<jupyter_start><jupyter_text>After every turn we need to decide what information from the previous turn we need to keep and what not. This process is complex because we have 3 types of NIU models in place: Pirkin 2 main/back up/ secondary.  Depending on the type of model used in the previous turn we have three different structures to work with. After the reset pipeline, the Memory update pipeline will decide how to combine past and present inputs of the user.<jupyter_code># IN: current SPCA
# OUT: an updated version of SPCA after the current turn is completed
# Insert code for:

# Resetting previous SPCA depending on AVM and current user reaction;
# Appending/resetting chitchats depending on AVM and current user reaction;
# Appending/resetting back-up answer depending on AVM and current user reaction;
# Resetting DER;
# Freeze means that no SPCA of inputs are updated until the end of the query/action.
# Append means that new input=past input (summarized) + present input = (memory update/summarization-augmentation)

# adapt code for table implementation:<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.04549590536851683, 'fraction_numerical': 0.0018198362147406734, 'mean_word_length': 4.945945945945946, 'pattern_counts': {'":': 0, '<': 5, '<?xml version=': 0, '>': 5, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '1', 'dir': 'jupyter-structured-clean-dedup', 'id': '73780', 'license': 'Apache-2.0', 'n_tokens_mistral': 288, 'n_tokens_neox': 278, 'n_words': 158, 'path': 'CPL Conversational Policy Learning/25_Reset_dummy_code.ipynb', 'repo_name': 'Cezanne-ai/project-2021', 'size': '1099'}