import matplotlib.pyplot as plt

import numpy as np

import pandas as pd

# Create sample performance chart

def create_performance_chart():

agents = ['ReAct-GPT4', 'AutoGPT', 'LangChain', 'CrewAI', 'BabyAGI']

accuracy = [0.87, 0.78, 0.82, 0.85, 0.76]

speed = [1200, 2100, 950, 1800, 2400]

fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 5))

# Accuracy chart

bars1 = ax1.bar(agents, accuracy, color=['#FF6B6B', '#4ECDC4', '#45B7D1', '#96CEB4', '#FECA57'])

ax1.set_title('AI Agent Accuracy Comparison')

ax1.set_ylabel('Accuracy Score')

ax1.set_ylim(0, 1)

ax1.tick_params(axis='x', rotation=45)

# Add value labels on bars

for bar in bars1:

height = bar.get_height()

ax1.text(bar.get_x() + bar.get_width()/2., height + 0.01,

f'{height:.2f}', ha='center', va='bottom')

# Speed chart

bars2 = ax2.bar(agents, speed, color=['#FF6B6B', '#4ECDC4', '#45B7D1', '#96CEB4', '#FECA57'])

ax2.set_title('AI Agent Response Time Comparison')

ax2.set_ylabel('Response Time (ms)')

ax2.tick_params(axis='x', rotation=45)

# Add value labels on bars

for bar in bars2:

height = bar.get_height()

ax2.text(bar.get_x() + bar.get_width()/2., height + 50,

f'{int(height)}ms', ha='center', va='bottom')

plt.tight_layout()

plt.savefig('data/multimodal_agents/images/agent_performance_comparison.png', dpi=300, bbox_inches='tight')

plt.close()

# Create architecture diagram

def create_architecture_diagram():

fig, ax = plt.subplots(1, 1, figsize=(10, 8))

# Define components

components = {

'User Query': (2, 7, '#FFE5B4'),

'Query Engine': (2, 6, '#B4E5FF'),

'Retriever': (1, 5, '#C8FFB4'),

'Vector Store': (0.5, 4, '#FFB4B4'),

'LLM': (3, 5, '#E5B4FF'),

'Documents': (0.5, 3, '#FFCCB4'),

'Response': (2, 2, '#B4FFE5')

}

# Draw components

for name, (x, y, color) in components.items():

rect = plt.Rectangle((x-0.4, y-0.3), 0.8, 0.6,

facecolor=color, edgecolor='black', linewidth=2)

ax.add_patch(rect)

ax.text(x, y, name, ha='center', va='center', fontsize=10, fontweight='bold')

# Draw arrows

arrows = [

((2, 6.7), (2, 6.3)), # User Query -> Query Engine

((2, 5.7), (1.4, 5.3)), # Query Engine -> Retriever

((2, 5.7), (2.6, 5.3)), # Query Engine -> LLM

((1, 4.7), (0.9, 4.3)), # Retriever -> Vector Store

((0.5, 3.7), (0.7, 3.3)), # Vector Store -> Documents

((2.5, 4.7), (2.3, 2.3)), # LLM -> Response

]

for start, end in arrows:

ax.annotate('', xy=end, xytext=start,

arrowprops=dict(arrowstyle='->', lw=2, color='red'))

ax.set_xlim(-0.5, 4)

ax.set_ylim(1, 8)

ax.set_title('RAG Agent Architecture', fontsize=16, fontweight='bold', pad=20)

ax.axis('off')

plt.tight_layout()

plt.savefig('data/multimodal_agents/images/rag_architecture_diagram.png', dpi=300, bbox_inches='tight')

plt.close()

# Create agent types visualization

def create_agent_types_chart():

# Agent types and their characteristics

agent_types = ['Reasoning\nAgents', 'Tool-Using\nAgents', 'Autonomous\nAgents',

'Multi-Agent\nSystems', 'Conversational\nAgents']

complexity = [3, 2, 5, 4, 2]

popularity = [4, 5, 3, 3, 4]

fig, ax = plt.subplots(figsize=(10, 6))

x = np.arange(len(agent_types))

width = 0.35

bars1 = ax.bar(x - width/2, complexity, width, label='Complexity', color='#FF6B6B', alpha=0.8)

bars2 = ax.bar(x + width/2, popularity, width, label='Popularity', color='#4ECDC4', alpha=0.8)

ax.set_xlabel('Agent Types')

ax.set_ylabel('Score (1-5)')

ax.set_title('AI Agent Types: Complexity vs Popularity')

ax.set_xticks(x)

ax.set_xticklabels(agent_types)

ax.legend()

ax.set_ylim(0, 6)

# Add value labels

for bars in [bars1, bars2]:

for bar in bars:

height = bar.get_height()

ax.text(bar.get_x() + bar.get_width()/2., height + 0.1,

f'{int(height)}', ha='center', va='bottom')

plt.tight_layout()

plt.savefig('data/multimodal_agents/images/agent_types_comparison.png', dpi=300, bbox_inches='tight')

plt.close()

if __name__ == "__main__":

print("Creating sample images...")

create_performance_chart()

create_architecture_diagram()

create_agent_types_chart()

print("Sample images created successfully!")