mirror of
https://github.com/ParisNeo/lollms.git
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1516 lines
83 KiB
Python
1516 lines
83 KiB
Python
from lollms.main_config import LOLLMSConfig
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from lollms.paths import LollmsPaths
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from lollms.personality import PersonalityBuilder, AIPersonality
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from lollms.binding import LLMBinding, BindingBuilder, ModelBuilder
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from lollms.databases.discussions_database import Message
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from lollms.extension import LOLLMSExtension, ExtensionBuilder
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from lollms.config import InstallOption
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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.terminal import MainMenu
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from lollms.types import MSG_TYPE, SENDER_TYPES
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from lollms.utilities import PromptReshaper
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from lollms.client_session import Client, Session
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from lollms.databases.skills_database import SkillsLibrary
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from lollms.tasks import TasksLibrary
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from lollmsvectordb.database_elements.chunk import Chunk
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from lollmsvectordb.vector_database import VectorDatabase
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from typing import Callable
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from pathlib import Path
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from datetime import datetime
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from functools import partial
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from socketio import AsyncServer
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from typing import Tuple, List, Dict
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import subprocess
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import importlib
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import sys, os
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import platform
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import gc
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import yaml
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import time
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from lollms.utilities import PackageManager
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class LollmsApplication(LoLLMsCom):
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def __init__(
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self,
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app_name:str,
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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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try_select_binding=False,
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try_select_model=False,
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callback=None,
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sio:AsyncServer=None,
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free_mode=False
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) -> None:
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"""
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Creates a LOLLMS Application
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"""
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super().__init__(sio)
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self.app_name = app_name
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self.config = config
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self.lollms_paths = lollms_paths
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# TODO : implement
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self.embedding_models = []
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self.menu = MainMenu(self, callback)
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self.mounted_personalities = []
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self.personality:AIPersonality = None
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self.mounted_extensions = []
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self.binding = None
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self.model:LLMBinding = None
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self.long_term_memory = None
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self.tts = None
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self.session = Session(lollms_paths)
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self.skills_library = SkillsLibrary(self.lollms_paths.personal_skills_path/(self.config.skills_lib_database_name+".db"))
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self.tasks_library = TasksLibrary(self)
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self.handle_generate_msg: Callable[[str, Dict], None] = None
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self.generate_msg_with_internet: Callable[[str, Dict], None] = None
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self.handle_continue_generate_msg_from: Callable[[str, Dict], None] = None
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# Trust store
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self.bk_store = None
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# services
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self.ollama = None
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self.vllm = None
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self.whisper = None
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self.xtts = None
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self.sd = None
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self.comfyui = None
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self.motion_ctrl = None
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self.tti = None
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self.tts = None
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self.stt = None
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self.ttm = None
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self.ttv = None
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self.rt_com = None
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if not free_mode:
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try:
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if config.auto_update:
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# Clone the repository to the target path
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if self.lollms_paths.lollms_core_path.exists():
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ASCIIColors.info("Lollms_core found in the app space.\nPulling last lollms_core")
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subprocess.run(["git", "-C", self.lollms_paths.lollms_core_path, "pull"])
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if self.lollms_paths.safe_store_path.exists():
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ASCIIColors.info("safe_store_path found in the app space.\nPulling last safe_store_path")
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subprocess.run(["git", "-C", self.lollms_paths.safe_store_path, "pull"])
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# Pull the repository if it already exists
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ASCIIColors.info("Bindings zoo found in your personal space.\nPulling last bindings zoo")
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subprocess.run(["git", "-C", self.lollms_paths.bindings_zoo_path, "pull"])
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# Pull the repository if it already exists
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ASCIIColors.info("Personalities zoo found in your personal space.\nPulling last personalities zoo")
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subprocess.run(["git", "-C", self.lollms_paths.personalities_zoo_path, "pull"])
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# Pull the repository if it already exists
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ASCIIColors.info("Extensions zoo found in your personal space.\nPulling last Extensions zoo")
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subprocess.run(["git", "-C", self.lollms_paths.extensions_zoo_path, "pull"])
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# Pull the repository if it already exists
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ASCIIColors.info("Models zoo found in your personal space.\nPulling last Models zoo")
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subprocess.run(["git", "-C", self.lollms_paths.models_zoo_path, "pull"])
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except Exception as ex:
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ASCIIColors.error("Couldn't pull zoos. Please contact the main dev on our discord channel and report the problem.")
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trace_exception(ex)
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if self.config.binding_name is None:
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ASCIIColors.warning(f"No binding selected")
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if try_select_binding:
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ASCIIColors.info("Please select a valid model or install a new one from a url")
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self.menu.select_binding()
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else:
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if load_binding:
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try:
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ASCIIColors.info(f">Loading binding {self.config.binding_name}. Please wait ...")
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self.binding = self.load_binding()
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except Exception as ex:
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ASCIIColors.error(f"Failed to load binding.\nReturned exception: {ex}")
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trace_exception(ex)
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if self.binding is not None:
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ASCIIColors.success(f"Binding {self.config.binding_name} loaded successfully.")
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if load_model:
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if self.config.model_name is None:
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ASCIIColors.warning(f"No model selected")
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if try_select_model:
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print("Please select a valid model")
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self.menu.select_model()
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if self.config.model_name is not None:
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try:
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ASCIIColors.info(f">Loading model {self.config.model_name}. Please wait ...")
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self.model = self.load_model()
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if self.model is not None:
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ASCIIColors.success(f"Model {self.config.model_name} loaded successfully.")
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except Exception as ex:
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ASCIIColors.error(f"Failed to load model.\nReturned exception: {ex}")
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trace_exception(ex)
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else:
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ASCIIColors.warning(f"Couldn't load binding {self.config.binding_name}.")
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self.mount_personalities()
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self.mount_extensions()
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try:
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self.load_rag_dbs()
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except Exception as ex:
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trace_exception(ex)
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def backup_trust_store(self):
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self.bk_store = None
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if 'REQUESTS_CA_BUNDLE' in os.environ:
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self.bk_store = os.environ['REQUESTS_CA_BUNDLE']
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del os.environ['REQUESTS_CA_BUNDLE']
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def restore_trust_store(self):
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if self.bk_store is not None:
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os.environ['REQUESTS_CA_BUNDLE'] = self.bk_store
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def model_path_to_binding_model(self, model_path:str):
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parts = model_path.strip().split("::")
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if len(parts)<2:
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raise Exception("Model path is not in the format binding:model_name!")
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binding = parts[0]
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model_name = parts[1]
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return binding, model_name
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def select_model(self, binding_name, model_name):
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self.config["binding_name"] = binding_name
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self.config["model_name"] = model_name
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print(f"New binding selected : {binding_name}")
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try:
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if self.binding:
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self.binding.destroy_model()
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self.binding = None
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self.model = None
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for per in self.mounted_personalities:
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if per is not None:
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per.model = None
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gc.collect()
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self.binding = BindingBuilder().build_binding(self.config, self.lollms_paths, InstallOption.INSTALL_IF_NECESSARY, lollmsCom=self)
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self.config["model_name"] = model_name
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self.model = self.binding.build_model()
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for per in self.mounted_personalities:
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if per is not None:
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per.model = self.model
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self.config.save_config()
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ASCIIColors.green("Binding loaded successfully")
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return True
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except Exception as ex:
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ASCIIColors.error(f"Couldn't build binding: [{ex}]")
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trace_exception(ex)
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return False
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def add_discussion_to_skills_library(self, client: Client):
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end_header_id_template = self.config.end_header_id_template
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separator_template = self.config.separator_template
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system_message_template = self.config.system_message_template
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messages = client.discussion.get_messages()
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# Extract relevant information from messages
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def cb(str, MSG_TYPE_=MSG_TYPE.MSG_TYPE_FULL, dict=None, list=None):
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if MSG_TYPE_!=MSG_TYPE.MSG_TYPE_CHUNK:
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self.ShowBlockingMessage(f"Learning\n{str}")
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bk_cb = self.tasks_library.callback
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self.tasks_library.callback = cb
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content = self._extract_content(messages, cb)
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self.tasks_library.callback = bk_cb
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# Generate title
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title_prompt = f"{separator_template}".join([
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f"{self.start_header_id_template}{system_message_template}{end_header_id_template}Generate a concise and descriptive title for the following content.",
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"The title should summarize the main topic or subject of the content.",
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"Do not mention the format of the content (e.g., bullet points, discussion, etc.) in the title.",
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"Provide only the title without any additional explanations or context.",
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f"{self.start_header_id_template}content{end_header_id_template}",
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f"{content}",
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f"{self.start_header_id_template}title{end_header_id_template}"
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])
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title = self._generate_text(title_prompt)
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# Determine category
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category_prompt = f"{self.system_full_header}Analyze the following title, and determine the most appropriate generic category that encompasses the main subject or theme. The category should be broad enough to include multiple related skill entries. Provide only the category name without any additional explanations or context:\n\nTitle:\n{title}\n{separator_template}{self.start_header_id_template}Category:\n"
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category = self._generate_text(category_prompt)
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# Add entry to skills library
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self.skills_library.add_entry(1, category, title, content)
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return category, title, content
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def _extract_content(self, messages:List[Message], callback = None):
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message_content = ""
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for message in messages:
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rank = message.rank
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sender = message.sender
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text = message.content
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message_content += f"Rank {rank} - {sender}: {text}\n"
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return self.tasks_library.summarize_text(
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message_content,
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"\n".join([
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"Act as Skills library maker.",
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"The objective is to find out important information from the discussion and store them as text that can be used in the future to remember those information.",
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"Format the output as sections if applicable:",
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"Global context: Explain in a sentense or two the subject of the discussion",
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"Interesting things (if applicable): If you find interesting information or something that was discovered or built in this discussion, list it here with enough details to be reproducible just by reading this text.",
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"Code snippet (if applicable): If there are important code snippets, write them here in a markdown code tag.",
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"Make the output easy to understand."
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]),
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doc_name="discussion",
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callback=callback)
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def _generate_text(self, prompt):
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max_tokens = min(self.config.ctx_size - self.model.get_nb_tokens(prompt),self.config.max_n_predict)
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generated_text = self.model.generate(prompt, max_tokens)
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return generated_text.strip()
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def get_uploads_path(self, client_id):
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return self.lollms_paths.personal_uploads_path
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def load_rag_dbs(self):
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self.active_rag_dbs = []
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for rag_db in self.config.rag_databases:
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parts = rag_db.split("::")
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db_name = parts[0]
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if parts[-1]=="mounted":
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try:
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if not PackageManager.check_package_installed("lollmsvectordb"):
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PackageManager.install_package("lollmsvectordb")
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from lollmsvectordb import VectorDatabase
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from lollmsvectordb.text_document_loader import TextDocumentsLoader
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from lollmsvectordb.lollms_tokenizers.tiktoken_tokenizer import TikTokenTokenizer
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if self.config.rag_vectorizer == "bert":
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self.backup_trust_store()
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from lollmsvectordb.lollms_vectorizers.bert_vectorizer import BERTVectorizer
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v = BERTVectorizer()
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self.restore_trust_store()
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elif self.config.rag_vectorizer == "tfidf":
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from lollmsvectordb.lollms_vectorizers.tfidf_vectorizer import TFIDFVectorizer
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v = TFIDFVectorizer()
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elif self.config.rag_vectorizer == "word2vec":
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from lollmsvectordb.lollms_vectorizers.word2vec_vectorizer import Word2VecVectorizer
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v = Word2VecVectorizer()
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vdb = VectorDatabase(Path(parts[1])/f"{db_name}.sqlite", v, self.model if self.model else TikTokenTokenizer(), n_neighbors=self.config.rag_n_chunks)
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self.active_rag_dbs.append({"name":parts[0],"path":parts[1],"vectorizer":vdb})
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except:
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ASCIIColors.error(f"Couldn't load "+str(Path(parts[1])/f"{db_name}.sqlite")+" consider revectorizing it")
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def start_servers(self):
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ASCIIColors.yellow("* - * - * - Starting services - * - * - *")
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ASCIIColors.blue("Loading local TTT services")
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if self.config.enable_ollama_service:
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try:
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from lollms.services.ollama.lollms_ollama import Service
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self.ollama = Service(self, base_url=self.config.ollama_base_url)
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except Exception as ex:
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trace_exception(ex)
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self.warning(f"Couldn't load Ollama")
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if self.config.enable_vllm_service:
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try:
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from lollms.services.vllm.lollms_vllm import Service
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self.vllm = Service(self, base_url=self.config.vllm_url)
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except Exception as ex:
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trace_exception(ex)
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self.warning(f"Couldn't load vllm")
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ASCIIColors.blue("Loading loacal STT services")
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if self.config.whisper_activate or self.config.active_stt_service == "whisper":
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try:
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from lollms.services.whisper.lollms_whisper import LollmsWhisper
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self.whisper = LollmsWhisper(self, self.config.whisper_model, self.lollms_paths.personal_outputs_path)
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except Exception as ex:
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trace_exception(ex)
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ASCIIColors.blue("Loading local TTS services")
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if self.config.active_tts_service == "xtts":
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ASCIIColors.yellow("Loading XTTS")
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try:
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from lollms.services.xtts.lollms_xtts import LollmsXTTS
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voice=self.config.xtts_current_voice
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if voice!="main_voice":
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voices_folder = self.lollms_paths.custom_voices_path
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else:
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voices_folder = Path(__file__).parent.parent.parent/"services/xtts/voices"
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self.xtts = LollmsXTTS(
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self,
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voices_folders=[voices_folder, self.lollms_paths.custom_voices_path],
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freq=self.config.xtts_freq
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)
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except Exception as ex:
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trace_exception(ex)
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self.warning(f"Couldn't load XTTS")
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ASCIIColors.blue("Loading local TTI services")
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if self.config.enable_sd_service:
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try:
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from lollms.services.sd.lollms_sd import LollmsSD
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self.sd = LollmsSD(self, auto_sd_base_url=self.config.sd_base_url)
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except:
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self.warning(f"Couldn't load SD")
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if self.config.enable_comfyui_service:
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try:
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from lollms.services.comfyui.lollms_comfyui import LollmsComfyUI
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self.comfyui = LollmsComfyUI(self, comfyui_base_url=self.config.comfyui_base_url)
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except:
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self.warning(f"Couldn't load SD")
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if self.config.enable_motion_ctrl_service:
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try:
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from lollms.services.motion_ctrl.lollms_motion_ctrl import Service
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self.motion_ctrl = Service(self, base_url=self.config.motion_ctrl_base_url)
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except Exception as ex:
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trace_exception(ex)
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self.warning(f"Couldn't load Motion control")
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ASCIIColors.blue("Activating TTI service")
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if self.config.active_tti_service == "diffusers":
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from lollms.services.diffusers.lollms_diffusers import LollmsDiffusers
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self.tti = LollmsDiffusers(self)
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elif self.config.active_tti_service == "autosd":
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if self.sd:
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self.tti = self.sd
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else:
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from lollms.services.sd.lollms_sd import LollmsSD
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self.tti = LollmsSD(self)
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elif self.config.active_tti_service == "dall-e":
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from lollms.services.dalle.lollms_dalle import LollmsDalle
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self.tti = LollmsDalle(self, self.config.dall_e_key)
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elif self.config.active_tti_service == "midjourney":
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from lollms.services.midjourney.lollms_midjourney import LollmsMidjourney
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self.tti = LollmsMidjourney(self, self.config.midjourney_key, self.config.midjourney_timeout, self.config.midjourney_retries)
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elif self.config.active_tti_service == "comfyui" and (self.tti is None or self.tti.name!="comfyui"):
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if self.comfyui:
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self.tti = self.comfyui
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else:
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from lollms.services.comfyui.lollms_comfyui import LollmsComfyUI
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self.tti = LollmsComfyUI(self, comfyui_base_url=self.config.comfyui_base_url)
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ASCIIColors.blue("Activating TTS services")
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if self.config.active_tts_service == "eleven_labs_tts":
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from lollms.services.eleven_labs_tts.lollms_eleven_labs_tts import LollmsElevenLabsTTS
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self.tts = LollmsElevenLabsTTS(self, self.config.elevenlabs_tts_model_id, self.config.elevenlabs_tts_voice_id, self.config.elevenlabs_tts_key, stability=self.config.elevenlabs_tts_voice_stability, similarity_boost=self.config.elevenlabs_tts_voice_boost)
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elif self.config.active_tts_service == "openai_tts":
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from lollms.services.open_ai_tts.lollms_openai_tts import LollmsOpenAITTS
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self.tts = LollmsOpenAITTS(self, self.config.openai_tts_model, self.config.openai_tts_voice, self.config.openai_tts_key)
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elif self.config.active_tts_service == "xtts" and self.xtts:
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self.tts = self.xtts
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ASCIIColors.blue("Loading STT services")
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if self.config.active_stt_service == "openai_whisper":
|
|
from lollms.services.openai_whisper.lollms_openai_whisper import LollmsOpenAIWhisper
|
|
self.stt = LollmsOpenAIWhisper(self, self.config.openai_whisper_model, self.config.openai_whisper_key)
|
|
elif self.config.active_stt_service == "whisper":
|
|
from lollms.services.whisper.lollms_whisper import LollmsWhisper
|
|
self.stt = LollmsWhisper(self, self.config.whisper_model)
|
|
|
|
|
|
def verify_servers(self, reload_all=False):
|
|
ASCIIColors.yellow("* - * - * - Verifying services - * - * - *")
|
|
|
|
try:
|
|
ASCIIColors.blue("Loading active local TTT services")
|
|
|
|
if self.config.enable_ollama_service and self.ollama is None:
|
|
try:
|
|
from lollms.services.ollama.lollms_ollama import Service
|
|
self.ollama = Service(self, base_url=self.config.ollama_base_url)
|
|
except Exception as ex:
|
|
trace_exception(ex)
|
|
self.warning(f"Couldn't load Ollama")
|
|
|
|
if self.config.enable_vllm_service and self.vllm is None:
|
|
try:
|
|
from lollms.services.vllm.lollms_vllm import Service
|
|
self.vllm = Service(self, base_url=self.config.vllm_url)
|
|
except Exception as ex:
|
|
trace_exception(ex)
|
|
self.warning(f"Couldn't load vllm")
|
|
|
|
ASCIIColors.blue("Loading local STT services")
|
|
|
|
if self.config.whisper_activate and self.whisper is None:
|
|
try:
|
|
from lollms.services.whisper.lollms_whisper import LollmsWhisper
|
|
self.whisper = LollmsWhisper(self, self.config.whisper_model, self.lollms_paths.personal_outputs_path)
|
|
except Exception as ex:
|
|
trace_exception(ex)
|
|
|
|
ASCIIColors.blue("Loading loacal TTS services")
|
|
if self.config.active_tts_service == "xtts" and self.xtts is None:
|
|
ASCIIColors.yellow("Loading XTTS")
|
|
try:
|
|
from lollms.services.xtts.lollms_xtts import LollmsXTTS
|
|
voice=self.config.xtts_current_voice
|
|
if voice!="main_voice":
|
|
voices_folder = self.lollms_paths.custom_voices_path
|
|
else:
|
|
voices_folder = Path(__file__).parent.parent.parent/"services/xtts/voices"
|
|
|
|
self.xtts = LollmsXTTS(
|
|
self,
|
|
voices_folders=[voices_folder, self.lollms_paths.custom_voices_path],
|
|
freq=self.config.xtts_freq
|
|
)
|
|
except Exception as ex:
|
|
trace_exception(ex)
|
|
self.warning(f"Couldn't load XTTS")
|
|
|
|
ASCIIColors.blue("Loading local TTI services")
|
|
if self.config.enable_sd_service and self.sd is None:
|
|
try:
|
|
from lollms.services.sd.lollms_sd import LollmsSD
|
|
self.sd = LollmsSD(self, auto_sd_base_url=self.config.sd_base_url)
|
|
except:
|
|
self.warning(f"Couldn't load SD")
|
|
|
|
if self.config.enable_comfyui_service and self.comfyui is None:
|
|
try:
|
|
from lollms.services.comfyui.lollms_comfyui import LollmsComfyUI
|
|
self.comfyui = LollmsComfyUI(self, comfyui_base_url=self.config.comfyui_base_url)
|
|
except:
|
|
self.warning(f"Couldn't load Comfyui")
|
|
|
|
if self.config.enable_motion_ctrl_service and self.motion_ctrl is None:
|
|
try:
|
|
from lollms.services.motion_ctrl.lollms_motion_ctrl import Service
|
|
self.motion_ctrl = Service(self, base_url=self.config.motion_ctrl_base_url)
|
|
except Exception as ex:
|
|
trace_exception(ex)
|
|
self.warning(f"Couldn't load Motion control")
|
|
|
|
|
|
ASCIIColors.blue("Activating TTI service")
|
|
if self.config.active_tti_service == "diffusers" and (self.tti is None or self.tti.name!="diffusers"):
|
|
from lollms.services.diffusers.lollms_diffusers import LollmsDiffusers
|
|
self.tti = LollmsDiffusers(self)
|
|
elif self.config.active_tti_service == "autosd" and (self.tti is None or self.tti.name!="stable_diffusion"):
|
|
if self.sd:
|
|
self.tti = self.sd
|
|
else:
|
|
from lollms.services.sd.lollms_sd import LollmsSD
|
|
self.tti = LollmsSD(self)
|
|
elif self.config.active_tti_service == "dall-e" and (self.tti is None or self.tti.name!="dall-e-2" or type(self.tti.name)!="dall-e-3"):
|
|
from lollms.services.dalle.lollms_dalle import LollmsDalle
|
|
self.tti = LollmsDalle(self, self.config.dall_e_key)
|
|
elif self.config.active_tti_service == "midjourney" and (self.tti is None or self.tti.name!="midjourney"):
|
|
from lollms.services.midjourney.lollms_midjourney import LollmsMidjourney
|
|
self.tti = LollmsMidjourney(self, self.config.midjourney_key, self.config.midjourney_timeout, self.config.midjourney_retries)
|
|
elif self.config.active_tti_service == "comfyui" and (self.tti is None or self.tti.name!="comfyui"):
|
|
if self.comfyui:
|
|
self.tti = self.comfyui
|
|
else:
|
|
from lollms.services.comfyui.lollms_comfyui import LollmsComfyUI
|
|
self.tti = LollmsComfyUI(self, comfyui_base_url=self.config.comfyui_base_url)
|
|
|
|
ASCIIColors.blue("Activating TTS service")
|
|
if self.config.active_tts_service == "eleven_labs_tts":
|
|
from lollms.services.eleven_labs_tts.lollms_eleven_labs_tts import LollmsElevenLabsTTS
|
|
self.tts = LollmsElevenLabsTTS(self, self.config.elevenlabs_tts_model_id, self.config.elevenlabs_tts_voice_id, self.config.elevenlabs_tts_key, stability=self.config.elevenlabs_tts_voice_stability, similarity_boost=self.config.elevenlabs_tts_voice_boost)
|
|
elif self.config.active_tts_service == "openai_tts" and (self.tts is None or self.tts.name!="openai_tts"):
|
|
from lollms.services.open_ai_tts.lollms_openai_tts import LollmsOpenAITTS
|
|
self.tts = LollmsOpenAITTS(self, self.config.openai_tts_model, self.config.openai_tts_voice, self.config.openai_tts_key)
|
|
elif self.config.active_tts_service == "xtts" and self.xtts:
|
|
self.tts = self.xtts
|
|
|
|
ASCIIColors.blue("Activating STT service")
|
|
if self.config.active_stt_service == "openai_whisper" and (self.tts is None or self.tts.name!="openai_whisper"):
|
|
from lollms.services.openai_whisper.lollms_openai_whisper import LollmsOpenAIWhisper
|
|
self.stt = LollmsOpenAIWhisper(self, self.config.openai_whisper_model, self.config.openai_whisper_key)
|
|
elif self.config.active_stt_service == "whisper" and (self.tts is None or self.tts.name!="whisper") :
|
|
from lollms.services.whisper.lollms_whisper import LollmsWhisper
|
|
self.stt = LollmsWhisper(self, self.config.whisper_model)
|
|
|
|
except Exception as ex:
|
|
trace_exception(ex)
|
|
|
|
|
|
|
|
def process_chunk(
|
|
self,
|
|
chunk:str,
|
|
message_type,
|
|
parameters:dict=None,
|
|
metadata:list=None,
|
|
personality=None
|
|
):
|
|
|
|
pass
|
|
|
|
def default_callback(self, chunk, type, generation_infos:dict):
|
|
if generation_infos["nb_received_tokens"]==0:
|
|
self.start_time = datetime.now()
|
|
dt =(datetime.now() - self.start_time).seconds
|
|
if dt==0:
|
|
dt=1
|
|
spd = generation_infos["nb_received_tokens"]/dt
|
|
ASCIIColors.green(f"Received {generation_infos['nb_received_tokens']} tokens (speed: {spd:.2f}t/s) ",end="\r",flush=True)
|
|
sys.stdout = sys.__stdout__
|
|
sys.stdout.flush()
|
|
if chunk:
|
|
generation_infos["generated_text"] += chunk
|
|
antiprompt = self.personality.detect_antiprompt(generation_infos["generated_text"])
|
|
if antiprompt:
|
|
ASCIIColors.warning(f"\n{antiprompt} detected. Stopping generation")
|
|
generation_infos["generated_text"] = self.remove_text_from_string(generation_infos["generated_text"],antiprompt)
|
|
return False
|
|
else:
|
|
generation_infos["nb_received_tokens"] += 1
|
|
generation_infos["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
|
|
|
|
def remove_text_from_string(self, string, text_to_find):
|
|
"""
|
|
Removes everything from the first occurrence of the specified text in the string (case-insensitive).
|
|
|
|
Parameters:
|
|
string (str): The original string.
|
|
text_to_find (str): The text to find in the string.
|
|
|
|
Returns:
|
|
str: The updated string.
|
|
"""
|
|
index = string.lower().find(text_to_find.lower())
|
|
|
|
if index != -1:
|
|
string = string[:index]
|
|
|
|
return string
|
|
|
|
def load_binding(self):
|
|
try:
|
|
binding = BindingBuilder().build_binding(self.config, self.lollms_paths, lollmsCom=self)
|
|
return binding
|
|
except Exception as ex:
|
|
self.error("Couldn't load binding")
|
|
self.info("Trying to reinstall binding")
|
|
trace_exception(ex)
|
|
try:
|
|
binding = BindingBuilder().build_binding(self.config, self.lollms_paths,installation_option=InstallOption.FORCE_INSTALL, lollmsCom=self)
|
|
except Exception as ex:
|
|
self.error("Couldn't reinstall binding")
|
|
trace_exception(ex)
|
|
return None
|
|
|
|
|
|
def load_model(self):
|
|
try:
|
|
model = ModelBuilder(self.binding).get_model()
|
|
for personality in self.mounted_personalities:
|
|
if personality is not None:
|
|
personality.model = model
|
|
except Exception as ex:
|
|
self.error("Couldn't load model.")
|
|
ASCIIColors.error(f"Couldn't load model. Please verify your configuration file at {self.lollms_paths.personal_configuration_path} or use the next menu to select a valid model")
|
|
ASCIIColors.error(f"Binding returned this exception : {ex}")
|
|
trace_exception(ex)
|
|
ASCIIColors.error(f"{self.config.get_model_path_infos()}")
|
|
print("Please select a valid model or install a new one from a url")
|
|
model = None
|
|
|
|
return model
|
|
|
|
|
|
def mount_extension(self, id:int, callback=None):
|
|
try:
|
|
extension = ExtensionBuilder().build_extension(self.config["extensions"][id], self.lollms_paths, self)
|
|
self.mounted_extensions.append(extension)
|
|
return extension
|
|
except Exception as ex:
|
|
ASCIIColors.error(f"Couldn't load extension. Please verify your configuration file at {self.lollms_paths.personal_configuration_path} or use the next menu to select a valid personality")
|
|
trace_exception(ex)
|
|
return None
|
|
|
|
|
|
def mount_personality(self, id:int, callback=None):
|
|
try:
|
|
personality = PersonalityBuilder(self.lollms_paths, self.config, self.model, self, callback=callback).build_personality(id)
|
|
if personality.model is not None:
|
|
self.cond_tk = personality.model.tokenize(personality.personality_conditioning)
|
|
self.n_cond_tk = len(self.cond_tk)
|
|
ASCIIColors.success(f"Personality {personality.name} mounted successfully")
|
|
else:
|
|
if personality.selected_language is not None:
|
|
ASCIIColors.success(f"Personality {personality.name} : {personality.selected_language} mounted successfully but no model is selected")
|
|
else:
|
|
ASCIIColors.success(f"Personality {personality.name} mounted successfully but no model is selected")
|
|
except Exception as ex:
|
|
ASCIIColors.error(f"Couldn't load personality. Please verify your configuration file at {self.lollms_paths.personal_configuration_path} or use the next menu to select a valid personality")
|
|
ASCIIColors.error(f"Binding returned this exception : {ex}")
|
|
trace_exception(ex)
|
|
ASCIIColors.error(f"{self.config.get_personality_path_infos()}")
|
|
if id == self.config.active_personality_id:
|
|
self.config.active_personality_id=len(self.config.personalities)-1
|
|
personality = None
|
|
|
|
self.mounted_personalities.append(personality)
|
|
return personality
|
|
|
|
def mount_personalities(self, callback = None):
|
|
self.mounted_personalities = []
|
|
to_remove = []
|
|
for i in range(len(self.config["personalities"])):
|
|
p = self.mount_personality(i, callback = None)
|
|
if p is None:
|
|
to_remove.append(i)
|
|
to_remove.sort(reverse=True)
|
|
for i in to_remove:
|
|
self.unmount_personality(i)
|
|
|
|
if self.config.active_personality_id>=0 and self.config.active_personality_id<len(self.mounted_personalities):
|
|
self.personality = self.mounted_personalities[self.config.active_personality_id]
|
|
else:
|
|
self.config["personalities"].insert(0, "generic/lollms")
|
|
self.mount_personality(0, callback = None)
|
|
self.config.active_personality_id = 0
|
|
self.personality = self.mounted_personalities[self.config.active_personality_id]
|
|
|
|
def mount_extensions(self, callback = None):
|
|
self.mounted_extensions = []
|
|
to_remove = []
|
|
for i in range(len(self.config["extensions"])):
|
|
p = self.mount_extension(i, callback = None)
|
|
if p is None:
|
|
to_remove.append(i)
|
|
to_remove.sort(reverse=True)
|
|
for i in to_remove:
|
|
self.unmount_extension(i)
|
|
|
|
|
|
def set_personalities_callbacks(self, callback: Callable[[str, int, dict], bool]=None):
|
|
for personality in self.mount_personalities:
|
|
personality.setCallback(callback)
|
|
|
|
def unmount_extension(self, id:int)->bool:
|
|
if id<len(self.config.extensions):
|
|
del self.config.extensions[id]
|
|
if id>=0 and id<len(self.mounted_extensions):
|
|
del self.mounted_extensions[id]
|
|
self.config.save_config()
|
|
return True
|
|
else:
|
|
return False
|
|
|
|
|
|
def unmount_personality(self, id:int)->bool:
|
|
if id<len(self.config.personalities):
|
|
del self.config.personalities[id]
|
|
del self.mounted_personalities[id]
|
|
if self.config.active_personality_id>=id:
|
|
self.config.active_personality_id-=1
|
|
|
|
self.config.save_config()
|
|
return True
|
|
else:
|
|
return False
|
|
|
|
|
|
def select_personality(self, id:int):
|
|
if id<len(self.config.personalities):
|
|
self.config.active_personality_id = id
|
|
self.personality = self.mounted_personalities[id]
|
|
self.config.save_config()
|
|
return True
|
|
else:
|
|
return False
|
|
|
|
|
|
def load_personality(self, callback=None):
|
|
try:
|
|
personality = PersonalityBuilder(self.lollms_paths, self.config, self.model, self, callback=callback).build_personality()
|
|
except Exception as ex:
|
|
ASCIIColors.error(f"Couldn't load personality. Please verify your configuration file at {self.configuration_path} or use the next menu to select a valid personality")
|
|
ASCIIColors.error(f"Binding returned this exception : {ex}")
|
|
ASCIIColors.error(f"{self.config.get_personality_path_infos()}")
|
|
print("Please select a valid model or install a new one from a url")
|
|
personality = None
|
|
return personality
|
|
|
|
@staticmethod
|
|
def reset_paths(lollms_paths:LollmsPaths):
|
|
lollms_paths.resetPaths()
|
|
|
|
@staticmethod
|
|
def reset_all_installs(lollms_paths:LollmsPaths):
|
|
ASCIIColors.info("Removeing all configuration files to force reinstall")
|
|
ASCIIColors.info(f"Searching files from {lollms_paths.personal_configuration_path}")
|
|
for file_path in lollms_paths.personal_configuration_path.iterdir():
|
|
if file_path.name!=f"{lollms_paths.tool_prefix}local_config.yaml" and file_path.suffix.lower()==".yaml":
|
|
file_path.unlink()
|
|
ASCIIColors.info(f"Deleted file: {file_path}")
|
|
|
|
|
|
#languages:
|
|
def get_personality_languages(self):
|
|
languages = []
|
|
# Construire le chemin vers le dossier contenant les fichiers de langue pour la personnalité actuelle
|
|
languages_dir = self.lollms_paths.personal_configuration_path / "personalities" / self.personality.name
|
|
if self.personality.language:
|
|
default_language = self.personality.language.lower().strip().split()[0]
|
|
else:
|
|
default_language = "english"
|
|
# Vérifier si le dossier existe
|
|
languages_dir.mkdir(parents=True, exist_ok=True)
|
|
|
|
# Itérer sur chaque fichier YAML dans le dossier
|
|
for language_file in languages_dir.glob("languages_*.yaml"):
|
|
# Improved extraction of the language code to handle names with underscores
|
|
parts = language_file.stem.split("_")
|
|
if len(parts) > 2:
|
|
language_code = "_".join(parts[1:]) # Rejoin all parts after "languages"
|
|
else:
|
|
language_code = parts[-1]
|
|
|
|
if language_code != default_language:
|
|
languages.append(language_code)
|
|
|
|
return [default_language] + languages
|
|
|
|
|
|
|
|
def set_personality_language(self, language:str):
|
|
if language is None or language == "":
|
|
return False
|
|
language = language.lower().strip().split()[0]
|
|
# Build the conditionning text block
|
|
default_language = self.personality.language.lower().strip().split()[0]
|
|
|
|
if language!= default_language:
|
|
language_path = self.lollms_paths.personal_configuration_path/"personalities"/self.personality.name/f"languages_{language}.yaml"
|
|
if not language_path.exists():
|
|
self.ShowBlockingMessage(f"This is the first time this personality speaks {language}\nLollms is reconditionning the persona in that language.\nThis will be done just once. Next time, the personality will speak {language} out of the box")
|
|
language_path.parent.mkdir(exist_ok=True, parents=True)
|
|
# Translating
|
|
conditionning = self.tasks_library.translate_conditionning(self.personality._personality_conditioning, self.personality.language, language)
|
|
welcome_message = self.tasks_library.translate_message(self.personality.welcome_message, self.personality.language, language)
|
|
with open(language_path,"w",encoding="utf-8", errors="ignore") as f:
|
|
yaml.safe_dump({"conditionning":conditionning,"welcome_message":welcome_message}, f)
|
|
self.HideBlockingMessage()
|
|
else:
|
|
with open(language_path,"r",encoding="utf-8", errors="ignore") as f:
|
|
language_pack = yaml.safe_load(f)
|
|
conditionning = language_pack["conditionning"]
|
|
self.config.current_language=language
|
|
self.config.save_config()
|
|
return True
|
|
|
|
def del_personality_language(self, language:str):
|
|
if language is None or language == "":
|
|
return False
|
|
|
|
language = language.lower().strip().split()[0]
|
|
default_language = self.personality.language.lower().strip().split()[0]
|
|
if language == default_language:
|
|
return False # Can't remove the default language
|
|
|
|
language_path = self.lollms_paths.personal_configuration_path/"personalities"/self.personality.name/f"languages_{language}.yaml"
|
|
if language_path.exists():
|
|
try:
|
|
language_path.unlink()
|
|
except Exception as ex:
|
|
return False
|
|
if self.config.current_language==language:
|
|
self.config.current_language="english"
|
|
self.config.save_config()
|
|
return True
|
|
|
|
def recover_discussion(self,client_id, message_index=-1):
|
|
messages = self.session.get_client(client_id).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
|
|
# -------------------------------------- Prompt preparing
|
|
def prepare_query(self, client_id: str, message_id: int = -1, is_continue: bool = False, n_tokens: int = 0, generation_type = None, force_using_internet=False, previous_chunk="") -> 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.
|
|
"""
|
|
skills = []
|
|
documentation_entries = []
|
|
start_ai_header_id_template = self.config.start_ai_header_id_template
|
|
end_ai_header_id_template = self.config.end_ai_header_id_template
|
|
|
|
system_message_template = self.config.system_message_template
|
|
|
|
if self.personality.callback is None:
|
|
self.personality.callback = partial(self.process_chunk, client_id=client_id)
|
|
# Get the list of messages
|
|
client = self.session.get_client(client_id)
|
|
discussion = client.discussion
|
|
messages = 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
|
|
default_language = self.personality.language.lower().strip().split()[0]
|
|
current_language = self.config.current_language.lower().strip().split()[0]
|
|
|
|
if self.config.current_language and current_language!= default_language:
|
|
language_path = self.lollms_paths.personal_configuration_path/"personalities"/self.personality.name/f"languages_{current_language}.yaml"
|
|
if not language_path.exists():
|
|
self.info(f"This is the first time this personality speaks {current_language}\nLollms is reconditionning the persona in that language.\nThis will be done just once. Next time, the personality will speak {current_language} out of the box")
|
|
language_path.parent.mkdir(exist_ok=True, parents=True)
|
|
# Translating
|
|
conditionning = self.tasks_library.translate_conditionning(self.personality._personality_conditioning, self.personality.language, current_language)
|
|
welcome_message = self.tasks_library.translate_message(self.personality.welcome_message, self.personality.language, current_language)
|
|
with open(language_path,"w",encoding="utf-8", errors="ignore") as f:
|
|
yaml.safe_dump({"conditionning":conditionning,"welcome_message":welcome_message}, f)
|
|
else:
|
|
with open(language_path,"r",encoding="utf-8", errors="ignore") as f:
|
|
language_pack = yaml.safe_load(f)
|
|
conditionning = language_pack["conditionning"]
|
|
else:
|
|
conditionning = self.personality._personality_conditioning
|
|
|
|
if len(conditionning)>0:
|
|
conditionning = self.start_header_id_template + system_message_template + self.end_header_id_template + self.personality.replace_keys(conditionning, self.personality.conditionning_commands) + ("" if conditionning[-1]==self.separator_template else self.separator_template)
|
|
|
|
# Check if there are document files to add to the prompt
|
|
internet_search_results = ""
|
|
internet_search_infos = []
|
|
documentation = ""
|
|
knowledge = ""
|
|
knowledge_infos = {"titles":[],"contents":[]}
|
|
|
|
|
|
# boosting information
|
|
if self.config.positive_boost:
|
|
positive_boost=f"{self.separator_template}{self.start_header_id_template}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=f"{self.separator_template}{self.start_header_id_template}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.fun_mode:
|
|
fun_mode=f"{self.separator_template}{self.start_header_id_template}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
|
|
|
|
discussion = None
|
|
if generation_type != "simple_question":
|
|
|
|
if self.config.activate_internet_search or force_using_internet or generation_type == "full_context_with_internet":
|
|
if discussion is None:
|
|
discussion = self.recover_discussion(client_id)
|
|
if self.config.internet_activate_search_decision:
|
|
self.personality.step_start(f"Requesting if {self.personality.name} needs to search internet to answer the user")
|
|
q = f"{self.separator_template}".join([
|
|
f"{self.start_header_id_template}{system_message_template}{self.end_header_id_template}",
|
|
f"Answer the question with yes or no. Don't add any extra explanation.",
|
|
f"{self.start_user_header_id_template}user{self.end_user_header_id_template}",
|
|
f"Do you have enough information to give a satisfactory answer to {self.config.user_name}'s request without internet search?",
|
|
"(If you do not know or you can't answer the question, return 0 (no)"
|
|
])
|
|
need = not self.personality.yes_no(q, discussion)
|
|
self.personality.step_end(f"Requesting if {self.personality.name} needs to search internet to answer the user")
|
|
self.personality.step("Yes" if need else "No")
|
|
else:
|
|
need=True
|
|
if need:
|
|
self.personality.step_start("Crafting internet search query")
|
|
q = f"{self.separator_template}".join([
|
|
f"{self.start_header_id_template}discussion{self.end_header_id_template}",
|
|
f"{discussion[-2048:]}{self.start_header_id_template}system{self.end_header_id_template}",
|
|
f"Read the discussion and craft a web search query suited to recover needed information to reply to last {self.config.user_name} message.",
|
|
f"Do not answer the prompt. Do not add explanations.",
|
|
f"{self.start_header_id_template}current date{self.end_header_id_template}{datetime.now()}",
|
|
f"{self.start_header_id_template}websearch query{self.end_header_id_template}"
|
|
])
|
|
query = self.personality.fast_gen(q, max_generation_size=256, show_progress=True, callback=self.personality.sink)
|
|
query = query.replace("\"","")
|
|
self.personality.step_end("Crafting internet search query")
|
|
self.personality.step(f"web search query: {query}")
|
|
|
|
if self.config.internet_quick_search:
|
|
self.personality.step_start("Performing Internet search (quick mode)")
|
|
else:
|
|
self.personality.step_start("Performing Internet search (advanced mode: slower but more advanced)")
|
|
|
|
internet_search_results=f"{self.system_full_header}Use the web search results data to answer {self.config.user_name}. Try to extract information from the web search and use it to perform the requested task or answer the question. Do not come up with information that is not in the websearch results. Try to stick to the websearch results and clarify if your answer was based on the resuts or on your own culture. If you don't know how to perform the task, then tell the user politely that you need more data inputs.{self.separator_template}{self.start_header_id_template}Web search results{self.end_header_id_template}\n"
|
|
|
|
chunks:List[Chunk] = self.personality.internet_search_with_vectorization(query, self.config.internet_quick_search, asses_using_llm=self.config.activate_internet_pages_judgement)
|
|
|
|
if len(chunks)>0:
|
|
for chunk in chunks:
|
|
internet_search_infos.append({
|
|
"title":chunk.doc.title,
|
|
"url":chunk.doc.path,
|
|
"brief":chunk.text
|
|
})
|
|
internet_search_results += self.system_custom_header("search result chunk")+f"\nchunk_infos:{chunk.doc.path}\nchunk_title:{chunk.doc.title}\ncontent:{chunk.text}\n"
|
|
else:
|
|
internet_search_results += "The search response was empty!\nFailed to recover useful information from the search engine.\n"
|
|
internet_search_results += self.system_custom_header("information") + "Use the search results to answer the user question."
|
|
if self.config.internet_quick_search:
|
|
self.personality.step_end("Performing Internet search (quick mode)")
|
|
else:
|
|
self.personality.step_end("Performing Internet search (advanced mode: slower but more advanced)")
|
|
|
|
if self.personality.persona_data_vectorizer:
|
|
if documentation=="":
|
|
documentation=f"{self.separator_template}{self.start_header_id_template}Documentation:\n"
|
|
|
|
if self.config.data_vectorization_build_keys_words:
|
|
if discussion is None:
|
|
discussion = self.recover_discussion(client_id)
|
|
query = self.personality.fast_gen(f"{self.separator_template}{self.start_header_id_template}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.{self.separator_template}{self.start_header_id_template}discussion:\n{discussion[-2048:]}{self.separator_template}{self.start_header_id_template}enhanced query: ", max_generation_size=256, show_progress=True)
|
|
ASCIIColors.cyan(f"Query:{query}")
|
|
else:
|
|
query = current_message.content
|
|
try:
|
|
chunks:List[Chunk] = self.personality.persona_data_vectorizer.search(query, int(self.config.rag_n_chunks))
|
|
for chunk in chunks:
|
|
if self.config.data_vectorization_put_chunk_informations_into_context:
|
|
documentation += f"{self.start_header_id_template}document chunk{self.end_header_id_template}\ndocument title: {chunk.doc.title}\nchunk content:\n{chunk.text}\n"
|
|
else:
|
|
documentation += f"{self.start_header_id_template}chunk{self.end_header_id_template}\n{chunk.text}\n"
|
|
|
|
documentation += f"{self.separator_template}{self.start_header_id_template}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.\n"
|
|
|
|
except Exception as ex:
|
|
trace_exception(ex)
|
|
self.warning("Couldn't add documentation to the context. Please verify the vector database")
|
|
if not self.personality.ignore_discussion_documents_rag:
|
|
query = None
|
|
if len(self.active_rag_dbs) > 0 :
|
|
if discussion is None:
|
|
discussion = self.recover_discussion(client_id)
|
|
|
|
if self.config.data_vectorization_build_keys_words:
|
|
self.personality.step_start("Building vector store query")
|
|
q = f"{self.separator_template}".join([
|
|
f"{self.start_header_id_template}instruction{self.end_header_id_template}Read the entire discussion and rewrite the last prompt for someone who hasn't read the discussion.",
|
|
"Do not answer the prompt. Do not provide any explanations.",
|
|
f"{self.start_header_id_template}discussion{self.end_header_id_template}",
|
|
f"{discussion[-2048:]}",
|
|
f"{self.start_header_id_template}enhanced_query{self.end_header_id_template}"
|
|
])
|
|
query = self.personality.fast_gen(q, max_generation_size=256, show_progress=True, callback=self.personality.sink)
|
|
self.personality.step_end("Building vector store query")
|
|
ASCIIColors.magenta(f"Query: {query}")
|
|
self.personality.step(f"Query: {query}")
|
|
else:
|
|
query = current_message.content
|
|
if documentation=="":
|
|
documentation=f"{self.separator_template}".join([
|
|
f"{self.separator_template}{self.start_header_id_template}important information{self.end_header_id_template}",
|
|
"Always refer to the provided documentation to answer user questions accurately.",
|
|
"Absence of Information: If the required information is not available in the documentation, inform the user that the requested information is not present in the documentation section.",
|
|
"Strict Adherence to Documentation: It is strictly prohibited to provide answers without concrete evidence from the documentation.",
|
|
"Cite Your Sources: After providing an answer, include the full path to the document where the information was found.",
|
|
self.system_custom_header("Documentation")])
|
|
documentation += f"{self.separator_template}"
|
|
full_documentation=""
|
|
if self.config.contextual_summary:
|
|
for db in self.active_rag_dbs:
|
|
v:VectorDatabase = db["vectorizer"]
|
|
docs = v.list_documents()
|
|
for doc in docs:
|
|
document=v.get_document(document_path = doc["path"])
|
|
self.personality.step_start(f"Summaryzing document {doc['path']}")
|
|
def post_process(summary):
|
|
return summary
|
|
summary = self.personality.summarize_text(document,
|
|
f"Extract information from the following text chunk to answer this request.\n{self.system_custom_header('query')}{query}", chunk_summary_post_processing=post_process, callback=self.personality.sink)
|
|
self.personality.step_end(f"Summaryzing document {doc['path']}")
|
|
document_infos = f"{self.separator_template}".join([
|
|
self.system_custom_header('document contextual summary'),
|
|
f"source_document_title:{doc['title']}",
|
|
f"source_document_path:{doc['path']}",
|
|
f"content:\n{summary}\n"
|
|
])
|
|
documentation_entries.append({
|
|
"document_title":doc['title'],
|
|
"document_path":doc['path'],
|
|
"chunk_content":summary,
|
|
"chunk_size":0,
|
|
"distance":0,
|
|
})
|
|
if summary!="":
|
|
v.add_summaries(doc['path'],[{"context":query, "summary":summary}])
|
|
full_documentation += document_infos
|
|
documentation += self.personality.summarize_text(full_documentation, f"Extract information from the current text chunk and previous text chunks to answer the query. If there is no information about the query, just return an empty string.\n{self.system_custom_header('query')}{query}", callback=self.personality.sink)
|
|
|
|
else:
|
|
results = []
|
|
recovered_ids=[[] for _ in range(len(self.active_rag_dbs))]
|
|
hop_id = 0
|
|
while( len(results)<self.config.rag_n_chunks and hop_id<self.config.rag_max_n_hops):
|
|
i=0
|
|
hop_id +=1
|
|
for db in self.active_rag_dbs:
|
|
v = db["vectorizer"]
|
|
r=v.search(query, self.config.rag_n_chunks, recovered_ids[i])
|
|
recovered_ids[i]+=[rg.chunk_id for rg in r]
|
|
if self.config.rag_activate_multi_hops:
|
|
r = [rg for rg in r if self.personality.verify_rag_entry(query, rg.text)]
|
|
results+=r
|
|
i+=1
|
|
if len(results)>=self.config.rag_n_chunks:
|
|
break
|
|
n_neighbors = self.active_rag_dbs[0]["vectorizer"].n_neighbors
|
|
sorted_results = sorted(results, key=lambda x: x.distance)[:n_neighbors]
|
|
|
|
for chunk in sorted_results:
|
|
document_infos = f"{self.separator_template}".join([
|
|
f"{self.start_header_id_template}document chunk{self.end_header_id_template}",
|
|
f"source_document_title:{chunk.doc.title}",
|
|
f"source_document_path:{chunk.doc.path}",
|
|
f"content:\n{chunk.text}\n"
|
|
])
|
|
documentation_entries.append({
|
|
"document_title":chunk.doc.title,
|
|
"document_path":chunk.doc.path,
|
|
"chunk_content":chunk.text,
|
|
"chunk_size":chunk.nb_tokens,
|
|
"distance":chunk.distance,
|
|
})
|
|
documentation += document_infos
|
|
|
|
if (len(client.discussion.text_files) > 0) and client.discussion.vectorizer is not None:
|
|
if discussion is None:
|
|
discussion = self.recover_discussion(client_id)
|
|
|
|
if documentation=="":
|
|
documentation=f"{self.separator_template}".join([
|
|
f"{self.separator_template}{self.start_header_id_template}important information{self.end_header_id_template}Utilize Documentation Data: Always refer to the provided documentation to answer user questions accurately.",
|
|
"Absence of Information: If the required information is not available in the documentation, inform the user that the requested information is not present in the documentation section.",
|
|
"Strict Adherence to Documentation: It is strictly prohibited to provide answers without concrete evidence from the documentation.",
|
|
"Cite Your Sources: After providing an answer, include the full path to the document where the information was found.",
|
|
f"{self.start_header_id_template}Documentation{self.end_header_id_template}"])
|
|
documentation += f"{self.separator_template}"
|
|
|
|
if query is None:
|
|
if self.config.data_vectorization_build_keys_words:
|
|
self.personality.step_start("Building vector store query")
|
|
query = self.personality.fast_gen(f"{self.separator_template}{self.start_header_id_template}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.{self.separator_template}{self.start_header_id_template}discussion:\n{discussion[-2048:]}{self.separator_template}{self.start_header_id_template}enhanced query: ", max_generation_size=256, show_progress=True, callback=self.personality.sink)
|
|
self.personality.step_end("Building vector store query")
|
|
ASCIIColors.cyan(f"Query: {query}")
|
|
else:
|
|
query = current_message.content
|
|
|
|
|
|
full_documentation=""
|
|
if self.config.contextual_summary:
|
|
v = client.discussion.vectorizer
|
|
docs = v.list_documents()
|
|
for doc in docs:
|
|
document=v.get_document(document_path = doc["path"])
|
|
self.personality.step_start(f"Summeryzing document {doc['path']}")
|
|
summary = self.personality.summarize_text(document, f"Extract information from the following text chunk to answer this request. If there is no information about the query, just return an empty string.\n{self.system_custom_header('query')}{query}", callback=self.personality.sink)
|
|
self.personality.step_end(f"Summeryzing document {doc['path']}")
|
|
document_infos = f"{self.separator_template}".join([
|
|
self.system_custom_header('document contextual summary'),
|
|
f"source_document_title:{doc['title']}",
|
|
f"source_document_path:{doc['path']}",
|
|
f"content:\n{summary}\n"
|
|
])
|
|
documentation_entries.append({
|
|
"document_title":doc['title'],
|
|
"document_path":doc['path'],
|
|
"chunk_content":summary,
|
|
"chunk_size":0,
|
|
"distance":0,
|
|
})
|
|
if summary!="":
|
|
v.add_summaries(doc['path'],[{"context":query, "summary":summary}])
|
|
full_documentation += document_infos
|
|
documentation += self.personality.summarize_text(full_documentation, f"Extract information from the current text chunk and previous text chunks to answer the query. If there is no information about the query, just return an empty string.\n{self.system_custom_header('query')}{query}", callback=self.personality.sink)
|
|
else:
|
|
try:
|
|
chunks:List[Chunk] = client.discussion.vectorizer.search(query, int(self.config.rag_n_chunks))
|
|
for chunk in chunks:
|
|
if self.config.data_vectorization_put_chunk_informations_into_context:
|
|
documentation += f"{self.start_header_id_template}document chunk{self.end_header_id_template}\ndocument title: {chunk.doc.title}\nchunk content:\n{chunk.text}\n"
|
|
else:
|
|
documentation += f"{self.start_header_id_template}chunk{self.end_header_id_template}\n{chunk.text}\n"
|
|
|
|
documentation += f"{self.separator_template}{self.start_header_id_template}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.\n"
|
|
except Exception as ex:
|
|
trace_exception(ex)
|
|
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.activate_skills_lib:
|
|
try:
|
|
self.personality.step_start("Querying skills library")
|
|
if discussion is None:
|
|
discussion = self.recover_discussion(client_id)
|
|
self.personality.step_start("Building query")
|
|
query = self.personality.fast_gen(f"{self.start_header_id_template}{system_message_template}{self.end_header_id_template}Your task is to carefully read the provided discussion and reformulate {self.config.user_name}'s request concisely. Return only the reformulated request without any additional explanations, commentary, or output.{self.separator_template}{self.start_header_id_template}discussion:\n{discussion[-2048:]}{self.separator_template}{self.start_header_id_template}search query: ", max_generation_size=256, show_progress=True, callback=self.personality.sink)
|
|
self.personality.step_end("Building query")
|
|
self.personality.step(f"query: {query}")
|
|
# skills = self.skills_library.query_entry(query)
|
|
self.personality.step_start("Adding skills")
|
|
if self.config.debug:
|
|
ASCIIColors.info(f"Query : {query}")
|
|
skill_titles, skills = self.skills_library.query_vector_db(query, top_k=3, max_dist=1000)#query_entry_fts(query)
|
|
knowledge_infos={"titles":skill_titles,"contents":skills}
|
|
if len(skills)>0:
|
|
if knowledge=="":
|
|
knowledge=f"{self.system_custom_header(knowledge)}\n"
|
|
for i,(title, content) in enumerate(zip(skill_titles,skills)):
|
|
knowledge += self.system_custom_header(f"knowledge {i}") +f"\ntitle:\n{title}\ncontent:\n{content}\n"
|
|
self.personality.step_end("Adding skills")
|
|
self.personality.step_end("Querying skills library")
|
|
except Exception as ex:
|
|
ASCIIColors.error(ex)
|
|
self.warning("Couldn't add long term memory information to the context. Please verify the vector database") # Add information about the user
|
|
self.personality.step_end("Adding skills")
|
|
self.personality.step_end("Querying skills library",False)
|
|
user_description=""
|
|
if self.config.use_user_informations_in_discussion:
|
|
user_description=f"{self.start_header_id_template}User description{self.end_header_id_template}\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 internet search results text and calculate its number of tokens
|
|
if len(internet_search_results)>0:
|
|
tokens_internet_search_results = self.model.tokenize(internet_search_results)
|
|
n_isearch_tk = len(tokens_internet_search_results)
|
|
else:
|
|
tokens_internet_search_results = []
|
|
n_isearch_tk = 0
|
|
|
|
|
|
# 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_isearch_tk + n_doc_tk + n_history_tk + n_user_description_tk + n_positive_boost + n_negative_boost + 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}\nn_isearch_tk:{n_isearch_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:
|
|
ASCIIColors.red(f"available_space:{available_space}")
|
|
ASCIIColors.red(f"n_doc_tk:{n_doc_tk}")
|
|
|
|
ASCIIColors.red(f"n_history_tk:{n_history_tk}")
|
|
ASCIIColors.red(f"n_isearch_tk:{n_isearch_tk}")
|
|
|
|
ASCIIColors.red(f"n_tokens:{n_tokens}")
|
|
ASCIIColors.red(f"self.config.max_n_predict:{self.config.max_n_predict}")
|
|
self.InfoMessage(f"Not enough space in context!!\nVerify that your vectorization settings for documents or internet search are realistic compared to your context size.\nYou are {available_space} short of 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(
|
|
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
|
|
if self.config.use_model_name_in_discussions:
|
|
if message.model:
|
|
msg = f"{self.separator_template}" + f"{start_ai_header_id_template if message.sender_type == SENDER_TYPES.SENDER_TYPES_AI else self.start_user_header_id_template}{message.sender}({message.model}){end_ai_header_id_template if message.sender_type == SENDER_TYPES.SENDER_TYPES_AI else self.end_user_header_id_template}" + message.content.strip()
|
|
else:
|
|
msg = f"{self.separator_template}" + f"{start_ai_header_id_template if message.sender_type == SENDER_TYPES.SENDER_TYPES_AI else self.start_user_header_id_template}{message.sender}{end_ai_header_id_template if message.sender_type == SENDER_TYPES.SENDER_TYPES_AI else self.end_user_header_id_template}" + message.content.strip()
|
|
message_tokenized = self.model.tokenize(msg)
|
|
else:
|
|
msg_value= f"{self.separator_template}" + f"{start_ai_header_id_template if message.sender_type == SENDER_TYPES.SENDER_TYPES_AI else self.start_user_header_id_template}{message.sender}{end_ai_header_id_template if message.sender_type == SENDER_TYPES.SENDER_TYPES_AI else self.end_user_header_id_template}" + message.content.strip()
|
|
message_tokenized = self.model.tokenize(
|
|
msg_value
|
|
)
|
|
# Check if adding the message will exceed the available space
|
|
if tokens_accumulated + len(message_tokenized) > available_space:
|
|
# Update the cumulative number of tokens
|
|
msg = message_tokenized[-(available_space-tokens_accumulated):]
|
|
tokens_accumulated += available_space-tokens_accumulated
|
|
full_message_list.insert(0, msg)
|
|
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):
|
|
|
|
if self.config.use_model_name_in_discussions:
|
|
if message.model:
|
|
msg = f"{self.separator_template}{start_ai_header_id_template if message.sender_type == SENDER_TYPES.SENDER_TYPES_AI else self.start_user_header_id_template}{message.sender}({message.model}){end_ai_header_id_template if message.sender_type == SENDER_TYPES.SENDER_TYPES_AI else self.end_user_header_id_template}" + message.content.strip()
|
|
else:
|
|
msg = f"{self.separator_template}{start_ai_header_id_template if message.sender_type == SENDER_TYPES.SENDER_TYPES_AI else self.start_user_header_id_template}{message.sender}{end_ai_header_id_template if message.sender_type == SENDER_TYPES.SENDER_TYPES_AI else self.end_user_header_id_template}" + message.content.strip()
|
|
message_tokenized = self.model.tokenize(msg)
|
|
else:
|
|
message_tokenized = self.model.tokenize(
|
|
f"{self.separator_template}{start_ai_header_id_template if message.sender_type == SENDER_TYPES.SENDER_TYPES_AI else self.start_user_header_id_template}{message.sender}{end_ai_header_id_template if message.sender_type == SENDER_TYPES.SENDER_TYPES_AI else self.end_user_header_id_template}" + 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 if not is_continue else len(full_message_list)):
|
|
message_tokens = full_message_list[i]
|
|
discussion_messages += self.model.detokenize(message_tokens)
|
|
|
|
if len(full_message_list)>0:
|
|
ai_prefix = self.personality.ai_message_prefix
|
|
else:
|
|
ai_prefix = ""
|
|
# Build the final prompt by concatenating the conditionning and discussion messages
|
|
prompt_data = conditionning + internet_search_results + documentation + knowledge + user_description + discussion_messages + positive_boost + negative_boost + fun_mode + (self.separator_template + start_ai_header_id_template + ai_prefix + end_ai_header_id_template if not is_continue else '' if not self.config.use_continue_message else end_ai_header_id_template + "CONTINUE FROM HERE And do not open a new markdown code tag" + self.separator_template + start_ai_header_id_template + ai_prefix + end_ai_header_id_template)
|
|
|
|
# Tokenize the prompt data
|
|
tokens = self.model.tokenize(prompt_data)
|
|
|
|
# Details
|
|
context_details = {
|
|
"client_id":client_id,
|
|
"conditionning":conditionning,
|
|
"internet_search_infos":internet_search_infos,
|
|
"internet_search_results":internet_search_results,
|
|
"documentation":documentation,
|
|
"documentation_entries":documentation_entries,
|
|
"knowledge":knowledge,
|
|
"knowledge_infos":knowledge_infos,
|
|
"user_description":user_description,
|
|
"discussion_messages":discussion_messages,
|
|
"positive_boost":positive_boost,
|
|
"negative_boost":negative_boost,
|
|
"current_language":self.config.current_language,
|
|
"fun_mode":fun_mode,
|
|
"ai_prefix":ai_prefix,
|
|
"extra":"",
|
|
"available_space":available_space,
|
|
"skills":skills,
|
|
"is_continue":is_continue,
|
|
"previous_chunk":previous_chunk
|
|
}
|
|
if self.config.debug:
|
|
ASCIIColors.highlight(documentation,"source_document_title", ASCIIColors.color_yellow, ASCIIColors.color_red, False)
|
|
# Return the prepared query, original message content, and tokenized query
|
|
return prompt_data, current_message.content, tokens, context_details, internet_search_infos
|
|
|
|
|
|
# Properties ===============================================
|
|
@property
|
|
def start_header_id_template(self) -> str:
|
|
"""Get the start_header_id_template."""
|
|
return self.config.start_header_id_template
|
|
|
|
@property
|
|
def end_header_id_template(self) -> str:
|
|
"""Get the end_header_id_template."""
|
|
return self.config.end_header_id_template
|
|
|
|
@property
|
|
def system_message_template(self) -> str:
|
|
"""Get the system_message_template."""
|
|
return self.config.system_message_template
|
|
|
|
|
|
@property
|
|
def separator_template(self) -> str:
|
|
"""Get the separator template."""
|
|
return self.config.separator_template
|
|
|
|
|
|
@property
|
|
def start_user_header_id_template(self) -> str:
|
|
"""Get the start_user_header_id_template."""
|
|
return self.config.start_user_header_id_template
|
|
@property
|
|
def end_user_header_id_template(self) -> str:
|
|
"""Get the end_user_header_id_template."""
|
|
return self.config.end_user_header_id_template
|
|
@property
|
|
def end_user_message_id_template(self) -> str:
|
|
"""Get the end_user_message_id_template."""
|
|
return self.config.end_user_message_id_template
|
|
|
|
|
|
|
|
|
|
# Properties ===============================================
|
|
@property
|
|
def start_header_id_template(self) -> str:
|
|
"""Get the start_header_id_template."""
|
|
return self.config.start_header_id_template
|
|
|
|
@property
|
|
def end_header_id_template(self) -> str:
|
|
"""Get the end_header_id_template."""
|
|
return self.config.end_header_id_template
|
|
|
|
@property
|
|
def system_message_template(self) -> str:
|
|
"""Get the system_message_template."""
|
|
return self.config.system_message_template
|
|
|
|
|
|
@property
|
|
def separator_template(self) -> str:
|
|
"""Get the separator template."""
|
|
return self.config.separator_template
|
|
|
|
|
|
@property
|
|
def start_user_header_id_template(self) -> str:
|
|
"""Get the start_user_header_id_template."""
|
|
return self.config.start_user_header_id_template
|
|
@property
|
|
def end_user_header_id_template(self) -> str:
|
|
"""Get the end_user_header_id_template."""
|
|
return self.config.end_user_header_id_template
|
|
@property
|
|
def end_user_message_id_template(self) -> str:
|
|
"""Get the end_user_message_id_template."""
|
|
return self.config.end_user_message_id_template
|
|
|
|
|
|
|
|
|
|
@property
|
|
def start_ai_header_id_template(self) -> str:
|
|
"""Get the start_ai_header_id_template."""
|
|
return self.config.start_ai_header_id_template
|
|
@property
|
|
def end_ai_header_id_template(self) -> str:
|
|
"""Get the end_ai_header_id_template."""
|
|
return self.config.end_ai_header_id_template
|
|
@property
|
|
def end_ai_message_id_template(self) -> str:
|
|
"""Get the end_ai_message_id_template."""
|
|
return self.config.end_ai_message_id_template
|
|
@property
|
|
def system_full_header(self) -> str:
|
|
"""Get the start_header_id_template."""
|
|
return f"{self.start_header_id_template}{self.system_message_template}{self.end_header_id_template}"
|
|
@property
|
|
def user_full_header(self) -> str:
|
|
"""Get the start_header_id_template."""
|
|
return f"{self.start_user_header_id_template}{self.config.user_name}{self.end_user_header_id_template}"
|
|
@property
|
|
def ai_full_header(self) -> str:
|
|
"""Get the start_header_id_template."""
|
|
return f"{self.start_user_header_id_template}{self.personality.name}{self.end_user_header_id_template}"
|
|
|
|
def system_custom_header(self, ai_name) -> str:
|
|
"""Get the start_header_id_template."""
|
|
return f"{self.start_user_header_id_template}{ai_name}{self.end_user_header_id_template}"
|
|
|
|
def ai_custom_header(self, ai_name) -> str:
|
|
"""Get the start_header_id_template."""
|
|
return f"{self.start_user_header_id_template}{ai_name}{self.end_user_header_id_template}"
|
|
|