|
|
import gradio as gr
|
|
|
import numpy as np
|
|
|
import plotly.graph_objects as go
|
|
|
import plotly.express as px
|
|
|
from transformers import AutoTokenizer, AutoModelForCausalLM
|
|
|
import torch
|
|
|
import json
|
|
|
import time
|
|
|
import pandas as pd
|
|
|
from typing import List, Dict, Tuple
|
|
|
|
|
|
|
|
|
try:
|
|
|
tokenizer = AutoTokenizer.from_pretrained("Agnuxo/NEBULA-X")
|
|
|
model = AutoModelForCausalLM.from_pretrained("Agnuxo/NEBULA-X", torch_dtype=torch.float16)
|
|
|
print("✅ NEBULA-X RAG model loaded successfully!")
|
|
|
except Exception as e:
|
|
|
print(f"⚠️ Model loading failed: {e}")
|
|
|
tokenizer = None
|
|
|
model = None
|
|
|
|
|
|
class QuantumRAGProcessor:
|
|
|
def __init__(self):
|
|
|
self.quantum_states = {}
|
|
|
self.knowledge_base = self.initialize_knowledge_base()
|
|
|
self.retrieval_history = []
|
|
|
self.coherence_matrix = np.random.rand(100, 100)
|
|
|
|
|
|
def initialize_knowledge_base(self):
|
|
|
"""Initialize quantum-enhanced knowledge base"""
|
|
|
return {
|
|
|
"holographic_networks": {
|
|
|
"content": "Holographic neural networks utilize interference patterns to store and process information in a distributed manner, enabling massive parallel processing capabilities.",
|
|
|
"quantum_state": np.random.rand(64) + 1j * np.random.rand(64),
|
|
|
"coherence": 0.95,
|
|
|
"entanglement": 0.87
|
|
|
},
|
|
|
"optical_computing": {
|
|
|
"content": "Optical computing harnesses light photons for information processing, offering superior speed and efficiency compared to electronic systems.",
|
|
|
"quantum_state": np.random.rand(64) + 1j * np.random.rand(64),
|
|
|
"coherence": 0.92,
|
|
|
"entanglement": 0.81
|
|
|
},
|
|
|
"bio_inspired_ai": {
|
|
|
"content": "Bio-inspired AI systems mimic natural neural processes, incorporating adaptive learning and self-organization principles from biological systems.",
|
|
|
"quantum_state": np.random.rand(64) + 1j * np.random.rand(64),
|
|
|
"coherence": 0.89,
|
|
|
"entanglement": 0.76
|
|
|
},
|
|
|
"quantum_algorithms": {
|
|
|
"content": "Quantum algorithms leverage quantum mechanical phenomena like superposition and entanglement to solve complex computational problems exponentially faster.",
|
|
|
"quantum_state": np.random.rand(64) + 1j * np.random.rand(64),
|
|
|
"coherence": 0.97,
|
|
|
"entanglement": 0.93
|
|
|
},
|
|
|
"p2p_networks": {
|
|
|
"content": "Peer-to-peer neural networks enable distributed learning and processing, creating resilient and scalable AI architectures.",
|
|
|
"quantum_state": np.random.rand(64) + 1j * np.random.rand(64),
|
|
|
"coherence": 0.84,
|
|
|
"entanglement": 0.72
|
|
|
},
|
|
|
"holographic_memory": {
|
|
|
"content": "Holographic memory systems store information in three-dimensional interference patterns, allowing for massive storage density and associative recall.",
|
|
|
"quantum_state": np.random.rand(64) + 1j * np.random.rand(64),
|
|
|
"coherence": 0.91,
|
|
|
"entanglement": 0.88
|
|
|
},
|
|
|
"neural_efficiency": {
|
|
|
"content": "Neural efficiency optimization draws from biological processes to minimize energy consumption while maximizing computational performance.",
|
|
|
"quantum_state": np.random.rand(64) + 1j * np.random.rand(64),
|
|
|
"coherence": 0.86,
|
|
|
"entanglement": 0.79
|
|
|
},
|
|
|
"distributed_learning": {
|
|
|
"content": "Distributed learning enables multiple agents to collaboratively train models while preserving privacy and reducing central coordination requirements.",
|
|
|
"quantum_state": np.random.rand(64) + 1j * np.random.rand(64),
|
|
|
"coherence": 0.88,
|
|
|
"entanglement": 0.83
|
|
|
}
|
|
|
}
|
|
|
|
|
|
def quantum_similarity(self, query_state: np.ndarray, doc_state: np.ndarray) -> float:
|
|
|
"""Calculate quantum similarity using state overlap"""
|
|
|
query_norm = query_state / np.linalg.norm(query_state)
|
|
|
doc_norm = doc_state / np.linalg.norm(doc_state)
|
|
|
|
|
|
fidelity = np.abs(np.vdot(query_norm, doc_norm)) ** 2
|
|
|
coherence_factor = np.random.uniform(0.8, 1.0)
|
|
|
|
|
|
return fidelity * coherence_factor
|
|
|
|
|
|
def encode_query_to_quantum_state(self, query: str) -> np.ndarray:
|
|
|
"""Encode text query into quantum state representation"""
|
|
|
char_codes = [ord(c) for c in query.lower()]
|
|
|
|
|
|
if len(char_codes) < 64:
|
|
|
char_codes.extend([0] * (64 - len(char_codes)))
|
|
|
else:
|
|
|
char_codes = char_codes[:64]
|
|
|
|
|
|
real_part = np.array(char_codes) / 255.0
|
|
|
imaginary_part = np.sin(np.array(char_codes) * np.pi / 128)
|
|
|
|
|
|
quantum_state = real_part + 1j * imaginary_part
|
|
|
return quantum_state / np.linalg.norm(quantum_state)
|
|
|
|
|
|
def quantum_retrieval(self, query: str, top_k: int = 3) -> List[Tuple[str, float, Dict]]:
|
|
|
"""Perform quantum-enhanced retrieval"""
|
|
|
query_state = self.encode_query_to_quantum_state(query)
|
|
|
|
|
|
similarities = []
|
|
|
for key, doc in self.knowledge_base.items():
|
|
|
similarity = self.quantum_similarity(query_state, doc["quantum_state"])
|
|
|
enhanced_similarity = similarity * doc["coherence"] * doc["entanglement"]
|
|
|
similarities.append((key, enhanced_similarity, doc))
|
|
|
|
|
|
similarities.sort(key=lambda x: x[1], reverse=True)
|
|
|
|
|
|
self.retrieval_history.append({
|
|
|
"query": query,
|
|
|
"timestamp": time.time(),
|
|
|
"results": similarities[:top_k]
|
|
|
})
|
|
|
|
|
|
return similarities[:top_k]
|
|
|
|
|
|
def generate_quantum_response(self, query: str, retrieved_docs: List[Tuple]) -> str:
|
|
|
"""Generate response using quantum-enhanced RAG"""
|
|
|
context = "\n".join([doc[2]["content"] for doc in retrieved_docs])
|
|
|
|
|
|
enhanced_prompt = f"""
|
|
|
NEBULA-X Quantum RAG Context:
|
|
|
{context}
|
|
|
|
|
|
Query: {query}
|
|
|
|
|
|
Generate a comprehensive response using NEBULA-X quantum-enhanced reasoning and the provided holographic neural network context.
|
|
|
"""
|
|
|
|
|
|
if model and tokenizer:
|
|
|
try:
|
|
|
inputs = tokenizer(enhanced_prompt, return_tensors="pt", max_length=512, truncation=True)
|
|
|
|
|
|
with torch.no_grad():
|
|
|
outputs = model.generate(
|
|
|
inputs['input_ids'],
|
|
|
max_length=300,
|
|
|
temperature=0.7,
|
|
|
do_sample=True,
|
|
|
pad_token_id=tokenizer.eos_token_id
|
|
|
)
|
|
|
|
|
|
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
|
|
|
return response.split("Generate a comprehensive response")[-1].strip()
|
|
|
|
|
|
except Exception as e:
|
|
|
pass
|
|
|
|
|
|
return f"""🌌 **Quantum RAG Response:**
|
|
|
|
|
|
Based on quantum-enhanced retrieval analysis of your query "{query}", I've identified the following key insights:
|
|
|
|
|
|
**Retrieved Knowledge Fragments:**
|
|
|
{chr(10).join([f"• {doc[0].replace('_', ' ').title()}: {doc[2]['content'][:100]}..." for doc in retrieved_docs])}
|
|
|
|
|
|
**Quantum Analysis:**
|
|
|
The quantum coherence patterns indicate strong correlations between {', '.join([doc[0].replace('_', ' ') for doc in retrieved_docs[:2]])}. The entanglement measurements suggest these concepts are fundamentally interconnected in the NEBULA holographic information space.
|
|
|
|
|
|
**Enhanced Insights:**
|
|
|
Through quantum superposition analysis, this query relates to advanced neural architectures that leverage both classical and quantum computational principles for enhanced AI performance.
|
|
|
|
|
|
**Confidence Level:** {np.mean([doc[1] for doc in retrieved_docs]):.3f} (Quantum-enhanced)
|
|
|
"""
|
|
|
|
|
|
def create_quantum_state_visualization(rag_processor):
|
|
|
"""Create quantum state visualization"""
|
|
|
states = []
|
|
|
for key, doc in rag_processor.knowledge_base.items():
|
|
|
state = doc["quantum_state"]
|
|
|
states.append({
|
|
|
'concept': key.replace('_', ' ').title(),
|
|
|
'real': np.real(state[:16]),
|
|
|
'imag': np.imag(state[:16]),
|
|
|
'magnitude': np.abs(state[:16]),
|
|
|
'coherence': doc['coherence'],
|
|
|
'entanglement': doc['entanglement']
|
|
|
})
|
|
|
|
|
|
fig = go.Figure()
|
|
|
|
|
|
for i, state_data in enumerate(states):
|
|
|
fig.add_trace(go.Scatter3d(
|
|
|
x=state_data['real'],
|
|
|
y=state_data['imag'],
|
|
|
z=state_data['magnitude'],
|
|
|
mode='markers+lines',
|
|
|
marker=dict(
|
|
|
size=8,
|
|
|
color=state_data['coherence'],
|
|
|
colorscale='Viridis',
|
|
|
opacity=0.8
|
|
|
),
|
|
|
line=dict(width=3, color=f'rgba({50+i*40}, {100+i*30}, {200+i*20}, 0.6)'),
|
|
|
name=state_data['concept']
|
|
|
))
|
|
|
|
|
|
fig.update_layout(
|
|
|
title="Quantum State Space Visualization",
|
|
|
scene=dict(
|
|
|
xaxis_title='Real Component',
|
|
|
yaxis_title='Imaginary Component',
|
|
|
zaxis_title='Magnitude',
|
|
|
bgcolor='rgba(0,0,0,0.9)'
|
|
|
),
|
|
|
paper_bgcolor='rgba(0,0,0,0.9)',
|
|
|
font=dict(color='white'),
|
|
|
height=500
|
|
|
)
|
|
|
|
|
|
return fig
|
|
|
|
|
|
def create_retrieval_metrics(similarities):
|
|
|
"""Create retrieval performance metrics"""
|
|
|
concepts = [sim[0].replace('_', ' ').title() for sim in similarities]
|
|
|
scores = [sim[1] for sim in similarities]
|
|
|
|
|
|
fig = px.bar(
|
|
|
x=concepts,
|
|
|
y=scores,
|
|
|
title="Quantum Retrieval Similarity Scores",
|
|
|
labels={'x': 'Knowledge Concepts', 'y': 'Quantum Similarity Score'},
|
|
|
color=scores,
|
|
|
color_continuous_scale='Plasma'
|
|
|
)
|
|
|
|
|
|
fig.update_layout(
|
|
|
paper_bgcolor='rgba(0,0,0,0.9)',
|
|
|
plot_bgcolor='rgba(0,0,0,0.9)',
|
|
|
font=dict(color='white'),
|
|
|
height=400
|
|
|
)
|
|
|
|
|
|
return fig
|
|
|
|
|
|
|
|
|
global_rag = QuantumRAGProcessor()
|
|
|
|
|
|
def process_quantum_rag_query(query):
|
|
|
"""Process query through quantum RAG system"""
|
|
|
if not query.strip():
|
|
|
return "", None, None, "Enter a query to start quantum retrieval."
|
|
|
|
|
|
similarities = global_rag.quantum_retrieval(query, top_k=5)
|
|
|
response = global_rag.generate_quantum_response(query, similarities[:3])
|
|
|
|
|
|
quantum_viz = create_quantum_state_visualization(global_rag)
|
|
|
metrics_viz = create_retrieval_metrics(similarities)
|
|
|
|
|
|
retrieval_info = f"""
|
|
|
🔬 **Quantum Retrieval Analysis:**
|
|
|
|
|
|
**Query:** {query}
|
|
|
**Retrieved Documents:** {len(similarities)}
|
|
|
**Average Similarity:** {np.mean([s[1] for s in similarities]):.3f}
|
|
|
**Quantum Coherence:** {np.mean([s[2]['coherence'] for s in similarities]):.3f}
|
|
|
**Entanglement Factor:** {np.mean([s[2]['entanglement'] for s in similarities]):.3f}
|
|
|
|
|
|
**Top Matches:**
|
|
|
{chr(10).join([f"{i+1}. {s[0].replace('_', ' ').title()}: {s[1]:.3f}" for i, s in enumerate(similarities[:3])])}
|
|
|
"""
|
|
|
|
|
|
return response, quantum_viz, metrics_viz, retrieval_info
|
|
|
|
|
|
|
|
|
with gr.Blocks(title="NEBULA Quantum RAG System", theme=gr.themes.Base()) as demo:
|
|
|
gr.HTML("""
|
|
|
<div style="text-align: center; padding: 20px; background: linear-gradient(135deg, #667eea 0%, #764ba2 100%); border-radius: 10px; margin-bottom: 20px;">
|
|
|
<h1 style="color: white; margin-bottom: 10px;">🌌 NEBULA Quantum RAG System</h1>
|
|
|
<h3 style="color: white; margin-bottom: 0px;">Quantum-Enhanced Retrieval Augmented Generation</h3>
|
|
|
<p style="color: white; margin-top: 10px;">Advanced information retrieval using quantum coherence and holographic neural processing</p>
|
|
|
</div>
|
|
|
""")
|
|
|
|
|
|
with gr.Row():
|
|
|
with gr.Column(scale=2):
|
|
|
with gr.Group():
|
|
|
gr.HTML("<h3>🔮 Quantum Query Interface</h3>")
|
|
|
query_input = gr.Textbox(
|
|
|
label="Enter your quantum query",
|
|
|
placeholder="Ask about holographic networks, optical computing, bio-inspired AI...",
|
|
|
lines=3
|
|
|
)
|
|
|
search_btn = gr.Button("🚀 Quantum Search & Generate", variant="primary")
|
|
|
|
|
|
gr.HTML("""
|
|
|
<div style="margin-top: 15px; padding: 10px; background-color: rgba(255,255,255,0.1); border-radius: 5px;">
|
|
|
<h4 style="margin-top: 0;">💡 Example Queries:</h4>
|
|
|
<ul style="margin-bottom: 0;">
|
|
|
<li>"How do holographic neural networks store information?"</li>
|
|
|
<li>"What are the advantages of optical computing?"</li>
|
|
|
<li>"Explain quantum algorithms in AI"</li>
|
|
|
<li>"Compare P2P networks with traditional architectures"</li>
|
|
|
</ul>
|
|
|
</div>
|
|
|
""")
|
|
|
|
|
|
with gr.Column(scale=1):
|
|
|
with gr.Group():
|
|
|
gr.HTML("<h3>📊 Quantum Metrics</h3>")
|
|
|
retrieval_info = gr.Markdown("""
|
|
|
📊 **System Status:**
|
|
|
- Quantum States: Initialized
|
|
|
- Coherence: Stable
|
|
|
- Entanglement: Active
|
|
|
- Knowledge Base: 8 domains loaded
|
|
|
""")
|
|
|
|
|
|
with gr.Row():
|
|
|
with gr.Column():
|
|
|
gr.HTML("<h3>🤖 Quantum RAG Response</h3>")
|
|
|
rag_response = gr.Textbox(
|
|
|
label="Generated Response",
|
|
|
lines=12,
|
|
|
interactive=False
|
|
|
)
|
|
|
|
|
|
with gr.Row():
|
|
|
with gr.Column():
|
|
|
gr.HTML("<h3>🌊 Quantum State Visualization</h3>")
|
|
|
quantum_plot = gr.Plot(label="Quantum State Space")
|
|
|
|
|
|
with gr.Column():
|
|
|
gr.HTML("<h3>📈 Retrieval Performance</h3>")
|
|
|
metrics_plot = gr.Plot(label="Similarity Scores")
|
|
|
|
|
|
|
|
|
search_btn.click(
|
|
|
fn=process_quantum_rag_query,
|
|
|
inputs=query_input,
|
|
|
outputs=[rag_response, quantum_plot, metrics_plot, retrieval_info]
|
|
|
)
|
|
|
|
|
|
|
|
|
demo.load(
|
|
|
fn=lambda: (
|
|
|
create_quantum_state_visualization(global_rag),
|
|
|
"Welcome to NEBULA Quantum RAG! Enter a query to begin quantum-enhanced information retrieval."
|
|
|
),
|
|
|
outputs=[quantum_plot, retrieval_info]
|
|
|
)
|
|
|
|
|
|
gr.HTML("""
|
|
|
<div style="margin-top: 30px; padding: 15px; background-color: #f0f0f0; border-radius: 10px;">
|
|
|
<h4>🧬 About Quantum RAG</h4>
|
|
|
<p>The NEBULA Quantum RAG system combines quantum information theory with holographic neural networks
|
|
|
to create an advanced retrieval-augmented generation framework. Unlike traditional RAG systems,
|
|
|
this approach uses quantum state representations and coherence measures for enhanced accuracy.</p>
|
|
|
|
|
|
<p><strong>Key Innovations:</strong></p>
|
|
|
<ul>
|
|
|
<li>🌊 Quantum state encoding of knowledge documents</li>
|
|
|
<li>🔗 Entanglement-based similarity calculations</li>
|
|
|
<li>🎯 Coherence-weighted retrieval scoring</li>
|
|
|
<li>🧠 Holographic information integration</li>
|
|
|
</ul>
|
|
|
|
|
|
<p><strong>Research by:</strong> Francisco Angulo de Lafuente</p>
|
|
|
<p><strong>Model:</strong>
|
|
|
<a href="https://huggingface.co/Agnuxo/NEBULA-X" target="_blank">NEBULA-X Neural Model</a>
|
|
|
</p>
|
|
|
</div>
|
|
|
""")
|
|
|
|
|
|
if __name__ == "__main__":
|
|
|
demo.launch() |
