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| import logging | |
| import os | |
| import streamlit as st | |
| import torch | |
| import sys | |
| from llama_index.core import VectorStoreIndex, SimpleDirectoryReader, Settings, StorageContext, load_index_from_storage | |
| from llama_index.core.chat_engine.types import ChatMode | |
| from llama_index.embeddings.huggingface import HuggingFaceEmbedding | |
| from llama_index.llms.openai_like import OpenAILike | |
| API_BASE_URL="https://chatapi.akash.network/api/v1" | |
| EMBEDDING_MODEL="sentence-transformers/paraphrase-MiniLM-L6-v2" #Modèle le plus rapide | |
| LLM_MODEL="DeepSeek-R1-Distill-Llama-70B" | |
| PERSIST_DIR = "./storage" | |
| # Ajuster le chemin de torch.classes pour éviter le conflit | |
| torch.classes.__path__ = [] | |
| st.set_page_config(page_title="Votre expert en règles de marché RTE", layout="centered", initial_sidebar_state="auto", menu_items=None) | |
| st.title("Votre expert en règles de marché RTE") | |
| custom_llm = OpenAILike(api_base=API_BASE_URL, model=LLM_MODEL, api_key=st.secrets["openai_key"], max_tokens=1500, temperature=0.2) | |
| Settings.embed_model = HuggingFaceEmbedding(model_name=EMBEDDING_MODEL) | |
| Settings.llm=custom_llm | |
| logging.basicConfig(stream=sys.stdout, level=logging.DEBUG) | |
| logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout)) | |
| # Load and index data | |
| def load_data(): | |
| if not os.path.exists(PERSIST_DIR): | |
| documents = SimpleDirectoryReader(input_dir="./data").load_data() | |
| document_index = VectorStoreIndex.from_documents(documents) | |
| document_index.storage_context.persist(persist_dir=PERSIST_DIR) | |
| else: | |
| storage_context = StorageContext.from_defaults(persist_dir=PERSIST_DIR) | |
| document_index = load_index_from_storage(storage_context) | |
| return document_index | |
| index = load_data() | |
| if "messages" not in st.session_state.keys(): # Initialize the chat messages history | |
| st.session_state.messages = [ | |
| { | |
| "role": "assistant", | |
| "content": "Posez-moi une question sur les règles de marché RTE :\nhttps://www.services-rte.com/fr/actualites/nouvelles-versions-des-r%C3%A8gles-de-march%C3%A9-applicables-au-01-avril-2024.html", | |
| } | |
| ] | |
| if "chat_engine" not in st.session_state.keys(): # Initialize the chat engine | |
| st.session_state.chat_engine = index.as_chat_engine(chat_mode=ChatMode.CONTEXT, similarity_top_k=5, verbose=True, streaming=True) | |
| if prompt := st.chat_input("Posez votre question"): # Prompt for user input and save to chat history | |
| st.session_state.messages.append({"role": "user", "content": prompt}) | |
| for message in st.session_state.messages: # Write message history to UI | |
| with st.chat_message(message["role"]): | |
| st.write(message["content"]) | |
| # If last message is not from assistant, generate a new response | |
| if st.session_state.messages[-1]["role"] != "assistant": | |
| with st.chat_message("assistant"): | |
| response_stream = st.session_state.chat_engine.stream_chat(prompt) | |
| st.write_stream(response_stream.response_gen) | |
| message = {"role": "assistant", "content": response_stream.response} | |
| # Add response to message history | |
| st.session_state.messages.append(message) | |