###### # Project : lollms-webui # File : api/__init__.py # Author : ParisNeo with the help of the community # license : Apache 2.0 # Description : # A simple api to communicate with lollms-webui and its models. ###### from flask import request from datetime import datetime from api.db import DiscussionsDB, Discussion from pathlib import Path from lollms.config import InstallOption from lollms.types import MSG_TYPE, SENDER_TYPES from lollms.extension import LOLLMSExtension, ExtensionBuilder from lollms.personality import AIPersonality, PersonalityBuilder from lollms.binding import LOLLMSConfig, BindingBuilder, LLMBinding, ModelBuilder, BindingType from lollms.paths import LollmsPaths from lollms.helpers import ASCIIColors, trace_exception from lollms.com import NotificationType, NotificationDisplayType, LoLLMsCom from lollms.app import LollmsApplication from lollms.utilities import File64BitsManager, PromptReshaper from lollms.media import WebcamImageSender, AudioRecorder from safe_store import TextVectorizer, VectorizationMethod, VisualizationMethod import threading from tqdm import tqdm import traceback import sys import gc import ctypes from functools import partial import json import shutil import re import string import requests from datetime import datetime from typing import List, Tuple def terminate_thread(thread): if thread: if not thread.is_alive(): ASCIIColors.yellow("Thread not alive") return thread_id = thread.ident exc = ctypes.py_object(SystemExit) res = ctypes.pythonapi.PyThreadState_SetAsyncExc(thread_id, exc) if res > 1: ctypes.pythonapi.PyThreadState_SetAsyncExc(thread_id, None) del thread gc.collect() raise SystemError("Failed to terminate the thread.") else: ASCIIColors.yellow("Canceled successfully") __author__ = "parisneo" __github__ = "https://github.com/ParisNeo/lollms-webui" __copyright__ = "Copyright 2023, " __license__ = "Apache 2.0" import subprocess import pkg_resources # =========================================================== # Manage automatic install scripts def is_package_installed(package_name): try: dist = pkg_resources.get_distribution(package_name) return True except pkg_resources.DistributionNotFound: return False def install_package(package_name): try: # Check if the package is already installed __import__(package_name) print(f"{package_name} is already installed.") except ImportError: print(f"{package_name} is not installed. Installing...") # Install the package using pip subprocess.check_call(["pip", "install", package_name]) print(f"{package_name} has been successfully installed.") def parse_requirements_file(requirements_path): with open(requirements_path, 'r') as f: for line in f: line = line.strip() if not line or line.startswith('#'): # Skip empty and commented lines continue package_name, _, version_specifier = line.partition('==') package_name, _, version_specifier = line.partition('>=') if is_package_installed(package_name): # The package is already installed print(f"{package_name} is already installed.") else: # The package is not installed, install it if version_specifier: install_package(f"{package_name}{version_specifier}") else: install_package(package_name) # =========================================================== class LoLLMsAPI(LollmsApplication): def __init__(self, config:LOLLMSConfig, socketio, config_file_path:str, lollms_paths: LollmsPaths) -> None: self.socketio = socketio super().__init__("Lollms_webui",config, lollms_paths, callback=self.process_chunk) self.busy = False self.nb_received_tokens = 0 self.config_file_path = config_file_path self.cancel_gen = False # Keeping track of current discussion and message self._current_user_message_id = 0 self._current_ai_message_id = 0 self._message_id = 0 self.db_path = config["db_path"] if Path(self.db_path).is_absolute(): # Create database object self.db = DiscussionsDB(self.db_path) else: # Create database object self.db = DiscussionsDB(self.lollms_paths.personal_databases_path/self.db_path) # If the database is empty, populate it with tables ASCIIColors.info("Checking discussions database... ",end="") self.db.create_tables() self.db.add_missing_columns() ASCIIColors.success("ok") # prepare vectorization if self.config.data_vectorization_activate and self.config.use_discussions_history: try: ASCIIColors.yellow("Loading long term memory") folder = self.lollms_paths.personal_databases_path/"vectorized_dbs" folder.mkdir(parents=True, exist_ok=True) self.build_long_term_skills_memory() ASCIIColors.yellow("Ready") except Exception as ex: trace_exception(ex) self.long_term_memory = None else: self.long_term_memory = None # This is used to keep track of messages self.download_infos={} self.connections = { 0:{ "current_discussion":None, "generated_text":"", "cancel_generation": False, "generation_thread": None, "processing":False, "schedule_for_deletion":False, "continuing": False, "first_chunk": True, } } try: self.webcam = WebcamImageSender(socketio,lollmsCom=self) except: self.webcam = None try: self.rec_output_folder = lollms_paths.personal_outputs_path/"audio_rec" self.rec_output_folder.mkdir(exist_ok=True, parents=True) self.summoned = False self.audio_cap = AudioRecorder(socketio,self.rec_output_folder/"rt.wav", callback=self.audio_callback,lollmsCom=self) except: self.rec_output_folder = None # ========================================================================================= # Socket IO stuff # ========================================================================================= @socketio.on('connect') def connect(): #Create a new connection information self.connections[request.sid] = { "current_discussion":self.db.load_last_discussion(), "generated_text":"", "continuing": False, "first_chunk": True, "cancel_generation": False, "generation_thread": None, "processing":False, "schedule_for_deletion":False } self.socketio.emit('connected', room=request.sid) ASCIIColors.success(f'Client {request.sid} connected') @socketio.on('disconnect') def disconnect(): try: self.socketio.emit('disconnected', room=request.sid) if self.connections[request.sid]["processing"]: self.connections[request.sid]["schedule_for_deletion"]=True else: del self.connections[request.sid] except Exception as ex: pass ASCIIColors.error(f'Client {request.sid} disconnected') @socketio.on('start_webcam_video_stream') def start_webcam_video_stream(): self.webcam.start_capture() @socketio.on('stop_webcam_video_stream') def stop_webcam_video_stream(): self.webcam.stop_capture() @socketio.on('start_audio_stream') def start_audio_stream(): self.audio_cap.start_recording() @socketio.on('stop_audio_stream') def stop_audio_stream(): self.audio_cap.stop_recording() @socketio.on('upgrade_vectorization') def upgrade_vectorization(): if self.config.data_vectorization_activate and self.config.use_discussions_history: try: self.socketio.emit('show_progress') self.socketio.sleep(0) ASCIIColors.yellow("0- Detected discussion vectorization request") folder = self.lollms_paths.personal_databases_path/"vectorized_dbs" folder.mkdir(parents=True, exist_ok=True) self.build_long_term_skills_memory() ASCIIColors.yellow("1- Exporting discussions") discussions = self.db.export_all_as_markdown_list_for_vectorization() ASCIIColors.yellow("2- Adding discussions to vectorizer") index = 0 nb_discussions = len(discussions) for (title,discussion) in tqdm(discussions): self.socketio.emit('update_progress',{'value':int(100*(index/nb_discussions))}) self.socketio.sleep(0) index += 1 if discussion!='': skill = self.learn_from_discussion(title, discussion) self.long_term_memory.add_document(title, skill, chunk_size=self.config.data_vectorization_chunk_size, overlap_size=self.config.data_vectorization_overlap_size, force_vectorize=False, add_as_a_bloc=False) ASCIIColors.yellow("3- Indexing database") self.long_term_memory.index() ASCIIColors.yellow("4- Saving database") self.long_term_memory.save_to_json() if self.config.data_vectorization_visualize_on_vectorization: self.long_term_memory.show_document(show_interactive_form=True) ASCIIColors.yellow("Ready") except Exception as ex: ASCIIColors.error(f"Couldn't vectorize database:{ex}") self.socketio.emit('hide_progress') self.socketio.sleep(0) @socketio.on('cancel_install') def cancel_install(data): try: model_name = data["model_name"] binding_folder = data["binding_folder"] model_url = data["model_url"] signature = f"{model_name}_{binding_folder}_{model_url}" self.download_infos[signature]["cancel"]=True self.socketio.emit('canceled', { 'status': True }, room=request.sid ) except Exception as ex: trace_exception(ex) self.socketio.emit('canceled', { 'status': False, 'error':str(ex) }, room=request.sid ) @socketio.on('install_model') def install_model(data): room_id = request.sid def install_model_(): print("Install model triggered") model_path = data["path"].replace("\\","/") if data["type"].lower() in model_path.lower(): model_type:str=data["type"] else: mtt = None for mt in self.binding.models_dir_names: if mt.lower() in model_path.lower(): mtt = mt break if mtt: model_type = mtt else: model_type:str=self.binding.models_dir_names[0] progress = 0 installation_dir = self.binding.searchModelParentFolder(model_path.split('/')[-1], model_type) if model_type=="gptq" or model_type=="awq": parts = model_path.split("/") if len(parts)==2: filename = parts[1] else: filename = parts[4] installation_path = installation_dir / filename elif model_type=="gpt4all": filename = data["variant_name"] model_path = "http://gpt4all.io/models/gguf/"+filename installation_path = installation_dir / filename else: filename = Path(model_path).name installation_path = installation_dir / filename print("Model install requested") print(f"Model path : {model_path}") model_name = filename binding_folder = self.config["binding_name"] model_url = model_path signature = f"{model_name}_{binding_folder}_{model_url}" try: self.download_infos[signature]={ "start_time":datetime.now(), "total_size":self.binding.get_file_size(model_path), "downloaded_size":0, "progress":0, "speed":0, "cancel":False } if installation_path.exists(): print("Error: Model already exists. please remove it first") socketio.emit('install_progress',{ 'status': False, 'error': f'model already exists. Please remove it first.\nThe model can be found here:{installation_path}', 'model_name' : model_name, 'binding_folder' : binding_folder, 'model_url' : model_url, 'start_time': self.download_infos[signature]['start_time'].strftime("%Y-%m-%d %H:%M:%S"), 'total_size': self.download_infos[signature]['total_size'], 'downloaded_size': self.download_infos[signature]['downloaded_size'], 'progress': self.download_infos[signature]['progress'], 'speed': self.download_infos[signature]['speed'], }, room=room_id ) return socketio.emit('install_progress',{ 'status': True, 'progress': progress, 'model_name' : model_name, 'binding_folder' : binding_folder, 'model_url' : model_url, 'start_time': self.download_infos[signature]['start_time'].strftime("%Y-%m-%d %H:%M:%S"), 'total_size': self.download_infos[signature]['total_size'], 'downloaded_size': self.download_infos[signature]['downloaded_size'], 'progress': self.download_infos[signature]['progress'], 'speed': self.download_infos[signature]['speed'], }, room=room_id) def callback(downloaded_size, total_size): progress = (downloaded_size / total_size) * 100 now = datetime.now() dt = (now - self.download_infos[signature]['start_time']).total_seconds() speed = downloaded_size/dt self.download_infos[signature]['downloaded_size'] = downloaded_size self.download_infos[signature]['speed'] = speed if progress - self.download_infos[signature]['progress']>2: self.download_infos[signature]['progress'] = progress socketio.emit('install_progress',{ 'status': True, 'model_name' : model_name, 'binding_folder' : binding_folder, 'model_url' : model_url, 'start_time': self.download_infos[signature]['start_time'].strftime("%Y-%m-%d %H:%M:%S"), 'total_size': self.download_infos[signature]['total_size'], 'downloaded_size': self.download_infos[signature]['downloaded_size'], 'progress': self.download_infos[signature]['progress'], 'speed': self.download_infos[signature]['speed'], }, room=room_id) if self.download_infos[signature]["cancel"]: raise Exception("canceled") if hasattr(self.binding, "download_model"): try: self.binding.download_model(model_path, installation_path, callback) except Exception as ex: ASCIIColors.warning(str(ex)) trace_exception(ex) socketio.emit('install_progress',{ 'status': False, 'error': 'canceled', 'model_name' : model_name, 'binding_folder' : binding_folder, 'model_url' : model_url, 'start_time': self.download_infos[signature]['start_time'].strftime("%Y-%m-%d %H:%M:%S"), 'total_size': self.download_infos[signature]['total_size'], 'downloaded_size': self.download_infos[signature]['downloaded_size'], 'progress': self.download_infos[signature]['progress'], 'speed': self.download_infos[signature]['speed'], }, room=room_id ) del self.download_infos[signature] try: if installation_path.is_dir(): shutil.rmtree(installation_path) else: installation_path.unlink() except Exception as ex: trace_exception(ex) ASCIIColors.error(f"Couldn't delete file. Please try to remove it manually.\n{installation_path}") return else: try: self.download_file(model_path, installation_path, callback) except Exception as ex: ASCIIColors.warning(str(ex)) trace_exception(ex) socketio.emit('install_progress',{ 'status': False, 'error': 'canceled', 'model_name' : model_name, 'binding_folder' : binding_folder, 'model_url' : model_url, 'start_time': self.download_infos[signature]['start_time'].strftime("%Y-%m-%d %H:%M:%S"), 'total_size': self.download_infos[signature]['total_size'], 'downloaded_size': self.download_infos[signature]['downloaded_size'], 'progress': self.download_infos[signature]['progress'], 'speed': self.download_infos[signature]['speed'], }, room=room_id ) del self.download_infos[signature] installation_path.unlink() return socketio.emit('install_progress',{ 'status': True, 'error': '', 'model_name' : model_name, 'binding_folder' : binding_folder, 'model_url' : model_url, 'start_time': self.download_infos[signature]['start_time'].strftime("%Y-%m-%d %H:%M:%S"), 'total_size': self.download_infos[signature]['total_size'], 'downloaded_size': self.download_infos[signature]['downloaded_size'], 'progress': 100, 'speed': self.download_infos[signature]['speed'], }, room=room_id) del self.download_infos[signature] except Exception as ex: trace_exception(ex) socketio.emit('install_progress',{ 'status': False, 'error': str(ex), 'model_name' : model_name, 'binding_folder' : binding_folder, 'model_url' : model_url, 'start_time': '', 'total_size': 0, 'downloaded_size': 0, 'progress': 0, 'speed': 0, }, room=room_id ) tpe = threading.Thread(target=install_model_, args=()) tpe.start() @socketio.on('uninstall_model') def uninstall_model(data): model_path = data['path'] model_type:str=data.get("type","ggml") installation_dir = self.binding.searchModelParentFolder(model_path) binding_folder = self.config["binding_name"] if model_type=="gptq" or model_type=="awq": filename = model_path.split("/")[4] installation_path = installation_dir / filename else: filename = Path(model_path).name installation_path = installation_dir / filename model_name = filename if not installation_path.exists(): socketio.emit('uninstall_progress',{ 'status': False, 'error': 'The model does not exist', 'model_name' : model_name, 'binding_folder' : binding_folder }, room=request.sid) try: if not installation_path.exists(): # Try to find a version model_path = installation_path.name.lower().replace("-ggml","").replace("-gguf","") candidates = [m for m in installation_dir.iterdir() if model_path in m.name] if len(candidates)>0: model_path = candidates[0] installation_path = model_path if installation_path.is_dir(): shutil.rmtree(installation_path) else: installation_path.unlink() socketio.emit('uninstall_progress',{ 'status': True, 'error': '', 'model_name' : model_name, 'binding_folder' : binding_folder }, room=request.sid) except Exception as ex: trace_exception(ex) ASCIIColors.error(f"Couldn't delete {installation_path}, please delete it manually and restart the app") socketio.emit('uninstall_progress',{ 'status': False, 'error': f"Couldn't delete {installation_path}, please delete it manually and restart the app", 'model_name' : model_name, 'binding_folder' : binding_folder }, room=request.sid) @socketio.on('new_discussion') def new_discussion(data): client_id = request.sid title = data["title"] if self.connections[client_id]["current_discussion"] is not None: if self.long_term_memory is not None: title, content = self.connections[client_id]["current_discussion"].export_for_vectorization() skill = self.learn_from_discussion(title, content) self.long_term_memory.add_document(title, skill, chunk_size=self.config.data_vectorization_chunk_size, overlap_size=self.config.data_vectorization_overlap_size, force_vectorize=False, add_as_a_bloc=False, add_to_index=True) ASCIIColors.yellow("4- Saving database") self.long_term_memory.save_to_json() self.connections[client_id]["current_discussion"] = self.db.create_discussion(title) # Get the current timestamp timestamp = datetime.now().strftime("%Y-%m-%d %H:%M:%S") # Return a success response if self.connections[client_id]["current_discussion"] is None: self.connections[client_id]["current_discussion"] = self.db.load_last_discussion() if self.personality.welcome_message!="": message = self.connections[client_id]["current_discussion"].add_message( message_type = MSG_TYPE.MSG_TYPE_FULL.value if self.personality.include_welcome_message_in_disucssion else MSG_TYPE.MSG_TYPE_FULL_INVISIBLE_TO_AI.value, sender_type = SENDER_TYPES.SENDER_TYPES_AI.value, sender = self.personality.name, content = self.personality.welcome_message, metadata = None, rank = 0, parent_message_id = -1, binding = self.config.binding_name, model = self.config.model_name, personality = self.config.personalities[self.config.active_personality_id], created_at=None, finished_generating_at=None ) self.socketio.emit('discussion_created', {'id':self.connections[client_id]["current_discussion"].discussion_id}, room=client_id ) else: self.socketio.emit('discussion_created', {'id':0}, room=client_id ) @socketio.on('load_discussion') def load_discussion(data): client_id = request.sid ASCIIColors.yellow(f"Loading discussion for client {client_id} ... ", end="") if "id" in data: discussion_id = data["id"] self.connections[client_id]["current_discussion"] = Discussion(discussion_id, self.db) else: if self.connections[client_id]["current_discussion"] is not None: discussion_id = self.connections[client_id]["current_discussion"].discussion_id self.connections[client_id]["current_discussion"] = Discussion(discussion_id, self.db) else: self.connections[client_id]["current_discussion"] = self.db.create_discussion() messages = self.connections[client_id]["current_discussion"].get_messages() jsons = [m.to_json() for m in messages] self.socketio.emit('discussion', jsons, room=client_id ) ASCIIColors.green(f"ok") @socketio.on('upload_file') def upload_file(data): ASCIIColors.yellow("Uploading file") file = data['file'] filename = file.filename save_path = self.lollms_paths.personal_uploads_path/filename # Specify the desired folder path try: if not self.personality.processor is None: file.save(save_path) self.personality.processor.add_file(save_path, partial(self.process_chunk, client_id = request.sid)) # File saved successfully socketio.emit('progress', {'status':True, 'progress': 100}) else: file.save(save_path) self.personality.add_file(save_path, partial(self.process_chunk, client_id = request.sid)) # File saved successfully socketio.emit('progress', {'status':True, 'progress': 100}) except Exception as e: # Error occurred while saving the file socketio.emit('progress', {'status':False, 'error': str(e)}) @socketio.on('cancel_generation') def cancel_generation(): client_id = request.sid self.cancel_gen = True #kill thread ASCIIColors.error(f'Client {request.sid} requested cancelling generation') terminate_thread(self.connections[client_id]['generation_thread']) ASCIIColors.error(f'Client {request.sid} canceled generation') self.busy=False @socketio.on('get_personality_files') def get_personality_files(data): client_id = request.sid self.connections[client_id]["generated_text"] = "" self.connections[client_id]["cancel_generation"] = False try: self.personality.setCallback(partial(self.process_chunk,client_id = client_id)) except Exception as ex: trace_exception(ex) @socketio.on('send_file_chunk') def send_file_chunk(data): client_id = request.sid filename = data['filename'] chunk = data['chunk'] offset = data['offset'] is_last_chunk = data['isLastChunk'] chunk_index = data['chunkIndex'] path:Path = self.lollms_paths.personal_uploads_path / self.personality.personality_folder_name path.mkdir(parents=True, exist_ok=True) file_path = path / data["filename"] # Save the chunk to the server or process it as needed # For example: if chunk_index==0: with open(file_path, 'wb') as file: file.write(chunk) else: with open(file_path, 'ab') as file: file.write(chunk) if is_last_chunk: print('File received and saved successfully') if self.personality.processor: result = self.personality.processor.add_file(file_path, partial(self.process_chunk, client_id=client_id)) else: result = self.personality.add_file(file_path, partial(self.process_chunk, client_id=client_id)) self.socketio.emit('file_received', {'status': True, 'filename': filename}) else: # Request the next chunk from the client self.socketio.emit('request_next_chunk', {'offset': offset + len(chunk)}) @self.socketio.on('cancel_text_generation') def cancel_text_generation(data): client_id = request.sid self.connections[client_id]["requested_stop"]=True print(f"Client {client_id} requested canceling generation") self.socketio.emit("generation_canceled", {"message":"Generation is canceled."}, room=client_id) self.socketio.sleep(0) self.busy = False @self.socketio.on('execute_python_code') def execute_python_code(data): """Executes Python code and returns the output.""" client_id = request.sid code = data["code"] # Import the necessary modules. import io import sys import time # Create a Python interpreter. interpreter = io.StringIO() sys.stdout = interpreter # Execute the code. start_time = time.time() exec(code) end_time = time.time() # Get the output. output = interpreter.getvalue() self.socketio.emit("execution_output", {"output":output,"execution_time":end_time - start_time}, room=client_id) @self.socketio.on('create_empty_message') def create_empty_message(data): client_id = request.sid type = data.get("type",0) if type==0: ASCIIColors.info(f"Building empty User message requested by : {client_id}") # send the message to the bot print(f"Creating an empty message for AI answer orientation") if self.connections[client_id]["current_discussion"]: if not self.model: self.error("No model selected. Please make sure you select a model before starting generation", client_id = client_id) return self.new_message(client_id, self.config.user_name, "", sender_type=SENDER_TYPES.SENDER_TYPES_USER, open=True) self.socketio.sleep(0.01) else: if self.personality is None: self.warning("Select a personality") return ASCIIColors.info(f"Building empty AI message requested by : {client_id}") # send the message to the bot print(f"Creating an empty message for AI answer orientation") if self.connections[client_id]["current_discussion"]: if not self.model: self.error("No model selected. Please make sure you select a model before starting generation", client_id=client_id) return self.new_message(client_id, self.personality.name, "[edit this to put your ai answer start]", open=True) self.socketio.sleep(0.01) # A copy of the original lollms-server generation code needed for playground @self.socketio.on('generate_text') def handle_generate_text(data): client_id = request.sid self.cancel_gen = False ASCIIColors.info(f"Text generation requested by client: {client_id}") if self.busy: self.socketio.emit("busy", {"message":"I am busy. Come back later."}, room=client_id) self.socketio.sleep(0) ASCIIColors.warning(f"OOps request {client_id} refused!! Server busy") return def generate_text(): self.busy = True try: model = self.model self.connections[client_id]["is_generating"]=True self.connections[client_id]["requested_stop"]=False prompt = data['prompt'] tokenized = model.tokenize(prompt) personality_id = data.get('personality', -1) n_crop = data.get('n_crop', len(tokenized)) if n_crop!=-1: prompt = model.detokenize(tokenized[-n_crop:]) n_predicts = data["n_predicts"] parameters = data.get("parameters",{ "temperature":self.config["temperature"], "top_k":self.config["top_k"], "top_p":self.config["top_p"], "repeat_penalty":self.config["repeat_penalty"], "repeat_last_n":self.config["repeat_last_n"], "seed":self.config["seed"] }) if personality_id==-1: # Raw text generation self.answer = {"full_text":""} def callback(text, message_type: MSG_TYPE, metadata:dict={}): if message_type == MSG_TYPE.MSG_TYPE_CHUNK: ASCIIColors.success(f"generated:{len(self.answer['full_text'].split())} words", end='\r') self.answer["full_text"] = self.answer["full_text"] + text self.socketio.emit('text_chunk', {'chunk': text, 'type':MSG_TYPE.MSG_TYPE_CHUNK.value}, room=client_id) self.socketio.sleep(0) if client_id in self.connections:# Client disconnected if self.connections[client_id]["requested_stop"]: return False else: return True else: return False tk = model.tokenize(prompt) n_tokens = len(tk) fd = model.detokenize(tk[-min(self.config.ctx_size-n_predicts,n_tokens):]) try: ASCIIColors.print("warming up", ASCIIColors.color_bright_cyan) generated_text = model.generate(fd, n_predict=n_predicts, callback=callback, temperature = parameters["temperature"], top_k = parameters["top_k"], top_p = parameters["top_p"], repeat_penalty = parameters["repeat_penalty"], repeat_last_n = parameters["repeat_last_n"], seed = parameters["seed"], ) ASCIIColors.success(f"\ndone") if client_id in self.connections: if not self.connections[client_id]["requested_stop"]: # Emit the generated text to the client self.socketio.emit('text_generated', {'text': generated_text}, room=client_id) self.socketio.sleep(0) except Exception as ex: self.socketio.emit('generation_error', {'error': str(ex)}, room=client_id) ASCIIColors.error(f"\ndone") self.busy = False else: try: personality: AIPersonality = self.personalities[personality_id] ump = self.config.discussion_prompt_separator +self.config.user_name.strip() if self.config.use_user_name_in_discussions else self.personality.user_message_prefix personality.model = model cond_tk = personality.model.tokenize(personality.personality_conditioning) n_cond_tk = len(cond_tk) # Placeholder code for text generation # Replace this with your actual text generation logic print(f"Text generation requested by client: {client_id}") self.answer["full_text"] = '' full_discussion_blocks = self.connections[client_id]["full_discussion_blocks"] if prompt != '': if personality.processor is not None and personality.processor_cfg["process_model_input"]: preprocessed_prompt = personality.processor.process_model_input(prompt) else: preprocessed_prompt = prompt if personality.processor is not None and personality.processor_cfg["custom_workflow"]: full_discussion_blocks.append(ump) full_discussion_blocks.append(preprocessed_prompt) else: full_discussion_blocks.append(ump) full_discussion_blocks.append(preprocessed_prompt) full_discussion_blocks.append(personality.link_text) full_discussion_blocks.append(personality.ai_message_prefix) full_discussion = personality.personality_conditioning + ''.join(full_discussion_blocks) def callback(text, message_type: MSG_TYPE, metadata:dict={}): if message_type == MSG_TYPE.MSG_TYPE_CHUNK: self.answer["full_text"] = self.answer["full_text"] + text self.socketio.emit('text_chunk', {'chunk': text}, room=client_id) self.socketio.sleep(0) try: if self.connections[client_id]["requested_stop"]: return False else: return True except: # If the client is disconnected then we stop talking to it return False tk = personality.model.tokenize(full_discussion) n_tokens = len(tk) fd = personality.model.detokenize(tk[-min(self.config.ctx_size-n_cond_tk-personality.model_n_predicts,n_tokens):]) if personality.processor is not None and personality.processor_cfg["custom_workflow"]: ASCIIColors.info("processing...") generated_text = personality.processor.run_workflow(prompt, previous_discussion_text=personality.personality_conditioning+fd, callback=callback) else: ASCIIColors.info("generating...") generated_text = personality.model.generate( personality.personality_conditioning+fd, n_predict=personality.model_n_predicts, callback=callback) if personality.processor is not None and personality.processor_cfg["process_model_output"]: generated_text = personality.processor.process_model_output(generated_text) full_discussion_blocks.append(generated_text.strip()) ASCIIColors.success("\ndone") # Emit the generated text to the client self.socketio.emit('text_generated', {'text': generated_text}, room=client_id) self.socketio.sleep(0) except Exception as ex: self.socketio.emit('generation_error', {'error': str(ex)}, room=client_id) ASCIIColors.error(f"\ndone") self.busy = False except Exception as ex: trace_exception(ex) self.socketio.emit('generation_error', {'error': str(ex)}, room=client_id) self.busy = False # Start the text generation task in a separate thread task = self.socketio.start_background_task(target=generate_text) @socketio.on('execute_command') def execute_command(data): client_id = request.sid command = data["command"] parameters = data["parameters"] if self.personality.processor is not None: self.start_time = datetime.now() self.personality.processor.callback = partial(self.process_chunk, client_id=client_id) self.personality.processor.execute_command(command, parameters) else: self.warning("Non scripted personalities do not support commands",client_id=client_id) self.close_message(client_id) @socketio.on('generate_msg') def generate_msg(data): client_id = request.sid self.cancel_gen = False self.connections[client_id]["generated_text"]="" self.connections[client_id]["cancel_generation"]=False self.connections[client_id]["continuing"]=False self.connections[client_id]["first_chunk"]=True if not self.model: ASCIIColors.error("Model not selected. Please select a model") self.error("Model not selected. Please select a model", client_id=client_id) return if not self.busy: if self.connections[client_id]["current_discussion"] is None: if self.db.does_last_discussion_have_messages(): self.connections[client_id]["current_discussion"] = self.db.create_discussion() else: self.connections[client_id]["current_discussion"] = self.db.load_last_discussion() prompt = data["prompt"] ump = self.config.discussion_prompt_separator +self.config.user_name.strip() if self.config.use_user_name_in_discussions else self.personality.user_message_prefix message = self.connections[client_id]["current_discussion"].add_message( message_type = MSG_TYPE.MSG_TYPE_FULL.value, sender_type = SENDER_TYPES.SENDER_TYPES_USER.value, sender = ump.replace(self.config.discussion_prompt_separator,"").replace(":",""), content=prompt, metadata=None, parent_message_id=self.message_id ) ASCIIColors.green("Starting message generation by "+self.personality.name) self.connections[client_id]['generation_thread'] = threading.Thread(target=self.start_message_generation, args=(message, message.id, client_id)) self.connections[client_id]['generation_thread'].start() self.socketio.sleep(0.01) ASCIIColors.info("Started generation task") self.busy=True #tpe = threading.Thread(target=self.start_message_generation, args=(message, message_id, client_id)) #tpe.start() else: self.error("I am busy. Come back later.", client_id=client_id) @socketio.on('generate_msg_from') def generate_msg_from(data): client_id = request.sid self.cancel_gen = False self.connections[client_id]["continuing"]=False self.connections[client_id]["first_chunk"]=True if self.connections[client_id]["current_discussion"] is None: ASCIIColors.warning("Please select a discussion") self.error("Please select a discussion first", client_id=client_id) return id_ = data['id'] generation_type = data.get('msg_type',None) if id_==-1: message = self.connections[client_id]["current_discussion"].current_message else: message = self.connections[client_id]["current_discussion"].load_message(id_) if message is None: return self.connections[client_id]['generation_thread'] = threading.Thread(target=self.start_message_generation, args=(message, message.id, client_id, False, generation_type)) self.connections[client_id]['generation_thread'].start() @socketio.on('continue_generate_msg_from') def handle_connection(data): client_id = request.sid self.cancel_gen = False self.connections[client_id]["continuing"]=True self.connections[client_id]["first_chunk"]=True if self.connections[client_id]["current_discussion"] is None: ASCIIColors.yellow("Please select a discussion") self.error("Please select a discussion", client_id=client_id) return id_ = data['id'] if id_==-1: message = self.connections[client_id]["current_discussion"].current_message else: message = self.connections[client_id]["current_discussion"].load_message(id_) self.connections[client_id]["generated_text"]=message.content self.connections[client_id]['generation_thread'] = threading.Thread(target=self.start_message_generation, args=(message, message.id, client_id, True)) self.connections[client_id]['generation_thread'].start() # generation status self.generating=False ASCIIColors.blue(f"Your personal data is stored here :",end="") ASCIIColors.green(f"{self.lollms_paths.personal_path}") def audio_callback(self, text): if self.summoned: client_id = 0 self.cancel_gen = False self.connections[client_id]["generated_text"]="" self.connections[client_id]["cancel_generation"]=False self.connections[client_id]["continuing"]=False self.connections[client_id]["first_chunk"]=True if not self.model: ASCIIColors.error("Model not selected. Please select a model") self.error("Model not selected. Please select a model", client_id=client_id) return if not self.busy: if self.connections[client_id]["current_discussion"] is None: if self.db.does_last_discussion_have_messages(): self.connections[client_id]["current_discussion"] = self.db.create_discussion() else: self.connections[client_id]["current_discussion"] = self.db.load_last_discussion() prompt = text ump = self.config.discussion_prompt_separator +self.config.user_name.strip() if self.config.use_user_name_in_discussions else self.personality.user_message_prefix message = self.connections[client_id]["current_discussion"].add_message( message_type = MSG_TYPE.MSG_TYPE_FULL.value, sender_type = SENDER_TYPES.SENDER_TYPES_USER.value, sender = ump.replace(self.config.discussion_prompt_separator,"").replace(":",""), content=prompt, metadata=None, parent_message_id=self.message_id ) ASCIIColors.green("Starting message generation by "+self.personality.name) self.connections[client_id]['generation_thread'] = threading.Thread(target=self.start_message_generation, args=(message, message.id, client_id)) self.connections[client_id]['generation_thread'].start() self.socketio.sleep(0.01) ASCIIColors.info("Started generation task") self.busy=True #tpe = threading.Thread(target=self.start_message_generation, args=(message, message_id, client_id)) #tpe.start() else: self.error("I am busy. Come back later.", client_id=client_id) else: if text.lower()=="lollms": self.summoned = True def rebuild_personalities(self, reload_all=False): if reload_all: self.mounted_personalities=[] loaded = self.mounted_personalities loaded_names = [f"{p.category}/{p.personality_folder_name}:{p.selected_language}" if p.selected_language else f"{p.category}/{p.personality_folder_name}" for p in loaded] mounted_personalities=[] ASCIIColors.success(f" ╔══════════════════════════════════════════════════╗ ") ASCIIColors.success(f" ║ Building mounted Personalities ║ ") ASCIIColors.success(f" ╚══════════════════════════════════════════════════╝ ") to_remove=[] for i,personality in enumerate(self.config['personalities']): if i==self.config["active_personality_id"]: ASCIIColors.red("*", end="") ASCIIColors.green(f" {personality}") else: ASCIIColors.yellow(f" {personality}") if personality in loaded_names: mounted_personalities.append(loaded[loaded_names.index(personality)]) else: personality_path = f"{personality}" if not ":" in personality else f"{personality.split(':')[0]}" try: personality = AIPersonality(personality_path, self.lollms_paths, self.config, model=self.model, app=self, selected_language=personality.split(":")[1] if ":" in personality else None, run_scripts=True) mounted_personalities.append(personality) except Exception as ex: ASCIIColors.error(f"Personality file not found or is corrupted ({personality_path}).\nReturned the following exception:{ex}\nPlease verify that the personality you have selected exists or select another personality. Some updates may lead to change in personality name or category, so check the personality selection in settings to be sure.") ASCIIColors.info("Trying to force reinstall") if self.config["debug"]: print(ex) try: personality = AIPersonality( personality_path, self.lollms_paths, self.config, self.model, app = self, run_scripts=True, selected_language=personality.split(":")[1] if ":" in personality else None, installation_option=InstallOption.FORCE_INSTALL) mounted_personalities.append(personality) if personality.processor: personality.processor.mounted() except Exception as ex: ASCIIColors.error(f"Couldn't load personality at {personality_path}") trace_exception(ex) ASCIIColors.info(f"Unmounting personality") to_remove.append(i) personality = AIPersonality(None, self.lollms_paths, self.config, self.model, app=self, run_scripts=True, installation_option=InstallOption.FORCE_INSTALL) mounted_personalities.append(personality) if personality.processor: personality.processor.mounted() ASCIIColors.info("Reverted to default personality") if self.config["active_personality_id"]>=0 and self.config["active_personality_id"]=len(self.config["personalities"]): self.config["active_personality_id"]=0 return mounted_personalities def rebuild_extensions(self, reload_all=False): if reload_all: self.mounted_extensions=[] loaded = self.mounted_extensions loaded_names = [f"{p.category}/{p.extension_folder_name}" for p in loaded] mounted_extensions=[] ASCIIColors.success(f" ╔══════════════════════════════════════════════════╗ ") ASCIIColors.success(f" ║ Building mounted Extensions ║ ") ASCIIColors.success(f" ╚══════════════════════════════════════════════════╝ ") to_remove=[] for i,extension in enumerate(self.config['extensions']): ASCIIColors.yellow(f" {extension}") if extension in loaded_names: mounted_extensions.append(loaded[loaded_names.index(extension)]) else: extension_path = self.lollms_paths.extensions_zoo_path/f"{extension}" try: extension = ExtensionBuilder().build_extension(extension_path,self.lollms_paths, self) mounted_extensions.append(extension) except Exception as ex: ASCIIColors.error(f"Extension file not found or is corrupted ({extension_path}).\nReturned the following exception:{ex}\nPlease verify that the personality you have selected exists or select another personality. Some updates may lead to change in personality name or category, so check the personality selection in settings to be sure.") trace_exception(ex) ASCIIColors.info("Trying to force reinstall") if self.config["debug"]: print(ex) ASCIIColors.success(f" ╔══════════════════════════════════════════════════╗ ") ASCIIColors.success(f" ║ Done ║ ") ASCIIColors.success(f" ╚══════════════════════════════════════════════════╝ ") # Sort the indices in descending order to ensure correct removal to_remove.sort(reverse=True) # Remove elements from the list based on the indices for index in to_remove: if 0 <= index < len(mounted_extensions): mounted_extensions.pop(index) self.config["extensions"].pop(index) ASCIIColors.info(f"removed personality {extension_path}") return mounted_extensions # ================================== LOLLMSApp #properties @property def message_id(self): return self._message_id @message_id.setter def message_id(self, id): self._message_id=id @property def current_user_message_id(self): return self._current_user_message_id @current_user_message_id.setter def current_user_message_id(self, id): self._current_user_message_id=id self._message_id = id @property def current_ai_message_id(self): return self._current_ai_message_id @current_ai_message_id.setter def current_ai_message_id(self, id): self._current_ai_message_id=id self._message_id = id def download_file(self, url, installation_path, callback=None): """ Downloads a file from a URL, reports the download progress using a callback function, and displays a progress bar. Args: url (str): The URL of the file to download. installation_path (str): The path where the file should be saved. callback (function, optional): A callback function to be called during the download with the progress percentage as an argument. Defaults to None. """ try: response = requests.get(url, stream=True) # Get the file size from the response headers total_size = int(response.headers.get('content-length', 0)) with open(installation_path, 'wb') as file: downloaded_size = 0 with tqdm(total=total_size, unit='B', unit_scale=True, ncols=80) as progress_bar: for chunk in response.iter_content(chunk_size=8192): if chunk: file.write(chunk) downloaded_size += len(chunk) if callback is not None: callback(downloaded_size, total_size) progress_bar.update(len(chunk)) if callback is not None: callback(total_size, total_size) print("File downloaded successfully") except Exception as e: print("Couldn't download file:", str(e)) def clean_string(self, input_string): # Remove extra spaces by replacing multiple spaces with a single space #cleaned_string = re.sub(r'\s+', ' ', input_string) # Remove extra line breaks by replacing multiple consecutive line breaks with a single line break cleaned_string = re.sub(r'\n\s*\n', '\n', input_string) # Create a string containing all punctuation characters punctuation_chars = string.punctuation # Define a regular expression pattern to match and remove non-alphanumeric characters #pattern = f'[^a-zA-Z0-9\s{re.escape(punctuation_chars)}]' # This pattern matches any character that is not a letter, digit, space, or punctuation pattern = f'[^a-zA-Z0-9\u00C0-\u017F\s{re.escape(punctuation_chars)}]' # Use re.sub to replace the matched characters with an empty string cleaned_string = re.sub(pattern, '', cleaned_string) return cleaned_string def make_discussion_title(self, discussion, client_id=None): """ Builds a title for a discussion """ # Get the list of messages messages = discussion.get_messages() discussion_messages = "!@>instruction: Create a short title to this discussion\n" discussion_title = "\n!@>Discussion title:" available_space = self.config.ctx_size - 150 - len(self.model.tokenize(discussion_messages))- len(self.model.tokenize(discussion_title)) # Initialize a list to store the full messages full_message_list = [] # Accumulate messages until the cumulative number of tokens exceeds available_space tokens_accumulated = 0 # Accumulate messages starting from message_index for message in messages: # Check if the message content is not empty and visible to the AI if message.content != '' and ( message.message_type <= MSG_TYPE.MSG_TYPE_FULL_INVISIBLE_TO_USER.value and message.message_type != MSG_TYPE.MSG_TYPE_FULL_INVISIBLE_TO_AI.value): # Tokenize the message content message_tokenized = self.model.tokenize( "\n" + self.config.discussion_prompt_separator + message.sender + ": " + message.content.strip()) # Check if adding the message will exceed the available space if tokens_accumulated + len(message_tokenized) > available_space: break # Add the tokenized message to the full_message_list full_message_list.insert(0, message_tokenized) # Update the cumulative number of tokens tokens_accumulated += len(message_tokenized) # Build the final discussion messages by detokenizing the full_message_list for message_tokens in full_message_list: discussion_messages += self.model.detokenize(message_tokens) discussion_messages += discussion_title title = [""] def receive( chunk:str, message_type:MSG_TYPE ): if chunk: title[0] += chunk antiprompt = self.personality.detect_antiprompt(title[0]) if antiprompt: ASCIIColors.warning(f"\nDetected hallucination with antiprompt: {antiprompt}") title[0] = self.remove_text_from_string(title[0],antiprompt) return False else: return True self._generate(discussion_messages, 150, client_id, receive) ASCIIColors.info(title[0]) return title[0] def prepare_reception(self, client_id): if not self.connections[client_id]["continuing"]: self.connections[client_id]["generated_text"] = "" self.connections[client_id]["first_chunk"]=True self.nb_received_tokens = 0 self.start_time = datetime.now() def prepare_query(self, client_id: str, message_id: int = -1, is_continue: bool = False, n_tokens: int = 0, generation_type = None) -> Tuple[str, str, List[str]]: """ Prepares the query for the model. Args: client_id (str): The client ID. message_id (int): The message ID. Default is -1. is_continue (bool): Whether the query is a continuation. Default is False. n_tokens (int): The number of tokens. Default is 0. Returns: Tuple[str, str, List[str]]: The prepared query, original message content, and tokenized query. """ # Get the list of messages messages = self.connections[client_id]["current_discussion"].get_messages() # Find the index of the message with the specified message_id message_index = -1 for i, message in enumerate(messages): if message.id == message_id: message_index = i break # Define current message current_message = messages[message_index] # Build the conditionning text block conditionning = self.personality.personality_conditioning # Check if there are document files to add to the prompt documentation = "" history = "" if generation_type != "simple_question": if self.personality.persona_data_vectorizer: if documentation=="": documentation="!@>Documentation:\n" if self.config.data_vectorization_build_keys_words: query = self.personality.fast_gen("!@>prompt:"+current_message.content+"\n!@>instruction: Convert the prompt to a web search query."+"\nDo not answer the prompt. Do not add explanations. Use comma separated syntax to make a list of keywords in the same line.\nThe keywords should reflect the ideas written in the prompt so that a seach engine can process them efficiently.\n!@>query: ", max_generation_size=256, show_progress=True) ASCIIColors.cyan(f"Query:{query}") else: query = current_message.content docs, sorted_similarities = self.personality.persona_data_vectorizer.recover_text(query, top_k=self.config.data_vectorization_nb_chunks) for doc, infos in zip(docs, sorted_similarities): documentation += f"document chunk:\n{doc}" if len(self.personality.text_files) > 0 and self.personality.vectorizer: if documentation=="": documentation="!@>Documentation:\n" if self.config.data_vectorization_build_keys_words: query = self.personality.fast_gen("!@>prompt:"+current_message.content+"\n!@>instruction: Convert the prompt to a web search query."+"\nDo not answer the prompt. Do not add explanations. Use comma separated syntax to make a list of keywords in the same line.\nThe keywords should reflect the ideas written in the prompt so that a seach engine can process them efficiently.\n!@>query: ", max_generation_size=256, show_progress=True) ASCIIColors.cyan(f"Query:{query}") else: query = current_message.content docs, sorted_similarities = self.personality.vectorizer.recover_text(query, top_k=self.config.data_vectorization_nb_chunks) for doc, infos in zip(docs, sorted_similarities): documentation += f"document chunk:\nchunk path: {infos[0]}\nchunk content:{doc}" # Check if there is discussion history to add to the prompt if self.config.use_discussions_history and self.long_term_memory is not None: if history=="": history="!@>previous discussions:\n" docs, sorted_similarities = self.long_term_memory.recover_text(current_message.content, top_k=self.config.data_vectorization_nb_chunks) for i,(doc, infos) in enumerate(zip(docs, sorted_similarities)): history += f"!@>previous discussion {i}:\n!@>discussion title:\n{infos[0]}\ndiscussion content:\n{doc}" # Add information about the user user_description="" if self.config.use_user_name_in_discussions: user_description="!@>User description:\n"+self.config.user_description # Tokenize the conditionning text and calculate its number of tokens tokens_conditionning = self.model.tokenize(conditionning) n_cond_tk = len(tokens_conditionning) # Tokenize the documentation text and calculate its number of tokens if len(documentation)>0: tokens_documentation = self.model.tokenize(documentation) n_doc_tk = len(tokens_documentation) else: tokens_documentation = [] n_doc_tk = 0 # Tokenize the history text and calculate its number of tokens if len(history)>0: tokens_history = self.model.tokenize(history) n_history_tk = len(tokens_history) else: tokens_history = [] n_history_tk = 0 # Tokenize user description if len(user_description)>0: tokens_user_description = self.model.tokenize(user_description) n_user_description_tk = len(tokens_user_description) else: tokens_user_description = [] n_user_description_tk = 0 # Calculate the total number of tokens between conditionning, documentation, and history total_tokens = n_cond_tk + n_doc_tk + n_history_tk + n_user_description_tk # Calculate the available space for the messages available_space = self.config.ctx_size - n_tokens - total_tokens # Raise an error if the available space is 0 or less if available_space<1: raise Exception("Not enough space in context!!") # Accumulate messages until the cumulative number of tokens exceeds available_space tokens_accumulated = 0 # Initialize a list to store the full messages full_message_list = [] # If this is not a continue request, we add the AI prompt if not is_continue: message_tokenized = self.model.tokenize( "\n" +self.personality.ai_message_prefix.strip() ) full_message_list.append(message_tokenized) # Update the cumulative number of tokens tokens_accumulated += len(message_tokenized) if generation_type != "simple_question": # Accumulate messages starting from message_index for i in range(message_index, -1, -1): message = messages[i] # Check if the message content is not empty and visible to the AI if message.content != '' and ( message.message_type <= MSG_TYPE.MSG_TYPE_FULL_INVISIBLE_TO_USER.value and message.message_type != MSG_TYPE.MSG_TYPE_FULL_INVISIBLE_TO_AI.value): # Tokenize the message content message_tokenized = self.model.tokenize( "\n" + self.config.discussion_prompt_separator + message.sender + ": " + message.content.strip()) # Check if adding the message will exceed the available space if tokens_accumulated + len(message_tokenized) > available_space: break # Add the tokenized message to the full_message_list full_message_list.insert(0, message_tokenized) # Update the cumulative number of tokens tokens_accumulated += len(message_tokenized) else: message = messages[message_index] # Check if the message content is not empty and visible to the AI if message.content != '' and ( message.message_type <= MSG_TYPE.MSG_TYPE_FULL_INVISIBLE_TO_USER.value and message.message_type != MSG_TYPE.MSG_TYPE_FULL_INVISIBLE_TO_AI.value): # Tokenize the message content message_tokenized = self.model.tokenize( "\n" + self.config.discussion_prompt_separator + message.sender + ": " + message.content.strip()) # Add the tokenized message to the full_message_list full_message_list.insert(0, message_tokenized) # Update the cumulative number of tokens tokens_accumulated += len(message_tokenized) # Build the final discussion messages by detokenizing the full_message_list discussion_messages = "" for message_tokens in full_message_list: discussion_messages += self.model.detokenize(message_tokens) # Build the final prompt by concatenating the conditionning and discussion messages prompt_data = conditionning + documentation + history + user_description + discussion_messages # Tokenize the prompt data tokens = self.model.tokenize(prompt_data) # if this is a debug then show prompt construction details if self.config["debug"]: ASCIIColors.bold("CONDITIONNING") ASCIIColors.yellow(conditionning) ASCIIColors.bold("DOC") ASCIIColors.yellow(documentation) ASCIIColors.bold("HISTORY") ASCIIColors.yellow(history) ASCIIColors.bold("DISCUSSION") ASCIIColors.hilight(discussion_messages,"!@>",ASCIIColors.color_yellow,ASCIIColors.color_bright_red,False) ASCIIColors.bold("Final prompt") ASCIIColors.hilight(prompt_data,"!@>",ASCIIColors.color_yellow,ASCIIColors.color_bright_red,False) ASCIIColors.info(f"prompt size:{len(tokens)} tokens") ASCIIColors.info(f"available space after doc and history:{available_space} tokens") # Return the prepared query, original message content, and tokenized query return prompt_data, current_message.content, tokens def get_discussion_to(self, client_id, message_id=-1): messages = self.connections[client_id]["current_discussion"].get_messages() full_message_list = [] ump = self.config.discussion_prompt_separator +self.config.user_name.strip() if self.config.use_user_name_in_discussions else self.personality.user_message_prefix for message in messages: if message["id"]<= message_id or message_id==-1: if message["type"]!=MSG_TYPE.MSG_TYPE_FULL_INVISIBLE_TO_USER: if message["sender"]==self.personality.name: full_message_list.append(self.personality.ai_message_prefix+message["content"]) else: full_message_list.append(ump + message["content"]) link_text = "\n"# self.personality.link_text if len(full_message_list) > self.config["nb_messages_to_remember"]: discussion_messages = self.personality.personality_conditioning+ link_text.join(full_message_list[-self.config["nb_messages_to_remember"]:]) else: discussion_messages = self.personality.personality_conditioning+ link_text.join(full_message_list) return discussion_messages # Removes the last return def notify( self, content, notification_type:NotificationType=NotificationType.NOTIF_SUCCESS, duration:int=4, client_id=None, display_type:NotificationDisplayType=NotificationDisplayType.TOAST, verbose=True ): self.socketio.emit('notification', { 'content': content,# self.connections[client_id]["generated_text"], 'notification_type': notification_type.value, "duration": duration, 'display_type':display_type.value }, room=client_id ) self.socketio.sleep(0.01) if verbose: if notification_type==NotificationType.NOTIF_SUCCESS: ASCIIColors.success(content) elif notification_type==NotificationType.NOTIF_INFO: ASCIIColors.info(content) elif notification_type==NotificationType.NOTIF_WARNING: ASCIIColors.warning(content) else: ASCIIColors.red(content) def new_message(self, client_id, sender=None, content="", parameters=None, metadata=None, ui=None, message_type:MSG_TYPE=MSG_TYPE.MSG_TYPE_FULL, sender_type:SENDER_TYPES=SENDER_TYPES.SENDER_TYPES_AI, open=False ): mtdt = metadata if metadata is None or type(metadata) == str else json.dumps(metadata, indent=4) if sender==None: sender= self.personality.name msg = self.connections[client_id]["current_discussion"].add_message( message_type = message_type.value, sender_type = sender_type.value, sender = sender, content = content, metadata = mtdt, ui = ui, rank = 0, parent_message_id = self.connections[client_id]["current_discussion"].current_message.id, binding = self.config["binding_name"], model = self.config["model_name"], personality = self.config["personalities"][self.config["active_personality_id"]], ) # first the content is empty, but we'll fill it at the end self.socketio.emit('new_message', { "sender": sender, "message_type": message_type.value, "sender_type": SENDER_TYPES.SENDER_TYPES_AI.value, "content": content, "parameters": parameters, "metadata": metadata, "ui": ui, "id": msg.id, "parent_message_id": msg.parent_message_id, 'binding': self.config["binding_name"], 'model' : self.config["model_name"], 'personality': self.config["personalities"][self.config["active_personality_id"]], 'created_at': self.connections[client_id]["current_discussion"].current_message.created_at, 'finished_generating_at': self.connections[client_id]["current_discussion"].current_message.finished_generating_at, 'open': open }, room=client_id ) def update_message(self, client_id, chunk, parameters=None, metadata=[], ui=None, msg_type:MSG_TYPE=None ): self.connections[client_id]["current_discussion"].current_message.finished_generating_at=datetime.now().strftime('%Y-%m-%d %H:%M:%S') mtdt = json.dumps(metadata, indent=4) if metadata is not None and type(metadata)== list else metadata if self.nb_received_tokens==1: self.socketio.emit('update_message', { "sender": self.personality.name, 'id':self.connections[client_id]["current_discussion"].current_message.id, 'content': "✍ warming up ...",# self.connections[client_id]["generated_text"], 'ui': ui, 'discussion_id':self.connections[client_id]["current_discussion"].discussion_id, 'message_type': MSG_TYPE.MSG_TYPE_STEP_END.value, 'finished_generating_at': self.connections[client_id]["current_discussion"].current_message.finished_generating_at, 'parameters':parameters, 'metadata':metadata }, room=client_id ) self.socketio.emit('update_message', { "sender": self.personality.name, 'id':self.connections[client_id]["current_discussion"].current_message.id, 'content': chunk,# self.connections[client_id]["generated_text"], 'ui': ui, 'discussion_id':self.connections[client_id]["current_discussion"].discussion_id, 'message_type': msg_type.value if msg_type is not None else MSG_TYPE.MSG_TYPE_CHUNK.value if self.nb_received_tokens>1 else MSG_TYPE.MSG_TYPE_FULL.value, 'finished_generating_at': self.connections[client_id]["current_discussion"].current_message.finished_generating_at, 'parameters':parameters, 'metadata':metadata }, room=client_id ) self.socketio.sleep(0.01) if msg_type != MSG_TYPE.MSG_TYPE_INFO: self.connections[client_id]["current_discussion"].update_message(self.connections[client_id]["generated_text"], new_metadata=mtdt, new_ui=ui) def close_message(self, client_id): if not self.connections[client_id]["current_discussion"]: return #fix halucination self.connections[client_id]["generated_text"]=self.connections[client_id]["generated_text"].split("!@>")[0] # Send final message self.connections[client_id]["current_discussion"].current_message.finished_generating_at=datetime.now().strftime('%Y-%m-%d %H:%M:%S') self.socketio.emit('close_message', { "sender": self.personality.name, "id": self.connections[client_id]["current_discussion"].current_message.id, "content":self.connections[client_id]["generated_text"], 'binding': self.config["binding_name"], 'model' : self.config["model_name"], 'personality':self.config["personalities"][self.config["active_personality_id"]], 'created_at': self.connections[client_id]["current_discussion"].current_message.created_at, 'finished_generating_at': self.connections[client_id]["current_discussion"].current_message.finished_generating_at, }, room=client_id ) def process_chunk( self, chunk:str, message_type:MSG_TYPE, parameters:dict=None, metadata:list=None, client_id:int=0, personality:AIPersonality=None ): """ Processes a chunk of generated text """ if chunk is None: return True if not client_id in list(self.connections.keys()): self.error("Connection lost", client_id=client_id) return if message_type == MSG_TYPE.MSG_TYPE_STEP: ASCIIColors.info("--> Step:"+chunk) if message_type == MSG_TYPE.MSG_TYPE_STEP_START: ASCIIColors.info("--> Step started:"+chunk) if message_type == MSG_TYPE.MSG_TYPE_STEP_END: if parameters['status']: ASCIIColors.success("--> Step ended:"+chunk) else: ASCIIColors.error("--> Step ended:"+chunk) if message_type == MSG_TYPE.MSG_TYPE_EXCEPTION: self.error(chunk, client_id=client_id) ASCIIColors.error("--> Exception from personality:"+chunk) if message_type == MSG_TYPE.MSG_TYPE_WARNING: self.warning(chunk,client_id=client_id) ASCIIColors.error("--> Exception from personality:"+chunk) if message_type == MSG_TYPE.MSG_TYPE_INFO: self.info(chunk, client_id=client_id) ASCIIColors.info("--> Info:"+chunk) if message_type == MSG_TYPE.MSG_TYPE_UI: self.update_message(client_id, "", parameters, metadata, chunk, MSG_TYPE.MSG_TYPE_UI) if message_type == MSG_TYPE.MSG_TYPE_NEW_MESSAGE: self.nb_received_tokens = 0 self.start_time = datetime.now() self.new_message( client_id, self.personality.name if personality is None else personality.name, chunk if parameters["type"]!=MSG_TYPE.MSG_TYPE_UI.value else '', metadata = [{ "title":chunk, "content":parameters["metadata"] } ] if parameters["type"]==MSG_TYPE.MSG_TYPE_JSON_INFOS.value else None, ui= chunk if parameters["type"]==MSG_TYPE.MSG_TYPE_UI.value else None, message_type= MSG_TYPE(parameters["type"])) elif message_type == MSG_TYPE.MSG_TYPE_FINISHED_MESSAGE: self.close_message(client_id) elif message_type == MSG_TYPE.MSG_TYPE_CHUNK: if self.nb_received_tokens==0: self.start_time = datetime.now() dt =(datetime.now() - self.start_time).seconds if dt==0: dt=1 spd = self.nb_received_tokens/dt ASCIIColors.green(f"Received {self.nb_received_tokens} tokens (speed: {spd:.2f}t/s) ",end="\r",flush=True) sys.stdout = sys.__stdout__ sys.stdout.flush() if chunk: self.connections[client_id]["generated_text"] += chunk antiprompt = self.personality.detect_antiprompt(self.connections[client_id]["generated_text"]) if antiprompt: ASCIIColors.warning(f"\nDetected hallucination with antiprompt: {antiprompt}") self.connections[client_id]["generated_text"] = self.remove_text_from_string(self.connections[client_id]["generated_text"],antiprompt) self.update_message(client_id, self.connections[client_id]["generated_text"], parameters, metadata, None, MSG_TYPE.MSG_TYPE_FULL) return False else: self.nb_received_tokens += 1 if self.connections[client_id]["continuing"] and self.connections[client_id]["first_chunk"]: self.update_message(client_id, self.connections[client_id]["generated_text"], parameters, metadata) else: self.update_message(client_id, chunk, parameters, metadata, msg_type=MSG_TYPE.MSG_TYPE_CHUNK) self.connections[client_id]["first_chunk"]=False # if stop generation is detected then stop if not self.cancel_gen: return True else: self.cancel_gen = False ASCIIColors.warning("Generation canceled") return False # Stream the generated text to the main process elif message_type == MSG_TYPE.MSG_TYPE_FULL: self.connections[client_id]["generated_text"] = chunk self.nb_received_tokens += 1 dt =(datetime.now() - self.start_time).seconds if dt==0: dt=1 spd = self.nb_received_tokens/dt ASCIIColors.green(f"Received {self.nb_received_tokens} tokens (speed: {spd:.2f}t/s) ",end="\r",flush=True) self.update_message(client_id, chunk, parameters, metadata, ui=None, msg_type=message_type) return True # Stream the generated text to the frontend else: self.update_message(client_id, chunk, parameters, metadata, ui=None, msg_type=message_type) return True def generate(self, full_prompt, prompt, n_predict, client_id, callback=None): if self.personality.processor is not None: ASCIIColors.info("Running workflow") try: self.personality.processor.run_workflow( prompt, full_prompt, callback) except Exception as ex: trace_exception(ex) # Catch the exception and get the traceback as a list of strings traceback_lines = traceback.format_exception(type(ex), ex, ex.__traceback__) # Join the traceback lines into a single string traceback_text = ''.join(traceback_lines) ASCIIColors.error(f"Workflow run failed.\nError:{ex}") ASCIIColors.error(traceback_text) if callback: callback(f"Workflow run failed\nError:{ex}", MSG_TYPE.MSG_TYPE_EXCEPTION) print("Finished executing the workflow") return self._generate(full_prompt, n_predict, client_id, callback) ASCIIColors.success("\nFinished executing the generation") def _generate(self, prompt, n_predict, client_id, callback=None): self.nb_received_tokens = 0 self.start_time = datetime.now() if self.model is not None: if self.model.binding_type==BindingType.TEXT_IMAGE and len(self.personality.image_files)>0: ASCIIColors.info(f"warmup for generating up to {n_predict} tokens") if self.config["override_personality_model_parameters"]: output = self.model.generate_with_images( prompt, self.personality.image_files, callback=callback, n_predict=n_predict, temperature=self.config['temperature'], top_k=self.config['top_k'], top_p=self.config['top_p'], repeat_penalty=self.config['repeat_penalty'], repeat_last_n = self.config['repeat_last_n'], seed=self.config['seed'], n_threads=self.config['n_threads'] ) else: output = self.model.generate_with_images( prompt, self.personality.image_files, callback=callback, n_predict=min(n_predict,self.personality.model_n_predicts), temperature=self.personality.model_temperature, top_k=self.personality.model_top_k, top_p=self.personality.model_top_p, repeat_penalty=self.personality.model_repeat_penalty, repeat_last_n = self.personality.model_repeat_last_n, seed=self.config['seed'], n_threads=self.config['n_threads'] ) else: ASCIIColors.info(f"warmup for generating up to {n_predict} tokens") if self.config["override_personality_model_parameters"]: output = self.model.generate( prompt, callback=callback, n_predict=n_predict, temperature=self.config['temperature'], top_k=self.config['top_k'], top_p=self.config['top_p'], repeat_penalty=self.config['repeat_penalty'], repeat_last_n = self.config['repeat_last_n'], seed=self.config['seed'], n_threads=self.config['n_threads'] ) else: output = self.model.generate( prompt, callback=callback, n_predict=min(n_predict,self.personality.model_n_predicts), temperature=self.personality.model_temperature, top_k=self.personality.model_top_k, top_p=self.personality.model_top_p, repeat_penalty=self.personality.model_repeat_penalty, repeat_last_n = self.personality.model_repeat_last_n, seed=self.config['seed'], n_threads=self.config['n_threads'] ) else: print("No model is installed or selected. Please make sure to install a model and select it inside your configuration before attempting to communicate with the model.") print("To do this: Install the model to your models/ folder.") print("Then set your model information in your local configuration file that you can find in configs/local_config.yaml") print("You can also use the ui to set your model in the settings page.") output = "" return output def start_message_generation(self, message, message_id, client_id, is_continue=False, generation_type=None): if self.personality is None: self.warning("Select a personality") return ASCIIColors.info(f"Text generation requested by client: {client_id}") # send the message to the bot print(f"Received message : {message.content}") if self.connections[client_id]["current_discussion"]: if not self.model: self.error("No model selected. Please make sure you select a model before starting generation", client_id=client_id) return # First we need to send the new message ID to the client if is_continue: self.connections[client_id]["current_discussion"].load_message(message_id) self.connections[client_id]["generated_text"] = message.content else: self.new_message(client_id, self.personality.name, "") self.update_message(client_id, "✍ warming up ...", msg_type=MSG_TYPE.MSG_TYPE_STEP_START) self.socketio.sleep(0.01) # prepare query and reception self.discussion_messages, self.current_message, tokens = self.prepare_query(client_id, message_id, is_continue, n_tokens=self.config.min_n_predict, generation_type=generation_type) self.prepare_reception(client_id) self.generating = True self.connections[client_id]["processing"]=True self.generate( self.discussion_messages, self.current_message, n_predict = self.config.ctx_size-len(tokens)-1, client_id=client_id, callback=partial(self.process_chunk,client_id = client_id) ) print() print("## Done Generation ##") print() self.cancel_gen = False # Send final message self.close_message(client_id) self.socketio.sleep(0.01) self.connections[client_id]["processing"]=False if self.connections[client_id]["schedule_for_deletion"]: del self.connections[client_id] ASCIIColors.success(f" ╔══════════════════════════════════════════════════╗ ") ASCIIColors.success(f" ║ Done ║ ") ASCIIColors.success(f" ╚══════════════════════════════════════════════════╝ ") if self.config.auto_title: d = self.connections[client_id]["current_discussion"] ttl = d.title() if ttl is None or ttl=="" or ttl=="untitled": title = self.make_discussion_title(d, client_id=client_id) d.rename(title) self.socketio.emit('disucssion_renamed',{ 'status': True, 'discussion_id':d.discussion_id, 'title':title }, room=client_id) self.busy=False else: ump = self.config.discussion_prompt_separator +self.config.user_name.strip() if self.config.use_user_name_in_discussions else self.personality.user_message_prefix self.cancel_gen = False #No discussion available ASCIIColors.warning("No discussion selected!!!") self.error("No discussion selected!!!", client_id=client_id) print() self.busy=False return ""