37 lines
2.4 KiB
Python
37 lines
2.4 KiB
Python
'''
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Close-domain QA Pipeline
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'''
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import argparse
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from toolbench.inference.Downstream_tasks.rapidapi import pipeline_runner
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import os
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if __name__ == "__main__":
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parser = argparse.ArgumentParser()
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parser.add_argument('--backbone_model', type=str, default="toolllama", required=False, help='chatgpt_function or davinci or toolllama')
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parser.add_argument('--openai_key', type=str, default="", required=False, help='openai key for chatgpt_function or davinci model')
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parser.add_argument('--model_path', type=str, default="your_model_path/", required=False, help='')
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parser.add_argument('--tool_root_dir', type=str, default="your_tools_path/", required=True, help='')
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parser.add_argument("--lora", action="store_true", help="Load lora model or not.")
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parser.add_argument('--lora_path', type=str, default="your_lora_path if lora", required=False, help='')
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parser.add_argument('--max_observation_length', type=int, default=1024, required=False, help='maximum observation length')
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parser.add_argument('--max_source_sequence_length', type=int, default=4096, required=False, help='original maximum model sequence length')
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parser.add_argument('--max_sequence_length', type=int, default=8192, required=False, help='maximum model sequence length')
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parser.add_argument('--observ_compress_method', type=str, default="truncate", choices=["truncate", "filter", "random"], required=False, help='observation compress method')
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parser.add_argument('--method', type=str, default="CoT@1", required=False, help='method for answer generation: CoT@n,Reflexion@n,BFS,DFS,UCT_vote')
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parser.add_argument('--input_query_file', type=str, default="", required=False, help='input path')
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parser.add_argument('--output_answer_file', type=str, default="",required=False, help='output path')
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parser.add_argument('--toolbench_key', type=str, default="",required=False, help='your toolbench key to request rapidapi service')
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parser.add_argument('--rapidapi_key', type=str, default="",required=False, help='your rapidapi key to request rapidapi service')
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parser.add_argument('--use_rapidapi_key', action="store_true", help="To use customized rapidapi service or not.")
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parser.add_argument('--api_customization', action="store_true", help="To use customized api or not.")
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args = parser.parse_args()
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os.makedirs(args.output_answer_file, exist_ok=True)
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pipeline_runner = pipeline_runner(args)
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pipeline_runner.run()
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