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Enhanced Holographic Neural Network

) } [V0_FILE]typescriptreact:file="components/Chat.tsx" import React from 'react' import { Button } from "@/components/ui/button" import { Input } from "@/components/ui/input" import { Card, CardContent, CardHeader, CardTitle } from "@/components/ui/card" export default function Chat({ chatHistory, inputText, setInputText, handleSubmit, predictedWords }) { return ( Enhanced Holographic Neural Network Chat

{chatHistory.map((message, index) => (

{message.text}

))}

{predictedWords.length > 0 && (

Predicted words: {predictedWords.join(', ')}

)} ) } [V0_FILE]typescriptreact:file="components/P2PNetwork.tsx" import React from 'react' import { Card, CardContent, CardHeader, CardTitle } from "@/components/ui/card" import { Input } from "@/components/ui/input" export default function P2PNetwork({ nodeId, p2pConnected, connectedPeers }) { return ( P2P Network

{p2pConnected ? 'Connected to P2P network' : 'Disconnected from P2P network'}

Connected Peers: {connectedPeers.length}

) } [V0_FILE]typescriptreact:file="components/Learning.tsx" import React from 'react' import { Button } from "@/components/ui/button" import { Input } from "@/components/ui/input" import { Card, CardContent, CardHeader, CardTitle } from "@/components/ui/card" export default function Learning({ learnInput, setLearnInput, learnResponse, setLearnResponse, handleLearn }) { return ( Learning setLearnInput(e.target.value)} placeholder="Input to learn" className="mb-2" /> setLearnResponse(e.target.value)} placeholder="Associated response" className="mb-2" /> ) } [V0_FILE]typescriptreact:file="components/KnowledgeManagement.tsx" import React from 'react' import { Button } from "@/components/ui/button" import { Input } from "@/components/ui/input" import { Card, CardContent, CardHeader, CardTitle } from "@/components/ui/card" import { Download, Upload } from 'lucide-react' export default function KnowledgeManagement({ handleSave, handleLoad }) { return ( Knowledge Management

) } [V0_FILE]typescriptreact:file="components/Training.tsx" import React from 'react' import { Button } from "@/components/ui/button" import { Card, CardContent, CardHeader, CardTitle } from "@/components/ui/card" import { Progress } from "@/components/ui/progress" export default function Training({ isTraining, trainingProgress, handleTrain }) { return ( Training {isTraining && (

{trainingProgress}% completed

)} ) } [V0_FILE]typescriptreact:file="components/FileProcessing.tsx" import React from 'react' import { Card, CardContent, CardHeader, CardTitle } from "@/components/ui/card" import { Input } from "@/components/ui/input" import { Progress } from "@/components/ui/progress" export default function FileProcessing({ handleFileUpload, isProcessing, progress }) { return ( File Processing {isProcessing && (

{Math.round(progress)}% processed

)} ) } [V0_FILE]typescriptreact:file="lib/EnhancedHolographicNeuralNetwork.ts" isQuickEdit="true" import { HolographicMemory } from './HolographicMemory' import * as THREE from 'three' import axios from 'axios' import { LLAMA_INDEX_API, NEMOTRON_70B_API, RAG_NVIDIA_API, NEMO_GUARDRAILS_API } from '@/src/config' export class EnhancedHolographicNeuralNetwork { neurons: any[] connections: any[] knowledgeBase: Record contextNodes: any[] holographicMemory: HolographicMemory constructor(numNeurons: number) { this.neurons = Array.from({ length: numNeurons }, () => ({ position: [ (Math.random() - 0.5) * 20, (Math.random() - 0.5) * 20, (Math.random() - 0.5) * 20 ], activation: 0 })) this.connections = this.initializeConnections() this.knowledgeBase = {} this.contextNodes = [] this.holographicMemory = new HolographicMemory() } initializeConnections() { const connections = [] for (let i = 0; i < this.neurons.length; i++) { for (let j = i + 1; j < this.neurons.length; j++) { if (Math.random() < 0.1) { // 10% chance of connection connections.push({ start: i, end: j, strength: Math.random() }) } } } return connections } activate(input: string) { const inputHash = this.hash(input) const initialNeuron = inputHash % this.neurons.length this.neurons.forEach((neuron, i) => { const sourcePosition = new THREE.Vector3(...this.neurons[initialNeuron].position) const targetPosition = new THREE.Vector3(...neuron.position) const lightIntensity = this.propagateLight(sourcePosition, targetPosition) neuron.activation = lightIntensity }) // Propagate activations through connections this.connections.forEach(conn => { const sourceActivation = this.neurons[conn.start].activation const targetActivation = this.neurons[conn.end].activation const interference = this.calculateInterference([sourceActivation, targetActivation]) this.neurons[conn.end].activation = interference }) // Normalize activations const maxActivation = Math.max(...this.neurons.map(n => n.activation)) this.neurons.forEach(n => n.activation /= maxActivation) return this.neurons.map(n => n.activation) } learn(input: string, output: string) { const activations = this.activate(input) this.knowledgeBase[input] = { output, activations } this.holographicMemory.encode(input, output) this.updateConnections(activations) } updateConnections(activations: number[]) { this.connections.forEach(conn => { const sourceActivation = activations[conn.start] const targetActivation = activations[conn.end] conn.strength = (conn.strength + Math.abs(sourceActivation - targetActivation)) / 2 }) } generateResponse(input: string) { const activations = this.activate(input) const similarities = Object.entries(this.knowledgeBase).map(([key, value]) => ({ key, similarity: this.cosineSimilarity(activations, value.activations) })) similarities.sort((a, b) => b.similarity - a.similarity) if (similarities[0] && similarities[0].similarity > 0.8) { return this.knowledgeBase[similarities[0].key].output } else { const reconstructedOutput = this.holographicMemory.decode(input) return reconstructedOutput || "I don't know how to respond to that." } } updateContextNodes(ragContext: { text: string, score: number }[]) { this.contextNodes = ragContext.map(node => ({ position: [ (Math.random() - 0.5) * 20, (Math.random() - 0.5) * 20, (Math.random() - 0.5) * 20 ], relevance: node.score })) } hash(input: string) { return input.split('').reduce((acc, char) => acc + char.charCodeAt(0), 0) } cosineSimilarity(a: number[], b: number[]) { const dotProduct = a.reduce((sum, _, i) => sum + a[i] * b[i], 0) const magnitudeA = Math.sqrt(a.reduce((sum, val) => sum + val * val, 0)) const magnitudeB = Math.sqrt(b.reduce((sum, val) => sum + val * val, 0)) return dotProduct / (magnitudeA * magnitudeB) } exportKnowledge() { return JSON.stringify({ knowledgeBase: this.knowledgeBase, neurons: this.neurons, connections: this.connections }) } importKnowledge(knowledge: string) { try { const parsedKnowledge = JSON.parse(knowledge) this.knowledgeBase = parsedKnowledge.knowledgeBase this.neurons = parsedKnowledge.neurons this.connections = parsedKnowledge.connections return true } catch (error) { console.error("Error importing knowledge:", error) return false } } // Methods for NVIDIA API integration async useLlamaIndex(input: string) { if (LLAMA_INDEX_API !== "YOUR-LLAMA-INDEX-API-KEY-HERE") { const response = await axios.post(LLAMA_INDEX_API, { query: input }) return response.data.result } return null } async useNemotron70B(input: string) { if (NEMOTRON_70B_API !== "YOUR-NEMOTRON-70B-API-KEY-HERE") { const response = await axios.post(NEMOTRON_70B_API, { text: input }) return response.data.generated_text } return null } async useRagNvidia(input: string) { if (RAG_NVIDIA_API !== "YOUR-RAG-NVIDIA-API-KEY-HERE") { const response = await axios.post(RAG_NVIDIA_API, { query: input }) return response.data.result } return null } async useNemoGuardrails(input: string) { if (NEMO_GUARDRAILS_API !== "YOUR-NEMO-GUARDRAILS-API-KEY-HERE") { const response = await axios.post(NEMO_GUARDRAILS_API, { text: input }) return response.data.safe_text } return null } // Method for generating multiple words generateWords(input: string, count = 5) { let currentWord = input const words = [currentWord] for (let i = 1; i < count; i++) { const nextWord = this.generateResponse(currentWord) if (nextWord === "I don't know how to respond to that.") { break } words.push(nextWord) currentWord = nextWord } return words } propagateLight(sourcePosition: THREE.Vector3, targetPosition: THREE.Vector3) { const distance = sourcePosition.distanceTo(targetPosition) const intensity = 1 / (distance * distance) return Math.min(intensity, 1) } calculateInterference(waves: number[]) { return waves.reduce((sum, wave) => sum + wave, 0) / waves.length } } [V0_FILE]typescriptreact:file="lib/HolographicMemory.ts" export class HolographicMemory { private memory: Map constructor() { this.memory = new Map() } encode(key: string, value: string) { const pattern = this.generateInterferencePattern(key, value) this.memory.set(key, pattern) } decode(key: string) { const pattern = this.memory.get(key) if (!pattern) return null return this.reconstructFromPattern(pattern) } private generateInterferencePattern(key: string, value: string) { // Simplified interference pattern generation const pattern = new Float32Array(1024) for (let i = 0; i < 1024; i++) { pattern[i] = Math.sin(i * key.length) * Math.cos(i * value.length) } return pattern } private reconstructFromPattern(pattern: Float32Array) { // Simplified reconstruction (this would be more complex in a real system) return pattern.reduce((sum, val) => sum + val, 0).toString(36) } } [V0_FILE]typescriptreact:file="components/HolographicNeuralNetwork/Scene.tsx" "use client" import React, { useEffect } from 'react' import * as THREE from 'three' import { Canvas, useThree } from '@react-three/fiber' import { OrbitControls, Stars } from '@react-three/drei' import { EffectComposer, SSR, Bloom, DepthOfField, Noise } from '@react-three/postprocessing' import { KernelSize } from 'postprocessing' import { Neuron } from './Neuron' import { Connection } from './Connection' import { HolographicPlane } from './HolographicPlane' interface SceneProps { neurons: any[] connections: any[] contextNodes: any[] } export function Scene({ neurons, connections, contextNodes }: SceneProps) { return (

) } interface NeuralNetworkProps { neurons: any[] connections: any[] contextNodes: any[] } function NeuralNetwork({ neurons, connections, contextNodes }: NeuralNetworkProps) { return ( {neurons.map((neuron, i) => ( ))} {contextNodes.map((node, i) => ( ))} {connections.map((connection, i) => ( ))} ) } function CameraSetup() { const { camera } = useThree() useEffect(() => { camera.position.set(0, 20, 40) }, [camera]) return null } [V0_FILE]typescriptreact:file="components/HolographicNeuralNetwork/Neuron.tsx" "use client" import React, { useRef, useState } from 'react' import * as THREE from 'three' import { useFrame } from '@react-three/fiber' import { Html, Trail, useTexture } from '@react-three/drei' const holographicVertexShader = ` varying vec2 vUv; varying vec3 vPosition; varying vec3 vNormal; void main() { vUv = uv; vPosition = position; vNormal = normal; gl_Position = projectionMatrix * modelViewMatrix * vec4(position, 1.0); } ` const holographicFragmentShader = ` uniform float time; uniform vec3 color; uniform sampler2D hologramTexture; varying vec2 vUv; varying vec3 vPosition; varying vec3 vNormal; float fresnel(vec3 normal, vec3 viewDir) { return pow(1.0 + dot(normal, viewDir), 3.0); } void main() { vec3 viewDir = normalize(cameraPosition - vPosition); float fresnelTerm = fresnel(vNormal, viewDir); vec2 uv = vUv + 0.1 * vec2(sin(time + vPosition.x * 10.0), cos(time + vPosition.y * 10.0)); vec3 hologram = texture2D(hologramTexture, uv).rgb; vec3 finalColor = mix(color, hologram, 0.5) + fresnelTerm * vec3(0.1, 0.3, 0.5); float alpha = 0.7 + 0.3 * sin(time * 2.0 + vPosition.z * 5.0); gl_FragColor = vec4(finalColor, alpha); } ` export function Neuron({ position, activation, relevance, isContextNode }) { const meshRef = useRef() const [hovered, setHovered] = useState(false) const hologramTexture = useTexture('/placeholder.svg?height=128&width=128') const color = isContextNode ? new THREE.Color(relevance, 0, 1 - relevance) : new THREE.Color(activation, 0, 1 - activation) useFrame((state) => { if (meshRef.current) { meshRef.current.rotation.x = Math.sin(state.clock.elapsedTime * 0.5) * 0.2 meshRef.current.rotation.y = Math.sin(state.clock.elapsedTime * 0.3) * 0.2 meshRef.current.material.uniforms.time.value = state.clock.elapsedTime } }) return ( setHovered(true)} onPointerOut={() => setHovered(false)} > t * t} > {hovered && (

{isContextNode ? `Relevance: ${relevance.toFixed(2)}` : `Activation: ${activation.toFixed(2)}`}

)} ) } [V0_FILE]typescriptreact:file="src/config.ts" // NVIDIA API placeholders export const LLAMA_INDEX_API = "YOUR-LLAMA-INDEX-API-KEY-HERE" export const NEMOTRON_70B_API = "YOUR-NEMOTRON-70B-API-KEY-HERE" export const RAG_NVIDIA_API = "YOUR-RAG-NVIDIA-API-KEY-HERE" export const NEMO_GUARDRAILS_API = "YOUR-NEMO-GUARDRAILS-API-KEY-HERE" // Global variable to control NVIDIA API usage export const useNvidiaAPIs = false [V0_FILE]typescriptreact:file="components/HolographicNeuralNetwork/Connection.tsx" import React, { useMemo } from 'react' import * as THREE from 'three' interface ConnectionProps { start: [number, number, number] end: [number, number, number] strength: number } export function Connection({ start, end, strength }: ConnectionProps) { const points = useMemo(() => [ new THREE.Vector3(...start), new THREE.Vector3(...end) ], [start, end]) return ( ) } [V0_FILE]typescriptreact:file="components/HolographicNeuralNetwork/HolographicPlane.tsx" "use client" import React, { useRef } from 'react' import * as THREE from 'three' import { useFrame } from '@react-three/fiber' import { useTexture } from '@react-three/drei' const holographicVertexShader = ` varying vec2 vUv; varying vec3 vPosition; varying vec3 vNormal; void main() { vUv = uv; vPosition = position; vNormal = normal; gl_Position = projectionMatrix * modelViewMatrix * vec4(position, 1.0); } ` const holographicFragmentShader = ` uniform float time; uniform vec3 color; uniform sampler2D hologramTexture; varying vec2 vUv; varying vec3 vPosition; varying vec3 vNormal; float fresnel(vec3 normal, vec3 viewDir) { return pow(1.0 + dot(normal, viewDir), 3.0); } void main() { vec3 viewDir = normalize(cameraPosition - vPosition); float fresnelTerm = fresnel(vNormal, viewDir); vec2 uv = vUv + 0.1 * vec2(sin(time + vPosition.x * 10.0), cos(time + vPosition.y * 10.0)); vec3 hologram = texture2D(hologramTexture, uv).rgb; vec3 finalColor = mix(color, hologram, 0.5) + fresnelTerm * vec3(0.1, 0.3, 0.5); float alpha = 0.7 + 0.3 * sin(time * 2.0 + vPosition.z * 5.0); gl_FragColor = vec4(finalColor, alpha); } ` export function HolographicPlane() { const shaderRef = useRef() const texture = useTexture('/placeholder.svg?height=1024&width=1024') useFrame(({ clock }) => { if (shaderRef.current) { shaderRef.current.uniforms.time.value = clock.getElapsedTime() } }) return ( ) } [V0_FILE]typescriptreact:file="src/nvidia/nemo/README.md" # NVIDIA NeMo NVIDIA NeMo is an open-source toolkit for building, training, and deploying conversational AI models. It provides a rich set of pre-built modules for ASR (Automatic Speech Recognition), NLP (Natural Language Processing), and TTS (Text-to-Speech) tasks. ## How it works NeMo is built on top of PyTorch and uses a modular approach to building neural networks. It provides: 1. Collections: Pre-built neural network architectures for ASR, NLP, and TTS. 2. Neural Modules: Building blocks that represent data layers, encoders, decoders, language models, loss functions, etc. 3. Models: High-level abstractions that string together Neural Modules to create full neural network graphs. NeMo makes it easy to compose complex neural architectures using pre-built modules, allowing researchers and developers to quickly experiment with different model configurations. ## Key Features - Pre-trained models for ASR, NLP, and TTS tasks - Easy-to-use API for training and fine-tuning models - Distributed training support - Mixed precision training - Deployment-ready with NVIDIA Triton Inference Server For more information and the full source code, visit the [NVIDIA NeMo GitHub repository](https://github.com/NVIDIA/NeMo). [V0_FILE]python:file="src/nvidia/nemo/nemo_example.py" import nemo import nemo.collections.asr as nemo_asr # Load a pre-trained ASR model asr_model = nemo_asr.models.EncDecCTCModel.from_pretrained(model_name="QuartzNet15x5Base-En") # Transcribe audio files = ['path/to/audio_file.wav'] transcriptions = asr_model.transcribe(paths2audio_files=files) print(transcriptions) # Fine-tune the model train_data = 'path/to/train_manifest.json' validation_data = 'path/to/val_manifest.json' asr_model.setup_training_data(train_data_config={ 'manifest_filepath': train_data, 'labels': asr_model.decoder.vocabulary, 'batch_size': 32, 'shuffle': True, }) asr_model.setup_validation_data(val_data_config={ 'manifest_filepath': validation_data, 'labels': asr_model.decoder.vocabulary, 'batch_size': 32, 'shuffle': False, }) trainer = nemo.core.PyTorchLightning.Trainer(max_epochs=50, gpus=1) trainer.fit(asr_model) # Save the fine-tuned model asr_model.save_to('path/to/save/model.nemo') [V0_FILE]typescriptreact:file="src/nvidia/rag/README.md" # NVIDIA RAG (Retrieval-Augmented Generation) NVIDIA's RAG (Retrieval-Augmented Generation) is a technique that enhances large language models by combining them with external knowledge retrieval. This approach allows the model to access and utilize information beyond its training data, improving the accuracy and relevance of its responses. ## How it works 1. Query Processing: The input query is processed and used to retrieve relevant information from an external knowledge base. 2. Knowledge Retrieval: A retrieval system searches the knowledge base for documents or passages that are most relevant to the query. 3. Context Augmentation: The retrieved information is combined with the original query to create an augmented prompt. 4. Generation: The augmented prompt is fed into a large language model, which generates a response based on both the query and the retrieved information. This process allows the model to provide more informed and accurate responses by leveraging external knowledge. ## Key Features - Improved accuracy and relevance of responses - Ability to access and utilize up-to-date information - Reduced hallucination and factual errors - Customizable knowledge bases for domain-specific applications For more information and example implementations, visit the [NVIDIA Generative AI Examples GitHub repository](https://github.com/NVIDIA/GenerativeAIExamples/tree/main/community/llm_video_series/video_2_multimodal-rag). [V0_FILE]python:file="src/nvidia/rag/rag_example.py" import torch from transformers import AutoTokenizer, AutoModelForCausalLM from langchain import PromptTemplate, LLMChain from langchain.llms import HuggingFacePipeline from langchain.vectorstores import FAISS from langchain.embeddings import HuggingFaceEmbeddings from langchain.text_splitter import RecursiveCharacterTextSplitter # Load model and tokenizer model_name = "EleutherAI/gpt-neo-1.3B" # You can replace this with a larger model tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForCausalLM.from_pretrained(model_name) # Create a HuggingFacePipeline pipeline = HuggingFacePipeline(pipeline=model) # Create a vector store embeddings = HuggingFaceEmbeddings() vector_store = FAISS.from_texts(["Your knowledge base text here"], embeddings) # Create a text splitter text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200) # Define a prompt template template = """ Context: {context} [V0_FILE]typescript:file="pages/api/nvidia-completion.ts" import { NextApiRequest, NextApiResponse } from 'next' import { OpenAI } from 'openai' const API_KEY = process.env.NVIDIA_API_KEY const nvidiaClient = new OpenAI({ baseURL: "https://integrate.api.nvidia.com/v1", apiKey: API_KEY }) export default async function handler(req: NextApiRequest, res: NextApiResponse) { if (req.method !== 'POST') { return res.status(405).json({ error: 'Method not allowed' }) } const { prompt } = req.body if (!prompt) { return res.status(400).json({ error: 'Prompt is required' }) } try { const completion = await nvidiaClient.chat.completions.create({ model: "nvidia/llama-3.1-nemotron-70b-instruct", messages: [{ role: "user", content: prompt }], temperature: 0.5, top_p: 1, max_tokens: 1024, }) res.status(200).json({ response: completion.choices[0].message.content }) } catch (error) { console.error('Error generating NVIDIA response:', error) res.status(500).json({ error: 'Error generating response' }) } } [V0_FILE]typescript:file="src/utils/nvidiaApi.ts" export async function generateNvidiaResponse(prompt: string): Promise { try { const response = await fetch('/api/nvidia-completion', { method: 'POST', headers: { 'Content-Type': 'application/json', }, body: JSON.stringify({ prompt }), }) if (!response.ok) { throw new Error('Failed to generate response') } const data = await response.json() return data.response } catch (error) { console.error('Error generating NVIDIA response:', error) throw error } } [V0_FILE]typescriptreact:file="components/EnhancedUnifiedHolographicNeuralNetwork.tsx" "use client" import React, { useState, useEffect, useRef } from 'react' import axios from 'axios' import * as pdfjs from 'pdfjs-dist' import { v4 as uuidv4 } from 'uuid' import Peer from 'peerjs' import localforage from 'localforage' import { Button } from "@/components/ui/button" import { Input } from "@/components/ui/input" import { Card, CardContent, CardHeader, CardTitle } from "@/components/ui/card" import { AlertCircle, Download, Upload } from 'lucide-react' import { Alert, AlertDescription, AlertTitle } from "@/components/ui/alert" import { Progress } from "@/components/ui/progress" import { EnhancedHolographicNeuralNetwork } from '@/lib/EnhancedHolographicNeuralNetwork' import { Scene } from '@/components/HolographicNeuralNetwork/Scene' import Chat from '@/components/Chat' import Learning from '@/components/Learning' import KnowledgeManagement from '@/components/KnowledgeManagement' import Training from '@/components/Training' import FileProcessing from '@/components/FileProcessing' import P2PNetwork from '@/components/P2PNetwork' import { LLAMA_INDEX_API, NEMOTRON_70B_API, RAG_NVIDIA_API, NEMO_GUARDRAILS_API, useNvidiaAPIs } from '@/src/config' import { generateNvidiaResponse } from '@/src/utils/nvidiaApi' pdfjs.GlobalWorkerOptions.workerSrc = `//cdnjs.cloudflare.com/ajax/libs/pdf.js/${pdfjs.version}/pdf.worker.min.js` export default function EnhancedUnifiedHolographicNeuralNetwork() { const [chatHistory, setChatHistory] = useState([]) const [inputText, setInputText] = useState('') const [hnn, setHnn] = useState(null) const [isLLMActive, setIsLLMActive] = useState(true) const [error, setError] = useState(null) const [isTraining, setIsTraining] = useState(false) const [trainingProgress, setTrainingProgress] = useState(0) const [learnInput, setLearnInput] = useState('') const [learnResponse, setLearnResponse] = useState('') const [isProcessing, setIsProcessing] = useState(false) const [progress, setProgress] = useState(0) const [predictedWords, setPredictedWords] = useState([]) const [nodeId, setNodeId] = useState('') const [p2pConnected, setP2pConnected] = useState(false) const [connectedPeers, setConnectedPeers] = useState([]) const peerRef = useRef(null) useEffect(() => { const initializeHNN = async () => { let storedHNN = await localforage.getItem('hnn') if (storedHNN) { const newHnn = new EnhancedHolographicNeuralNetwork(100) newHnn.importKnowledge(storedHNN) setHnn(newHnn) } else { setHnn(new EnhancedHolographicNeuralNetwork(100)) } } initializeHNN() initializeP2P() }, []) useEffect(() => { if (hnn) { const saveHNN = async () => { await localforage.setItem('hnn', hnn.exportKnowledge()) } saveHNN() } }, [hnn]) const initializeP2P = () => { const peer = new Peer(uuidv4()) peerRef.current = peer peer.on('open', (id) => { setNodeId(id) setP2pConnected(true) }) peer.on('connection', (conn) => { conn.on('data', (data) => { handlePeerData(data) }) setConnectedPeers(prev => [...prev, conn.peer]) }) peer.on('error', (error) => { console.error('P2P Error:', error) setError('P2P connection error') }) } const handlePeerData = (data) => { if (data.type === 'knowledge') { hnn.importKnowledge(data.knowledge) } } const broadcastKnowledge = () => { const knowledge = hnn.exportKnowledge() connectedPeers.forEach(peerId => { const conn = peerRef.current.connect(peerId) conn.on('open', () => { conn.send({ type: 'knowledge', knowledge }) }) }) } const handleSubmit = async (e) => { e.preventDefault() if (!inputText.trim() || !hnn) return let response let ragContext = [] if (isLLMActive) { try { if (useNvidiaAPIs) { const llamaIndexResult = await hnn.useLlamaIndex(inputText) const nemotronResult = await hnn.useNemotron70B(inputText) const ragResult = await hnn.useRagNvidia(inputText) const guardrailsResult = await hnn.useNemoGuardrails(inputText) response = `LlamaIndex: ${llamaIndexResult} Nemotron: ${nemotronResult} RAG: ${ragResult} Guardrails: ${guardrailsResult}` } else { ragContext = [ { text: "Relevant context 1", score: 0.9 }, { text: "Relevant context 2", score: 0.7 }, { text: "Relevant context 3", score: 0.5 } ] hnn.updateContextNodes(ragContext) response = await generateNvidiaResponse(inputText) } } catch (error) { console.error('Error generating response:', error) response = "Sorry, I couldn't generate a response." } } else { response = hnn.generateResponse(inputText) } hnn.learn(inputText, response) setChatHistory(prev => [...prev, { type: 'user', text: inputText }, { type: 'bot', text: response }]) setInputText('') // Update predicted words const words = hnn.generateWords(inputText.split(' ').pop(), 10) setPredictedWords(words) // Broadcast updated knowledge to peers broadcastKnowledge() } const handleLearn = () => { if (learnInput.trim() && learnResponse.trim() && hnn) { hnn.learn(learnInput, learnResponse) setLearnInput('') setLearnResponse('') alert('Learning completed') broadcastKnowledge() } } const handleSave = () => { if (hnn) { const knowledge = hnn.exportKnowledge() const blob = new Blob([knowledge], { type: 'application/json' }) const url = URL.createObjectURL(blob) const a = document.createElement('a') a.href = url a.download = 'holographic_nn_knowledge.json' a.click() } } const handleLoad = (event) => { const file = event.target.files?.[0] if (file && hnn) { const reader = new FileReader() reader.onload = (e) => { const knowledge = e.target?.result if (typeof knowledge === 'string') { const success = hnn.importKnowledge(knowledge) if (success) { alert('Knowledge loaded successfully') broadcastKnowledge() } else { alert('Error loading knowledge') } } } reader.readAsText(file) } } const handleTrain = () => { setIsTraining(true) setTrainingProgress(0) const trainStep = (i) => { if (i >= 100) { // Train on 100 random samples setIsTraining(false) setTrainingProgress(0) alert('Training completed successfully') broadcastKnowledge() return } const input = `Random input ${i}` const output = `Random output ${i}` hnn.learn(input, output) setTrainingProgress(Math.round(((i + 1) / 100) * 100)) setTimeout(() => trainStep(i + 1), 100) } trainStep(0) } const processPDF = async (file) => { const arrayBuffer = await file.arrayBuffer() const pdf = await pdfjs.getDocument(arrayBuffer).promise let text = '' for (let i = 1; i <= pdf.numPages; i++) { const page = await pdf.getPage(i) const content = await page.getTextContent() text += content.items.map((item) => item.str).join(' ') + ' ' setProgress((i / pdf.numPages) * 100) } return text } const handleFileUpload = async (event) => { const file = event.target.files?.[0] if (file) { setIsProcessing(true) setProgress(0) try { let text = '' if (file.name.endsWith('.txt')) { text = await file.text() } else if (file.name.endsWith('.pdf')) { text = await processPDF(file) } else { throw new Error('Unsupported file type') } const words = text.toLowerCase().match(/\b\w+\b/g) || [] for (let i = 0; i < words.length - 1; i++) { hnn.learn(words[i], words[i + 1]) } setIsProcessing(false) setError(null) alert('File processed successfully') broadcastKnowledge() } catch (err) { setError('Error processing file. Please try again.') setIsProcessing(false) } } } return (

{error && ( Error {error} )}

Enhanced Holographic Neural Network

{chatHistory.map((message, index) => (

{message.text}

))}

{predictedWords.length > 0 && (

Predicted words: {predictedWords.join(', ')}

{p2pConnected ? 'Connected to P2P network' : 'Disconnected from P2P network'}

Connected Peers: {connectedPeers.length}

{trainingProgress}% completed

{Math.round(progress)}% processed

{isContextNode ? `Relevance: ${relevance.toFixed(2)}` : `Activation: ${activation.toFixed(2)}`}

{error && ( Error {error} )}

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