mirror of
https://github.com/ParisNeo/lollms-webui.git
synced 2024-12-30 09:08:51 +00:00
1472 lines
72 KiB
Python
1472 lines
72 KiB
Python
"""
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File: lollms_web_ui.py
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Author: ParisNeo
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Description: Singleton class for the LoLLMS web UI.
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This class provides a singleton instance of the LoLLMS web UI, allowing access to its functionality and data across multiple endpoints.
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"""
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from lollms.server.elf_server import LOLLMSElfServer
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from flask import request
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from datetime import datetime
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from api.db import DiscussionsDB, Discussion
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from pathlib import Path
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from lollms.config import InstallOption
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from lollms.types import MSG_TYPE, SENDER_TYPES
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from lollms.extension import LOLLMSExtension, ExtensionBuilder
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from lollms.personality import AIPersonality, PersonalityBuilder
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from lollms.binding import LOLLMSConfig, BindingBuilder, LLMBinding, ModelBuilder, BindingType
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from lollms.paths import LollmsPaths
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from lollms.helpers import ASCIIColors, trace_exception
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from lollms.com import NotificationType, NotificationDisplayType, LoLLMsCom
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from lollms.app import LollmsApplication
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from lollms.utilities import File64BitsManager, PromptReshaper, PackageManager, find_first_available_file_index, run_async, is_asyncio_loop_running
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import git
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import asyncio
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import os
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try:
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from lollms.media import WebcamImageSender, AudioRecorder
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Media_on=True
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except:
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ASCIIColors.warning("Couldn't load media library.\nYou will not be able to perform any of the media linked operations. please verify the logs and install any required installations")
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Media_on=False
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from safe_store import TextVectorizer, VectorizationMethod, VisualizationMethod
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import threading
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from tqdm import tqdm
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import traceback
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import sys
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import gc
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import ctypes
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from functools import partial
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import json
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import shutil
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import re
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import string
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import requests
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from datetime import datetime
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from typing import List, Tuple
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import time
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import numpy as np
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from lollms.utilities import find_first_available_file_index, convert_language_name
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if not PackageManager.check_package_installed("requests"):
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PackageManager.install_package("requests")
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if not PackageManager.check_package_installed("bs4"):
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PackageManager.install_package("beautifulsoup4")
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import requests
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from flask_socketio import SocketIO
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from bs4 import BeautifulSoup
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def terminate_thread(thread):
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if thread:
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if not thread.is_alive():
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ASCIIColors.yellow("Thread not alive")
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return
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thread_id = thread.ident
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exc = ctypes.py_object(SystemExit)
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res = ctypes.pythonapi.PyThreadState_SetAsyncExc(thread_id, exc)
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if res > 1:
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ctypes.pythonapi.PyThreadState_SetAsyncExc(thread_id, None)
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del thread
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gc.collect()
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raise SystemError("Failed to terminate the thread.")
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else:
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ASCIIColors.yellow("Canceled successfully")# The current version of the webui
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lollms_webui_version="9.0 (alpha)"
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class LOLLMSWebUI(LOLLMSElfServer):
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__instance = None
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@staticmethod
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def build_instance(
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config: LOLLMSConfig,
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lollms_paths: LollmsPaths,
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load_binding=True,
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load_model=True,
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load_voice_service=True,
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load_sd_service=True,
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try_select_binding=False,
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try_select_model=False,
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callback=None,
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socketio = None
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):
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if LOLLMSWebUI.__instance is None:
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LOLLMSWebUI(
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config,
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lollms_paths,
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load_binding=load_binding,
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load_model=load_model,
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load_sd_service=load_sd_service,
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load_voice_service=load_voice_service,
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try_select_binding=try_select_binding,
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try_select_model=try_select_model,
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callback=callback,
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socketio=socketio
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)
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return LOLLMSWebUI.__instance
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def __init__(
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self,
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config: LOLLMSConfig,
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lollms_paths: LollmsPaths,
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load_binding=True,
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load_model=True,
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load_voice_service=True,
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load_sd_service=True,
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try_select_binding=False,
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try_select_model=False,
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callback=None,
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socketio=None
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) -> None:
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super().__init__(
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config,
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lollms_paths,
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load_binding=load_binding,
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load_model=load_model,
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load_sd_service=load_sd_service,
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load_voice_service=load_voice_service,
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try_select_binding=try_select_binding,
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try_select_model=try_select_model,
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callback=callback,
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socketio=socketio
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)
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self.app_name:str = "LOLLMSWebUI"
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self.version:str = lollms_webui_version
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self.busy = False
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self.nb_received_tokens = 0
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self.config_file_path = config.file_path
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self.cancel_gen = False
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if self.config.auto_update:
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if self.check_update_():
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ASCIIColors.info("New version found. Updating!")
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self.run_update_script()
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# Keeping track of current discussion and message
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self._current_user_message_id = 0
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self._current_ai_message_id = 0
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self._message_id = 0
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self.db_path = config["db_path"]
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if Path(self.db_path).is_absolute():
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# Create database object
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self.db = DiscussionsDB(self.db_path)
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else:
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# Create database object
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self.db = DiscussionsDB(self.lollms_paths.personal_databases_path/self.db_path)
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# If the database is empty, populate it with tables
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ASCIIColors.info("Checking discussions database... ",end="")
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self.db.create_tables()
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self.db.add_missing_columns()
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ASCIIColors.success("ok")
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# prepare vectorization
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if self.config.data_vectorization_activate and self.config.use_discussions_history:
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try:
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ASCIIColors.yellow("Loading long term memory")
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folder = self.lollms_paths.personal_databases_path/"vectorized_dbs"
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folder.mkdir(parents=True, exist_ok=True)
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self.build_long_term_skills_memory()
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ASCIIColors.yellow("Ready")
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except Exception as ex:
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trace_exception(ex)
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self.long_term_memory = None
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else:
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self.long_term_memory = None
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# This is used to keep track of messages
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self.download_infos={}
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self.connections = {
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0:{
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"current_discussion":None,
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"generated_text":"",
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"cancel_generation": False,
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"generation_thread": None,
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"processing":False,
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"schedule_for_deletion":False,
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"continuing": False,
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"first_chunk": True,
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}
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}
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if Media_on:
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try:
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self.webcam = WebcamImageSender(socketio,lollmsCom=self)
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except:
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self.webcam = None
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try:
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self.rec_output_folder = lollms_paths.personal_outputs_path/"audio_rec"
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self.rec_output_folder.mkdir(exist_ok=True, parents=True)
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self.summoned = False
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self.audio_cap = AudioRecorder(socketio,self.rec_output_folder/"rt.wav", callback=self.audio_callback,lollmsCom=self)
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except:
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self.audio_cap = None
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self.rec_output_folder = None
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else:
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self.webcam = None
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self.rec_output_folder = None
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# Define a WebSocket event handler
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@socketio.event
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async def connect(sid, environ):
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#Create a new connection information
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self.connections[sid] = {
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"current_discussion":self.db.load_last_discussion(),
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"generated_text":"",
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"continuing": False,
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"first_chunk": True,
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"cancel_generation": False,
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"generation_thread": None,
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"processing":False,
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"schedule_for_deletion":False
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}
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await self.socketio.emit('connected', to=sid)
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ASCIIColors.success(f'Client {sid} connected')
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@socketio.event
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def disconnect(sid):
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try:
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if self.connections[sid]["processing"]:
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self.connections[sid]["schedule_for_deletion"]=True
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else:
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del self.connections[sid]
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except Exception as ex:
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pass
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ASCIIColors.error(f'Client {sid} disconnected')
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# generation status
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self.generating=False
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ASCIIColors.blue(f"Your personal data is stored here :",end="")
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ASCIIColors.green(f"{self.lollms_paths.personal_path}")
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# Other methods and properties of the LoLLMSWebUI singleton class
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def check_module_update_(self, repo_path, branch_name="main"):
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try:
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# Open the repository
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ASCIIColors.yellow(f"Checking for updates from {repo_path}")
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repo = git.Repo(repo_path)
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# Fetch updates from the remote for the specified branch
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repo.remotes.origin.fetch(refspec=f"refs/heads/{branch_name}:refs/remotes/origin/{branch_name}")
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# Compare the local and remote commit IDs for the specified branch
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local_commit = repo.head.commit
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remote_commit = repo.remotes.origin.refs[branch_name].commit
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# Check if the local branch is behind the remote branch
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is_behind = repo.is_ancestor(local_commit, remote_commit) and local_commit!= remote_commit
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ASCIIColors.yellow(f"update availability: {is_behind}")
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# Return True if the local branch is behind the remote branch
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return is_behind
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except Exception as e:
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# Handle any errors that may occur during the fetch process
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# trace_exception(e)
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return False
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def check_update_(self, branch_name="main"):
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try:
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# Open the repository
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repo_path = str(Path(__file__).parent)
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if self.check_module_update_(repo_path, branch_name):
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return True
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repo_path = str(Path(__file__).parent/"lollms_core")
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if self.check_module_update_(repo_path, branch_name):
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return True
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repo_path = str(Path(__file__).parent/"utilities/safe_store")
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if self.check_module_update_(repo_path, branch_name):
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return True
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return False
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except Exception as e:
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# Handle any errors that may occur during the fetch process
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# trace_exception(e)
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return False
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def run_update_script(self, args=None):
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update_script = Path(__file__).parent/"update_script.py"
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# Convert Namespace object to a dictionary
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if args:
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args_dict = vars(args)
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else:
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args_dict = {}
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# Filter out any key-value pairs where the value is None
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valid_args = {key: value for key, value in args_dict.items() if value is not None}
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# Save the arguments to a temporary file
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temp_file = Path(__file__).parent/"temp_args.txt"
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with open(temp_file, "w") as file:
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# Convert the valid_args dictionary to a string in the format "key1 value1 key2 value2 ..."
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arg_string = " ".join([f"--{key} {value}" for key, value in valid_args.items()])
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file.write(arg_string)
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os.system(f"python {update_script}")
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sys.exit(0)
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def run_restart_script(self, args):
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restart_script = Path(__file__).parent/"restart_script.py"
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# Convert Namespace object to a dictionary
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args_dict = vars(args)
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# Filter out any key-value pairs where the value is None
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valid_args = {key: value for key, value in args_dict.items() if value is not None}
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# Save the arguments to a temporary file
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temp_file = Path(__file__).parent/"temp_args.txt"
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with open(temp_file, "w") as file:
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# Convert the valid_args dictionary to a string in the format "key1 value1 key2 value2 ..."
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arg_string = " ".join([f"--{key} {value}" for key, value in valid_args.items()])
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file.write(arg_string)
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os.system(f"python {restart_script}")
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sys.exit(0)
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def audio_callback(self, text):
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if self.summoned:
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client_id = 0
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self.cancel_gen = False
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self.connections[client_id]["generated_text"]=""
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self.connections[client_id]["cancel_generation"]=False
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self.connections[client_id]["continuing"]=False
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self.connections[client_id]["first_chunk"]=True
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if not self.model:
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ASCIIColors.error("Model not selected. Please select a model")
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self.error("Model not selected. Please select a model", client_id=client_id)
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return
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if not self.busy:
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if self.connections[client_id]["current_discussion"] is None:
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if self.db.does_last_discussion_have_messages():
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self.connections[client_id]["current_discussion"] = self.db.create_discussion()
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else:
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self.connections[client_id]["current_discussion"] = self.db.load_last_discussion()
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prompt = text
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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
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message = self.connections[client_id]["current_discussion"].add_message(
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message_type = MSG_TYPE.MSG_TYPE_FULL.value,
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sender_type = SENDER_TYPES.SENDER_TYPES_USER.value,
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sender = ump.replace(self.config.discussion_prompt_separator,"").replace(":",""),
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content=prompt,
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metadata=None,
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parent_message_id=self.message_id
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)
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ASCIIColors.green("Starting message generation by "+self.personality.name)
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self.connections[client_id]['generation_thread'] = threading.Thread(target=self.start_message_generation, args=(message, message.id, client_id))
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self.connections[client_id]['generation_thread'].start()
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self.socketio.sleep(0.01)
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ASCIIColors.info("Started generation task")
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self.busy=True
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#tpe = threading.Thread(target=self.start_message_generation, args=(message, message_id, client_id))
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#tpe.start()
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else:
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self.error("I am busy. Come back later.", client_id=client_id)
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else:
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if output["text"].lower()=="lollms":
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self.summoned = True
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def scrape_and_save(self, url, file_path):
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# Send a GET request to the URL
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response = requests.get(url)
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# Parse the HTML content using BeautifulSoup
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soup = BeautifulSoup(response.content, 'html.parser')
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# Find all the text content in the webpage
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text_content = soup.get_text()
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# Remove extra returns and spaces
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text_content = ' '.join(text_content.split())
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# Save the text content as a text file
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with open(file_path, 'w', encoding="utf-8") as file:
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file.write(text_content)
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self.info(f"Webpage content saved to {file_path}")
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def rebuild_personalities(self, reload_all=False):
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if reload_all:
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self.mounted_personalities=[]
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loaded = self.mounted_personalities
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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]
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mounted_personalities=[]
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ASCIIColors.success(f" ╔══════════════════════════════════════════════════╗ ")
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ASCIIColors.success(f" ║ Building mounted Personalities ║ ")
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ASCIIColors.success(f" ╚══════════════════════════════════════════════════╝ ")
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to_remove=[]
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for i,personality in enumerate(self.config['personalities']):
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if i==self.config["active_personality_id"]:
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ASCIIColors.red("*", end="")
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ASCIIColors.green(f" {personality}")
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else:
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ASCIIColors.yellow(f" {personality}")
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if personality in loaded_names:
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mounted_personalities.append(loaded[loaded_names.index(personality)])
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else:
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personality_path = f"{personality}" if not ":" in personality else f"{personality.split(':')[0]}"
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try:
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personality = AIPersonality(personality_path,
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self.lollms_paths,
|
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self.config,
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model=self.model,
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app=self,
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selected_language=personality.split(":")[1] if ":" in personality else None,
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run_scripts=True)
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mounted_personalities.append(personality)
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if self.config.enable_voice_service and self.config.auto_read and len(personality.audio_samples)>0:
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try:
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from lollms.services.xtts.lollms_xtts import LollmsXTTS
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if self.tts is None:
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self.tts = LollmsXTTS(self, voice_samples_path=Path(__file__).parent.parent/"voices")
|
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except:
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|
self.warning(f"Personality {personality.name} request using custom voice but couldn't load XTTS")
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|
except Exception as ex:
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|
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.")
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ASCIIColors.info("Trying to force reinstall")
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if self.config["debug"]:
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print(ex)
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|
try:
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personality = AIPersonality(
|
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personality_path,
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self.lollms_paths,
|
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self.config,
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self.model,
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app = self,
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run_scripts=True,
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selected_language=personality.split(":")[1] if ":" in personality else None,
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installation_option=InstallOption.FORCE_INSTALL)
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mounted_personalities.append(personality)
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if personality.processor:
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|
personality.processor.mounted()
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|
except Exception as ex:
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ASCIIColors.error(f"Couldn't load personality at {personality_path}")
|
|
trace_exception(ex)
|
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ASCIIColors.info(f"Unmounting personality")
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to_remove.append(i)
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|
personality = AIPersonality(None,
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self.lollms_paths,
|
|
self.config,
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self.model,
|
|
app=self,
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run_scripts=True,
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|
installation_option=InstallOption.FORCE_INSTALL)
|
|
mounted_personalities.append(personality)
|
|
if personality.processor:
|
|
personality.processor.mounted()
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|
|
|
ASCIIColors.info("Reverted to default personality")
|
|
if self.config["active_personality_id"]>=0 and self.config["active_personality_id"]<len(self.config["personalities"]):
|
|
ASCIIColors.success(f'selected model : {self.config["personalities"][self.config["active_personality_id"]]}')
|
|
else:
|
|
ASCIIColors.warning('An error was encountered while trying to mount personality')
|
|
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_personalities):
|
|
mounted_personalities.pop(index)
|
|
self.config["personalities"].pop(index)
|
|
ASCIIColors.info(f"removed personality {personality_path}")
|
|
|
|
if 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 recover_discussion(self,client_id, message_index=-1):
|
|
messages = self.connections[client_id]["current_discussion"].get_messages()
|
|
discussion=""
|
|
for msg in messages:
|
|
if message_index!=-1 and msg>message_index:
|
|
break
|
|
discussion += "\n" + self.config.discussion_prompt_separator + msg.sender + ": " + msg.content.strip()
|
|
return discussion
|
|
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 = ""
|
|
knowledge = ""
|
|
|
|
|
|
# boosting information
|
|
if self.config.positive_boost:
|
|
positive_boost="\n!@>important information: "+self.config.positive_boost+"\n"
|
|
n_positive_boost = len(self.model.tokenize(positive_boost))
|
|
else:
|
|
positive_boost=""
|
|
n_positive_boost = 0
|
|
|
|
if self.config.negative_boost:
|
|
negative_boost="\n!@>important information: "+self.config.negative_boost+"\n"
|
|
n_negative_boost = len(self.model.tokenize(negative_boost))
|
|
else:
|
|
negative_boost=""
|
|
n_negative_boost = 0
|
|
|
|
if self.config.force_output_language_to_be:
|
|
force_language="\n!@>important information: Answer the user in this language :"+self.config.force_output_language_to_be+"\n"
|
|
n_force_language = len(self.model.tokenize(force_language))
|
|
else:
|
|
force_language=""
|
|
n_force_language = 0
|
|
|
|
if self.config.fun_mode:
|
|
fun_mode="\n!@>important information: Fun mode activated. In this mode you must answer in a funny playful way. Do not be serious in your answers. Each answer needs to make the user laugh.\n"
|
|
n_fun_mode = len(self.model.tokenize(positive_boost))
|
|
else:
|
|
fun_mode=""
|
|
n_fun_mode = 0
|
|
|
|
|
|
if generation_type != "simple_question":
|
|
if self.personality.persona_data_vectorizer:
|
|
if documentation=="":
|
|
documentation="!@>Documentation:\n"
|
|
|
|
if self.config.data_vectorization_build_keys_words:
|
|
discussion = self.recover_discussion(client_id)[-512:]
|
|
query = self.personality.fast_gen(f"\n!@>instruction: Read the discussion and rewrite the last prompt for someone who didn't read the entire discussion.\nDo not answer the prompt. Do not add explanations.\n!@>discussion:\n{discussion}\n!@>enhanced query: ", max_generation_size=256, show_progress=True)
|
|
ASCIIColors.cyan(f"Query:{query}")
|
|
else:
|
|
query = current_message.content
|
|
try:
|
|
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}"
|
|
except:
|
|
self.warning("Couldn't add documentation to the context. Please verify the vector database")
|
|
|
|
if len(self.personality.text_files) > 0 and self.personality.vectorizer:
|
|
if documentation=="":
|
|
documentation="!@>Documentation:\n"
|
|
|
|
if self.config.data_vectorization_build_keys_words:
|
|
discussion = self.recover_discussion(client_id)[-512:]
|
|
query = self.personality.fast_gen(f"\n!@>instruction: Read the discussion and rewrite the last prompt for someone who didn't read the entire discussion.\nDo not answer the prompt. Do not add explanations.\n!@>discussion:\n{discussion}\n!@>enhanced query: ", max_generation_size=256, show_progress=True)
|
|
ASCIIColors.cyan(f"Query:{query}")
|
|
else:
|
|
query = current_message.content
|
|
|
|
try:
|
|
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}"
|
|
documentation += "\n!@>important information: Use the documentation data to answer the user questions. If the data is not present in the documentation, please tell the user that the information he is asking for does not exist in the documentation section. It is strictly forbidden to give the user an answer without having actual proof from the documentation."
|
|
except:
|
|
self.warning("Couldn't add documentation to the context. Please verify the vector database")
|
|
# Check if there is discussion knowledge to add to the prompt
|
|
if self.config.use_discussions_history and self.long_term_memory is not None:
|
|
if knowledge=="":
|
|
knowledge="!@>knowledge:\n"
|
|
|
|
try:
|
|
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)):
|
|
knowledge += f"!@>knowledge {i}:\n!@>title:\n{infos[0]}\ncontent:\n{doc}"
|
|
except:
|
|
self.warning("Couldn't add long term memory information to the context. Please verify the vector database") # Add information about the user
|
|
user_description=""
|
|
if self.config.use_user_name_in_discussions:
|
|
user_description="!@>User description:\n"+self.config.user_description+"\n"
|
|
|
|
|
|
# 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 knowledge text and calculate its number of tokens
|
|
if len(knowledge)>0:
|
|
tokens_history = self.model.tokenize(knowledge)
|
|
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 knowledge
|
|
total_tokens = n_cond_tk + n_doc_tk + n_history_tk + n_user_description_tk + n_positive_boost + n_negative_boost + n_force_language + n_fun_mode
|
|
|
|
# Calculate the available space for the messages
|
|
available_space = self.config.ctx_size - n_tokens - total_tokens
|
|
|
|
if self.config.debug:
|
|
self.info(f"Tokens summary:\nConditionning:{n_cond_tk}\ndoc:{n_doc_tk}\nhistory:{n_history_tk}\nuser description:{n_user_description_tk}\nAvailable space:{available_space}",10)
|
|
|
|
# Raise an error if the available space is 0 or less
|
|
if available_space<1:
|
|
self.error("Not enough space in context!!")
|
|
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 i in range(len(full_message_list)-1):
|
|
message_tokens = full_message_list[i]
|
|
discussion_messages += self.model.detokenize(message_tokens)
|
|
|
|
ai_prefix = self.model.detokenize(full_message_list[-1])
|
|
|
|
# Build the final prompt by concatenating the conditionning and discussion messages
|
|
prompt_data = conditionning + documentation + knowledge + user_description + discussion_messages + positive_boost + negative_boost + force_language + fun_mode + ai_prefix
|
|
|
|
# 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(knowledge)
|
|
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 knowledge:{available_space} tokens")
|
|
|
|
self.info(f"Tokens summary:\nPrompt size:{len(tokens)}\nTo generate:{available_space}",10)
|
|
|
|
# Details
|
|
context_details = {
|
|
"conditionning":conditionning,
|
|
"documentation":documentation,
|
|
"knowledge":knowledge,
|
|
"user_description":user_description,
|
|
"discussion_messages":discussion_messages,
|
|
"positive_boost":positive_boost,
|
|
"negative_boost":negative_boost,
|
|
"force_language":force_language,
|
|
"fun_mode":fun_mode,
|
|
"ai_prefix":ai_prefix
|
|
|
|
}
|
|
|
|
# Return the prepared query, original message content, and tokenized query
|
|
return prompt_data, current_message.content, tokens, context_details
|
|
|
|
|
|
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,
|
|
):
|
|
|
|
run_async(partial(self.socketio.emit,'notification', {
|
|
'content': content,# self.connections[client_id]["generated_text"],
|
|
'notification_type': notification_type.value,
|
|
"duration": duration,
|
|
'display_type':display_type.value
|
|
}, to=client_id
|
|
)
|
|
)
|
|
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
|
|
run_async(partial(
|
|
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
|
|
}, to=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:
|
|
run_async(
|
|
partial(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
|
|
}, to=client_id
|
|
)
|
|
)
|
|
|
|
run_async(
|
|
partial(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
|
|
}, to=client_id
|
|
)
|
|
)
|
|
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')
|
|
run_async(
|
|
partial(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,
|
|
|
|
}, to=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)
|
|
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
|
|
|
|
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, context_details, n_predict, client_id, callback=None):
|
|
if self.personality.processor is not None:
|
|
ASCIIColors.info("Running workflow")
|
|
try:
|
|
self.personality.callback = callback
|
|
self.personality.processor.run_workflow( prompt, full_prompt, callback, context_details)
|
|
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/<binding name> 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, context_details = 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
|
|
try:
|
|
self.generate(
|
|
self.discussion_messages,
|
|
self.current_message,
|
|
context_details=context_details,
|
|
n_predict = self.config.ctx_size-len(tokens)-1,
|
|
client_id=client_id,
|
|
callback=partial(self.process_chunk,client_id = client_id)
|
|
)
|
|
if self.config.enable_voice_service and self.config.auto_read and len(self.personality.audio_samples)>0:
|
|
try:
|
|
self.process_chunk("Generating voice output",MSG_TYPE.MSG_TYPE_STEP_START,client_id=client_id)
|
|
from lollms.services.xtts.lollms_xtts import LollmsXTTS
|
|
if self.tts is None:
|
|
self.tts = LollmsXTTS(self, voice_samples_path=Path(__file__).parent.parent/"voices")
|
|
language = convert_language_name(self.personality.language)
|
|
self.tts.set_speaker_folder(Path(self.personality.audio_samples[0]).parent)
|
|
fn = self.personality.name.lower().replace(' ',"_").replace('.','')
|
|
fn = f"{fn}_{message_id}.wav"
|
|
url = f"audio/{fn}"
|
|
self.tts.tts_to_file(self.connections[client_id]["generated_text"], Path(self.personality.audio_samples[0]).name, f"{fn}", language=language)
|
|
fl = f"""
|
|
<audio controls>
|
|
<source src="{url}" type="audio/wav">
|
|
Your browser does not support the audio element.
|
|
</audio>
|
|
"""
|
|
self.process_chunk("Generating voice output", MSG_TYPE.MSG_TYPE_STEP_END, {'status':True},client_id=client_id)
|
|
self.process_chunk(fl,MSG_TYPE.MSG_TYPE_UI, client_id=client_id)
|
|
|
|
"""
|
|
self.info("Creating audio output",10)
|
|
self.personality.step_start("Creating audio output")
|
|
if not PackageManager.check_package_installed("tortoise"):
|
|
PackageManager.install_package("tortoise-tts")
|
|
from tortoise import utils, api
|
|
import sounddevice as sd
|
|
if self.tts is None:
|
|
self.tts = api.TextToSpeech( kv_cache=True, half=True)
|
|
reference_clips = [utils.audio.load_audio(str(p), 22050) for p in self.personality.audio_samples]
|
|
tk = self.model.tokenize(self.connections[client_id]["generated_text"])
|
|
if len(tk)>100:
|
|
chunk_size = 100
|
|
|
|
for i in range(0, len(tk), chunk_size):
|
|
chunk = self.model.detokenize(tk[i:i+chunk_size])
|
|
if i==0:
|
|
pcm_audio = self.tts.tts_with_preset(chunk, voice_samples=reference_clips, preset='fast').numpy().flatten()
|
|
else:
|
|
pcm_audio = np.concatenate([pcm_audio, self.tts.tts_with_preset(chunk, voice_samples=reference_clips, preset='ultra_fast').numpy().flatten()])
|
|
else:
|
|
pcm_audio = self.tts.tts_with_preset(self.connections[client_id]["generated_text"], voice_samples=reference_clips, preset='fast').numpy().flatten()
|
|
sd.play(pcm_audio, 22050)
|
|
self.personality.step_end("Creating audio output")
|
|
"""
|
|
|
|
|
|
|
|
except Exception as ex:
|
|
ASCIIColors.error("Couldn't read")
|
|
trace_exception(ex)
|
|
print()
|
|
ASCIIColors.success("## Done Generation ##")
|
|
print()
|
|
except Exception as ex:
|
|
trace_exception(ex)
|
|
print()
|
|
ASCIIColors.error("## Generation Error ##")
|
|
print()
|
|
|
|
self.cancel_gen = False
|
|
|
|
# Send final message
|
|
self.close_message(client_id)
|
|
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)
|
|
asyncio.run(
|
|
self.socketio.emit('disucssion_renamed',{
|
|
'status': True,
|
|
'discussion_id':d.discussion_id,
|
|
'title':title
|
|
}, to=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 ""
|