RYU Introduction

Your AI Partner powered by PandaV2 SDK

Next Generation AI Companion

Meet RYU (龍一ryu)

RYU represents the pinnacle of AI companion technology, combining advanced machine learning with emotional intelligence to create meaningful, personalized interactions that adapt and evolve with each user.

Overview

RYU is an advanced AI companion platform built on the robust PandaV2 SDK infrastructure. Designed to provide authentic, emotionally intelligent interactions, RYU combines cutting-edge natural language processing with sophisticated personality modeling to create a truly engaging companion experience.

Advanced AI Architecture

Built on PandaV2 SDK with multi-layered neural networks for sophisticated understanding and response generation.

Emotional Intelligence

Recognizes and responds to emotional cues, building deeper connections through empathetic interactions.

Real-time Adaptation

Continuously learns and adapts to user preferences, conversation patterns, and personality traits.

Privacy-First Design

End-to-end encryption and privacy-preserving techniques ensure user data remains secure and confidential.

Quick Start Guide

Get up and running with RYU in minutes

Install PandaV2 SDK

Set up the foundational AI framework

# Install PandaV2 SDK
pip install pandav2-sdk>=2.1.0

# Verify installation
python -c "import pandav2; print(pandav2.__version__)"

Initialize RYU Companion

Create your first RYU instance

from pandav2 import RYUCompanion

# Initialize RYU with default configuration
ryu = RYUCompanion(
    api_key="your_api_key",
    model_version="v2.2.0",
    personality_profile="friendly_adaptive"
)

# Start conversation
response = ryu.chat("Hello RYU!")

Configure Personality

Customize RYU's personality traits

# Set personality parameters
ryu.configure_personality({
    "empathy_level": 0.8,
    "humor_style": "witty",
    "formality": "casual",
    "interests": ["technology", "art", "philosophy"]
})

# Enable emotional intelligence
ryu.enable_emotion_detection(True)

Start Interacting

Begin your conversation with RYU

# Basic conversation
messages = [
    {"role": "user", "content": "Tell me about yourself"},
    {"role": "assistant", "content": ryu.chat("Tell me about yourself")}
]

# Enable memory for persistent conversations
ryu.enable_memory(session_id="user_123")
print(ryu.chat("What did we talk about yesterday?"))

Installation

Complete installation guide for PandaV2 SDK and RYU

System Requirements

Minimum Requirements

Python 3.9+
8GB RAM
2GB Storage
Internet Connection

Installation Methods

PIP Installation (Recommended)

# Install latest stable version
pip install pandav2-sdk

# Install specific version
pip install pandav2-sdk==2.2.0

# Install with optional dependencies
pip install pandav2-sdk[voice,vision,gpu]

Note: GPU acceleration requires CUDA 11.8+ and compatible GPU drivers.

Docker Installation

# Pull official Docker image
docker pull pandav2/ryu-companion:latest

# Run with basic configuration
docker run -d \
  --name ryu-companion \
  -p 8080:8080 \
  -e API_KEY=your_api_key \
  pandav2/ryu-companion:latest

# Run with GPU support
docker run --gpus all -d \
  --name ryu-companion-gpu \
  -p 8080:8080 \
  pandav2/ryu-companion:gpu-latest

Development Installation

# Clone repository
git clone https://github.com/pandav2/ryu-companion.git
cd ryu-companion

# Create virtual environment
python -m venv ryu-env
source ryu-env/bin/activate  # On Windows: ryu-env\Scripts\activate

# Install in development mode
pip install -e .[dev]

# Run tests
pytest tests/

Initial Configuration

API Key Setup

Configure your PandaV2 API credentials:

# Method 1: Environment Variables
export PANDAV2_API_KEY="your_api_key_here"
export PANDAV2_MODEL_VERSION="v2.2.0"

# Method 2: Configuration File
echo '{
  "api_key": "your_api_key_here",
  "model_version": "v2.2.0",
  "personality_profile": "default"
}' > ~/.pandav2/config.json

# Method 3: Programmatic Setup
from pandav2 import configure
configure(api_key="your_api_key_here")

Basic Configuration

Create your first RYU configuration:

# config.py
from pandav2 import RYUCompanion

# Basic configuration
config = {
    "personality": {
        "empathy_level": 0.8,
        "humor_style": "witty",
        "formality": "casual"
    },
    "capabilities": {
        "voice": True,
        "vision": False,
        "memory": True
    },
    "limits": {
        "max_conversation_length": 1000,
        "response_timeout": 30
    }
}

# Initialize RYU
ryu = RYUCompanion(config=config)

Verify Installation

Run this verification script to ensure everything is working:

# verify_installation.py
from pandav2 import RYUCompanion
import sys

try:
    # Initialize RYU
    ryu = RYUCompanion()
    
    # Test basic functionality
    response = ryu.chat("Hello, are you working correctly?")
    print(f"✅ Installation successful!")
    print(f"RYU Response: {response}")
    
except Exception as e:
    print(f"❌ Installation failed: {e}")
    sys.exit(1)

AI Companion Architecture

Deep dive into RYU's technical architecture and design principles

System Architecture

Multi-Layer Processing

Hierarchical processing layers for understanding, reasoning, and response generation

Input Processing

Context Analysis

Response Generation

Output Formatting

Distributed Intelligence

Modular AI components that work together seamlessly

Language Model

Personality Engine

Memory System

Emotion Detector

Real-time Pipeline

Low-latency processing pipeline for immediate responses

Stream Processing

Parallel Execution

Caching Layer

Load Balancing

Core Components

Natural Language Understanding (NLU)

Processing Pipeline

Tokenization & Preprocessing

Intent Classification

Entity Extraction

Sentiment Analysis

Context Integration

Example Code

# NLU Processing
nlu = RYUCompanion.NLU()

# Process user input
result = nlu.process(
    text="I'm feeling sad today",
    context=conversation_history
)

print(result.intent)      # "emotional_support"
print(result.sentiment)   # "negative"
print(result.entities)    # {"emotion": "sad"}

Response Generation Engine

Generation Strategies

Template-based

Structured responses for common scenarios

Neural Generation

AI-generated responses using language models

Hybrid Approach

Combination of templates and neural generation

Personality Filtering

Responses adjusted for personality consistency

Generation Pipeline

# Response Generation
generator = RYUCompanion.Generator()

response = generator.generate(
    intent=nlu_result.intent,
    context=conversation_context,
    personality=user_personality,
    constraints={
        "max_length": 150,
        "tone": "empathetic",
        "include_emotion": True
    }
)

print(response.text)
print(response.confidence)
print(response.emotion_tags)

Context Management System

RYU maintains sophisticated context awareness across conversations, enabling coherent and meaningful interactions that build upon previous exchanges.

Short-term Context

Current conversation thread and immediate context

Current session

Medium-term Context

Recent conversation patterns and preferences

Past 7 days

Long-term Context

Persistent user profile and relationship history

Lifetime

Performance & Scalability

Performance Metrics

Response Time

Average response latency

< 500ms

Throughput

Concurrent request handling

10K req/sec

Accuracy

Intent classification accuracy

94.5%

Uptime

Service availability

99.9%

Scalability Features

Horizontal auto-scaling

Load balancing across regions

Caching and CDN optimization

Database sharding and replication

Microservices architecture

Container orchestration

PandaV2 SDK Integration

Comprehensive guide to leveraging PandaV2 SDK capabilities in RYU

SDK Overview

PandaV2 SDK Features

Advanced neural language models
Multi-modal processing capabilities
Real-time inference optimization
Scalable cloud infrastructure
Enterprise-grade security
Extensive customization options

SDK Integration

from pandav2 import (
    LanguageModel,
    PersonalityEngine,
    MemorySystem,
    EmotionDetector
)

# Initialize core components
model = LanguageModel("gpt-4-ryu")
personality = PersonalityEngine()
memory = MemorySystem()
emotions = EmotionDetector()

# Create RYU instance
ryu = RYUCompanion(
    model=model,
    personality=personality,
    memory=memory,
    emotions=emotions
)

Core SDK Components

Language Model Engine

Model Capabilities

Text Generation

High-quality natural language generation with contextual awareness

max_tokenstemperaturetop_p

Text Understanding

Deep semantic understanding and intent recognition

context_windowattention_layers

Multi-turn Conversations

Coherent dialogue management across multiple exchanges

conversation_historymemory_integration

Usage Example

# Initialize language model
model = LanguageModel(
    model_name="gpt-4-ryu",
    temperature=0.7,
    max_tokens=150,
    context_window=4096
)

# Generate response
response = model.generate(
    prompt="User: I had a difficult day at work",
    context=conversation_history,
    personality_traits={
        "empathy": 0.9,
        "supportiveness": 0.8
    }
)

print(response.text)
print(f"Confidence: {response.confidence}")
print(f"Tokens used: {response.token_usage}")

Multi-Modal Processing

Text Processing

Advanced NLP with sentiment analysis and intent recognition

Voice Processing

Speech-to-text, voice synthesis, and audio analysis

Vision Processing

Image recognition, scene understanding, and visual context

Multi-Modal Implementation

# Multi-modal processing
from pandav2 import MultiModalProcessor

processor = MultiModalProcessor()

# Process different input types
text_result = processor.process_text("How are you feeling?")
voice_result = processor.process_audio(audio_file)
image_result = processor.process_image(image_data)

# Combine modalities for context
combined_context = processor.combine_modalities([
    text_result, voice_result, image_result
])

# Generate contextually aware response
response = ryu.generate_response(
    context=combined_context,
    response_modality="text+voice"
)

SDK Configuration & Optimization

Performance Tuning

Batch Size

Optimal batch size for inference

8-32

Cache Size

Memory allocation for response caching

1000MB

Timeout

Maximum response generation time

30s

Concurrency

Maximum concurrent requests

50

Configuration Example

# SDK Configuration
config = {
    "model": {
        "name": "gpt-4-ryu",
        "temperature": 0.7,
        "max_tokens": 200,
        "top_p": 0.9
    },
    "performance": {
        "batch_size": 16,
        "cache_enabled": True,
        "cache_size_mb": 1000,
        "timeout_seconds": 30
    },
    "features": {
        "voice_enabled": True,
        "vision_enabled": True,
        "memory_enabled": True,
        "emotion_detection": True
    }
}

# Apply configuration
pandav2.configure(config)

API Reference

Core Classes

RYUCompanion

Main companion interface

LanguageModel

Text generation and understanding

PersonalityEngine

Personality trait management

MemorySystem

Conversation memory and context

EmotionDetector

Emotional state analysis

MultiModalProcessor

Multi-modal input processing

Key Methods

chat(message)

Send message and get response

configure_personality(traits)

Set personality parameters

enable_memory(session_id)

Enable conversation memory

process_emotion(text)

Detect emotional content

generate_response(context)

Generate contextual response

get_conversation_history()

Retrieve chat history

Personality Engine

Create consistent, believable personality traits that evolve with interactions

Personality System Overview

RYU's personality system creates consistent, believable character traits through:

Dynamic Trait Modeling

Adaptive personality parameters that evolve based on interactions

Contextual Responses

Situation-aware responses that maintain character consistency

Personality Configuration

Basic Personality Setup

# Configure RYU's personality
personality_config = {
    "core_traits": {
        "empathy_level": 0.8,        # 0.0 - 1.0
        "humor_style": "witty",       # witty, playful, dry, warm
        "formality": "casual",        # formal, casual, friendly
        "optimism": 0.7,             # 0.0 - 1.0
        "assertiveness": 0.6         # 0.0 - 1.0
    },
    "communication_style": {
        "verbosity": "moderate",      # brief, moderate, detailed
        "emotional_expressiveness": 0.8,
        "use_emojis": True,
        "conversational_initiative": 0.7
    },
    "interests_and_knowledge": {
        "primary_interests": ["technology", "philosophy", "art"],
        "expertise_areas": ["AI", "programming", "creative_writing"],
        "curiosity_level": 0.9
    }
}

ryu.configure_personality(personality_config)

Adaptive Learning

Personality traits that evolve based on user interactions and feedback

Key Features

User preference learning
Communication style adaptation
Emotional response refinement
Interest alignment over time

Code Example

# Enable adaptive learning
ryu.personality.enable_adaptation(
    learning_rate=0.1,
    adaptation_areas=["empathy", "humor", "interests"],
    feedback_integration=True
)

Emotional Intelligence

Advanced emotional recognition and appropriate response generation

Key Features

Sentiment analysis
Emotional state tracking
Empathetic response generation
Mood-based adaptation

Code Example

# Configure emotional intelligence
ryu.emotions.configure({
    "sensitivity": 0.8,
    "emotional_memory": True,
    "mood_tracking": True,
    "empathy_threshold": 0.7
})

Consistency Engine

Ensures personality consistency across all interactions and contexts

Key Features

Trait coherence validation
Response consistency checking
Character arc maintenance
Behavioral pattern analysis

Code Example

# Ensure personality consistency
consistency_check = ryu.personality.validate_response(
    response_text=generated_response,
    context=conversation_context,
    strictness=0.8
)

Memory System

Advanced memory architecture for persistent relationships and context

Memory Architecture

Advanced memory architecture enables RYU to build lasting relationships:

Long-term Memory: Persistent storage of user preferences and conversation history

Episodic Recall: Contextual memory of significant events and interactions

Adaptive Learning: Continuous improvement through interaction patterns

Memory Types

Short-term Memory

Current conversation thread and immediate context

Duration:Current session

Capacity:2K tokens

Medium-term Memory

Recent conversation patterns and preferences

Duration:Past 7 days

Capacity:50K tokens

Long-term Memory

Persistent user profile and relationship history

Duration:Lifetime

Capacity:Unlimited

Memory Implementation

Memory System Setup

# Initialize memory system
memory_config = {
    "short_term": {
        "capacity_tokens": 2000,
        "retention_policy": "session_based"
    },
    "medium_term": {
        "capacity_tokens": 50000,
        "retention_days": 7,
        "compression_enabled": True
    },
    "long_term": {
        "persistent_storage": True,
        "encryption_enabled": True,
        "backup_frequency": "daily"
    },
    "episodic_memory": {
        "event_detection": True,
        "significance_threshold": 0.7,
        "relationship_tracking": True
    }
}

# Configure memory system
ryu.memory.configure(memory_config)

# Enable memory for user session
ryu.memory.enable_session("user_12345")

# Store important information
ryu.memory.store_fact("user_preferences", {
    "communication_style": "casual",
    "interests": ["AI", "music", "travel"],
    "important_dates": {"birthday": "1990-05-15"}
})

# Retrieve relevant memories
relevant_memories = ryu.memory.recall(
    query="music preferences", 
    memory_types=["medium_term", "long_term"],
    max_results=5
)

Conversation Flows

Design natural, engaging conversation patterns with RYU

Conversation Flow Types

Greeting & Introduction

First-time user interactions and session openings

Flow Steps

User enters conversation

RYU detects new/returning user

Personalized greeting based on history

Context establishment

Topic suggestion or question

Implementation

# Greeting flow implementation
def handle_greeting(user_id, is_returning_user=False):
    if is_returning_user:
        last_conversation = ryu.memory.get_last_interaction(user_id)
        greeting = ryu.generate_contextual_greeting(last_conversation)
    else:
        greeting = ryu.generate_introduction_greeting()
    
    return {
        "message": greeting,
        "suggested_topics": ryu.get_conversation_starters(),
        "personality_introduction": True
    }

Topic Exploration

Deep-dive conversations on user interests

Flow Steps

User expresses interest in topic

RYU assesses knowledge level

Tailored explanation or discussion

Follow-up questions

Implementation

# Topic exploration flow
def explore_topic(topic, user_knowledge_level):
    context = ryu.knowledge.get_topic_context(topic)
    
    response = ryu.generate_response(
        topic=topic,
        user_level=user_knowledge_level,
        conversation_style="exploratory",
        include_questions=True
    )
    
    return {
        "explanation": response.content,
        "follow_up_questions": response.questions,
        "related_topics": ryu.get_related_topics(topic)
    }

Emotional Support

Providing comfort and empathy during difficult times

Flow Steps

Emotional state detection

Empathetic acknowledgment

Supportive response generation

Coping strategy suggestions

Check-in and follow-up

Implementation

# Emotional support flow
def provide_emotional_support(user_message):
    emotion_analysis = ryu.emotions.analyze(user_message)
    
    if emotion_analysis.distress_level > 0.7:
        response = ryu.generate_supportive_response(
            emotion_state=emotion_analysis,
            empathy_level=0.9,
            include_coping_strategies=True
        )
    
    # Schedule follow-up check
    ryu.schedule_checkin(user_id, hours=24)
    
    return response

Emotional Intelligence

Advanced emotional recognition and empathetic response generation

Emotion Detection & Analysis

Detection Methods

Lexical Analysis

Keyword and phrase-based emotion detection

Syntactic Patterns

Grammar and sentence structure analysis

Contextual Understanding

Situation-aware emotional interpretation

Multimodal Fusion

Combined text, voice, and visual cues

Emotion Detection Code

# Emotion detection pipeline
emotion_result = ryu.emotions.analyze(
    text="I'm feeling overwhelmed with work",
    context=conversation_history,
    include_intensity=True,
    detect_subtext=True
)

print(emotion_result.primary_emotion)  # "stress"
print(emotion_result.intensity)       # 0.8
print(emotion_result.secondary_emotions)  # ["anxiety", "fatigue"]
print(emotion_result.confidence)      # 0.92

# Generate empathetic response
response = ryu.generate_empathetic_response(
    emotion_state=emotion_result,
    support_level="high"
)

Joy

happinessexcitementsatisfaction

Sadness

grieflonelinessdisappointment

Anger

frustrationirritationrage

Fear

anxietyworrynervousness

Personalization

Tailored experiences that adapt to individual user preferences

Personalization Engine

Personalization Implementation

# User profile creation and management
user_profile = ryu.personalization.create_profile(
    user_id="user_12345",
    preferences={
        "communication_style": "casual",
        "interests": ["technology", "music", "cooking"],
        "learning_pace": "moderate",
        "humor_preference": "witty",
        "conversation_depth": "detailed"
    },
    personality_traits={
        "openness": 0.8,
        "extraversion": 0.6,
        "agreeableness": 0.9
    }
)

# Adaptive response generation
response = ryu.generate_personalized_response(
    user_message="Tell me about AI",
    user_profile=user_profile,
    adaptation_level=0.8
)

# Learning from interactions
ryu.personalization.learn_from_interaction(
    user_id="user_12345",
    interaction_data={
        "response_quality_rating": 4.5,
        "engagement_level": "high",
        "topic_interest": "very_interested"
    }
)

Behavioral Adaptation

RYU learns from user interactions and adjusts behavior accordingly

Response style learning

Topic preference tracking

Conversation pattern analysis

Content Customization

Tailored content delivery based on user interests and knowledge level

Interest-based recommendations

Difficulty level adjustment

Format preferences

Relationship Building

Long-term relationship development through personalized interactions

Milestone recognition

Personal history integration

Emotional connection building

Multi-Modal Interaction

Seamless communication across text, voice, and visual channels

Supported Modalities

Text Communication

Natural language processing

Rich text formatting

Emoji and emoticon support

Multi-language support

# Text processing
text_response = ryu.process_text(
    message="Hello RYU!",
    format_rich_text=True,
    include_emojis=True,
    language="auto_detect"
)

Voice Interaction

Speech-to-text conversion

Natural voice synthesis

Emotion recognition in voice

Multiple voice profiles

# Voice processing
voice_response = ryu.process_voice(
    audio_input=audio_data,
    voice_profile="natural_female",
    emotion_synthesis=True,
    response_format="audio"
)

Visual Elements

Image understanding

Visual context analysis

Chart and diagram generation

Facial expression recognition

# Visual processing
visual_response = ryu.process_image(
    image_data=image_input,
    analyze_context=True,
    generate_description=True,
    detect_emotions=True
)

AI Model Pipeline

Advanced AI processing pipeline for intelligent response generation

Pipeline Architecture

Input Processing

< 50ms

Raw input normalization and preprocessing

Text tokenizationAudio preprocessingImage normalizationContext extraction

Understanding

< 100ms

Deep semantic analysis and intent recognition

NLU processingEntity extractionSentiment analysisContext integration

Reasoning

< 200ms

Logical reasoning and response planning

Knowledge retrievalLogical inferenceResponse planningPersonality filtering

Generation

< 150ms

Natural language and multimodal response generation

Text generationVoice synthesisVisual elementsQuality assurance

Environment Setup

Complete guide to setting up your RYU development environment

Development Environment

Environment Variables

# .env configuration file
PANDAV2_API_KEY=your_api_key_here
PANDAV2_MODEL_VERSION=v2.2.0
PANDAV2_ENDPOINT=https://api.pandav2.ai
PANDAV2_REGION=us-west-2

# Optional configurations
RYU_PERSONALITY_PROFILE=default
RYU_MEMORY_ENABLED=true
RYU_VOICE_ENABLED=true
RYU_VISION_ENABLED=false

# Performance settings
RYU_MAX_TOKENS=200
RYU_TIMEOUT_SECONDS=30
RYU_CACHE_SIZE_MB=500

# Security settings
RYU_ENCRYPTION_ENABLED=true
RYU_LOG_LEVEL=INFO
RYU_DEBUG_MODE=false

Development Setup

Virtual Environment

python -m venv ryu-env

Activate Environment

source ryu-env/bin/activate

Install Dependencies

pip install -r requirements.txt

Set Environment Variables

cp .env.example .env

Initialize Database

ryu init-db

Run Tests

pytest tests/

Production Setup

Container Build

docker build -t ryu-companion .

Environment Config

kubectl create secret generic ryu-config

Deploy Application

kubectl apply -f deployment.yaml

Configure Load Balancer

kubectl apply -f service.yaml

Set Up Monitoring

helm install prometheus monitoring/

Enable Auto-scaling

kubectl apply -f hpa.yaml

Deployment Options

Multiple deployment strategies for different use cases and scales

Deployment Methods

Cloud Deployment

Managed cloud infrastructure with auto-scaling

Auto-scaling enabled

Load balancing

Global CDN

99.9% uptime SLA

From $99/month

On-Premise

Complete control over infrastructure and data

Full data control

Custom security

Private network

Compliance ready

Enterprise license

Hybrid Deployment

Best of both cloud and on-premise solutions

Flexible scaling

Data sovereignty

Cost optimization

Disaster recovery

Custom pricing

Real-time Processing

High-performance real-time processing for instant AI responses

Real-time Architecture

Processing Pipeline

Stream Processing

5-10ms

Continuous data stream processing

Parallel Execution

20-50ms

Multi-threaded AI model inference

Cache Layer

1-3ms

Intelligent response caching

Load Balancing

2-5ms

Request distribution across nodes

Real-time Configuration

# Real-time processing setup
realtime_config = {
    "stream_processing": {
        "buffer_size": 1024,
        "batch_timeout": 10,  # ms
        "max_concurrent": 100
    },
    "caching": {
        "enabled": True,
        "ttl_seconds": 300,
        "max_entries": 10000
    },
    "load_balancing": {
        "algorithm": "least_connections",
        "health_check_interval": 30,
        "retry_attempts": 3
    }
}

# Initialize real-time processor
processor = RYURealtimeProcessor(realtime_config)
response = await processor.process_stream(user_input)

Data Management

Comprehensive data storage, retrieval, and management systems

Data Storage Architecture

Conversation Data

Storage:Document Store

Retention:User controlled

Encryption:AES-256

User Profiles

Storage:Relational DB

Retention:Account lifetime

Encryption:Field-level

Analytics Data

Storage:Time Series DB

Retention:90 days

Encryption:At rest & transit

Scalability

Enterprise-grade scalability for millions of concurrent users

Scaling Strategies

Horizontal Scaling

Add more servers to handle increased load

1000+

Max Instances

< 60s

Auto-scale Time

99.9%

Load Distribution

< 5s

Failover Time

Vertical Scaling

Increase server resources for higher performance

Up to 64 cores

CPU Scaling

Up to 512GB

Memory Scaling

Multiple A100s

GPU Support

Up to 100TB

Storage

Database Scaling

Distributed database architecture for data scalability

50+

Read Replicas

Auto-managed

Sharding

< 10ms

Query Performance

Strong

Data Consistency

API Configuration

Complete API setup and configuration guide

API Configuration

API Configuration Example

# api_config.py
from pandav2 import APIConfig

config = APIConfig(
    # Authentication
    api_key="your_pandav2_api_key",
    secret_key="your_secret_key",
    
    # Endpoints
    base_url="https://api.pandav2.ai/v2",
    websocket_url="wss://ws.pandav2.ai/v2",
    
    # Rate Limiting
    rate_limit={
        "requests_per_minute": 1000,
        "requests_per_hour": 50000,
        "burst_limit": 100
    },
    
    # Model Configuration
    models={
        "default": "gpt-4-ryu",
        "voice": "ryu-voice-v2",
        "vision": "ryu-vision-v1"
    },
    
    # Response Settings
    response_config={
        "max_tokens": 200,
        "temperature": 0.7,
        "stream": True,
        "format": "json"
    }
)

# Initialize RYU with configuration
ryu = RYUCompanion(config=config)

Core Endpoints

POST/chat/completions

Generate AI responses

POST/chat/stream

Streaming conversations

GET/models

List available models

POST/embeddings

Generate text embeddings

POST/voice/synthesize

Text-to-speech conversion

POST/vision/analyze

Image analysis

Authentication

API Key Authentication

curl -X POST \
  https://api.pandav2.ai/v2/chat/completions \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "gpt-4-ryu",
    "messages": [
      {"role": "user", "content": "Hello RYU!"}
    ]
  }'

API keys are encrypted in transit

Rate limiting per API key

Usage analytics and monitoring

Automatic key rotation support

Authentication

Secure authentication and authorization systems

Authentication Methods

API Key Authentication

Simple and secure API key-based authentication

Bearer token authentication

Automatic key rotation

Usage monitoring

Rate limiting per key

OAuth 2.0

Industry-standard OAuth 2.0 implementation

Authorization code flow

Refresh token support

Scope-based permissions

Third-party integration

JWT Tokens

JSON Web Tokens for stateless authentication

Stateless authentication

Custom claims support

Token expiration

Digital signatures

Compliance

Industry-standard compliance and regulatory adherence

Compliance Standards

Data Protection Regulations

GDPR

European Union

Compliant

CCPA

California, USA

Compliant

PIPEDA

Canada

Compliant

LGPD

Brazil

Compliant

Security & Industry Standards

SOC 2 Type II

Global

Certified

ISO 27001

Global

Certified

HIPAA

USA Healthcare

Available

FedRAMP

US Government

In Progress

Data Protection

Enterprise-grade security and privacy protection for all user interactions

Security Features

End-to-End Encryption

Active

All conversations are encrypted with AES-256 encryption

Zero-Knowledge Architecture

Implemented

RYU processes data without storing personal information

Data Anonymization

Enforced

Personal identifiers are stripped from all training data

Secure Infrastructure

Certified

Cloud infrastructure with SOC 2 Type II compliance

Privacy Controls

User Controls

Data export and deletion requests

Conversation history management

Privacy settings customization

Consent management

Third-party integration controls

Compliance

GDPRCompliant

CCPACompliant

SOC 2 Type IICertified

ISO 27001Certified

HIPAAAvailable

RYU Introduction

Meet RYU (龍一ryu)

Overview

Advanced AI Architecture

Emotional Intelligence

Real-time Adaptation

Privacy-First Design

Quick Start Guide

Install PandaV2 SDK

Initialize RYU Companion

Configure Personality

Start Interacting

Next Steps

Explore Personality Engine

Configure Memory System

Multi-Modal Interaction

Production Deployment

Installation

System Requirements

Minimum Requirements

Recommended

Installation Methods

PIP Installation (Recommended)

Docker Installation

Development Installation

Initial Configuration

API Key Setup

Basic Configuration

Verify Installation

AI Companion Architecture

System Architecture

Multi-Layer Processing

Distributed Intelligence

Real-time Pipeline

Core Components

Natural Language Understanding (NLU)

Processing Pipeline

Example Code

Response Generation Engine

Generation Strategies

Template-based

Neural Generation

Hybrid Approach

Personality Filtering

Generation Pipeline

Context Management System

Short-term Context

Medium-term Context

Long-term Context

Performance & Scalability

Performance Metrics

Scalability Features

PandaV2 SDK Integration

SDK Overview

PandaV2 SDK Features

SDK Integration

Core SDK Components

Language Model Engine

Model Capabilities

Text Generation

Text Understanding

Multi-turn Conversations

Usage Example

Multi-Modal Processing

Text Processing

Voice Processing

Vision Processing

Multi-Modal Implementation

SDK Configuration & Optimization

Performance Tuning

Configuration Example

API Reference

Core Classes

Key Methods

Personality Engine

Personality System Overview

Dynamic Trait Modeling

Contextual Responses

Personality Configuration

Basic Personality Setup