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@@ -7,7 +7,10 @@ cfg = Config()
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prev_index = 0
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current_memory = ""
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def get_newly_trimmed_messages(full_message_history: List[Dict[str, str]], current_context: List[Dict[str, str]]) -> Tuple[List[Dict[str, str]], int]:
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def get_newly_trimmed_messages(
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full_message_history: List[Dict[str, str]], current_context: List[Dict[str, str]]
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) -> Tuple[List[Dict[str, str]], int]:
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"""
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This function returns a list of dictionaries contained in full_message_history
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with an index higher than prev_index that are absent from current_context, and the new index.
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@@ -27,7 +30,9 @@ def get_newly_trimmed_messages(full_message_history: List[Dict[str, str]], curre
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new_messages = [msg for i, msg in enumerate(full_message_history) if i > prev_index]
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# Remove messages that are already present in current_context
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new_messages_not_in_context = [msg for msg in new_messages if msg not in current_context]
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new_messages_not_in_context = [
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msg for msg in new_messages if msg not in current_context
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]
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# Find the index of the last message processed
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new_index = prev_index
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@@ -59,7 +64,7 @@ def update_running_summary(new_events: List[Dict]) -> str:
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"""
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global current_memory
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prompt = f'''Your task is to create a concise running summary of actions in the provided text, focusing on key and potentially important information to remember.
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You will receive the current summary and the latest development. Combine them, adding relevant key information from the latest development in 1st person past tense and keeping the summary concise.
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@@ -85,8 +90,8 @@ Latest Development:
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current_memory = create_chat_completion(messages, cfg.fast_llm_model)
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message_to_return = {
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"role": "system",
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"content": f"This reminds you of these events from your past: \n{current_memory}",
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}
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"role": "system",
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"content": f"This reminds you of these events from your past: \n{current_memory}",
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}
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return message_to_return
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return message_to_return
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