": "arn:aws:lambda:us-east-1:123456789012:function:weather-function"
            }
        }
    )
    
    # Step 2: Create a knowledge base for the agent
    knowledge_base_response = bedrock.create_knowledge_base(
        name="TravelKnowledgeBase",
        description="Knowledge base with travel information",
        roleArn="arn:aws:iam::123456789012:role/BedrockKnowledgeBaseRole",
        knowledgeBaseConfiguration={
            "type": "VECTOR",
            "vectorKnowledgeBaseConfiguration": {
                "embeddingModelArn": "arn:aws:bedrock:us-east-1::foundation-model/amazon.titan-embed-text-v1"
            }
        },
        storageConfiguration={
            "type": "OPENSEARCH_SERVERLESS",
            "opensearchServerlessConfiguration": {
                "collectionArn": "arn:aws:aoss:us-east-1:123456789012:collection/my-collection"
            }
        }
    )
    
    # Step 3: Create the agent
    agent_response = bedrock.create_agent(
        agentName="TravelAssistant",
        description="An assistant that helps with travel planning",
        instruction="You are a travel assistant. Help users plan trips by providing weather information and travel recommendations. Use the provided knowledge base for travel information and the weather tool to get current weather data.",
        foundationModel="anthropic.claude-v2",
        idleSessionTTLInSeconds=1800,  # 30 minutes
        customerEncryptionKeyArn="arn:aws:kms:us-east-1:123456789012:key/your-key-id",
        agentResourceRoleArn="arn:aws:iam::123456789012:role/BedrockAgentRole"
    )
    
    # Step 4: Associate knowledge base with agent
    kb_association_response = bedrock.associate_agent_knowledge_base(
        agentId=agent_response["agentId"],
        agentVersion="DRAFT",
        knowledgeBaseId=knowledge_base_response["knowledgeBaseId"],
        description="Travel information knowledge base"
    )
    
    # Step 5: Prepare agent for deployment
    prepare_response = bedrock.prepare_agent(
        agentId=agent_response["agentId"],
        agentVersion="DRAFT"
    )
    
    # Check preparation status
    waiter_config = {
        "delay": 5,
        "maxAttempts": 60
    }
    
    # Wait for preparation to complete
    while True:
        status_response = bedrock.get_agent(
            agentId=agent_response["agentId"],
            agentVersion="DRAFT"
        )
        
        if status_response["agentStatus"] == "PREPARED":
            break
        
        if status_response["agentStatus"] == "FAILED":
            raise Exception("Agent preparation failed")
        
        time.sleep(waiter_config["delay"])
        waiter_config["maxAttempts"] -= 1
        
        if waiter_config["maxAttempts"] <= 0:
            raise Exception("Timed out waiting for agent preparation")
    
    # Step 6: Create alias for the agent
    alias_response = bedrock.create_agent_alias(
        agentId=agent_response["agentId"],
        agentAliasName="prod",
        description="Production version of the travel assistant",
        routingConfiguration=[
            {
                "agentVersion": "DRAFT"
            }
        ]
    )
    
    return {
        "agent_id": agent_response["agentId"],
        "alias_id": alias_response["agentAliasId"]
    }

# AWS Lambda function for the weather tool
def lambda_handler(event, context):
    """
    AWS Lambda function that handles weather API requests from Bedrock agent
    """
    # Extract parameters from the request
    request_body = json.loads(event["body"])
    action_group = request_body.get("actionGroup")
    api_path = request_body.get("apiPath")
    parameters = request_body.get("parameters", [])
    
    # Process request based on API path
    if api_path == "/getWeather":
        # Extract location parameter
        location = None
        for param in parameters:
            if param["name"] == "location":
                location = param["value"]
                break
        
        if not location:
            return {
                "statusCode": 400,
                "body": json.dumps({
                    "error": "Location parameter is required"
                })
            }
        
        # In a real implementation, you would call a weather API here
        # For this example, we'll return mock data
        weather_data = {
            "temperature": 22.5,
            "condition": "Partly Cloudy",
            "humidity": 65
        }
        
        return {
            "statusCode": 200,
            "body": json.dumps(weather_data)
        }
    
    return {
        "statusCode": 404,
        "body": json.dumps({
            "error": "Not found"
        })
    }

# GCP Vertex AI Agents Implementation
from google.cloud import aiplatform
from google.protobuf import json_format
from google.protobuf.struct_pb2 import Struct
import vertexai
from vertexai.preview.generative_models import GenerativeModel
from vertexai.preview.generative_models import Tool, FunctionDeclaration, ToolConfig

def create_gcp_vertex_ai_agent():
    # Initialize Vertex AI
    vertexai.init(project="your-project-id", location="us-central1")
    
    # Step 1: Define tools (function declarations)
    weather_tool = Tool(
        function_declarations=[
            FunctionDeclaration(
                name="get_weather",
                description="Get the current weather for a location",
                parameters=Struct(
                    fields={
                        "location": json_format.ParseDict(
                            {"type": "string", "description": "The city and state or country"}, 
                            Struct()
                        )
                    }
                ),
                response=Struct(
                    fields={
                        "temperature": json_format.ParseDict(
                            {"type": "number", "description": "Temperature in Celsius"}, 
                            Struct()
                        ),
                        "condition": json_format.ParseDict(
                            {"type": "string", "description": "Weather condition description"}, 
                            Struct()
                        ),
                        "humidity": json_format.ParseDict(
                            {"type": "number", "description": "Humidity percentage"}, 
                            Struct()
                        )
                    }
                )
            )
        ]
    )
    
    # Step 2: Load Gemini model with tools
    model = GenerativeModel(
        model_name="gemini-pro",
        tools=[weather_tool],
        tool_config=ToolConfig(
            function_calling_config={"mode": "AUTO"}
        )
    )
    
    # Function to handle tool calls
    def handle_tool_calls(tool_calls):
        results = []
        
        for tool_call in tool_calls:
            function_name = tool_call.function_name
            function_args = tool_call.function_args
            
            if function_name == "get_weather":
                location = function_args.get("location", "")
                
                # In a real implementation, you would call a weather API here
                # For this example, we'll return mock data
                weather_result = {
                    "temperature": 22.5,
                    "condition": "Partly Cloudy",
                    "humidity": 65
                }
                
                results.append(weather_result)
        
        return results
    
    # Step 3: Create a prediction function for the agent
    def predict(prompt):
        response = model.generate_content(
            prompt,
            generation_config={
                "max_output_tokens": 1024,
                "temperature": 0.4
            }
        )
        
        if hasattr(response, "candidates") and response.candidates:
            candidate = response.candidates[0]
            if hasattr(candidate, "content") and candidate.content:
                content = candidate.content
                
                # Check if there are any tool calls to process
                if hasattr(content, "parts"):
                    for part in content.parts:
                        if hasattr(part, "function_call"):
                            # Handle tool calls
                            tool_results = handle_tool_calls([part.function_call])
                            
                            # Continue the conversation with tool results
                            follow_up_response = model.generate_content(
                                [
                                    {"role": "user", "parts": [prompt]},
                                    {"role": "model", "parts": [part]},
                                    {"role": "tool", "parts": [{"function_response": tool_results[0]}]}
                                ]
                            )
                            
                            return follow_up_response.text
                
                return response.text
        
        return "I couldn't process your request."
    
    # Step 4: Deploy the agent (in a real implementation, this would be a more complex setup)
    # For example, creating a Cloud Function or Cloud Run service that uses this prediction function
    
    return {
        "agent_type": "vertex_ai_gemini",
        "prediction_function": predict,
        "model": model
    }

# GCP Cloud Function to serve the agent
"""
from flask import Flask, request, jsonify
import vertexai
from vertexai.preview.generative_models import GenerativeModel
import json
import functions_framework

app = Flask(__name__)

# Initialize Vertex AI
vertexai.init(project="your-project-id", location="us-central1")

# Create tools and model as defined above
# ... (tool and model definitions) ...

# Function to handle tool calls
def handle_tool_calls(tool_calls):
    # ... (as defined above) ...
    pass

@functions_framework.http
def agent_endpoint(request):
    # Parse request
    request_json = request.get_json(silent=True)
    
    if not request_json or 'prompt' not in request_json:
        return jsonify({'error': 'No prompt provided'}), 400
    
    prompt = request_json['prompt']
    
    # Generate response using the agent
    try:
        response = model.generate_content(prompt)
        
        # Process any tool calls in the response
        # ... (tool call processing logic) ...
        
        return jsonify({'response': response.text})
    except Exception as e:
        return jsonify({'error': str(e)}), 500
"""

# Azure OpenAI Assistants Implementation
import os
import json
import time
from openai import AzureOpenAI

def create_azure_openai_assistant():
    # Initialize Azure OpenAI client
    client = AzureOpenAI(
        api_key=os.getenv("AZURE_OPENAI_API_KEY"),
        api_version="2023-12-01-preview",
        azure_endpoint=os.getenv("AZURE_OPENAI_ENDPOINT")
    )
    
    # Step 1: Define functions/tools
    tools = [
        {
            "type": "function",
            "function": {
                "name": "get_weather",
                "description": "Get the current weather for a location",
                "parameters": {
                    "type": "object",
                    "properties": {
                        "location": {
                            "type": "string",
                            "description": "The city and state or country"
                        }
                    },
                    "required": ["location"]
                }
            }
        }
    ]
    
    # Step 2: Create a file with travel information for the assistant
    with open("travel_info.txt", "w") as f:
        f.write("""
        # Travel Guide Information
        
        ## Popular Destinations
        
        ### Paris, France
        - Best time to visit: April to June, September to October
        - Famous for: Eiffel Tower, Louvre Museum, Notre Dame Cathedral
        - Local cuisine: Croissants, Escargot, Coq au Vin
        
        ### Tokyo, Japan
        - Best time to visit: March to May, September to November
        - Famous for: Cherry blossoms, Shibuya Crossing, Tokyo Skytree
        - Local cuisine: Sushi, Ramen, Tempura
        
        ## Travel Tips
        
        - Always check visa requirements before booking flights
        - Purchase travel insurance for international trips
        - Inform your bank of travel plans to avoid card issues
        - Make copies of important documents
        """)
    
    # Upload the file
    file_response = client.files.create(
        file=open("travel_info.txt", "rb"),
        purpose="assistants"
    )
    
    # Step 3: Create the assistant
    assistant = client.beta.assistants.create(
        name="Travel Assistant",
        description="An assistant that helps with travel planning",
        instructions="You are a travel assistant. Help users plan trips by providing weather information and travel recommendations. Use the provided knowledge base for travel information and the weather tool to get current weather data.",
        model="gpt-4",
        tools=tools,
        file_ids=[file_response.id]
    )
    
    # Step 4: Create a function to handle the assistant's tool calls
    def handle_azure_tool_calls(tool_calls):
        tool_results = []
        
        for tool_call in tool_calls:
            function_name = tool_call.function.name
            function_args = json.loads(tool_call.function.arguments)
            
            if function_name == "get_weather":
                location = function_args.get("location", "")
                
                # In a real implementation, you would call a weather API here
                # For this example, we'll return mock data
                weather_result = {
                    "temperature": 22.5,
                    "condition": "Partly Cloudy",
                    "humidity": 65
                }
                
                tool_results.append({
                    "tool_call_id": tool_call.id,
                    "output": json.dumps(weather_result)
                })
        
        return tool_results
    
    # Step 5: Create a conversation helper function
    def converse_with_assistant(prompt):
        # Create a thread
        thread = client.beta.threads.create()
        
        # Add a message to the thread
        client.beta.threads.messages.create(
            thread_id=thread.id,
            role="user",
            content=prompt
        )
        
        # Run the assistant on the thread
        run = client.beta.threads.runs.create(
            thread_id=thread.id,
            assistant_id=assistant.id
        )
        
        # Wait for the run to complete
        while True:
            run_status = client.beta.threads.runs.retrieve(
                thread_id=thread.id,
                run_id=run.id
            )
            
            if run_status.status == "completed":
                break
            
            if run_status.status == "requires_action":
                # Handle tool calls
                tool_calls = run_status.required_action.submit_tool_outputs.tool_calls
                tool_outputs = handle_azure_tool_calls(tool_calls)
                
                # Submit tool outputs
                client.beta.threads.runs.submit_tool_outputs(
                    thread_id=thread.id,
                    run_id=run.id,
                    tool_outputs=tool_outputs
                )
            
            if run_status.status in ["failed", "cancelled", "expired"]:
                raise Exception(f"Run failed with status: {run_status.status}")
            
            time.sleep(1)
        
        # Get messages from the thread
        messages = client.beta.threads.messages.list(
            thread_id=thread.id
        )
        
        # Return the assistant's response
        for message in messages.data:
            if message.role == "assistant":
                return message.content[0].text.value
        
        return "No response from assistant."
    
    return {
        "assistant_id": assistant.id,
        "converse_function": converse_with_assistant
    }

# Azure Function App for the Assistant
"""
import logging
import azure.functions as func
import json
import os
from openai import AzureOpenAI

# Initialize Azure OpenAI client
client = AzureOpenAI(
    api_key=os.getenv("AZURE_OPENAI_API_KEY"),
    api_version="2023-12-01-preview",
    azure_endpoint=os.getenv("AZURE_OPENAI_ENDPOINT")
)

# Get assistant ID from environment variable
ASSISTANT_ID = os.getenv("ASSISTANT_ID")

# Function to handle tool calls
def handle_tool_calls(tool_calls):
    # ... (as defined above) ...
    pass

app = func.FunctionApp()

@app.route(route="travelbot", auth_level=func.AuthLevel.FUNCTION)
def travelbot(req: func.HttpRequest) -> func.HttpResponse:
    logging.info('Python HTTP trigger function processed a request.')

    try:
        req_body = req.get_json()
        prompt = req_body.get('prompt')
        
        if not prompt:
            return func.HttpResponse(
                json.dumps({"error": "No prompt provided"}),
                mimetype="application/json",
                status_code=400
            )
        
        # Create a thread
        thread = client.beta.threads.create()
        
        # Add a message to the thread
        client.beta.threads.messages.create(
            thread_id=thread.id,
            role="user",
            content=prompt
        )
        
        # Run the assistant on the thread
        run = client.beta.threads.runs.create(
            thread_id=thread.id,
            assistant_id=ASSISTANT_ID
        )
        
        # Wait for the run to complete (with tool call handling)
        # ... (run completion logic) ...
        
        # Get messages from the thread
        messages = client.beta.threads.messages.list(
            thread_id=thread.id
        )
        
        # Return the assistant's response
        for message in messages.data:
            if message.role == "assistant":
                return func.HttpResponse(
                    json.dumps({"response": message.content[0].text.value}),
                    mimetype="application/json"
                )
        
        return func.HttpResponse(
            json.dumps({"error": "No response from assistant"}),
            mimetype="application/json",
            status_code=500
        )
    
    except Exception as e:
        return func.HttpResponse(
            json.dumps({"error": str(e)}),
            mimetype="application/json",
            status_code=500
        )
"""

# LangChain Agent Implementation (Cloud-Independent)
import os
from langchain.agents import initialize_agent, Tool
from langchain.agents import AgentType
from langchain.chat_models import ChatOpenAI
from langchain.memory import ConversationBufferMemory
from langchain.prompts import MessagesPlaceholder
from langchain.tools import BaseTool
from langchain.utilities import SerpAPIWrapper
from langchain.chains import RetrievalQA
from langchain.vectorstores import Chroma
from langchain.embeddings import HuggingFaceEmbeddings
from typing import Dict, List, Any, Optional

class WeatherTool(BaseTool):
    name = "weather_tool"
    description = "Get the current weather for a location"
    
    def _run(self, location: str) -> Dict[str, Any]:
        """Get weather data for the specified location"""
        # In a real implementation, you would call a weather API here
        # For this example, we'll return mock data
        return {
            "temperature": 22.5,
            "condition": "Partly Cloudy",
            "humidity": 65
        }
    
    def _arun(self, location: str) -> Dict[str, Any]:
        """Get weather data asynchronously"""
        return self._run(location)

def create_langchain_agent():
    # Step 1: Initialize tools
    # Weather tool
    weather_tool = WeatherTool()
    
    # Search tool
    search = SerpAPIWrapper(serpapi_api_key=os.getenv("SERPAPI_API_KEY"))
    search_tool = Tool(
        name="search",
        func=search.run,
        description="Useful for answering questions about current events or the current state of the world"
    )
    
    # Knowledge base tool using vector store
    embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
    
    # Load or create vector store (this assumes you've already populated it)
    vector_store = Chroma(
        persist_directory="./chroma_db",
        embedding_function=embeddings
    )
    
    retriever = vector_store.as_retriever(search_kwargs={"k": 5})
    
    # Create QA chain for travel information
    qa = RetrievalQA.from_chain_type(
        llm=ChatOpenAI(temperature=0, model="gpt-4"),
        chain_type="stuff",
        retriever=retriever
    )
    
    travel_kb_tool = Tool(
        name="travel_knowledge",
        func=qa.run,
        description="Useful for answering questions about travel destinations, tips, and recommendations."
    )
    
    tools = [weather_tool, search_tool, travel_kb_tool]
    
    # Step 2: Set up memory
    agent_kwargs = {
        "extra_prompt_messages": [MessagesPlaceholder(variable_name="memory")],
    }
    memory = ConversationBufferMemory(memory_key="memory", return_messages=True)
    
    # Step 3: Initialize the agent
    llm = ChatOpenAI(temperature=0, model="gpt-4")
    agent = initialize_agent(
        tools,
        llm,
        agent=AgentType.OPENAI_FUNCTIONS,
        verbose=True,
        agent_kwargs=agent_kwargs,
        memory=memory
    )
    
    return {
        "agent": agent,
        "tools": tools,
        "memory": memory
    }

# Example usage of the LangChain agent
def process_query_with_langchain(agent, query):
    try:
        response = agent.run(query)
        return response
    except Exception as e:
        return f"Error processing your request: {str(e)}"