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587f33e | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 | # Copyright 2026 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
Main script to launch PaperVizAgent
"""
import asyncio
import json
import argparse
from pathlib import Path
import aiofiles
import numpy as np
from agents.vanilla_agent import VanillaAgent
from agents.planner_agent import PlannerAgent
from agents.visualizer_agent import VisualizerAgent
from agents.stylist_agent import StylistAgent
from agents.critic_agent import CriticAgent
from agents.retriever_agent import RetrieverAgent
from agents.polish_agent import PolishAgent
from utils import config, paperviz_processor
async def main():
"""Main function"""
# add command line args
parser = argparse.ArgumentParser(description="PaperVizAgent processing script")
parser.add_argument(
"--dataset_name",
type=str,
default="PaperBananaBench",
help="name of the dataset to use (default: PaperBananaBench)",
)
parser.add_argument(
"--task_name",
type=str,
default="diagram",
choices=["diagram", "plot"],
help="task type: diagram or plot (default: diagram)",
)
parser.add_argument(
"--split_name",
type=str,
default="test",
help="split of the dataset to use (default: test)",
)
parser.add_argument(
"--exp_mode",
type=str,
default="dev",
help="name of the experiment to use (default: dev)",
)
parser.add_argument(
"--retrieval_setting",
type=str,
default="auto",
choices=["auto", "manual", "random", "none"],
help="retrieval setting for planner agent (default: auto)",
)
parser.add_argument(
"--max_critic_rounds",
type=int,
default=3,
help="maximum number of critic rounds (default: 3)",
)
parser.add_argument(
"--main_model_name",
type=str,
default="",
help="main model name to use (default: "")",
)
parser.add_argument(
"--image_gen_model_name",
type=str,
default="",
help="image generation model name to use (default: "")",
)
args = parser.parse_args()
exp_config = config.ExpConfig(
dataset_name=args.dataset_name,
task_name=args.task_name,
split_name=args.split_name,
exp_mode=args.exp_mode,
retrieval_setting=args.retrieval_setting,
max_critic_rounds=args.max_critic_rounds,
main_model_name=args.main_model_name,
image_gen_model_name=args.image_gen_model_name,
work_dir=Path(__file__).parent,
)
base_path = Path(__file__).parent / "data" / exp_config.dataset_name
input_filename = base_path / exp_config.task_name / f"{exp_config.split_name}.json"
output_filename = exp_config.result_dir / f"{exp_config.exp_name}.json"
print(f"Input file: {input_filename}", f"Output file: {output_filename}")
with open(input_filename, "r", encoding="utf-8") as f:
data_list = json.load(f)
# Create processor
processor = paperviz_processor.PaperVizProcessor(
exp_config=exp_config,
vanilla_agent=VanillaAgent(exp_config=exp_config),
planner_agent=PlannerAgent(exp_config=exp_config),
visualizer_agent=VisualizerAgent(exp_config=exp_config),
stylist_agent=StylistAgent(exp_config=exp_config),
critic_agent=CriticAgent(exp_config=exp_config),
retriever_agent=RetrieverAgent(exp_config=exp_config),
polish_agent=PolishAgent(exp_config=exp_config),
)
# Batch process documents
concurrent_num = 10
print(f"Using max concurrency: {concurrent_num}")
all_result_list = []
async def save_results_and_scores(current_results):
print(f"Incremental saving results (count: {len(current_results)}) to {output_filename}")
async with aiofiles.open(
output_filename, "w", encoding="utf-8", errors="surrogateescape"
) as f:
json_string = json.dumps(current_results, ensure_ascii=False, indent=4)
json_string = json_string.encode("utf-8", "ignore").decode("utf-8")
await f.write(json_string)
# Process samples incrementally
idx = 0
async for result_data in processor.process_queries_batch(
data_list, max_concurrent=concurrent_num
):
all_result_list.append(result_data)
idx += 1
if idx % 10 == 0:
await save_results_and_scores(all_result_list)
# Final save
await save_results_and_scores(all_result_list)
print("Processing completed.")
if __name__ == "__main__":
asyncio.run(main())
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