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import re | |
def parse_agents(agent_string): | |
""" | |
Parse a string containing agent names separated by ->, (, ), or commas | |
and return a list of agent names. | |
""" | |
if not agent_string or not agent_string.strip(): | |
return [] | |
# Replace parentheses with spaces to handle cases with parentheses | |
import re | |
cleaned_string = re.sub(r'\(.*?\)', '', agent_string) | |
# Split by -> to get individual agent segments | |
agent_segments = cleaned_string.split('->') | |
# Process each segment to extract agent names | |
agents = [] | |
for segment in agent_segments: | |
# Split by comma and strip whitespace | |
segment_agents = [agent.strip() for agent in segment.split(',') if agent.strip()] | |
agents.extend(segment_agents) | |
return agents[0] if isinstance(agents, list) else agents | |
# sample = """preprocessing_agent(dataset, goal -> code, summary | |
# instructions='Given a user-defined analysis goal and a pre-loaded dataset df, \nI will generate Python code using NumPy and Pandas to build an exploratory analytics pipeline.\nThe goal is to simplify the preprocessing and introductory analysis of the dataset.\n\nIMPORTANT: You may be provided with previous interaction history. The section marked "##""" | |
# print(parse_agents(sample)) |