text-generation-webui/extensions/superboogav2/chat_handler.py

126 lines
4.6 KiB
Python

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