EVOLUTION-NINJA

Edit File: chat_handler.py

"""
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)