mirror of
https://github.com/ParisNeo/lollms.git
synced 2024-12-20 21:23:17 +00:00
added stuff
This commit is contained in:
Saifeddine ALOUI
parent
83633c1559
commit
019bb6027b
@ -7,7 +7,7 @@ import yaml
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from pathlib import Path
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from ascii_colors import ASCIIColors
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from safe_store import TextVectorizer, GenericDataLoader, VisualizationMethod, VectorizationMethod
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from typing import Tuple
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from typing import Tuple, List
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context={
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"discussion":""
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@ -59,164 +59,148 @@ async def on_member_join(member):
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if channel is not None:
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await channel.send(f'Welcome {member.mention} to the server! How can I assist you today?')
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# def prepare_query(prompt, n_tokens: int = 0) -> Tuple[str, str, List[str]]:
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# """
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# Prepares the query for the model.
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def prepare_query(discussion, prompt, n_tokens: int = 0) -> Tuple[str, str, List[str]]:
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"""
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Prepares the query for the model.
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# Args:
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# client_id (str): The client ID.
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# message_id (int): The message ID. Default is -1.
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# is_continue (bool): Whether the query is a continuation. Default is False.
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# n_tokens (int): The number of tokens. Default is 0.
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Args:
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client_id (str): The client ID.
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message_id (int): The message ID. Default is -1.
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is_continue (bool): Whether the query is a continuation. Default is False.
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n_tokens (int): The number of tokens. Default is 0.
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# Returns:
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# Tuple[str, str, List[str]]: The prepared query, original message content, and tokenized query.
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# """
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# # Define current message
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# current_message = messages[message_index]
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Returns:
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Tuple[str, str, List[str]]: The prepared query, original message content, and tokenized query.
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"""
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# # Build the conditionning text block
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# conditionning = self.personality.personality_conditioning
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# Define current message
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current_message = prompt
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# # Check if there are document files to add to the prompt
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# documentation = ""
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# if self.personality.persona_data_vectorizer:
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# if documentation=="":
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# documentation="!@>Documentation:\n"
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# docs, sorted_similarities = self.personality.persona_data_vectorizer.recover_text(current_message.content, top_k=self.config.data_vectorization_nb_chunks)
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# for doc, infos in zip(docs, sorted_similarities):
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# documentation += f"document chunk:\n{doc}"
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# Build the conditionning text block
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conditionning = lollms_app.personality.personality_conditioning
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# if len(self.personality.text_files) > 0 and self.personality.vectorizer:
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# if documentation=="":
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# documentation="!@>Documentation:\n"
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# docs, sorted_similarities = self.personality.vectorizer.recover_text(current_message.content, top_k=self.config.data_vectorization_nb_chunks)
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# for doc, infos in zip(docs, sorted_similarities):
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# documentation += f"document chunk:\nchunk path: {infos[0]}\nchunk content:{doc}"
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# # Check if there is discussion history to add to the prompt
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# history = ""
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# if self.config.use_discussions_history and self.discussions_store is not None:
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# if history=="":
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# documentation="!@>History:\n"
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# docs, sorted_similarities = self.discussions_store.recover_text(current_message.content, top_k=self.config.data_vectorization_nb_chunks)
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# for doc, infos in zip(docs, sorted_similarities):
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# history += f"discussion chunk:\ndiscussion title: {infos[0]}\nchunk content:{doc}"
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# # Add information about the user
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# user_description=""
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# if self.config.use_user_name_in_discussions:
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# user_description="!@>User description:\n"+self.config.user_description
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# Check if there are document files to add to the prompt
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documentation = ""
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if lollms_app.personality.persona_data_vectorizer:
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if documentation=="":
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documentation="!@>Documentation:\n"
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docs, sorted_similarities = lollms_app.personality.persona_data_vectorizer.recover_text(current_message.content, top_k=lollms_app.config.data_vectorization_nb_chunks)
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for doc, infos in zip(docs, sorted_similarities):
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documentation += f"document chunk:\n{doc}"
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# # Tokenize the conditionning text and calculate its number of tokens
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# tokens_conditionning = self.model.tokenize(conditionning)
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# n_cond_tk = len(tokens_conditionning)
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if len(lollms_app.personality.text_files) > 0 and lollms_app.personality.vectorizer:
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if documentation=="":
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documentation="!@>Documentation:\n"
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docs, sorted_similarities = lollms_app.personality.vectorizer.recover_text(current_message.content, top_k=lollms_app.config.data_vectorization_nb_chunks)
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for doc, infos in zip(docs, sorted_similarities):
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documentation += f"document chunk:\nchunk path: {infos[0]}\nchunk content:{doc}"
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# # Tokenize the documentation text and calculate its number of tokens
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# if len(documentation)>0:
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# tokens_documentation = self.model.tokenize(documentation)
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# n_doc_tk = len(tokens_documentation)
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# else:
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# tokens_documentation = []
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# n_doc_tk = 0
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# Check if there is discussion history to add to the prompt
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history = ""
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if lollms_app.config.use_discussions_history and lollms_app.discussions_store is not None:
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if history=="":
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documentation="!@>History:\n"
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docs, sorted_similarities = lollms_app.discussions_store.recover_text(current_message.content, top_k=lollms_app.config.data_vectorization_nb_chunks)
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for doc, infos in zip(docs, sorted_similarities):
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history += f"discussion chunk:\ndiscussion title: {infos[0]}\nchunk content:{doc}"
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# # Tokenize the history text and calculate its number of tokens
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# if len(history)>0:
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# tokens_history = self.model.tokenize(history)
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# n_history_tk = len(tokens_history)
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# else:
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# tokens_history = []
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# n_history_tk = 0
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# Add information about the user
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user_description=""
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if lollms_app.config.use_user_name_in_discussions:
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user_description="!@>User description:\n"+lollms_app.config.user_description
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# # Tokenize user description
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# if len(user_description)>0:
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# tokens_user_description = self.model.tokenize(user_description)
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# n_user_description_tk = len(tokens_user_description)
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# else:
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# tokens_user_description = []
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# n_user_description_tk = 0
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# Tokenize the conditionning text and calculate its number of tokens
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tokens_conditionning = lollms_app.model.tokenize(conditionning)
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n_cond_tk = len(tokens_conditionning)
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# Tokenize the documentation text and calculate its number of tokens
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if len(documentation)>0:
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tokens_documentation = lollms_app.model.tokenize(documentation)
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n_doc_tk = len(tokens_documentation)
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else:
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tokens_documentation = []
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n_doc_tk = 0
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# Tokenize the history text and calculate its number of tokens
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if len(history)>0:
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tokens_history = lollms_app.model.tokenize(history)
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n_history_tk = len(tokens_history)
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else:
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tokens_history = []
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n_history_tk = 0
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# # Calculate the total number of tokens between conditionning, documentation, and history
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# total_tokens = n_cond_tk + n_doc_tk + n_history_tk + n_user_description_tk
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# # Calculate the available space for the messages
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# available_space = self.config.ctx_size - n_tokens - total_tokens
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# # Raise an error if the available space is 0 or less
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# if available_space<1:
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# raise Exception("Not enough space in context!!")
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# # Accumulate messages until the cumulative number of tokens exceeds available_space
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# tokens_accumulated = 0
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# Tokenize user description
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if len(user_description)>0:
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tokens_user_description = lollms_app.model.tokenize(user_description)
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n_user_description_tk = len(tokens_user_description)
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else:
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tokens_user_description = []
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n_user_description_tk = 0
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# # Initialize a list to store the full messages
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# full_message_list = []
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# # If this is not a continue request, we add the AI prompt
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# if not is_continue:
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# message_tokenized = self.model.tokenize(
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# "\n" +self.personality.ai_message_prefix.strip()
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# )
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# full_message_list.append(message_tokenized)
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# # Update the cumulative number of tokens
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# tokens_accumulated += len(message_tokenized)
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# Calculate the total number of tokens between conditionning, documentation, and history
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total_tokens = n_cond_tk + n_doc_tk + n_history_tk + n_user_description_tk
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# Calculate the available space for the messages
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available_space = lollms_app.config.ctx_size - n_tokens - total_tokens
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# Raise an error if the available space is 0 or less
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if available_space<1:
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raise Exception("Not enough space in context!!")
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# Accumulate messages until the cumulative number of tokens exceeds available_space
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tokens_accumulated = 0
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# # Accumulate messages starting from message_index
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# for i in range(message_index, -1, -1):
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# message = messages[i]
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# Initialize a list to store the full messages
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full_message_list = []
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# If this is not a continue request, we add the AI prompt
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message_tokenized = lollms_app.model.tokenize(
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"\n" +lollms_app.personality.ai_message_prefix.strip()
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)
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full_message_list.append(message_tokenized)
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# Update the cumulative number of tokens
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tokens_accumulated += len(message_tokenized)
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# # Check if the message content is not empty and visible to the AI
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# if message.content != '' and (
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# 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):
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# # Tokenize the message content
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# message_tokenized = self.model.tokenize(
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# "\n" + self.config.discussion_prompt_separator + message.sender + ": " + message.content.strip())
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message_tokenized = lollms_app.model.tokenize(discussion)
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if len(message_tokenized)>lollms_app.config.ctx_size-1024:
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pos = message_tokenized[-(lollms_app.config.ctx_size-1024)]
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detokenized = lollms_app.model.detokenize(message_tokenized[pos:pos+10])
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position = discussion.find(detokenized)
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if position!=-1:
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discussion_messages = discussion[-position:]
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else:
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discussion_messages = discussion
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else:
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discussion_messages = discussion
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# # Check if adding the message will exceed the available space
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# if tokens_accumulated + len(message_tokenized) > available_space:
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# break
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# Build the final prompt by concatenating the conditionning and discussion messages
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prompt_data = conditionning + documentation + history + user_description + discussion_messages
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# # Add the tokenized message to the full_message_list
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# full_message_list.insert(0, message_tokenized)
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# Tokenize the prompt data
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tokens = lollms_app.model.tokenize(prompt_data)
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# # Update the cumulative number of tokens
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# tokens_accumulated += len(message_tokenized)
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# if this is a debug then show prompt construction details
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if lollms_app.config["debug"]:
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ASCIIColors.bold("CONDITIONNING")
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ASCIIColors.yellow(conditionning)
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ASCIIColors.bold("DOC")
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ASCIIColors.yellow(documentation)
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ASCIIColors.bold("HISTORY")
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ASCIIColors.yellow(history)
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ASCIIColors.bold("DISCUSSION")
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ASCIIColors.hilight(discussion_messages,"!@>",ASCIIColors.color_yellow,ASCIIColors.color_bright_red,False)
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ASCIIColors.bold("Final prompt")
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ASCIIColors.hilight(prompt_data,"!@>",ASCIIColors.color_yellow,ASCIIColors.color_bright_red,False)
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ASCIIColors.info(f"prompt size:{len(tokens)} tokens")
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ASCIIColors.info(f"available space after doc and history:{available_space} tokens")
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# # Build the final discussion messages by detokenizing the full_message_list
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# discussion_messages = ""
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# for message_tokens in full_message_list:
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# discussion_messages += self.model.detokenize(message_tokens)
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# # Build the final prompt by concatenating the conditionning and discussion messages
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# prompt_data = conditionning + documentation + history + user_description + discussion_messages
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# # Tokenize the prompt data
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# tokens = self.model.tokenize(prompt_data)
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# # if this is a debug then show prompt construction details
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# if self.config["debug"]:
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# ASCIIColors.bold("CONDITIONNING")
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# ASCIIColors.yellow(conditionning)
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# ASCIIColors.bold("DOC")
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# ASCIIColors.yellow(documentation)
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# ASCIIColors.bold("HISTORY")
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# ASCIIColors.yellow(history)
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# ASCIIColors.bold("DISCUSSION")
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# ASCIIColors.hilight(discussion_messages,"!@>",ASCIIColors.color_yellow,ASCIIColors.color_bright_red,False)
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# ASCIIColors.bold("Final prompt")
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# ASCIIColors.hilight(prompt_data,"!@>",ASCIIColors.color_yellow,ASCIIColors.color_bright_red,False)
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# ASCIIColors.info(f"prompt size:{len(tokens)} tokens")
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# ASCIIColors.info(f"available space after doc and history:{available_space} tokens")
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# # Return the prepared query, original message content, and tokenized query
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# return prompt_data, current_message.content, tokens
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# Return the prepared query, original message content, and tokenized query
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return prompt_data
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@client.event
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async def on_message(message):
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@ -224,6 +208,8 @@ async def on_message(message):
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return
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if message.content.startswith(config["summoning_word"]):
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prompt = message.content[len(config["summoning_word"])+1:]
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context['discussion'] = prepare_query(context['discussion'], prompt, 512)
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context['discussion']+= "\n!@>" + message.author.name +": "+ prompt + "\n" + f"{lollms_app.personality.ai_message_prefix}"
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context['current_response']=""
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print("Chatting")
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@ -364,9 +364,12 @@ Date: {{date}}
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self.bot_says = bot_says
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return True
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def generate(self, prompt, max_size, temperature = None, top_k = None, top_p=None, repeat_penalty=None, callback=None ):
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def generate(self, prompt, max_size, temperature = None, top_k = None, top_p=None, repeat_penalty=None, callback=None, debug=False ):
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ASCIIColors.info("Text generation started: Warming up")
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self.bot_says = ""
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if debug:
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self.print_prompt("gen",prompt)
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self.model.generate(
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prompt,
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max_size,
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@ -1413,7 +1416,7 @@ class APScript(StateMachine):
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def add_file(self, path, callback=None):
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if callback is not None:
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callback("File added successfully",MSG_TYPE.MSG_TYPE_INFO)
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self.text_files.append(path)
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self.personality.add_file(path)
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return True
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def remove_file(self, path):
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@ -1470,8 +1473,8 @@ class APScript(StateMachine):
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yaml.dump(data, file)
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def generate(self, prompt, max_size, temperature = None, top_k = None, top_p=None, repeat_penalty=None, callback=None ):
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return self.personality.generate(prompt, max_size, temperature, top_k, top_p, repeat_penalty, callback)
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def generate(self, prompt, max_size, temperature = None, top_k = None, top_p=None, repeat_penalty=None, callback=None, debug=False ):
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return self.personality.generate(prompt, max_size, temperature, top_k, top_p, repeat_penalty, callback, debug=debug)
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def run_workflow(self, prompt:str, previous_discussion_text:str="", callback: Callable[[str, MSG_TYPE, dict, list], bool]=None):
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"""
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Reference in New Issue
Block a user