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""" | ||
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 | ||
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) | ||
""" | ||
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import os | ||
import sys | ||
from argparse import ArgumentParser | ||
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sys.path.append('./src') | ||
from lm import OpenAIModel | ||
from storm_wiki.engine import STORMWikiRunnerArguments, STORMWikiRunner, STORMWikiLMConfigs | ||
from utils import load_api_key | ||
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def main(args): | ||
load_api_key(toml_file_path='secrets.toml') | ||
lm_configs = STORMWikiLMConfigs() | ||
openai_kwargs = { | ||
'api_key': os.getenv("OPENAI_API_KEY"), | ||
'api_provider': os.getenv('OPENAI_API_TYPE'), | ||
'temperature': 1.0, | ||
'top_p': 0.9, | ||
'api_base': os.getenv('AZURE_API_BASE'), | ||
'api_version': os.getenv('AZURE_API_VERSION'), | ||
} | ||
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# 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 = OpenAIModel(model='gpt-3.5-turbo', max_tokens=500, **openai_kwargs) | ||
question_asker_lm = OpenAIModel(model='gpt-3.5-turbo', max_tokens=500, **openai_kwargs) | ||
outline_gen_lm = OpenAIModel(model='gpt-4-0125-preview', max_tokens=400, **openai_kwargs) | ||
article_gen_lm = OpenAIModel(model='gpt-4-0125-preview', max_tokens=700, **openai_kwargs) | ||
article_polish_lm = OpenAIModel(model='gpt-4-0125-preview', max_tokens=4000, **openai_kwargs) | ||
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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) | ||
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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, | ||
) | ||
runner = STORMWikiRunner(engine_args, lm_configs) | ||
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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() | ||
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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.') | ||
# 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.') | ||
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main(parser.parse_args()) |
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""" | ||
STORM Wiki pipeline powered by Mistral-7B-Instruct-v0.2 hosted by VLLM server and You.com search engine. | ||
You need to set up the following environment variables to run this script: | ||
- YDC_API_KEY: You.com 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) | ||
""" | ||
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import sys | ||
from argparse import ArgumentParser | ||
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from dspy import Example | ||
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sys.path.append('./src') | ||
from lm import VLLMClient | ||
from storm_wiki.engine import STORMWikiRunnerArguments, STORMWikiRunner, STORMWikiLMConfigs | ||
from utils import load_api_key | ||
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def main(args): | ||
load_api_key(toml_file_path='secrets.toml') | ||
lm_configs = STORMWikiLMConfigs() | ||
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mistral_kwargs = { | ||
"model": "mistralai/Mistral-7B-Instruct-v0.2", | ||
"port": args.port, | ||
"url": args.url, | ||
"stop": ('\n\n---',) # dspy uses "\n\n---" to separate examples. Open models sometimes generate this. | ||
} | ||
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conv_simulator_lm = VLLMClient(max_tokens=500, **mistral_kwargs) | ||
question_asker_lm = VLLMClient(max_tokens=500, **mistral_kwargs) | ||
outline_gen_lm = VLLMClient(max_tokens=400, **mistral_kwargs) | ||
article_gen_lm = VLLMClient(max_tokens=700, **mistral_kwargs) | ||
article_polish_lm = VLLMClient(max_tokens=4000, **mistral_kwargs) | ||
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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) | ||
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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, | ||
) | ||
runner = STORMWikiRunner(engine_args, lm_configs) | ||
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# Open LMs are generally weaker in following output format. | ||
# One way for mitigation is to add one-shot example to the prompt to exemplify the desired output format. | ||
# For example, we can add the following examples to the two prompts used in StormPersonaGenerator. | ||
# Note that the example should be an object of dspy.Example with fields matching the InputField | ||
# and OutputField in the prompt (i.e., dspy.Signature). | ||
find_related_topic_example = Example( | ||
topic="Knowledge Curation", | ||
related_topics="https://en.wikipedia.org/wiki/Knowledge_management\n" | ||
"https://en.wikipedia.org/wiki/Information_science\n" | ||
"https://en.wikipedia.org/wiki/Library_science\n" | ||
) | ||
gen_persona_example = Example( | ||
topic="Knowledge Curation", | ||
examples="Title: Knowledge management\n" | ||
"Table of Contents: History\nResearch\n Dimensions\n Strategies\n Motivations\nKM technologies" | ||
"\nKnowledge barriers\nKnowledge retention\nKnowledge audit\nKnowledge protection\n" | ||
" Knowledge protection methods\n Formal methods\n Informal methods\n" | ||
" Balancing knowledge protection and knowledge sharing\n Knowledge protection risks", | ||
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" | ||
"2. Information Science Professional: With insights from 'Information science', this editor will explore the foundational theories, definitions, and philosophy that underpin knowledge curation\n" | ||
"3. Digital Librarian: This editor will delve into the specifics of how digital libraries operate, including software, metadata, digital preservation.\n" | ||
"4. Technical expert: This editor will focus on the technical aspects of knowledge curation, such as common features of content management systems.\n" | ||
"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." | ||
) | ||
runner.storm_knowledge_curation_module.persona_generator.create_writer_with_persona.find_related_topic.demos = [ | ||
find_related_topic_example] | ||
runner.storm_knowledge_curation_module.persona_generator.create_writer_with_persona.gen_persona.demos = [ | ||
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 | ||
# examples may be very long (e.g., an example for writing a section based on the given information), which may | ||
# confuse the model. For these cases, you can create a pseudo-example that is short and easy to understand to steer | ||
# the model's output format. | ||
# For example, we can add the following pseudo-examples to the prompt used in WritePageOutlineFromConv and | ||
# ConvToSection. | ||
write_page_outline_example = Example( | ||
topic="Example Topic", | ||
conv="Wikipedia Writer: ...\nExpert: ...\nWikipedia Writer: ...\nExpert: ...", | ||
old_outline="# Section 1\n## Subsection 1\n## Subsection 2\n" | ||
"# Section 2\n## Subsection 1\n## Subsection 2\n" | ||
"# Section 3", | ||
outline="# New Section 1\n## New Subsection 1\n## New Subsection 2\n" | ||
"# New Section 2\n" | ||
"# New Section 3\n## New Subsection 1\n## New Subsection 2\n## New Subsection 3" | ||
) | ||
runner.storm_outline_generation_module.write_outline.write_page_outline.demos = [write_page_outline_example] | ||
write_section_example = Example( | ||
info="[1]\nInformation in document 1\n[2]\nInformation in document 2\n[3]\nInformation in document 3", | ||
topic="Example Topic", | ||
section="Example Section", | ||
output="# Example Topic\n## Subsection 1\n" | ||
"This is an example sentence [1]. This is another example sentence [2][3].\n" | ||
"## Subsection 2\nThis is one more example sentence [1]." | ||
) | ||
runner.storm_article_generation.section_gen.write_section.demos = [write_section_example] | ||
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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() | ||
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if __name__ == '__main__': | ||
parser = ArgumentParser() | ||
# global arguments | ||
parser.add_argument('--url', type=str, default='http://localhost', | ||
help='URL of the VLLM server.') | ||
parser.add_argument('--port', type=int, default=8000, | ||
help='Port of the VLLM server.') | ||
parser.add_argument('--output-dir', type=str, default='./results/mistral_7b', | ||
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.') | ||
# 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.') | ||
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main(parser.parse_args()) |