mirror of
https://github.com/ParisNeo/lollms-webui.git
synced 2024-12-19 20:37:51 +00:00
Merge branch 'main' into dev
This commit is contained in:
AndzejsP
commit
8f90bb5038
120
api/__init__.py
120
api/__init__.py
@ -28,6 +28,7 @@ import traceback
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import sys
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from lollms.console import MainMenu
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import urllib
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import traceback
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__author__ = "parisneo"
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__github__ = "https://github.com/ParisNeo/lollms-webui"
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@ -444,6 +445,19 @@ class LoLLMsAPPI():
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ASCIIColors.green(f"{self.lollms_paths.personal_path}")
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@socketio.on('continue_generate_msg_from')
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def handle_connection(data):
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message_id = int(data['id'])
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message = data["prompt"]
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self.current_user_message_id = message_id
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tpe = threading.Thread(target=self.start_message_generation, args=(message, message_id))
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tpe.start()
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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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def rebuild_personalities(self):
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loaded = self.mounted_personalities
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loaded_names = [f"{p.language}/{p.category}/{p.personality_folder_name}" for p in loaded]
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@ -645,7 +659,7 @@ class LoLLMsAPPI():
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self.condition_chatbot()
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return timestamp
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def prepare_query(self, message_id=-1):
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def prepare_query(self, message_id=-1, is_continue=False):
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messages = self.current_discussion.get_messages()
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self.full_message_list = []
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for message in messages:
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@ -658,17 +672,20 @@ class LoLLMsAPPI():
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else:
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break
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if self.personality.processor is not None:
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preprocessed_prompt = self.personality.processor.process_model_input(message["content"])
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else:
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preprocessed_prompt = message["content"]
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if preprocessed_prompt is not None:
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self.full_message_list.append(self.personality.user_message_prefix+preprocessed_prompt+self.personality.link_text+self.personality.ai_message_prefix)
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else:
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self.full_message_list.append(self.personality.user_message_prefix+message["content"]+self.personality.link_text+self.personality.ai_message_prefix)
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link_text = self.personality.link_text
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if not is_continue:
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if self.personality.processor is not None:
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preprocessed_prompt = self.personality.processor.process_model_input(message["content"])
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else:
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preprocessed_prompt = message["content"]
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if preprocessed_prompt is not None:
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self.full_message_list.append(self.personality.user_message_prefix+preprocessed_prompt+self.personality.link_text+self.personality.ai_message_prefix)
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else:
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self.full_message_list.append(self.personality.user_message_prefix+message["content"]+self.personality.link_text+self.personality.ai_message_prefix)
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else:
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self.full_message_list.append(self.personality.ai_message_prefix+message["content"])
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discussion_messages = self.personality.personality_conditioning+ link_text.join(self.full_message_list)
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@ -719,6 +736,8 @@ class LoLLMsAPPI():
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1 : a notification message
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2 : A hidden message
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"""
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if message_type == MSG_TYPE.MSG_TYPE_STEP:
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ASCIIColors.info("--> Step:"+chunk)
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if message_type == MSG_TYPE.MSG_TYPE_STEP_START:
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ASCIIColors.info("--> Step started:"+chunk)
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if message_type == MSG_TYPE.MSG_TYPE_STEP_END:
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@ -797,7 +816,12 @@ class LoLLMsAPPI():
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output = self.personality.processor.run_workflow( prompt, full_prompt, self.process_chunk)
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self.process_chunk(output, MSG_TYPE.MSG_TYPE_FULL)
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except Exception as ex:
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# Catch the exception and get the traceback as a list of strings
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traceback_lines = traceback.format_exception(type(ex), ex, ex.__traceback__)
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# Join the traceback lines into a single string
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traceback_text = ''.join(traceback_lines)
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ASCIIColors.error(f"Workflow run failed.\nError:{ex}")
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ASCIIColors.error(traceback_text)
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self.process_chunk(f"Workflow run failed\nError:{ex}", MSG_TYPE.MSG_TYPE_EXCEPTION)
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print("Finished executing the workflow")
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return
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@ -843,41 +867,44 @@ class LoLLMsAPPI():
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output = ""
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return output
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def start_message_generation(self, message, message_id):
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def start_message_generation(self, message, message_id, is_continue=False):
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ASCIIColors.info(f"Text generation requested by client: {self.current_room_id}")
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# send the message to the bot
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print(f"Received message : {message}")
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if self.current_discussion:
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# First we need to send the new message ID to the client
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self.current_ai_message_id = self.current_discussion.add_message(
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self.personality.name,
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"",
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parent = self.current_user_message_id,
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binding = self.config["binding_name"],
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model = self.config["model_name"],
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personality = self.config["personalities"][self.config["active_personality_id"]]
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) # first the content is empty, but we'll fill it at the end
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self.socketio.emit('infos',
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{
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"status":'generation_started',
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"type": "input_message_infos",
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"bot": self.personality.name,
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"user": self.personality.user_name,
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"message":message,#markdown.markdown(message),
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"user_message_id": self.current_user_message_id,
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"ai_message_id": self.current_ai_message_id,
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if is_continue:
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self.current_ai_message_id = message_id
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else:
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self.current_ai_message_id = self.current_discussion.add_message(
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self.personality.name,
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"",
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parent = self.current_user_message_id,
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binding = self.config["binding_name"],
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model = self.config["model_name"],
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personality = self.config["personalities"][self.config["active_personality_id"]]
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) # first the content is empty, but we'll fill it at the end
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self.socketio.emit('infos',
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{
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"status":'generation_started',
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"type": "input_message_infos",
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"bot": self.personality.name,
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"user": self.personality.user_name,
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"message":message,#markdown.markdown(message),
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"user_message_id": self.current_user_message_id,
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"ai_message_id": self.current_ai_message_id,
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'binding': self.current_discussion.current_message_binding,
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'model': self.current_discussion.current_message_model,
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'personality': self.current_discussion.current_message_personality,
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'created_at': self.current_discussion.current_message_created_at,
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'finished_generating_at': self.current_discussion.current_message_finished_generating_at,
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}, room=self.current_room_id
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'binding': self.current_discussion.current_message_binding,
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'model': self.current_discussion.current_message_model,
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'personality': self.current_discussion.current_message_personality,
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'created_at': self.current_discussion.current_message_created_at,
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'finished_generating_at': self.current_discussion.current_message_finished_generating_at,
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}, room=self.current_room_id
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)
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self.socketio.sleep(0)
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# prepare query and reception
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self.discussion_messages, self.current_message = self.prepare_query(message_id)
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self.discussion_messages, self.current_message = self.prepare_query(message_id, is_continue)
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self.prepare_reception()
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self.generating = True
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self.generate(self.discussion_messages, self.current_message, n_predict = self.config['n_predict'], callback=self.process_chunk)
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@ -913,14 +940,21 @@ class LoLLMsAPPI():
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)
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self.socketio.sleep(0)
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print()
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print("## Done ##")
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print()
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ASCIIColors.success(f" ╔══════════════════════════════════════════════════╗ ")
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ASCIIColors.success(f" ║ Done ║ ")
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ASCIIColors.success(f" ╚══════════════════════════════════════════════════╝ ")
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else:
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#No discussion available
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print("No discussion selected!!!")
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print("## Done ##")
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print()
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self.cancel_gen = False
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#No discussion available
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ASCIIColors.warning("No discussion selected!!!")
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self.socketio.emit('message', {
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'data': "No discussion selected!!!",
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'user_message_id':self.current_user_message_id,
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'ai_message_id':self.current_ai_message_id,
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'discussion_id':0,
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'message_type': MSG_TYPE.MSG_TYPE_EXCEPTION.value
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}, room=self.current_room_id
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)
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print()
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return ""
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@ -1,88 +0,0 @@
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######
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# Project : lollms-webui
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# File : binding.py
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# Author : ParisNeo with the help of the community
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# Supported by Nomic-AI
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# license : Apache 2.0
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# Description :
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# This is an interface class for lollms-webui bindings.
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######
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from pathlib import Path
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from typing import Callable
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import inspect
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import yaml
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import sys
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__author__ = "parisneo"
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__github__ = "https://github.com/ParisNeo/lollms-webui"
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__copyright__ = "Copyright 2023, "
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__license__ = "Apache 2.0"
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class LLMBinding:
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file_extension='*.bin'
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binding_path = Path(__file__).parent
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def __init__(self, config:dict, inline:bool) -> None:
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self.config = config
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self.inline = inline
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def generate(self,
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prompt:str,
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n_predict: int = 128,
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new_text_callback: Callable[[str], None] = None,
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verbose: bool = False,
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**gpt_params ):
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"""Generates text out of a prompt
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This should ber implemented by child class
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Args:
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prompt (str): The prompt to use for generation
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n_predict (int, optional): Number of tokens to prodict. Defaults to 128.
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new_text_callback (Callable[[str], None], optional): A callback function that is called everytime a new text element is generated. Defaults to None.
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verbose (bool, optional): If true, the code will spit many informations about the generation process. Defaults to False.
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"""
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pass
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def tokenize(self, prompt):
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"""
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Tokenizes the given prompt using the model's tokenizer.
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Args:
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prompt (str): The input prompt to be tokenized.
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Returns:
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list: A list of tokens representing the tokenized prompt.
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"""
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pass
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def detokenize(self, tokens_list):
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"""
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Detokenizes the given list of tokens using the model's tokenizer.
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Args:
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tokens_list (list): A list of tokens to be detokenized.
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Returns:
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str: The detokenized text as a string.
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"""
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pass
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@staticmethod
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def list_models(config:dict):
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"""Lists the models for this binding
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"""
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models_dir = Path('./models')/config["binding_name"] # replace with the actual path to the models folder
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return [f.name for f in models_dir.glob(LLMBinding.file_extension)]
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@staticmethod
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def get_available_models():
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# Create the file path relative to the child class's directory
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binding_path = Path(__file__).parent
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file_path = binding_path/"models.yaml"
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with open(file_path, 'r') as file:
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yaml_data = yaml.safe_load(file)
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return yaml_data
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@ -1,84 +0,0 @@
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try:
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from langchain.llms.base import LLM
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except ImportError:
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raise ImportError(
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'To use the ctransformers.langchain module, please install the '
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'`langchain` python package: `pip install langchain`')
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from typing import Any, Dict, Optional, Sequence
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from pydantic import root_validator
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from langchain.callbacks.manager import CallbackManagerForLLMRun
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from api.binding import LLMBinding
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class GenericBinding(LLM):
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"""Wrapper around All compatible LLM interfaces.
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Thanks to Marella for providing the base for this work.
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To follow him, here is his github profile:
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To use, you should have the `langchain` python package installed.
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"""
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client: Any #: :meta private:
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model: str
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"""The path to a model file or directory or the name of a Hugging Face Hub
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model repo."""
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model_type: Optional[str] = None
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"""The model type."""
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model_file: Optional[str] = None
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"""The name of the model file in repo or directory."""
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config: Optional[Dict[str, Any]] = None
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"""The config parameters."""
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lib: Optional[Any] = None
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"""The path to a shared library or one of `avx2`, `avx`, `basic`."""
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@property
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def _identifying_params(self) -> Dict[str, Any]:
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"""Get the identifying parameters."""
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return {
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'model': self.model,
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'model_type': self.model_type,
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'model_file': self.model_file,
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'config': self.config,
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}
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@property
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def _llm_type(self) -> str:
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"""Return type of llm."""
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return 'generic_binding'
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@root_validator()
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def validate_environment(cls, values: Dict) -> Dict:
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"""Validate and load model from a local file or remote repo."""
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config = values['config'] or {}
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values['client'] = LLMBinding(config, True)
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return values
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def _call(
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self,
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prompt: str,
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stop: Optional[Sequence[str]] = None,
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run_manager: Optional[CallbackManagerForLLMRun] = None,
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) -> str:
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"""Generate text from a prompt.
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Args:
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prompt: The prompt to generate text from.
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stop: A list of sequences to stop generation when encountered.
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Returns:
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The generated text.
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"""
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text = []
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for chunk in self.client(prompt, stop=stop, stream=True):
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text.append(chunk)
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if run_manager:
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run_manager.on_llm_new_token(chunk, verbose=self.verbose)
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return ''.join(text)
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8
app.py
8
app.py
@ -507,7 +507,13 @@ class LoLLMsWebUI(LoLLMsAPPI):
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try:
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self.model = self.binding.build_model()
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except Exception as ex:
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print(f"Couldn't load model: [{ex}]")
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# Catch the exception and get the traceback as a list of strings
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traceback_lines = traceback.format_exception(type(ex), ex, ex.__traceback__)
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# Join the traceback lines into a single string
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traceback_text = ''.join(traceback_lines)
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ASCIIColors.error(f"Couldn't load model: [{ex}]")
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ASCIIColors.error(traceback_text)
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return jsonify({ "status":False, 'error':str(ex)})
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print("update_settings : New model selected")
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@ -15,7 +15,6 @@ import 'highlight.js/styles/tokyo-night-dark.css';
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import hljs from 'highlight.js/lib/common';
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import 'highlight.js/styles/tomorrow-night-blue.css';
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import 'highlight.js/styles/tokyo-night-dark.css';
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import attrs from 'markdown-it-attrs';
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