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run_storm_wiki_claude.py
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"""
STORM Wiki pipeline powered by Claude family models and You.com search engine.
You need to set up the following environment variables to run this script:
- ANTHROPIC_API_KEY: Anthropic API key
- YDC_API_KEY: You.com API key; or, BING_SEARCH_API_KEY: Bing Search API key
Output will be structured as below
args.output_dir/
topic_name/ # topic_name will follow convention of underscore-connected topic name w/o space and slash
conversation_log.json # Log of information-seeking conversation
raw_search_results.json # Raw search results from search engine
direct_gen_outline.txt # Outline directly generated with LLM's parametric knowledge
storm_gen_outline.txt # Outline refined with collected information
url_to_info.json # Sources that are used in the final article
storm_gen_article.txt # Final article generated
storm_gen_article_polished.txt # Polished final article (if args.do_polish_article is True)
"""
import os
from argparse import ArgumentParser
from knowledge_storm import STORMWikiRunnerArguments, STORMWikiRunner, STORMWikiLMConfigs
from knowledge_storm.lm import ClaudeModel
from knowledge_storm.rm import YouRM, BingSearch, BraveRM
from knowledge_storm.utils import load_api_key
def main(args):
load_api_key(toml_file_path='secrets.toml')
lm_configs = STORMWikiLMConfigs()
claude_kwargs = {
'api_key': os.getenv("ANTHROPIC_API_KEY"),
'temperature': 1.0,
'top_p': 0.9
}
# STORM is a LM system so different components can be powered by different models.
# For a good balance between cost and quality, you can choose a cheaper/faster model for conv_simulator_lm
# which is used to split queries, synthesize answers in the conversation. We recommend using stronger models
# for outline_gen_lm which is responsible for organizing the collected information, and article_gen_lm
# which is responsible for generating sections with citations.
conv_simulator_lm = ClaudeModel(model='claude-3-haiku-20240307', max_tokens=500, **claude_kwargs)
question_asker_lm = ClaudeModel(model='claude-3-sonnet-20240229', max_tokens=500, **claude_kwargs)
outline_gen_lm = ClaudeModel(model='claude-3-opus-20240229', max_tokens=400, **claude_kwargs)
article_gen_lm = ClaudeModel(model='claude-3-opus-20240229', max_tokens=700, **claude_kwargs)
article_polish_lm = ClaudeModel(model='claude-3-opus-20240229', max_tokens=4000, **claude_kwargs)
lm_configs.set_conv_simulator_lm(conv_simulator_lm)
lm_configs.set_question_asker_lm(question_asker_lm)
lm_configs.set_outline_gen_lm(outline_gen_lm)
lm_configs.set_article_gen_lm(article_gen_lm)
lm_configs.set_article_polish_lm(article_polish_lm)
engine_args = STORMWikiRunnerArguments(
output_dir=args.output_dir,
max_conv_turn=args.max_conv_turn,
max_perspective=args.max_perspective,
search_top_k=args.search_top_k,
max_thread_num=args.max_thread_num,
)
# STORM is a knowledge curation system which consumes information from the retrieval module.
# Currently, the information source is the Internet and we use search engine API as the retrieval module.
if args.retriever == 'bing':
rm = BingSearch(bing_search_api=os.getenv('BING_SEARCH_API_KEY'), k=engine_args.search_top_k)
elif args.retriever == 'you':
rm = YouRM(ydc_api_key=os.getenv('YDC_API_KEY'), k=engine_args.search_top_k)
elif args.retriever == 'brave':
rm = BraveRM(brave_search_api_key=os.getenv('BRAVE_API_KEY'), k=engine_args.search_top_k)
runner = STORMWikiRunner(engine_args, lm_configs, rm)
topic = input('Topic: ')
runner.run(
topic=topic,
do_research=args.do_research,
do_generate_outline=args.do_generate_outline,
do_generate_article=args.do_generate_article,
do_polish_article=args.do_polish_article,
)
runner.post_run()
runner.summary()
if __name__ == '__main__':
parser = ArgumentParser()
# global arguments
parser.add_argument('--output-dir', type=str, default='./results/claude',
help='Directory to store the outputs.')
parser.add_argument('--max-thread-num', type=int, default=3,
help='Maximum number of threads to use. The information seeking part and the article generation'
'part can speed up by using multiple threads. Consider reducing it if keep getting '
'"Exceed rate limit" error when calling LM API.')
parser.add_argument('--retriever', type=str, choices=['bing', 'you', 'brave'],
help='The search engine API to use for retrieving information.')
# stage of the pipeline
parser.add_argument('--do-research', action='store_true',
help='If True, simulate conversation to research the topic; otherwise, load the results.')
parser.add_argument('--do-generate-outline', action='store_true',
help='If True, generate an outline for the topic; otherwise, load the results.')
parser.add_argument('--do-generate-article', action='store_true',
help='If True, generate an article for the topic; otherwise, load the results.')
parser.add_argument('--do-polish-article', action='store_true',
help='If True, polish the article by adding a summarization section and (optionally) removing '
'duplicate content.')
# hyperparameters for the pre-writing stage
parser.add_argument('--max-conv-turn', type=int, default=3,
help='Maximum number of questions in conversational question asking.')
parser.add_argument('--max-perspective', type=int, default=3,
help='Maximum number of perspectives to consider in perspective-guided question asking.')
parser.add_argument('--search-top-k', type=int, default=3,
help='Top k search results to consider for each search query.')
# hyperparameters for the writing stage
parser.add_argument('--retrieve-top-k', type=int, default=3,
help='Top k collected references for each section title.')
parser.add_argument('--remove-duplicate', action='store_true',
help='If True, remove duplicate content from the article.')
main(parser.parse_args())