PlutosAI

<<<<<<< Current (Your changes)

PlutosAI — Enterprise Family Office Platform

A production-ready, AI-native family office and asset management platform built with microservices architecture. Designed for regulatory compliance, scalability, and AI-driven insights.

🏗️ Architecture Overview

This platform follows a microservices architecture with domain-driven design, event-driven communication, and comprehensive observability.

Core Services

🎯 Client Service (/clients) - Client onboarding, KYC, profile management
📊 Investment Service (/portfolios, /transactions) - Portfolio management, trading, valuations
⚖️ Compliance Service (/compliance) - AML/KYC checks, monitoring, audit trails
📈 Reporting Service (/reports) - Client reports, analytics, dashboards
🤖 AI Service (/insights) - ML models, recommendations, risk analysis
⛓️ Blockchain Service (/wallets, /transactions, /nfts) - Digital asset management, DeFi, NFTs
🚪 Gateway Service - API gateway, authentication, rate limiting

Technology Stack

Backend:

Language: Python 3.11
Framework: FastAPI (async, high-performance)
Database: PostgreSQL (async SQLAlchemy)
Message Queue: NATS (event-driven architecture)
Cache: Redis
Authentication: JWT with role-based access control

Infrastructure:

Containerization: Docker
Orchestration: Kubernetes
CI/CD: GitHub Actions
Monitoring: Prometheus + Grafana
Security: Container scanning, secrets management

Development:

Linting: Ruff
Type Checking: MyPy
Testing: Pytest with async support
API Docs: Auto-generated OpenAPI/Swagger

🚀 Quick Start

Prerequisites

Python 3.11+
Docker & Docker Compose
kubectl (for Kubernetes deployment)

Local Development

Clone and setup:

git clone https://github.com/your-org/plutosai.git
cd plutosai
python3.11 -m venv .venv
source .venv/bin/activate

Start infrastructure:

cd infrastructure/docker
docker-compose up -d

Install dependencies and run client service:

cd services/client-service
pip install -e '.[dev]'
uvicorn src.api:app --reload --host 0.0.0.0 --port 8001

Access services:
- API Gateway: http://localhost:8000
- Client Service: http://localhost:8001
- Grafana: http://localhost:3000 (admin/admin)
- Prometheus: http://localhost:9090

API Examples

Create a client:

curl -X POST "http://localhost:8001/clients" \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer YOUR_JWT_TOKEN" \
  -d '{
    "type": "individual",
    "first_name": "John",
    "last_name": "Doe",
    "email": "john.doe@example.com"
  }'

Get client details:

curl -X GET "http://localhost:8001/clients/{client_id}" \
  -H "Authorization: Bearer YOUR_JWT_TOKEN"

📁 Project Structure

plutosai/
├── services/                    # Microservices
│   ├── client-service/         # Client management
│   ├── investment-ops/         # Portfolio & trading
│   ├── compliance-service/     # Compliance & monitoring
│   ├── reporting-service/      # Reports & analytics
│   ├── ai-service/            # AI/ML features
│   ├── blockchain-service/    # Digital asset management
│   └── gateway-service/       # API gateway
├── shared/                     # Shared libraries
│   ├── domain/                # Domain models
│   │   ├── client.py         # Client domain models
│   │   ├── investment.py     # Investment domain models
│   │   ├── compliance.py     # Compliance domain models
│   │   └── blockchain.py     # Blockchain domain models
│   ├── auth/                  # Authentication utilities
│   ├── events/                # Event definitions
│   └── utils/                 # Common utilities
├── infrastructure/            # Infrastructure as Code
│   ├── docker/               # Docker Compose & configs
│   └── k8s/                  # Kubernetes manifests
├── monitoring/                # Observability
│   ├── prometheus/           # Metrics collection
│   └── grafana/             # Dashboards
├── tests/                     # Test suites
│   ├── integration/          # Integration tests
│   └── e2e/                  # End-to-end tests
├── docs/                      # Documentation
└── .github/workflows/         # CI/CD pipelines

🔐 Security & Compliance

Authentication & Authorization

JWT-based authentication with refresh tokens
Role-based access control (RBAC)
Multi-factor authentication support
Session management with Redis

Regulatory Compliance

AML/KYC: Integrated screening and monitoring
Audit Trails: Comprehensive logging of all actions
Data Privacy: GDPR/CCPA compliant data handling
Risk Management: Real-time risk scoring and alerts

Security Features

Container Security: Trivy vulnerability scanning
Secrets Management: Encrypted secrets in Kubernetes
Network Security: Service mesh with mTLS
API Security: Rate limiting, CORS, input validation

📊 Monitoring & Observability

Metrics

Application Metrics: Request latency, error rates, throughput
Business Metrics: Client onboarding funnel, portfolio performance
System Metrics: CPU, memory, disk usage, network I/O

Logging

Structured logging with correlation IDs
Centralized log aggregation
Log levels: DEBUG, INFO, WARN, ERROR
Retention policies and archival

Alerting

Real-time alerts for system issues
Compliance violation notifications
Performance degradation alerts
Automated incident response

🚀 Deployment

Environments

Development: Local Docker Compose
Staging: Kubernetes cluster for testing
Production: Highly available Kubernetes cluster

CI/CD Pipeline

Code Quality: Lint, type check, security scan
Build: Docker image creation and registry push
Test: Unit, integration, and E2E tests
Deploy: Automated deployment with rollbacks
Monitor: Health checks and alerting setup

🤖 AI/ML Features

Portfolio Analysis

Risk Assessment: Monte Carlo simulations, VaR calculations
Performance Attribution: Factor analysis and benchmarking
Rebalancing Recommendations: Tax-efficient portfolio optimization

Client Insights

Personalized Recommendations: ML-driven investment suggestions
Behavioral Analysis: Client interaction patterns and preferences
Market Intelligence: Real-time news and sentiment analysis

Compliance Automation

Transaction Monitoring: ML-based anomaly detection
KYC Enhancement: Automated document processing and verification
Risk Scoring: Predictive models for client risk assessment

⛓️ Blockchain Integration

Digital Asset Management

Multi-Blockchain Support: Ethereum, Polygon, BSC, Arbitrum, Bitcoin
Wallet Management: Hot, cold, multi-signature, and hardware wallets
Asset Types: Cryptocurrencies, tokens, NFTs, security tokens
DeFi Integration: Lending, staking, liquidity provision
Cross-Chain Bridges: Seamless asset transfers between networks

Key Features

Secure Custody: Institutional-grade wallet security
Transaction Monitoring: Real-time blockchain transaction tracking
NFT Portfolio: Complete NFT management and valuation
Yield Optimization: Automated DeFi yield farming
Compliance Integration: Blockchain transaction compliance reporting

Example Use Cases

# Create a multi-signature Ethereum wallet
curl -X POST "http://localhost:8006/wallets" \
  -H "Authorization: Bearer TOKEN" \
  -d '{
    "client_id": "client-123",
    "name": "ETH Multi-Sig",
    "wallet_type": "multisig_wallet",
    "blockchain": "ethereum",
    "required_signatures": 2,
    "total_signers": 3
  }'

# Register an NFT in the portfolio
curl -X POST "http://localhost:8006/nfts" \
  -H "Authorization: Bearer TOKEN" \
  -d '{
    "wallet_id": "wallet-123",
    "contract_address": "0xBC4CA0EdA7647A8aB7C2061c2E118A18a936f13D",
    "token_id": "1234",
    "token_standard": "ERC-721"
  }'

🤝 Contributing

Fork the repository
Create a feature branch: git checkout -b feature/amazing-feature
Make your changes and add tests
Run the full test suite: make test-all
Commit your changes: git commit -m 'Add amazing feature'
Push to the branch: git push origin feature/amazing-feature
Open a Pull Request

Development Setup

# Install development dependencies
pip install -e '.[dev]'

# Run tests
pytest

# Run linter
ruff check .

# Run type checker
mypy shared/

# Start local services
docker-compose up -d

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙋 Support

Issues: GitHub Issues
Discussions: GitHub Discussions
Documentation: Wiki

🎯 Roadmap

Phase 1 (Current)

Phase 2 (Next)

Phase 3 (Future)

Blockchain integration
Advanced analytics
Regulatory reporting automation
Global expansion features

Built with ❤️ for the future of wealth management.

A comprehensive AI-powered platform for intelligent data processing and analysis.

Overview

PlutosAI is a modern, scalable platform that provides intelligent data processing capabilities through a comprehensive set of APIs and components. The platform is designed to handle various AI tasks including data analysis, natural language processing, and machine learning operations.

Key Features

RESTful API: Complete REST API for all operations
Real-time Processing: Stream-based data processing capabilities
Scalable Architecture: Built for high-performance and scalability
Comprehensive SDK: Multiple language support (JavaScript, Python, Go)
Web Components: Reusable UI components for rapid development

Installation

Prerequisites

Node.js 18+ (for JavaScript/TypeScript usage)
Python 3.8+ (for Python SDK)
Go 1.19+ (for Go SDK)

Quick Setup

# Clone the repository
git clone https://github.com/your-org/plutos-ai.git
cd plutos-ai

# Install dependencies
npm install

# Set up environment variables
cp .env.example .env
# Edit .env with your configuration

# Start the development server
npm run dev

Quick Start

Using the JavaScript SDK

import { PlutosAI } from '@plutos-ai/sdk';

const client = new PlutosAI({
  apiKey: 'your-api-key',
  environment: 'production'
});

// Process data
const result = await client.processData({
  input: 'Your input data',
  model: 'gpt-4',
  options: {
    temperature: 0.7,
    maxTokens: 1000
  }
});

console.log(result.output);

Using the Python SDK

from plutos_ai import PlutosAI

client = PlutosAI(
    api_key="your-api-key",
    environment="production"
)

# Process data
result = client.process_data(
    input="Your input data",
    model="gpt-4",
    options={
        "temperature": 0.7,
        "max_tokens": 1000
    }
)

print(result.output)

API Documentation

Authentication

All API requests require authentication using an API key. Include your API key in the request headers:

Authorization: Bearer YOUR_API_KEY

Base URL

Production: https://api.plutos-ai.com/v1
Staging: https://staging-api.plutos-ai.com/v1
Development: http://localhost:3000/v1

Core Endpoints

Data Processing

POST /process

Process data using AI models.

Request Body:

{
  "input": "string",
  "model": "string",
  "options": {
    "temperature": "number",
    "maxTokens": "number",
    "topP": "number"
  }
}

Response:

{
  "id": "string",
  "output": "string",
  "model": "string",
  "usage": {
    "promptTokens": "number",
    "completionTokens": "number",
    "totalTokens": "number"
  },
  "createdAt": "string"
}

Example:

curl -X POST https://api.plutos-ai.com/v1/process \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "input": "Analyze this text for sentiment",
    "model": "gpt-4",
    "options": {
      "temperature": 0.7,
      "maxTokens": 1000
    }
  }'

Batch Processing

POST /batch

Process multiple inputs in a single request.

Request Body:

{
  "inputs": ["string"],
  "model": "string",
  "options": {
    "temperature": "number",
    "maxTokens": "number"
  }
}

Response:

{
  "id": "string",
  "results": [
    {
      "input": "string",
      "output": "string",
      "usage": {
        "promptTokens": "number",
        "completionTokens": "number",
        "totalTokens": "number"
      }
    }
  ],
  "totalUsage": {
    "promptTokens": "number",
    "completionTokens": "number",
    "totalTokens": "number"
  }
}

Stream Processing

POST /stream

Stream data processing results in real-time.

Request Body:

{
  "input": "string",
  "model": "string",
  "options": {
    "temperature": "number",
    "maxTokens": "number"
  }
}

Response (Server-Sent Events):

data: {"chunk": "partial", "output": "Hello"}

data: {"chunk": "partial", "output": " world"}

data: {"chunk": "final", "output": "Hello world", "usage": {...}}

Error Handling

All API endpoints return standard HTTP status codes:

200 - Success
400 - Bad Request
401 - Unauthorized
403 - Forbidden
404 - Not Found
429 - Rate Limited
500 - Internal Server Error

Error responses include detailed information:

{
  "error": {
    "code": "string",
    "message": "string",
    "details": "object"
  }
}

Components

Web Components

PlutosAI provides a set of reusable web components for building AI-powered interfaces.

PlutosAIProcessor

A component for processing data with AI models.

<plutos-ai-processor
  api-key="your-api-key"
  model="gpt-4"
  placeholder="Enter your text here..."
  @result="handleResult"
  @error="handleError">
</plutos-ai-processor>

Properties:

api-key (string): Your PlutosAI API key
model (string): AI model to use
placeholder (string): Input placeholder text
temperature (number): Model temperature (0-1)
max-tokens (number): Maximum tokens to generate

Events:

result: Fired when processing completes
error: Fired when an error occurs
loading: Fired when processing starts

PlutosAIStream

A component for real-time streaming AI responses.

<plutos-ai-stream
  api-key="your-api-key"
  model="gpt-4"
  @chunk="handleChunk"
  @complete="handleComplete">
</plutos-ai-stream>

Properties:

api-key (string): Your PlutosAI API key
model (string): AI model to use
auto-start (boolean): Start streaming automatically

Events:

chunk: Fired for each stream chunk
complete: Fired when streaming completes
error: Fired when an error occurs

React Components

usePlutosAI Hook

A React hook for integrating PlutosAI functionality.

import { usePlutosAI } from '@plutos-ai/react';

function MyComponent() {
  const { processData, isLoading, error, result } = usePlutosAI({
    apiKey: 'your-api-key',
    model: 'gpt-4'
  });

  const handleSubmit = async (input) => {
    await processData(input);
  };

  return (
    <div>
      {isLoading && <div>Processing...</div>}
      {error && <div>Error: {error.message}</div>}
      {result && <div>Result: {result.output}</div>}
    </div>
  );
}

PlutosAIProvider

A React context provider for PlutosAI configuration.

import { PlutosAIProvider } from '@plutos-ai/react';

function App() {
  return (
    <PlutosAIProvider
      apiKey="your-api-key"
      environment="production"
    >
      <MyComponent />
    </PlutosAIProvider>
  );
}

Examples

Basic Text Processing

import { PlutosAI } from '@plutos-ai/sdk';

const client = new PlutosAI({ apiKey: 'your-api-key' });

// Simple text processing
const result = await client.processData({
  input: 'What is the capital of France?',
  model: 'gpt-4'
});

console.log(result.output); // "The capital of France is Paris."

Sentiment Analysis

const sentimentResult = await client.processData({
  input: 'I love this product! It works perfectly.',
  model: 'gpt-4',
  options: {
    systemPrompt: 'Analyze the sentiment of the following text. Respond with only: positive, negative, or neutral.',
    temperature: 0.1
  }
});

console.log(sentimentResult.output); // "positive"

Batch Processing

const batchResult = await client.batchProcess({
  inputs: [
    'Hello, how are you?',
    'What is the weather like?',
    'Tell me a joke'
  ],
  model: 'gpt-4'
});

batchResult.results.forEach((result, index) => {
  console.log(`Input ${index + 1}:`, result.output);
});

Streaming Responses

const stream = await client.streamProcess({
  input: 'Write a short story about a robot.',
  model: 'gpt-4'
});

for await (const chunk of stream) {
  if (chunk.chunk === 'partial') {
    process.stdout.write(chunk.output);
  } else if (chunk.chunk === 'final') {
    console.log('\nComplete!');
  }
}

Web Component Integration

<!DOCTYPE html>
<html>
<head>
  <script src="https://unpkg.com/@plutos-ai/components"></script>
</head>
<body>
  <plutos-ai-processor
    api-key="your-api-key"
    model="gpt-4"
    placeholder="Ask me anything..."
    @result="handleResult">
  </plutos-ai-processor>

  <script>
    function handleResult(event) {
      console.log('Result:', event.detail);
    }
  </script>
</body>
</html>

React Integration

import React, { useState } from 'react';
import { usePlutosAI } from '@plutos-ai/react';

function ChatInterface() {
  const [messages, setMessages] = useState([]);
  const { processData, isLoading } = usePlutosAI({
    apiKey: 'your-api-key',
    model: 'gpt-4'
  });

  const sendMessage = async (text) => {
    const userMessage = { role: 'user', content: text };
    setMessages(prev => [...prev, userMessage]);

    const result = await processData(text);
    const aiMessage = { role: 'assistant', content: result.output };
    setMessages(prev => [...prev, aiMessage]);
  };

  return (
    <div>
      {messages.map((msg, index) => (
        <div key={index} className={msg.role}>
          {msg.content}
        </div>
      ))}
      {isLoading && <div>AI is thinking...</div>}
    </div>
  );
}

Contributing

We welcome contributions! Please see our Contributing Guide for details.

Development Setup

# Fork and clone the repository
git clone https://github.com/your-username/plutos-ai.git
cd plutos-ai

# Install dependencies
npm install

# Run tests
npm test

# Start development server
npm run dev

# Build for production
npm run build

Code Style

Follow the existing code style
Write tests for new features
Update documentation for API changes
Use conventional commits

License

This project is licensed under the MIT License - see the LICENSE file for details.

Support

Documentation: docs.plutos-ai.com
API Reference: api.plutos-ai.com
Issues: GitHub Issues
Discussions: GitHub Discussions
Email: support@plutos-ai.com

PlutosAI - Intelligent data processing for the modern web.

Incoming (Background Agent changes)

Name		Name	Last commit message	Last commit date
Latest commit History 2 Commits
.cursor		.cursor
.github/workflows		.github/workflows
.vscode		.vscode
CodexGPT		CodexGPT
data/dbt/models/gold		data/dbt/models/gold
docs		docs
infrastructure		infrastructure
monitoring		monitoring
reference		reference
services		services
shared		shared
src		src
templates		templates
tests		tests
.DS_Store		.DS_Store
.python-version		.python-version
.ruff.toml		.ruff.toml
API_REFERENCE.md		API_REFERENCE.md
COMPONENT_DOCUMENTATION.md		COMPONENT_DOCUMENTATION.md
EXAMPLES_AND_TUTORIALS.md		EXAMPLES_AND_TUTORIALS.md
Makefile		Makefile
README.md		README.md
SDK_DOCUMENTATION.md		SDK_DOCUMENTATION.md
plutosai.db		plutosai.db
pyproject.toml		pyproject.toml

Folders and files

Latest commit

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PlutosAI — Enterprise Family Office Platform

🏗️ Architecture Overview

Core Services

Technology Stack

🚀 Quick Start

Prerequisites

Local Development

API Examples

📁 Project Structure

🔐 Security & Compliance

Authentication & Authorization

Regulatory Compliance

Security Features

📊 Monitoring & Observability

Metrics

Logging

Alerting

🚀 Deployment

Environments

CI/CD Pipeline

🤖 AI/ML Features

Portfolio Analysis

Client Insights

Compliance Automation

⛓️ Blockchain Integration

Digital Asset Management

Key Features

Example Use Cases

🤝 Contributing

Development Setup

📄 License

🙋 Support

🎯 Roadmap

Phase 1 (Current)

Phase 2 (Next)

Phase 3 (Future)

Built with ❤️ for the future of wealth management.

PlutosAI

Table of Contents

Overview

Key Features

Installation

Prerequisites

Quick Setup

Quick Start

Using the JavaScript SDK

Using the Python SDK

API Documentation

Authentication

Base URL

Core Endpoints

Data Processing

POST /process

Batch Processing

POST /batch

Stream Processing

POST /stream

Error Handling

Components

Web Components

PlutosAIProcessor

PlutosAIStream

React Components

usePlutosAI Hook

PlutosAIProvider

Examples

Basic Text Processing

Sentiment Analysis

Batch Processing

Streaming Responses

Web Component Integration

React Integration

Contributing

Development Setup

Code Style

License

Packages