RAG_loi / app.py
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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
)