rag_pipeline.py

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
from sentence_transformers import SentenceTransformer
import numpy as np
import time

class RAGPipeline:
    def __init__(self):
        print("[RAG] جاري تحميل النموذج والمحول...")
        self.tokenizer = AutoTokenizer.from_pretrained("facebook/bart-large")
        self.model = AutoModelForSeq2SeqLM.from_pretrained("facebook/bart-large")
        self.embedder = SentenceTransformer('all-MiniLM-L6-v2')
        self.index = None
        self.chunks = []
        self.chunk_embeddings = []
        print("[RAG] تم التحميل بنجاح.")

    def build_index(self, chunks, logs=None):
        self.chunks = chunks
        self.chunk_embeddings = self.embedder.encode(chunks, convert_to_numpy=True)
        if logs is not None:
            logs.append(f"[RAG] تم بناء الفهرس بأبعاد {self.chunk_embeddings.shape}")
        self.index = np.array(self.chunk_embeddings)

    def answer(self, question):
        question_embedding = self.embedder.encode([question], convert_to_numpy=True)
        # بحث عن أقرب 5 مقاطع
        similarities = np.dot(self.index, question_embedding.T).squeeze()
        top_idx = similarities.argsort()[-5:][::-1]
        context = "\n".join([self.chunks[i] for i in top_idx])
        inputs = self.tokenizer.encode(question + " " + context, return_tensors="pt", max_length=512, truncation=True)
        outputs = self.model.generate(inputs, max_length=200)
        answer = self.tokenizer.decode(outputs[0], skip_special_tokens=True)
        sources = [self.chunks[i] for i in top_idx]
        return answer, sources