Integrations & Extensibility¶

Section: 7-integrations-extensibility
Document: Third-Party Integrations & Platform Extensibility
Audience: Developers, Integration Partners, Technical Decision Makers
Last Updated: 2025-12-30
Version: 1.0

🎯 Executive Summary¶

MachineAvatars integrates with best-in-class third-party services to deliver AI-powered chatbot experiences. This section documents all external integrations, API dependencies, and extensibility points.

Integration Categories:

🤖 AI/ML Providers - OpenAI, Anthropic, Google (9 LLM models)
☁️ Cloud Infrastructure - Microsoft Azure (Cosmos DB, Blob Storage, Container Apps)
💳 Payment Processing - Razorpay (India's leading payment gateway)
📊 Analytics & Monitoring - PostHog, DataDog, Loki/Grafana
🔍 Vector Database - Milvus (open-source, self-hosted)

🤖 AI/ML Provider Integrations¶

OpenAI / Azure OpenAI Service¶

Status: ✅ Primary LLM Provider

Models Used:

GPT-4-0613 - Complex reasoning, high-quality responses
GPT-3.5 Turbo 16K - Cost-effective, fast responses
GPT-4o Mini - Balanced cost/performance
text-embedding-ada-002 - Vector embeddings (1536 dimensions)

Integration Type: REST API

Endpoints:

# Azure OpenAI Service
BASE_URL = "https://{deployment}.openai.azure.com/openai/deployments/{model}/chat/completions"
API_VERSION = "2023-05-15"

# Headers
headers = {
    "api-key": "{AZURE_OPENAI_KEY}",  # From Azure Key Vault
    "Content-Type": "application/json"
}

# Request payload
payload = {
    "messages": [
        {"role": "system", "content": "You are a helpful assistant..."},
        {"role": "user", "content": "User query here"}
    ],
    "temperature": 0.7,
    "max_tokens": 500
}

Configuration:

Authentication: API key-based (rotating every 90 days)
Rate Limiting: 60 requests/minute per deployment
Timeout: 30 seconds
Retry Logic: Exponential backoff (3 retries)

Data Residency:

Azure OpenAI East US - Default for US/global customers
Azure OpenAI West Europe - For EU customers (GDPR)
Azure OpenAI Central India - For Indian customers (planned Q2 2025, DPDPA)

Cost Optimization:

Dynamic model routing (GPT-4 for complex, GPT-3.5 for simple queries)
Token usage monitoring per user/plan
Caching for repeated queries (planned)

Reference: AI/ML Architecture - Model Selection

Anthropic (Claude)¶

Status: ✅ Fallback/Alternative LLM Provider

Models Used:

Claude 3.5 Sonnet - Complex analysis, creative tasks

Integration Type: REST API

Endpoints:

BASE_URL = "https://api.anthropic.com/v1/messages"
API_KEY = "{ANTHROPIC_API_KEY}"  # From environment variables

headers = {
    "x-api-key": API_KEY,
    "anthropic-version": "2023-06-01",
    "content-type": "application/json"
}

payload = {
    "model": "claude-3-5-sonnet-20241022",
    "max_tokens": 1024,
    "messages": [
        {"role": "user", "content": "User query"}
    ]
}

Use Cases:

Primary LLM failover (if OpenAI unavailable)
Specialized tasks (long-form content, analysis)
Cost optimization (cheaper than GPT-4 for certain workloads)

Reference: AI/ML Architecture

Google (Gemini, Vertex AI TTS)¶

Status: ✅ Supplementary AI Provider

Services Used:

Gemini 2.0 Flash - Multimodal LLM (text, future: images)
Vertex AI Text-to-Speech - Voice synthesis

Integration Type: Google Cloud Platform SDK

Configuration:

from google.cloud import aiplatform
from google.cloud import texttospeech

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

# TTS example
client = texttospeech.TextToSpeechClient()
synthesis_input = texttospeech.SynthesisInput(text="Hello world")
voice = texttospeech.VoiceSelectionParams(
    language_code="en-US",
    name="en-US-Neural2-C"
)
audio_config = texttospeech.AudioConfig(
    audio_encoding=texttospeech.AudioEncoding.MP3
)
response = client.synthesize_speech(
    input=synthesis_input, voice=voice, audio_config=audio_config
)

Use Cases:

Gemini: Budget tier LLM (Free/Basic plans)
Vertex AI TTS: Alternative to Azure TTS, multilingual support

Authentication: Service account JSON key (stored in Azure Key Vault, planned)

☁️ Cloud Infrastructure Integrations¶

Microsoft Azure¶

Status: ✅ Primary Cloud Provider

Services Used:

Service	Purpose	Integration Type
Azure Cosmos DB (MongoDB API)	Primary database (9 collections)	PyMongo driver
Azure Blob Storage	File storage (documents, audio, avatars)	Azure SDK
Azure Container Apps	Microservices hosting (23 services)	Docker + Azure CLI
Azure OpenAI Service	LLM inference	REST API
Azure Key Vault (planned)	Secrets management	Azure SDK
Azure Neural TTS	Voice synthesis	Azure SDK

Cosmos DB Integration:

from pymongo import MongoClient

MONGO_URI = "mongodb://{account}.mongo.cosmos.azure.com:10255/?ssl=true&retrywrites=false"
client = MongoClient(MONGO_URI)
db = client['Machine_agent_prod']

# Collections
users = db['users_multichatbot_v2']
chatbots = db['chatbot_selections']
history = db['chatbot_history']

Blob Storage Integration:

from azure.storage.blob import BlobServiceClient

STORAGE_URL = "https://qablobmachineagents.blob.core.windows.net"
blob_service_client = BlobServiceClient(account_url=STORAGE_URL, credential="{key}")

# Upload file
blob_client = blob_service_client.get_blob_client(container="pdf-documents-prod", blob="file.pdf")
blob_client.upload_blob(data)

Reference: Infrastructure Documentation

💳 Payment Integration¶

Razorpay¶

Status: ✅ Primary Payment Gateway (India)

Integration: Razorpay Payment Gateway API

Supported Payment Methods:

Credit/Debit cards (Visa, Mastercard, RuPay, Amex)
UPI (Google Pay, PhonePe, Paytm)
Net banking (60+ banks)
Wallets (Paytm, PhonePe, Amazon Pay)

Integration Flow:

// Frontend: Initialize Razorpay checkout
const options = {
  key: "rzp_live_XXXX", // Razorpay API key
  amount: 49900, // Amount in paise (₹499.00)
  currency: "INR",
  name: "MachineAvatars",
  description: "Pro Plan Subscription",
  order_id: "order_XXXX", // From backend
  callback_url: "/api/payment/verify",
  prefill: {
    email: user.email,
    contact: user.phone,
  },
};
const rzp = new Razorpay(options);
rzp.open();

# Backend: Create order
import razorpay

client = razorpay.Client(auth=("rzp_live_XXXX", "secret_key"))
order = client.order.create({
    "amount": 49900,  # paise
    "currency": "INR",
    "receipt": f"receipt_{user_id}",
    "payment_capture": 1
})

# Verify payment signature
params_dict = {
    'razorpay_order_id': order_id,
    'razorpay_payment_id': payment_id,
    'razorpay_signature': signature
}
client.utility.verify_payment_signature(params_dict)

Webhooks:

payment.captured - Payment successful
payment.failed - Payment failed
subscription.charged - Recurring payment
subscription.cancelled - Subscription cancelled

Security:

✅ PCI DSS Level 1 compliant (Razorpay handles card data)
✅ Signature verification for all webhooks
✅ HTTPS-only communication

Reference: IP Licensing - Razorpay

📊 Analytics & Monitoring Integrations¶

PostHog (Product Analytics)¶

Status: ✅ Active

Integration Type: JavaScript SDK (frontend) + Python SDK (backend)

Events Tracked:

// Frontend events
posthog.capture("chatbot_created", {
  avatar_type: "3d",
  model: "gpt-4",
  subscription_tier: "pro",
});

posthog.capture("message_sent", {
  chatbot_id: "xxx",
  response_time_ms: 1847,
});

Use Cases:

User behavior analysis (funnel conversions)
Feature usage tracking
A/B testing (planned)

DataDog (APM & Monitoring)¶

Status: ✅ Active (planned)

Integration Type: DataDog Agent + Python tracing library

Configuration:

from ddtrace import tracer

@tracer.wrap(service="response-3d-service")
def generate_response(query):
    # Automatically traced
    pass

Metrics Collected:

Request latency (p50, p95, p99)
Error rates by service
Database query performance
External API latency (OpenAI, Claude)

Loki + Grafana (Logging & Dashboards)¶

Status: ✅ Active

Components:

Promtail: Log collector (runs on each container)
Loki: Log aggregation & storage
Grafana: Visualization & dashboards

Log Query Example:

{service="response-3d"} |= "error" | json | status_code >= 500

Reference: Monitoring & Observability

🔍 Vector Database Integration¶

Milvus (Open Source)¶

Status: ✅ Self-Hosted on Azure Container Instance

Integration Type: Python SDK (pymilvus)

Configuration:

from pymilvus import connections, Collection

# Connect to Milvus
connections.connect(
    alias="default",
    host="milvus-prod.eastus.azurecontainerapps.io",
    port="19530"
)

# Query collection
collection = Collection("embeddings")
search_params = {"metric_type": "L2", "params": {"nprobe": 10}}
results = collection.search(
    data=[query_embedding],
    anns_field="embedding",
    param=search_params,
    limit=5,
    partition_names=[f"User_{user_id}_Project_{project_id}"]
)

Why Self-Hosted:

✅ Full control over data (no vendor lock-in)
✅ Cost savings vs. managed vector DB
✅ Apache 2.0 license (commercial-friendly)

Reference: Vector Store Architecture

🔌 Extensibility & Future Integrations¶

Planned Integrations (2025 Roadmap)¶

Q1 2025:

Slack integration (chatbot deployment in Slack workspaces)
Microsoft Teams integration
Webhook support (outbound notifications)

Q2 2025:

Zapier integration (connect to 5,000+ apps)
WhatsApp Business API (conversational commerce)
Telegram Bot API

Q3 2025:

Custom LLM support (bring your own model)
GraphQL API (in addition to REST)

🔐 Security Best Practices¶

API Keys Management:

✅ Never hardcode keys in source code
✅ Use environment variables (transitioning to Azure Key Vault)
✅ Rotate keys regularly (90-180 days)
✅ Least privilege principle (separate keys per environment)

Network Security:

✅ HTTPS-only for all external APIs
✅ IP whitelisting where possible (Azure NSG)
✅ Rate limiting to prevent API abuse
✅ Request signing for webhooks

📞 Integration Support¶

Technical Questions: integrations@askgalore.com
Partnership Inquiries: partnerships@askgalore.com
API Documentation: Section 6 - API Documentation

AI/ML Architecture - LLM provider details
Infrastructure - Azure services configuration
IP Licensing - Third-party license audit
Security Architecture - API security, secrets management

"Great integrations enable great products." 🔌✨

Section 7 Complete: All Major Integrations Documented 🎯

Integrations & Extensibility¶

🎯 Executive Summary¶

🤖 AI/ML Provider Integrations¶

OpenAI / Azure OpenAI Service¶

Anthropic (Claude)¶

Google (Gemini, Vertex AI TTS)¶

☁️ Cloud Infrastructure Integrations¶

Microsoft Azure¶

💳 Payment Integration¶

Razorpay¶

📊 Analytics & Monitoring Integrations¶

PostHog (Product Analytics)¶

DataDog (APM & Monitoring)¶

Loki + Grafana (Logging & Dashboards)¶

🔍 Vector Database Integration¶

Milvus (Open Source)¶

🔌 Extensibility & Future Integrations¶

Planned Integrations (2025 Roadmap)¶

🔐 Security Best Practices¶

📞 Integration Support¶

🔗 Related Documentation¶