stanford-oval--storm
zhou jian
179 行
10 KiB
Python
179 行
10 KiB
Python
"""
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STORM Wiki pipeline powered by local model hosted by Ollama server and You.com or Bing search engine.
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You need to set up the following environment variables to run this script:
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- YDC_API_KEY: You.com API key; or, BING_SEARCH_API_KEY: Bing Search API key
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You also need to have a Ollama server running with the llama3 model or other. Specify `--url`, `--port` and `--model` accordingly.
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Output will be structured as below
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args.output_dir/
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topic_name/ # topic_name will follow convention of underscore-connected topic name w/o space and slash
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conversation_log.json # Log of information-seeking conversation
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raw_search_results.json # Raw search results from search engine
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direct_gen_outline.txt # Outline directly generated with LLM's parametric knowledge
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storm_gen_outline.txt # Outline refined with collected information
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url_to_info.json # Sources that are used in the final article
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storm_gen_article.txt # Final article generated
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storm_gen_article_polished.txt # Polished final article (if args.do_polish_article is True)
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"""
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import os
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import sys
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from argparse import ArgumentParser
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from dspy import Example
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sys.path.append('./src')
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from lm import OllamaClient
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from rm import YouRM, BingSearch
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from storm_wiki.engine import STORMWikiRunnerArguments, STORMWikiRunner, STORMWikiLMConfigs
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from utils import load_api_key
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def main(args):
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load_api_key(toml_file_path='secrets.toml')
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lm_configs = STORMWikiLMConfigs()
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ollama_kwargs = {
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"model": args.model,
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"port": args.port,
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"url": args.url,
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"stop": ('\n\n---',) # dspy uses "\n\n---" to separate examples. Open models sometimes generate this.
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}
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conv_simulator_lm = OllamaClient(max_tokens=500, **ollama_kwargs)
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question_asker_lm = OllamaClient(max_tokens=500, **ollama_kwargs)
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outline_gen_lm = OllamaClient(max_tokens=400, **ollama_kwargs)
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article_gen_lm = OllamaClient(max_tokens=700, **ollama_kwargs)
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article_polish_lm = OllamaClient(max_tokens=4000, **ollama_kwargs)
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lm_configs.set_conv_simulator_lm(conv_simulator_lm)
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lm_configs.set_question_asker_lm(question_asker_lm)
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lm_configs.set_outline_gen_lm(outline_gen_lm)
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lm_configs.set_article_gen_lm(article_gen_lm)
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lm_configs.set_article_polish_lm(article_polish_lm)
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engine_args = STORMWikiRunnerArguments(
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output_dir=args.output_dir,
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max_conv_turn=args.max_conv_turn,
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max_perspective=args.max_perspective,
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search_top_k=args.search_top_k,
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max_thread_num=args.max_thread_num,
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)
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# STORM is a knowledge curation system which consumes information from the retrieval module.
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# Currently, the information source is the Internet and we use search engine API as the retrieval module.
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if args.retriever == 'bing':
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rm = BingSearch(bing_search_api=os.getenv('BING_SEARCH_API_KEY'), k=engine_args.search_top_k)
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elif args.retriever == 'you':
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rm = YouRM(ydc_api_key=os.getenv('YDC_API_KEY'), k=engine_args.search_top_k)
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runner = STORMWikiRunner(engine_args, lm_configs, rm)
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# Open LMs are generally weaker in following output format.
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# One way for mitigation is to add one-shot example to the prompt to exemplify the desired output format.
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# For example, we can add the following examples to the two prompts used in StormPersonaGenerator.
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# Note that the example should be an object of dspy.Example with fields matching the InputField
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# and OutputField in the prompt (i.e., dspy.Signature).
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find_related_topic_example = Example(
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topic="Knowledge Curation",
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related_topics="https://en.wikipedia.org/wiki/Knowledge_management\n"
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"https://en.wikipedia.org/wiki/Information_science\n"
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"https://en.wikipedia.org/wiki/Library_science\n"
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)
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gen_persona_example = Example(
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topic="Knowledge Curation",
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examples="Title: Knowledge management\n"
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"Table of Contents: History\nResearch\n Dimensions\n Strategies\n Motivations\nKM technologies"
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"\nKnowledge barriers\nKnowledge retention\nKnowledge audit\nKnowledge protection\n"
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" Knowledge protection methods\n Formal methods\n Informal methods\n"
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" Balancing knowledge protection and knowledge sharing\n Knowledge protection risks",
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personas="1. Historian of Knowledge Systems: This editor will focus on the history and evolution of knowledge curation. They will provide context on how knowledge curation has changed over time and its impact on modern practices.\n"
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"2. Information Science Professional: With insights from 'Information science', this editor will explore the foundational theories, definitions, and philosophy that underpin knowledge curation\n"
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"3. Digital Librarian: This editor will delve into the specifics of how digital libraries operate, including software, metadata, digital preservation.\n"
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"4. Technical expert: This editor will focus on the technical aspects of knowledge curation, such as common features of content management systems.\n"
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"5. Museum Curator: The museum curator will contribute expertise on the curation of physical items and the transition of these practices into the digital realm."
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)
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runner.storm_knowledge_curation_module.persona_generator.create_writer_with_persona.find_related_topic.demos = [
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find_related_topic_example]
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runner.storm_knowledge_curation_module.persona_generator.create_writer_with_persona.gen_persona.demos = [
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gen_persona_example]
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# A trade-off of adding one-shot example is that it will increase the input length of the prompt. Also, some
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# examples may be very long (e.g., an example for writing a section based on the given information), which may
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# confuse the model. For these cases, you can create a pseudo-example that is short and easy to understand to steer
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# the model's output format.
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# For example, we can add the following pseudo-examples to the prompt used in WritePageOutlineFromConv and
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# ConvToSection.
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write_page_outline_example = Example(
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topic="Example Topic",
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conv="Wikipedia Writer: ...\nExpert: ...\nWikipedia Writer: ...\nExpert: ...",
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old_outline="# Section 1\n## Subsection 1\n## Subsection 2\n"
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"# Section 2\n## Subsection 1\n## Subsection 2\n"
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"# Section 3",
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outline="# New Section 1\n## New Subsection 1\n## New Subsection 2\n"
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"# New Section 2\n"
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"# New Section 3\n## New Subsection 1\n## New Subsection 2\n## New Subsection 3"
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)
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runner.storm_outline_generation_module.write_outline.write_page_outline.demos = [write_page_outline_example]
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write_section_example = Example(
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info="[1]\nInformation in document 1\n[2]\nInformation in document 2\n[3]\nInformation in document 3",
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topic="Example Topic",
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section="Example Section",
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output="# Example Topic\n## Subsection 1\n"
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"This is an example sentence [1]. This is another example sentence [2][3].\n"
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"## Subsection 2\nThis is one more example sentence [1]."
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)
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runner.storm_article_generation.section_gen.write_section.demos = [write_section_example]
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topic = input('Topic: ')
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runner.run(
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topic=topic,
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do_research=args.do_research,
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do_generate_outline=args.do_generate_outline,
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do_generate_article=args.do_generate_article,
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do_polish_article=args.do_polish_article,
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)
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runner.post_run()
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runner.summary()
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if __name__ == '__main__':
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parser = ArgumentParser()
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# global arguments
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parser.add_argument('--url', type=str, default='http://localhost',
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help='URL of the Ollama server.')
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parser.add_argument('--port', type=int, default=11434,
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help='Port of the Ollama server.')
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parser.add_argument('--model', type=str, default='llama3:latest',
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help='Model of the Ollama server.')
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parser.add_argument('--output-dir', type=str, default='./results/ollama',
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help='Directory to store the outputs.')
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parser.add_argument('--max-thread-num', type=int, default=3,
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help='Maximum number of threads to use. The information seeking part and the article generation'
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'part can speed up by using multiple threads. Consider reducing it if keep getting '
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'"Exceed rate limit" error when calling LM API.')
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parser.add_argument('--retriever', type=str, choices=['bing', 'you'],
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help='The search engine API to use for retrieving information.')
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# stage of the pipeline
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parser.add_argument('--do-research', action='store_true',
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help='If True, simulate conversation to research the topic; otherwise, load the results.')
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parser.add_argument('--do-generate-outline', action='store_true',
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help='If True, generate an outline for the topic; otherwise, load the results.')
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parser.add_argument('--do-generate-article', action='store_true',
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help='If True, generate an article for the topic; otherwise, load the results.')
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parser.add_argument('--do-polish-article', action='store_true',
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help='If True, polish the article by adding a summarization section and (optionally) removing '
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'duplicate content.')
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# hyperparameters for the pre-writing stage
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parser.add_argument('--max-conv-turn', type=int, default=3,
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help='Maximum number of questions in conversational question asking.')
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parser.add_argument('--max-perspective', type=int, default=3,
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help='Maximum number of perspectives to consider in perspective-guided question asking.')
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parser.add_argument('--search-top-k', type=int, default=3,
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help='Top k search results to consider for each search query.')
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# hyperparameters for the writing stage
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parser.add_argument('--retrieve-top-k', type=int, default=3,
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help='Top k collected references for each section title.')
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parser.add_argument('--remove-duplicate', action='store_true',
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help='If True, remove duplicate content from the article.')
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main(parser.parse_args()) |