New RM Serper

examples/helper/process_kaggle_arxiv_abstract_dataset.py

@@ -25,4 +25,4 @@
     df['description'] = ''
 
     print(f'The downsampled dataset has {len(df)} samples.')
-    df.to_csv(args.output_path, index=False)
+    df.to_csv(args.output_path, index=False)

examples/run_storm_wiki_claude.py

@@ -114,4 +114,4 @@ def main(args):
     parser.add_argument('--remove-duplicate', action='store_true',
                         help='If True, remove duplicate content from the article.')
 
-    main(parser.parse_args())
+    main(parser.parse_args())

examples/run_storm_wiki_gpt.py

@@ -126,4 +126,4 @@ def main(args):
     parser.add_argument('--remove-duplicate', action='store_true',
                         help='If True, remove duplicate content from the article.')
 
-    main(parser.parse_args())
+    main(parser.parse_args())

examples/run_storm_wiki_gpt_with_VectorRM.py

@@ -169,4 +169,4 @@ def main(args):
                         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())
+    main(parser.parse_args())

examples/run_storm_wiki_mistral.py

@@ -172,4 +172,4 @@ def main(args):
     parser.add_argument('--remove-duplicate', action='store_true',
                         help='If True, remove duplicate content from the article.')
 
-    main(parser.parse_args())
+    main(parser.parse_args())

examples/run_storm_wiki_serper.py

@@ -0,0 +1,175 @@
+"""
+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())