Communication Tools

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from aimatrix.tools.communication import GmailTool

# Initialize Gmail tool
gmail = GmailTool(
    credentials_file="gmail_credentials.json",
    scopes=["https://www.googleapis.com/auth/gmail.readonly",
            "https://www.googleapis.com/auth/gmail.send"]
)

# AI agent can now manage emails
agent = AIAgent(
    name="email-assistant",
    tools=[gmail],
    instructions="""
    You are an email assistant. You can:
    1. Read and summarize incoming emails
    2. Draft and send professional responses
    3. Organize emails into folders
    4. Schedule follow-up reminders
    """
)

# Example: Process all unread emails
unread_emails = await gmail.get_unread_messages()
for email in unread_emails:
    summary = await agent.process_email(email)
    if summary.requires_response:
        response = await agent.draft_response(email, summary)
        await gmail.send_message(response)

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from aimatrix.tools.communication import SMTPTool, IMAPTool

# Setup SMTP for sending
smtp = SMTPTool(
    server="smtp.company.com",
    port=587,
    username="bot@company.com",
    password="secure_password",
    use_tls=True
)

# Setup IMAP for reading
imap = IMAPTool(
    server="imap.company.com",
    port=993,
    username="bot@company.com",
    password="secure_password",
    use_ssl=True
)

# Send automated responses
await smtp.send_email(
    to="customer@example.com",
    subject="Thank you for your inquiry",
    body="We've received your message and will respond within 24 hours.",
    attachments=["company_brochure.pdf"]
)

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# Automatic email categorization
email_categories = await gmail.categorize_emails(
    labels=["urgent", "sales", "support", "spam"]
)

# Smart reply suggestions
reply_suggestions = await gmail.generate_smart_replies(
    email_content=email.body,
    context=customer_history,
    tone="professional"
)

# Email sentiment analysis
sentiment = await gmail.analyze_sentiment(email.body)
if sentiment.score < -0.5:
    await escalate_to_human(email, "Negative sentiment detected")

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from aimatrix.tools.communication import WhatsAppTool

whatsapp = WhatsAppTool(
    phone_number_id="your_phone_number_id",
    access_token="your_access_token"
)

# Handle incoming messages
@whatsapp.on_message
async def handle_message(message):
    if message.type == "text":
        # Process with AI agent
        response = await agent.process_message(message.text)
        
        # Send response
        await whatsapp.send_message(
            to=message.from_number,
            text=response.text
        )
    
    elif message.type == "image":
        # Process image with vision AI
        analysis = await agent.analyze_image(message.image_url)
        await whatsapp.send_message(
            to=message.from_number,
            text=f"I can see: {analysis.description}"
        )

# Send multimedia messages
await whatsapp.send_template_message(
    to="1234567890",
    template="order_confirmation",
    parameters={
        "customer_name": "John Doe",
        "order_number": "ORD-12345",
        "delivery_date": "Tomorrow"
    }
)

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from aimatrix.tools.communication import TwilioTool

twilio = TwilioTool(
    account_sid="your_account_sid",
    auth_token="your_auth_token",
    from_number="+1234567890"
)

# Send automated SMS notifications
await twilio.send_sms(
    to="+0987654321",
    body="Your order #12345 has been shipped and will arrive tomorrow."
)

# Send MMS with images
await twilio.send_mms(
    to="+0987654321",
    body="Here's your order tracking information:",
    media_urls=["https://example.com/tracking_image.png"]
)

# Handle incoming SMS with AI
@twilio.on_sms_received
async def handle_sms(message):
    # Process with AI agent
    response = await agent.process_sms(
        text=message.body,
        sender=message.from_number
    )
    
    # Send intelligent response
    await twilio.send_sms(
        to=message.from_number,
        body=response.text
    )

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from aimatrix.tools.communication import TelegramTool

telegram = TelegramTool(
    bot_token="your_bot_token"
)

# Handle various message types
@telegram.on_message
async def handle_telegram_message(update):
    message = update.message
    
    if message.text:
        # Text message processing
        response = await agent.process_message(message.text)
        await telegram.send_message(
            chat_id=message.chat.id,
            text=response.text,
            parse_mode="HTML"
        )
    
    elif message.photo:
        # Image processing
        file_info = await telegram.get_file(message.photo[-1].file_id)
        image_analysis = await agent.analyze_image(file_info.file_path)
        
        await telegram.send_message(
            chat_id=message.chat.id,
            text=f"🖼️ I see: {image_analysis.description}"
        )

# Create custom keyboards
keyboard = telegram.create_inline_keyboard([
    [{"text": "📞 Contact Sales", "callback_data": "contact_sales"}],
    [{"text": "💬 Support Chat", "callback_data": "support_chat"}],
    [{"text": "📋 Check Order", "callback_data": "check_order"}]
])

await telegram.send_message(
    chat_id=chat_id,
    text="How can I help you today?",
    reply_markup=keyboard
)

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from aimatrix.tools.communication import WebRTCTool

webrtc = WebRTCTool(
    stun_servers=["stun:stun.l.google.com:19302"],
    ice_servers=["turn:turnserver.com"]
)

# Start voice call with AI agent
@webrtc.on_call_started
async def handle_call(call_session):
    # Start AI voice assistant
    voice_agent = await agent.start_voice_session(
        language="en-US",
        voice="neural-female"
    )
    
    # Stream audio to AI and get responses
    async for audio_chunk in call_session.audio_stream:
        # Convert speech to text
        text = await speech_to_text(audio_chunk)
        
        # Process with AI
        response = await voice_agent.process_speech(text)
        
        # Convert response to speech and stream back
        audio_response = await text_to_speech(response.text)
        await call_session.send_audio(audio_response)

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from aimatrix.tools.communication import ZoomTool

zoom = ZoomTool(
    api_key="your_api_key",
    api_secret="your_api_secret",
    account_id="your_account_id"
)

# Create and manage meetings
meeting = await zoom.create_meeting(
    topic="AI Agent Consultation",
    start_time="2025-01-21T10:00:00Z",
    duration=30,
    settings={
        "waiting_room": True,
        "recording": "local",
        "auto_recording": "cloud"
    }
)

# AI agent joins meeting
@zoom.on_meeting_started
async def join_meeting(meeting_id):
    # Start meeting bot
    meeting_bot = await agent.join_zoom_meeting(
        meeting_id=meeting_id,
        meeting_password=meeting.password
    )
    
    # Real-time transcription and responses
    async for transcript in meeting_bot.live_transcript:
        # Process conversation with AI
        response = await agent.process_meeting_transcript(transcript)
        
        if response.should_respond:
            # AI agent speaks in meeting
            await meeting_bot.speak(response.text)

# Post-meeting analysis
@zoom.on_meeting_ended
async def analyze_meeting(meeting_id, recording_url):
    # Download and analyze recording
    analysis = await agent.analyze_meeting_recording(recording_url)
    
    # Generate and send meeting summary
    summary = await agent.generate_meeting_summary(analysis)
    await gmail.send_email(
        to=meeting.participants,
        subject=f"Meeting Summary: {meeting.topic}",
        body=summary.formatted_text
    )

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from aimatrix.tools.communication import VoiceTool

voice = VoiceTool(
    provider="elevenlabs",  # or "azure", "google", "aws"
    api_key="your_api_key"
)

# Text-to-speech with custom voices
audio_data = await voice.synthesize_speech(
    text="Hello! I'm your AI assistant. How can I help you today?",
    voice_id="custom_voice_clone",
    style="friendly",
    speed=1.0
)

# Speech-to-text with live transcription
@voice.on_speech_detected
async def handle_speech(audio_stream):
    # Real-time transcription
    transcript = await voice.transcribe_live(
        audio_stream=audio_stream,
        language="en-US",
        punctuation=True
    )
    
    # Process with AI agent
    response = await agent.process_voice_input(transcript.text)
    
    # Respond with synthesized voice
    response_audio = await voice.synthesize_speech(
        text=response.text,
        voice_id="ai_assistant_voice"
    )
    
    return response_audio

# Voice cloning for personalized responses
custom_voice = await voice.clone_voice(
    audio_samples=[
        "sample1.wav", "sample2.wav", "sample3.wav"
    ],
    voice_name="customer_service_voice"
)

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from aimatrix.tools.communication import SlackTool

slack = SlackTool(
    bot_token="xoxb-your-bot-token",
    app_token="xapp-your-app-token"
)

# Handle messages and mentions
@slack.on_mention
async def handle_mention(event):
    # Process mention with AI agent
    response = await agent.process_slack_message(
        text=event.text,
        channel=event.channel,
        user=event.user
    )
    
    # Reply in thread
    await slack.post_message(
        channel=event.channel,
        text=response.text,
        thread_ts=event.ts
    )

# Create interactive workflows
@slack.on_slash_command("/support")
async def handle_support_command(command):
    # Create interactive modal
    modal = {
        "type": "modal",
        "title": {"type": "plain_text", "text": "Support Request"},
        "blocks": [
            {
                "type": "input",
                "label": {"type": "plain_text", "text": "Issue Description"},
                "element": {"type": "plain_text_input", "multiline": True}
            },
            {
                "type": "input",
                "label": {"type": "plain_text", "text": "Priority"},
                "element": {
                    "type": "static_select",
                    "options": [
                        {"text": {"type": "plain_text", "text": "Low"}, "value": "low"},
                        {"text": {"type": "plain_text", "text": "Medium"}, "value": "medium"},
                        {"text": {"type": "plain_text", "text": "High"}, "value": "high"},
                        {"text": {"type": "plain_text", "text": "Critical"}, "value": "critical"}
                    ]
                }
            }
        ]
    }
    
    await slack.views_open(
        trigger_id=command.trigger_id,
        view=modal
    )

# Automated channel management
@slack.on_channel_created
async def setup_new_channel(channel):
    # Add AI agent to new channels
    await slack.join_channel(channel.id)
    
    # Send welcome message
    await slack.post_message(
        channel=channel.id,
        text=f"👋 Hi everyone! I'm your AI assistant for #{channel.name}. Type @ai-assistant to get help!"
    )

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from aimatrix.tools.communication import TeamsTool

teams = TeamsTool(
    client_id="your_client_id",
    client_secret="your_client_secret",
    tenant_id="your_tenant_id"
)

# Handle chat messages
@teams.on_message
async def handle_teams_message(activity):
    # Process message with AI agent
    response = await agent.process_teams_message(
        text=activity.text,
        conversation=activity.conversation,
        from_user=activity.from_property
    )
    
    # Send adaptive card response
    card = {
        "type": "AdaptiveCard",
        "version": "1.3",
        "body": [
            {
                "type": "TextBlock",
                "text": response.text,
                "wrap": True
            }
        ],
        "actions": [
            {
                "type": "Action.Submit",
                "title": "Mark as Resolved",
                "data": {"action": "resolve", "id": response.id}
            }
        ]
    }
    
    await teams.send_adaptive_card(
        conversation_id=activity.conversation.id,
        card=card
    )

# Meeting integration
@teams.on_meeting_started
async def join_meeting(meeting):
    # AI agent joins as participant
    meeting_agent = await agent.join_teams_meeting(meeting.join_url)
    
    # Take meeting notes
    async for transcript in meeting_agent.live_transcript:
        # Process and store notes
        notes = await agent.process_meeting_notes(transcript)
        await store_meeting_notes(meeting.id, notes)
    
    # Generate action items
    action_items = await agent.extract_action_items(meeting.full_transcript)
    
    # Create follow-up tasks
    for item in action_items:
        await teams.create_task(
            assignee=item.assignee,
            title=item.title,
            description=item.description,
            due_date=item.due_date
        )

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from aimatrix.tools.communication import CommunicationOrchestrator

orchestrator = CommunicationOrchestrator([
    gmail, slack, whatsapp, teams, telegram
])

# Unified message handling
@orchestrator.on_message
async def handle_any_message(message, platform):
    # Process message with context from all platforms
    customer = await get_customer_by_contact(message.sender, platform)
    
    response = await agent.process_message(
        text=message.text,
        context={
            "customer": customer,
            "platform": platform,
            "history": customer.communication_history
        }
    )
    
    # Send response on the same platform
    await platform.send_message(
        to=message.sender,
        text=response.text
    )

# Cross-platform notifications
async def notify_customer(customer_id, message, urgency="normal"):
    customer = await get_customer(customer_id)
    
    if urgency == "critical":
        # Use all available channels
        channels = customer.get_all_channels()
    else:
        # Use preferred channel
        channels = [customer.preferred_channel]
    
    for channel in channels:
        await orchestrator.send_message(
            platform=channel.platform,
            to=channel.address,
            text=message
        )

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from aimatrix.analytics import CommunicationAnalytics

analytics = CommunicationAnalytics([
    gmail, slack, whatsapp, teams
])

# Real-time metrics
metrics = await analytics.get_real_time_metrics()
print(f"Messages processed: {metrics.messages_processed}")
print(f"Average response time: {metrics.avg_response_time}s")
print(f"Customer satisfaction: {metrics.satisfaction_score}/5")

# Communication insights
insights = await analytics.generate_insights(
    timeframe="last_30_days",
    include=["sentiment", "topics", "resolution_rate"]
)

# Create dashboard
dashboard = await analytics.create_dashboard([
    "message_volume_over_time",
    "response_time_by_channel",
    "customer_satisfaction_trends",
    "agent_performance_metrics"
])

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from aimatrix.security import EncryptionManager

encryption = EncryptionManager(
    key_management="azure_key_vault",  # or "aws_kms", "google_kms"
    encryption_standard="AES-256-GCM"
)

# Encrypt outgoing messages
encrypted_message = await encryption.encrypt_message(
    message="Sensitive customer data...",
    recipient_key="customer_public_key"
)

# Decrypt incoming messages
decrypted_message = await encryption.decrypt_message(
    encrypted_message=incoming_message,
    private_key="agent_private_key"
)

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from aimatrix.compliance import ComplianceManager

compliance = ComplianceManager(
    standards=["GDPR", "HIPAA", "SOX", "PCI_DSS"]
)

# Data retention policies
@compliance.enforce_retention
async def handle_message(message):
    # Process message
    response = await agent.process_message(message.text)
    
    # Auto-delete after retention period
    await compliance.schedule_deletion(
        data=message,
        retention_period="7_years"  # or based on regulation
    )

# Audit logging
@compliance.audit_log
async def send_message(platform, recipient, message):
    await platform.send_message(recipient, message)
    
    # Automatic audit trail
    await compliance.log_communication(
        platform=platform.name,
        sender="ai_agent",
        recipient=recipient,
        message_hash=hash(message),
        timestamp=datetime.utcnow()
    )

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# Handle different message types intelligently
async def process_message_by_type(message):
    if message.type == "question":
        return await agent.answer_question(message.text)
    elif message.type == "complaint":
        return await agent.handle_complaint(message.text, escalate=True)
    elif message.type == "order_inquiry":
        return await agent.check_order_status(message.text)
    else:
        return await agent.general_response(message.text)

# Maintain conversation context
conversation_context = {}

async def maintain_context(user_id, message):
    if user_id not in conversation_context:
        conversation_context[user_id] = []
    
    conversation_context[user_id].append(message)
    
    # Keep last 10 messages for context
    if len(conversation_context[user_id]) > 10:
        conversation_context[user_id] = conversation_context[user_id][-10:]

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from aimatrix.tools.communication import RetryManager

retry_manager = RetryManager(
    max_retries=3,
    backoff_strategy="exponential",
    retry_on=["network_error", "rate_limit", "temporary_failure"]
)

@retry_manager.with_retry
async def send_critical_message(platform, recipient, message):
    try:
        await platform.send_message(recipient, message)
    except RateLimitError:
        # Wait and retry
        await asyncio.sleep(60)
        raise  # Will trigger retry
    except NetworkError:
        # Switch to backup platform
        backup_platform = get_backup_platform(platform)
        await backup_platform.send_message(recipient, message)

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# Install communication tools
pip install aimatrix[communication]

# Configure credentials
aimatrix configure --service gmail
aimatrix configure --service slack
aimatrix configure --service whatsapp

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from aimatrix import Agent
from aimatrix.tools.communication import *

# Create multi-channel agent
agent = Agent(
    name="customer-service-bot",
    tools=[
        GmailTool(),
        SlackTool(), 
        WhatsAppTool(),
        TwilioTool()
    ],
    instructions="""
    You are a customer service agent that handles inquiries across multiple channels.
    Always be helpful, professional, and accurate.
    Escalate complex issues to human agents when needed.
    """
)

# Deploy and start handling messages
agent.deploy(environment="production")