human_prefix=self.human_prefix,
                ai_prefix=self.ai_prefix,
            )
        return {self.memory_key: final_buffer}
[docs]    def save_context(self, inputs: Dict[str, Any], outputs: Dict[str, Any]) -> None:
        """Save context from this conversation to buffer. Pruned."""
        input_str, output_str = self._get_input_output(inputs, outputs)
        self.chat_memory.add_user_message(input_str)
        steps = format_to_openai_functions(outputs[self.intermediate_steps_key])
        for msg in steps:
            self.chat_memory.add_message(msg)
        self.chat_memory.add_ai_message(output_str)
        # Prune buffer if it exceeds max token limit
        buffer = self.chat_memory.messages
        curr_buffer_length = self.llm.get_num_tokens_from_messages(buffer)
        if curr_buffer_length > self.max_token_limit:
            while curr_buffer_length > self.max_token_limit:
                buffer.pop(0)
                curr_buffer_length = self.llm.get_num_tokens_from_messages(buffer)