/examples/run_storm_wiki_serper.py

"""
STORM Wiki pipeline powered by Claude family models and serper search engine.
You need to set up the following environment variables to run this script:
    - ANTHROPIC_API_KEY: Anthropic API key
    - SERPER_API_KEY: Serper.dev 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 SerperRM
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,
    )
    # Documentation to generate the data is available here:
    # https://serper.dev/playground
    # Important to note that tbs(date range is hardcoded values).
    # num is results per pages and is recommended to use in increments of 10(10, 20, etc).
    # page is how many pages will be searched.
    # h1 is where the google search will orginate from.
    topic = input("topic: ")
    data = {"autocorrect": True, "num": 10, "page": 1}
    rm = SerperRM(serper_search_api_key=os.getenv("SERPER_API_KEY"), query_params=data)

    runner = STORMWikiRunner(engine_args, lm_configs, rm)

    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/serper",
        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", "serper"],
        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())