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input [{里面很多键},{}] len = batch
gathered_messages = [[{...}, {...}], [{...}, {...}]] # {'role': 'system', 'content': [...]}, {'role': 'user', 'content': [...]}] # 二维数组，每个元素是一个对话
multimodal_cache = [{}, {}] # len = batch size
rolling_messages = self._update_rolling_messages( ## 历史调用工具记录
rolling_messages, [[{...}, {...}, {...}, {...}], [{...}, {...}]] # 2维度数组 # 二维数组，每个元素是一系列对话
responses_msg, [{'role': 'assistant', 'content': [...]}, {'role': 'assistant', 'content': [...]}] # 一维数组
next_obs, [{'role': 'tool', 'name': 'seek_video_frames', 'arguments': {...}, 'content': [...]}, None] # 一维数组
)
for i,s in enumerate(generated_messages[0]):
if s['role'] == 'system':
print(generated_messages[0][i]['content'])
debug = [[s for s in msg if s['role'] != 'system'] for msg in generated_messages]
new_system = {
'role': 'system',
'content': [{'type': 'text', 'text': 'system_message'}]
}
for i, messages in enumerate(debug):
messages.insert(0, deepcopy(new_system))
visual_trace_batch_filtered # [[{...}, {...}], [{...}, {...}]] # 二维数组，每个元素是系列对话
visual_trace_batch_prediction_texts = [cleanup_llm_response(s) for s in visual_trace_batch_prediction_texts]
visual_trace_batch_prediction_texts, visual_trace_batch_prediction_msg = example_level_pad(visual_trace_batch_prediction_texts)
def cleanup_llm_response(response_str: str) -> str: # 截取关键信息
if '<think>' in response_str:
response_str = '<think>' + response_str.split('<think>')[-1]
if '</tool_call>' in response_str:
return response_str.split('</tool_call>')[0] + '</tool_call>'
elif '</answer>' in response_str:
return response_str.split('</answer>')[0] + '</answer>'
else:
return response_str
def example_level_pad(responses_str):
"""
Pad responses for non-active examples with empty messages.
"""
batch_size = len(responses_str)
padded_responses_str = [''] * batch_size
padded_responses_msg = [None] * batch_size
for i in range(len(responses_str)):
padded_responses_str[i] = responses_str[i]
padded_responses_msg[i] = {
"role": "assistant",
"content": [{
"type": "text",
"text": responses_str[i],
}]
}
return padded_responses_str, padded_responses_msg |