mirror of
https://github.com/oobabooga/text-generation-webui.git
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125 lines
4.6 KiB
Python
125 lines
4.6 KiB
Python
"""
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This module is responsible for modifying the chat prompt and history.
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"""
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import re
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import extensions.superboogav2.parameters as parameters
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from modules import chat, shared
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from modules.text_generation import get_encoded_length
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from modules.logging_colors import logger
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from modules.chat import load_character_memoized
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from extensions.superboogav2.utils import create_context_text, create_metadata_source
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from .data_processor import process_and_add_to_collector
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from .chromadb import ChromaCollector
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CHAT_METADATA = create_metadata_source('automatic-chat-insert')
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def _remove_tag_if_necessary(user_input: str):
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if not parameters.get_is_manual():
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return user_input
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return re.sub(r'^\s*!c\s*|\s*!c\s*$', '', user_input)
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def _should_query(input: str):
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if not parameters.get_is_manual():
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return True
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if re.search(r'^\s*!c|!c\s*$', input, re.MULTILINE):
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return True
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return False
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def _format_single_exchange(name, text):
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if re.search(r':\s*$', name):
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return '{} {}\n'.format(name, text)
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else:
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return '{}: {}\n'.format(name, text)
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def _get_names(state: dict):
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default_char = shared.settings.get('character', "Assistant")
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default_user = shared.settings.get('name1', "You")
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character = state.get('character', default_char)
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user_name = state.get('name1', default_user)
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user_name, bot_name, _, _, _ = load_character_memoized(character, user_name, '')
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return user_name, bot_name
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def _concatinate_history(history: dict, state: dict):
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full_history_text = ''
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user_name, bot_name = _get_names(state)
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# Grab the internal history.
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internal_history = history['internal']
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assert isinstance(internal_history, list)
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# Iterate through the history.
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for exchange in internal_history:
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assert isinstance(exchange, list)
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if len(exchange) >= 1:
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full_history_text += _format_single_exchange(user_name, exchange[0])
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if len(exchange) >= 2:
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full_history_text += _format_single_exchange(bot_name, exchange[1])
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return full_history_text[:-1] # Remove the last new line.
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def _hijack_last(context_text: str, history: dict, max_len: int, state: dict):
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num_context_tokens = get_encoded_length(context_text)
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names = _get_names(state)[::-1]
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history_tokens = 0
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replace_position = None
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for i, messages in enumerate(reversed(history['internal'])):
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for j, message in enumerate(reversed(messages)):
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num_message_tokens = get_encoded_length(_format_single_exchange(names[j], message))
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# TODO: This is an extremely naive solution. A more robust implementation must be made.
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if history_tokens + num_context_tokens <= max_len:
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# This message can be replaced
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replace_position = (i, j)
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history_tokens += num_message_tokens
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if replace_position is None:
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logger.warn("The provided context_text is too long to replace any message in the history.")
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else:
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# replace the message at replace_position with context_text
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i, j = replace_position
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history['internal'][-i-1][-j-1] = context_text
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def custom_generate_chat_prompt_internal(user_input: str, state: dict, collector: ChromaCollector, **kwargs):
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if parameters.get_add_chat_to_data():
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# Get the whole history as one string
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history_as_text = _concatinate_history(kwargs['history'], state)
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if history_as_text:
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# Delete all documents that were auto-inserted
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collector.delete(ids_to_delete=None, where=CHAT_METADATA)
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# Insert the processed history
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process_and_add_to_collector(history_as_text, collector, False, CHAT_METADATA)
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if _should_query(user_input):
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user_input = _remove_tag_if_necessary(user_input)
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results = collector.get_sorted_by_dist(user_input, n_results=parameters.get_chunk_count(), max_token_count=int(parameters.get_max_token_count()))
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# Check if the strategy is to modify the last message. If so, prepend or append to the user query.
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if parameters.get_injection_strategy() == parameters.APPEND_TO_LAST:
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user_input = user_input + create_context_text(results)
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elif parameters.get_injection_strategy() == parameters.PREPEND_TO_LAST:
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user_input = create_context_text(results) + user_input
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elif parameters.get_injection_strategy() == parameters.HIJACK_LAST_IN_CONTEXT:
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_hijack_last(create_context_text(results), kwargs['history'], state['truncation_length'], state)
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return chat.generate_chat_prompt(user_input, state, **kwargs)
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