README.md

---
license: gemma
metrics:
- bleu
---

onst tf = require('@tensorflow/tfjs-node');
const mongoose = require('mongoose');
const axios = require('axios');
const cheerio = require('cheerio');
const ytdl = require('ytdl-core');
const speech = require('@google-cloud/speech');
const fs = require('fs');
const gpt3Encoder = require('gpt-3-encoder'); // For encoding text for GPT-Neo
const { RecursiveCharacterTextSplitter } = require('langchain/text_splitter');
const { HNSWLib } = require("langchain/vectorstores/hnswlib");
const { OpenAIEmbeddings } = require("langchain/embeddings/openai");
const { RetrievalQA } = require("langchain/chains");

// ... (MongoDB connection and data schemas as before)

// 4. Data Collection Functions (Enhanced)
// ... (No changes here, same as before)

// 6. Model Definition and Training (Enhanced)
async function trainModel() {
 // ... (Load or create a TensorFlow.js model)

 // Load the GPT-Neo model
 const modelName = 'EleutherAI/gpt-neo-1.3B'; // Choose the desired GPT-Neo model size
 const gptNeoModel = await tf.loadGraphModel(`https://tfhub.dev/google/${modelName}/1`);

 // ... (Train the model using TensorFlow.js and the loaded GPT-Neo model)
}

// 8. Kemet AI API (Enhanced)
async function answerQuestion(question, userId) {
 // Load the HNSWLib vectorstore
 const vectorStore = await HNSWLib.load(DATA_DIR + 'hnswlib/', new OpenAIEmbeddings());

 // Encode the question using GPT-3 encoder
 const encodedQuestion = gpt3Encoder.encode(question);

 // Retrieve relevant documents from the vectorstore
 const relevantDocuments = await vectorStore.similaritySearch(encodedQuestion, k=3); // Get top 3 documents

 // Prepare context for GPT-Neo
 let context = "";
 for (const doc of relevantDocuments) {
  context += doc.pageContent + "\n";
 }

 // Generate answer using GPT-Neo
 const input_ids = gpt2_tokenizer.encode(context + question, return_tensors='tf');
 const output = await gptNeoModel.generate(input_ids, max_length=100, num_return_sequences=1);
 const answer = gpt2_tokenizer.decode(output[0], skip_special_tokens=True);

 // Store user feedback
 const feedback = await getFeedback(userId, question, answer);
 if (feedback) {
  // Update the vectorstore with user feedback
  // ...
 }

 return { answer, sourceDocuments: relevantDocuments };
}

// ... (Other API functions)

// 9. Main Function
async function main() {
 // ... (Data collection, model training, and evaluation as before)

 // Start the Kemet AI API server
 // ...
}

main();
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// ... (باقي الـ dependencies زي ما هي)
const { AutoModelForCausalLM, AutoTokenizer } = require('@xenova/transformers');

// ... (باقي الكود زي ما هو)

// 6. Model Definition and Training (Enhanced)
async function trainModel() {
  const { tokenizer, model } = await loadModel(); // حمل النموذج من Hugging Face

  // ... (استخدم الـ tokenizer والـ model في تدريب النموذج)
}

// 8. Kemet AI API (Enhanced)
async function answerQuestion(question, userId) {
  const { tokenizer, model } = await loadModel(); // حمل النموذج من Hugging Face

  // ... (استخدم الـ tokenizer والـ model لتوليد الإجابة)
}

// دالة مساعدة لتحميل النموذج من Hugging Face
async function loadModel() {
  const tokenizer = await AutoTokenizer.from_pretrained("your-username/your-model-name");
  const model = await AutoModelForCausalLM.from_pretrained("your-username/your-model-name");
  return { tokenizer, model };
}