""" STORM Wiki pipeline powered by GPT-3.5/4 and You.com search engine. You need to set up the following environment variables to run this script: - OPENAI_API_KEY: OpenAI API key - OPENAI_API_TYPE: OpenAI API type (e.g., 'openai' or 'azure') - AZURE_API_BASE: Azure API base URL if using Azure API - AZURE_API_VERSION: Azure API version if using Azure API - 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 OpenAIModel, AzureOpenAIModel from knowledge_storm.rm import YouRM, BingSearch from knowledge_storm.utils import load_api_key def main(args): load_api_key(toml_file_path='secrets.toml') lm_configs = STORMWikiLMConfigs() openai_kwargs = { 'api_key': os.getenv("OPENAI_API_KEY"), 'temperature': 1.0, 'top_p': 0.9, } ModelClass = OpenAIModel if os.getenv('OPENAI_API_TYPE') == 'openai' else AzureOpenAIModel # If you are using Azure service, make sure the model name matches your own deployed model name. # The default name here is only used for demonstration and may not match your case. gpt_35_model_name = 'gpt-3.5-turbo' if os.getenv('OPENAI_API_TYPE') == 'openai' else 'gpt-35-turbo' gpt_4_model_name = 'gpt-4o' if os.getenv('OPENAI_API_TYPE') == 'azure': openai_kwargs['api_base'] = os.getenv('AZURE_API_BASE') openai_kwargs['api_version'] = os.getenv('AZURE_API_VERSION') # 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 = ModelClass(model=gpt_35_model_name, max_tokens=500, **openai_kwargs) question_asker_lm = ModelClass(model=gpt_35_model_name, max_tokens=500, **openai_kwargs) outline_gen_lm = ModelClass(model=gpt_4_model_name, max_tokens=400, **openai_kwargs) article_gen_lm = ModelClass(model=gpt_4_model_name, max_tokens=700, **openai_kwargs) article_polish_lm = ModelClass(model=gpt_4_model_name, max_tokens=4000, **openai_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) 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/gpt', 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'], 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())