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(); expand_more
عرض المسودّات expand_more // ... (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 }; }