import transformers import re from transformers import AutoConfig, AutoTokenizer, AutoModel, AutoModelForCausalLM from vllm import LLM, SamplingParams import torch import gradio as gr import json import os import shutil import requests import chromadb import pandas as pd from chromadb.config import Settings from chromadb.utils import embedding_functions device = "cuda:0" sentence_transformer_ef = embedding_functions.SentenceTransformerEmbeddingFunction(model_name="intfloat/multilingual-e5-base", device = "cuda") client = chromadb.PersistentClient(path="mfs_vector") collection = client.get_collection(name="sp_expanded", embedding_function = sentence_transformer_ef) # Define the device device = "cuda" if torch.cuda.is_available() else "cpu" #Define variables temperature=0.2 max_new_tokens=1000 top_p=0.92 repetition_penalty=1.7 model_name = "AgentPublic/Guillaume-Tell" llm = LLM(model_name, max_model_len=4096) #Vector search over the database def vector_search(collection, text): results = collection.query( query_texts=[text], n_results=5, ) document = [] document_html = [] id_list = "" list_elm = 0 for ids in results["ids"][0]: first_link = str(results["metadatas"][0][list_elm]["identifier"]) first_title = results["documents"][0][list_elm] list_elm = list_elm+1 document.append(first_link + " : " + first_title) document_html.append('") document = "\n\n".join(document) document_html = '
' + "".join(document_html) + "
" # Replace this with the actual implementation of the vector search return document, document_html #CSS for references formatting css = """ .generation { margin-left:2em; margin-right:2em; size:1.2em; } :target { background-color: #CCF3DF; /* Change the text color to red */ } .source { float:left; max-width:17%; margin-left:2%; } .tooltip { position: relative; cursor: pointer; font-variant-position: super; color: #97999b; } .tooltip:hover::after { content: attr(data-text); position: absolute; left: 0; top: 120%; /* Adjust this value as needed to control the vertical spacing between the text and the tooltip */ white-space: pre-wrap; /* Allows the text to wrap */ width: 500px; /* Sets a fixed maximum width for the tooltip */ max-width: 500px; /* Ensures the tooltip does not exceed the maximum width */ z-index: 1; background-color: #f9f9f9; color: #000; border: 1px solid #ddd; border-radius: 5px; padding: 5px; display: block; box-shadow: 0 4px 8px rgba(0,0,0,0.1); /* Optional: Adds a subtle shadow for better visibility */ }""" #Curtesy of chatgpt def format_references(text): # Define start and end markers for the reference ref_start_marker = '', start_pos) if end_pos == -1: # Malformed reference, break to avoid infinite loop break # Extract the reference text ref_text = text[start_pos + len(ref_start_marker):end_pos].replace('\n', ' ').strip() ref_text_encoded = ref_text.replace("&", "&").replace("<", "<").replace(">", ">") # Find the end of the reference tag ref_end_pos = text.find(ref_end_marker, end_pos) if ref_end_pos == -1: # Malformed reference, break to avoid infinite loop break # Extract the reference ID ref_id = text[end_pos + 2:ref_end_pos].strip() # Create the HTML for the tooltip tooltip_html = f'[' + str(ref_number) +']' parts.append(tooltip_html) # Update current_pos to the end of the current reference current_pos = ref_end_pos + len(ref_end_marker) ref_number = ref_number + 1 # Join and return the parts parts = ''.join(parts) return parts # Class to encapsulate the Falcon chatbot class MistralChatBot: def __init__(self, system_prompt="Le dialogue suivant est une conversation"): self.system_prompt = system_prompt def predict(self, user_message): fiches, fiches_html = vector_search(collection, user_message) sampling_params = SamplingParams(temperature=.7, top_p=.95, max_tokens=2000, presence_penalty = 1.5, stop = ["``"]) detailed_prompt = """<|im_start|>system Tu es Albert, le chatbot des Maisons France Service qui donne des réponses sourcées.<|im_end|> <|im_start|>user Ecrit un texte référencé en réponse à cette question : """ + user_message + """ Les références doivent être citées de cette manière : texte rédigé[\"identifiant de la référence\"]Si les références ne permettent pas de répondre, qu'il n'y a pas de réponse. Les cinq références disponibles : """ + fiches + "<|im_end|>\n<|im_start|>assistant\n" print(detailed_prompt) prompts = [detailed_prompt] outputs = llm.generate(prompts, sampling_params, use_tqdm = False) generated_text = outputs[0].outputs[0].text generated_text = '

Réponse

\n
' + format_references(generated_text) + "
" fiches_html = '

Sources

\n' + fiches_html return generated_text, fiches_html # Create the Falcon chatbot instance mistral_bot = MistralChatBot() # Define the Gradio interface title = "Guillaume-Tell" description = "Le LLM répond à des questions administratives sur l'éducation nationale à partir de sources fiables." examples = [ [ "Qui peut bénéficier de l'AIP?", # user_message 0.7 # temperature ] ] additional_inputs=[ gr.Slider( label="Température", value=0.2, # Default value minimum=0.05, maximum=1.0, step=0.05, interactive=True, info="Des valeurs plus élevées donne plus de créativité, mais aussi d'étrangeté", ), ] demo = gr.Blocks() with gr.Blocks(theme='JohnSmith9982/small_and_pretty', css=css) as demo: gr.HTML("""

Albert (Guillaume-Tell)

""") text_input = gr.Textbox(label="Votre question ou votre instruction.", type="text", lines=1) text_button = gr.Button("Interroger Albert") text_output = gr.HTML(label="La réponse d'Albert") embedding_output = gr.HTML(label="Les sources utilisées") text_button.click(mistral_bot.predict, inputs=text_input, outputs=[text_output, embedding_output]) if __name__ == "__main__": demo.queue().launch()