""" STORM Wiki pipeline powered by llama3-70b-8192 hosted by Groq server and You.com search engine. You need to set up the following environment variables to run this script: - GROQ_API_KEY: You can get your Groq API Key at https://console.groq.com/keys - 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 You also need to have a VLLM server running with the Mistral-7B-Instruct-v0.2 model. Specify `--url` and `--port` accordingly. 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 import re from argparse import ArgumentParser from knowledge_storm import ( STORMWikiRunnerArguments, STORMWikiRunner, STORMWikiLMConfigs, ) # Now import lm directly import lm from lm import GroqModel from knowledge_storm.rm import ( YouRM, BingSearch, BraveRM, SerperRM, DuckDuckGoSearchRM, TavilySearchRM, SearXNG, ) from knowledge_storm.utils import load_api_key def sanitize_topic(topic): """ Sanitize the topic name for use in file names. Remove or replace characters that are not allowed in file names. """ # Replace spaces with underscores topic = topic.replace(" ", "_") # Remove any character that isn't alphanumeric, underscore, or hyphen topic = re.sub(r"[^a-zA-Z0-9_-]", "", topic) # Ensure the topic isn't empty after sanitization if not topic: topic = "unnamed_topic" return topic def main(args): load_api_key(toml_file_path="secrets.toml") lm_configs = STORMWikiLMConfigs() # Ensure GROQ_API_KEY is set if not os.getenv("GROQ_API_KEY"): raise ValueError( "GROQ_API_KEY environment variable is not set. Please set it in your secrets.toml file." ) groq_kwargs = { "api_key": os.getenv("GROQ_API_KEY"), "api_base": "https://api.groq.com/openai/v1", "temperature": args.temperature, "top_p": args.top_p, } # Groq currently offers the "llama3-70b-8192" model with generous free API credits and the llama3.1 family of models as a preview for paying customers conv_simulator_lm = GroqModel( model="llama3-70b-8192", max_tokens=500, **groq_kwargs ) question_asker_lm = GroqModel( model="llama3-70b-8192", max_tokens=500, **groq_kwargs ) outline_gen_lm = GroqModel(model="llama3-70b-8192", max_tokens=400, **groq_kwargs) article_gen_lm = GroqModel(model="llama3-70b-8192", max_tokens=700, **groq_kwargs) article_polish_lm = GroqModel( model="llama3-70b-8192", max_tokens=4000, **groq_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: ") sanitized_topic = sanitize_topic(topic) try: runner.run( topic=sanitized_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, remove_duplicate=args.remove_duplicate, ) runner.post_run() runner.summary() except Exception as e: logger.exception(f"An error occurred: {str(e)}") raise if __name__ == "__main__": parser = ArgumentParser() # global arguments parser.add_argument( "--output-dir", type=str, default="./results/groq", 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.", ) parser.add_argument( "--temperature", type=float, default=1.0, help="Sampling temperature to use." ) parser.add_argument( "--top_p", type=float, default=0.9, help="Top-p sampling parameter." ) # 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())