""" 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; BING_SEARCH_API_KEY: Bing Search API key, SERPER_API_KEY: Serper API key, BRAVE_API_KEY: Brave API key, or TAVILY_API_KEY: Tavily 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, SerperRM, DuckDuckGoSearchRM, TavilySearchRM, SearXNG 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. match args.retriever: case 'bing': rm = BingSearch(bing_search_api=os.getenv('BING_SEARCH_API_KEY'), k=engine_args.search_top_k) case 'you': rm = YouRM(ydc_api_key=os.getenv('YDC_API_KEY'), k=engine_args.search_top_k) case 'brave': rm = BraveRM(brave_search_api_key=os.getenv('BRAVE_API_KEY'), k=engine_args.search_top_k) case 'duckduckgo': rm = DuckDuckGoSearchRM(k=engine_args.search_top_k, safe_search='On', region='us-en') case 'serper': rm = SerperRM(serper_search_api_key=os.getenv('SERPER_API_KEY'), query_params={'autocorrect': True, 'num': 10, 'page': 1}) case 'tavily': rm = TavilySearchRM(tavily_search_api_key=os.getenv('TAVILY_API_KEY'), k=engine_args.search_top_k, include_raw_content=True) case 'searxng': rm = SearXNG(searxng_api_key=os.getenv('SEARXNG_API_KEY'), k=engine_args.search_top_k) case _: raise ValueError(f'Invalid retriever: {args.retriever}. Choose either "bing", "you", "brave", "duckduckgo", "serper", "tavily", or "searxng"') 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', 'serper', 'duckduckgo', 'tavily', 'searxng'], 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())