import gradio as gr from langchain_mistralai.chat_models import ChatMistralAI from langchain.prompts import ChatPromptTemplate import os from pathlib import Path import json import faiss import numpy as np from langchain.schema import Document import pickle import re import requests from functools import lru_cache import torch from sentence_transformers import SentenceTransformer import threading from queue import Queue import concurrent.futures class OptimizedRAGLoader: def __init__(self, docs_folder: str = "./docs", splits_folder: str = "./splits", index_folder: str = "./index"): self.docs_folder = Path(docs_folder) self.splits_folder = Path(splits_folder) self.index_folder = Path(index_folder) # Create folders if they don't exist for folder in [self.splits_folder, self.index_folder]: folder.mkdir(parents=True, exist_ok=True) # File paths self.splits_path = self.splits_folder / "splits.json" self.index_path = self.index_folder / "faiss.index" self.documents_path = self.index_folder / "documents.pkl" # Initialize components self.index = None self.indexed_documents = None # Initialize encoder model self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu") self.encoder = SentenceTransformer("intfloat/multilingual-e5-large") self.encoder.to(self.device) # Initialize thread pool self.executor = concurrent.futures.ThreadPoolExecutor(max_workers=4) # Initialize response cache self.response_cache = {} @lru_cache(maxsize=1000) def encode(self, text: str): """Cached encoding function""" with torch.no_grad(): embeddings = self.encoder.encode( text, convert_to_numpy=True, normalize_embeddings=True ) return embeddings def batch_encode(self, texts: list): """Batch encoding for multiple texts""" with torch.no_grad(): embeddings = self.encoder.encode( texts, batch_size=32, convert_to_numpy=True, normalize_embeddings=True, show_progress_bar=False ) return embeddings def load_and_split_texts(self): if self._splits_exist(): return self._load_existing_splits() documents = [] futures = [] for file_path in self.docs_folder.glob("*.txt"): future = self.executor.submit(self._process_file, file_path) futures.append(future) for future in concurrent.futures.as_completed(futures): documents.extend(future.result()) self._save_splits(documents) return documents def _process_file(self, file_path): with open(file_path, 'r', encoding='utf-8') as file: text = file.read() chunks = [s.strip() for s in re.split(r'(?<=[.!?])\s+', text) if s.strip()] return [ Document( page_content=chunk, metadata={ 'source': file_path.name, 'chunk_id': i, 'total_chunks': len(chunks) } ) for i, chunk in enumerate(chunks) ] def load_index(self) -> bool: """ Charge l'index FAISS et les documents associés s'ils existent Returns: bool: True si l'index a été chargé, False sinon """ if not self._index_exists(): print("Aucun index trouvé.") return False print("Chargement de l'index existant...") try: # Charger l'index FAISS self.index = faiss.read_index(str(self.index_path)) # Charger les documents associés with open(self.documents_path, 'rb') as f: self.indexed_documents = pickle.load(f) print(f"Index chargé avec {self.index.ntotal} vecteurs") return True except Exception as e: print(f"Erreur lors du chargement de l'index: {e}") return False def create_index(self, documents=None): if documents is None: documents = self.load_and_split_texts() if not documents: return False texts = [doc.page_content for doc in documents] embeddings = self.batch_encode(texts) dimension = embeddings.shape[1] self.index = faiss.IndexFlatL2(dimension) if torch.cuda.is_available(): # Use GPU for FAISS if available res = faiss.StandardGpuResources() self.index = faiss.index_cpu_to_gpu(res, 0, self.index) self.index.add(np.array(embeddings).astype('float32')) self.indexed_documents = documents # Save index and documents cpu_index = faiss.index_gpu_to_cpu(self.index) if torch.cuda.is_available() else self.index faiss.write_index(cpu_index, str(self.index_path)) with open(self.documents_path, 'wb') as f: pickle.dump(documents, f) return True def _index_exists(self) -> bool: """Vérifie si l'index et les documents associés existent""" return self.index_path.exists() and self.documents_path.exists() def get_retriever(self, k: int = 10): if self.index is None: if not self.load_index(): if not self.create_index(): raise ValueError("Unable to load or create index") def retriever_function(query: str) -> list: # Check cache first cache_key = f"{query}_{k}" if cache_key in self.response_cache: return self.response_cache[cache_key] query_embedding = self.encode(query) distances, indices = self.index.search( np.array([query_embedding]).astype('float32'), k ) results = [ self.indexed_documents[idx] for idx in indices[0] if idx != -1 ] # Cache the results self.response_cache[cache_key] = results return results return retriever_function # Initialize components llm = ChatMistralAI( model="mistral-large-latest", # mistral_api_key=mistral_api_key, temperature=0.1 # Lower temperature for faster responses ) rag_loader = OptimizedRAGLoader() retriever = rag_loader.get_retriever(k=5) # Reduced k for faster retrieval # Cache for processed questions question_cache = {} prompt_template = ChatPromptTemplate.from_messages([ ("system", """أنت مساعد مفيد يجيب على الأسئلة باللغة العربية باستخدام المعلومات المقدمة. استخدم المعلومات التالية للإجابة على السؤال: {context} إذا لم تكن المعلومات كافية للإجابة على السؤال بشكل كامل، قم بتوضيح ذلك. أجب بشكل موجز ودقيق."""), ("human", "{question}") ]) def process_question(question: str) -> tuple[str, str]: # Check cache first if question in question_cache: return question_cache[question] relevant_docs = retriever(question) context = "\n".join([doc.page_content for doc in relevant_docs]) prompt = prompt_template.format_messages( context=context, question=question ) response = llm(prompt) result = (response.content, context) # Cache the result question_cache[question] = result return result # Custom CSS for right-aligned text in textboxes custom_css = """ .rtl-text { text-align: right !important; direction: rtl !important; } .rtl-text textarea { text-align: right !important; direction: rtl !important; } """ # Gradio interface with queue with gr.Blocks(css=custom_css) as iface: with gr.Column(): input_text = gr.Textbox( label="السؤال", placeholder="اكتب سؤالك هنا...", lines=2, elem_classes="rtl-text" ) with gr.Row(): answer_box = gr.Textbox( label="الإجابة", lines=4, elem_classes="rtl-text" ) context_box = gr.Textbox( label="السياق المستخدم", lines=8, elem_classes="rtl-text" ) submit_btn = gr.Button("إرسال") submit_btn.click( fn=process_question, inputs=input_text, outputs=[answer_box, context_box], api_name="predict" ) if __name__ == "__main__": iface.launch( share=True, server_name="0.0.0.0", server_port=7860, max_threads=3, # Controls concurrency show_error=True )