Costorm integration

examples/costorm_examples/run_costorm_gpt.py

@@ -0,0 +1,241 @@
+"""
+Co-STORM pipeline powered by GPT-4o/4o-mini and Bing 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
+    - BING_SEARCH_API_KEY: Biang search 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
+
+Output will be structured as below
+args.output_dir/
+    log.json           # Log of information-seeking conversation
+    report.txt         # Final article generated
+"""
+
+import os
+import json
+from argparse import ArgumentParser
+from knowledge_storm.collaborative_storm.engine import CollaborativeStormLMConfigs, RunnerArgument, CoStormRunner
+from knowledge_storm.collaborative_storm.modules.callback import LocalConsolePrintCallBackHandler
+from knowledge_storm.lm import OpenAIModel, AzureOpenAIModel
+from knowledge_storm.logging_wrapper import LoggingWrapper
+from knowledge_storm.rm import YouRM, BingSearch, BraveRM, SerperRM, DuckDuckGoSearchRM, TavilySearchRM, SearXNG
+from knowledge_storm.utils import load_api_key
+
+
+def main(args):
+    load_api_key(toml_file_path='secrets.toml')
+    lm_config: CollaborativeStormLMConfigs = CollaborativeStormLMConfigs()
+    openai_kwargs = {
+        "api_key": os.getenv("OPENAI_API_KEY"),
+        "api_provider": "openai",
+        "temperature": 1.0,
+        "top_p": 0.9,
+        "api_base": None,
+    } if os.getenv('OPENAI_API_TYPE') == 'openai' else {
+        "api_key": os.getenv("AZURE_API_KEY"),
+        "temperature": 1.0,
+        "top_p": 0.9,
+        "api_base": os.getenv("AZURE_API_BASE"),
+        "api_version": os.getenv("AZURE_API_VERSION"),
+    }
+
+    ModelClass = OpenAIModel if os.getenv('OPENAI_API_TYPE') == 'openai' else AzureOpenAIModel
+    # If you are using Azure service, make sure the model name matches your own deployed model name.
+    # The default name here is only used for demonstration and may not match your case.
+    gpt_4o_mini_model_name = 'gpt-4o-mini'
+    gpt_4o_model_name = 'gpt-4o'
+    if os.getenv('OPENAI_API_TYPE') == 'azure':
+        openai_kwargs['api_base'] = os.getenv('AZURE_API_BASE')
+        openai_kwargs['api_version'] = os.getenv('AZURE_API_VERSION')
+
+    # 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.
+    question_answering_lm = ModelClass(model=gpt_4o_model_name, max_tokens=1000, **openai_kwargs)
+    discourse_manage_lm = ModelClass(model=gpt_4o_model_name, max_tokens=500, **openai_kwargs)
+    utterance_polishing_lm = ModelClass(model=gpt_4o_model_name, max_tokens=2000, **openai_kwargs)
+    warmstart_outline_gen_lm = ModelClass(model=gpt_4o_model_name, max_tokens=500, **openai_kwargs)
+    question_asking_lm = ModelClass(model=gpt_4o_model_name, max_tokens=300, **openai_kwargs)
+    knowledge_base_lm = ModelClass(model=gpt_4o_model_name, max_tokens=1000, **openai_kwargs)
+
+    lm_config.set_question_answering_lm(question_answering_lm)
+    lm_config.set_discourse_manage_lm(discourse_manage_lm)
+    lm_config.set_utterance_polishing_lm(utterance_polishing_lm)
+    lm_config.set_warmstart_outline_gen_lm(warmstart_outline_gen_lm)
+    lm_config.set_question_asking_lm(question_asking_lm)
+    lm_config.set_knowledge_base_lm(knowledge_base_lm)
+
+    topic = input('Topic: ')
+    runner_argument = RunnerArgument(
+        topic=topic,
+        retrieve_top_k=args.retrieve_top_k,
+        max_search_queries=args.max_search_queries,
+        total_conv_turn=args.total_conv_turn,
+        max_search_thread=args.max_search_thread,
+        max_search_queries_per_turn=args.max_search_queries_per_turn,
+        warmstart_max_num_experts=args.warmstart_max_num_experts,
+        warmstart_max_turn_per_experts=args.warmstart_max_turn_per_experts,
+        warmstart_max_thread=args.warmstart_max_thread,
+        max_thread_num=args.max_thread_num,
+        max_num_round_table_experts=args.max_num_round_table_experts,
+        moderator_override_N_consecutive_answering_turn=args.moderator_override_N_consecutive_answering_turn,
+        node_expansion_trigger_count=args.node_expansion_trigger_count)
+    logging_wrapper = LoggingWrapper(lm_config)
+    callback_handler = LocalConsolePrintCallBackHandler() if args.enable_log_print else None
+
+    # Co-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=runner_argument.retrieve_top_k)
+        case 'you':
+             rm = YouRM(ydc_api_key=os.getenv('YDC_API_KEY'), k=runner_argument.retrieve_top_k)
+        case 'brave':
+            rm = BraveRM(brave_search_api_key=os.getenv('BRAVE_API_KEY'), k=runner_argument.retrieve_top_k)
+        case 'duckduckgo':
+            rm = DuckDuckGoSearchRM(k=runner_argument.retrieve_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=runner_argument.retrieve_top_k, include_raw_content=True)
+        case 'searxng':
+            rm = SearXNG(searxng_api_key=os.getenv('SEARXNG_API_KEY'), k=runner_argument.retrieve_top_k)
+        case _:
+             raise ValueError(f'Invalid retriever: {args.retriever}. Choose either "bing", "you", "brave", "duckduckgo", "serper", "tavily", or "searxng"')
+
+    costorm_runner = CoStormRunner(lm_config=lm_config,
+                                   runner_argument=runner_argument,
+                                   logging_wrapper=logging_wrapper,
+                                   rm=rm,
+                                   callback_handler=callback_handler)
+
+    # warm start the system
+    costorm_runner.warm_start()
+
+    # Below is an example of how users may interact with Co-STORM to seek information together
+    # In actual deployment, we suggest allowing the user to decide whether to observe the agent utterance or inject a turn
+
+    # observing Co-STORM LLM agent utterance for 5 turns
+    for _ in range(1):
+        conv_turn = costorm_runner.step()
+        print(f"**{conv_turn.role}**: {conv_turn.utterance}\n")
+
+    # active engaging by injecting your utterance
+    your_utterance = input('Your utterance: ')
+    costorm_runner.step(user_utterance=your_utterance)
+
+    # continue observing
+    conv_turn = costorm_runner.step()
+    print(f"**{conv_turn.role}**: {conv_turn.utterance}\n")
+
+    # generate report
+    costorm_runner.knowledge_base.reogranize()
+    article = costorm_runner.generate_report()
+
+    # save results
+    os.makedirs(args.output_dir, exist_ok=True)
+
+    # Save article
+    with open(os.path.join(args.output_dir, "report.md"), "w") as f:
+        f.write(article)
+
+    # Save logging
+    log_dump = costorm_runner.dump_logging_and_reset()
+    with open(os.path.join(args.output_dir, "log.json"), "w") as f:
+        json.dump(log_dump, f, indent=2)
+
+
+if __name__ == '__main__':
+    parser = ArgumentParser()
+    # global arguments
+    parser.add_argument('--output-dir', type=str, default='./results/co-storm',
+                        help='Directory to store the outputs.')
+    parser.add_argument('--retriever', type=str, choices=['bing', 'you', 'brave', 'serper', 'duckduckgo', 'tavily', 'searxng'],
+                        help='The search engine API to use for retrieving information.')
+    # hyperparameters for co-storm
+    parser.add_argument(
+        '--retrieve_top_k',
+        type=int,
+        default=10,
+        help='Retrieve top k results for each query in retriever.'
+    )
+    parser.add_argument(
+        '--max_search_queries',
+        type=int,
+        default=2,
+        help='Maximum number of search queries to consider for each question.'
+    )
+    parser.add_argument(
+        '--total_conv_turn',
+        type=int,
+        default=20,
+        help='Maximum number of turns in conversation.'
+    )
+    parser.add_argument(
+        '--max_search_thread',
+        type=int,
+        default=5,
+        help='Maximum number of parallel threads for retriever.'
+    )
+    parser.add_argument(
+        '--max_search_queries_per_turn',
+        type=int,
+        default=3,
+        help='Maximum number of search queries to consider in each turn.'
+    )
+    parser.add_argument(
+        '--warmstart_max_num_experts',
+        type=int,
+        default=3,
+        help='Max number of experts in perspective-guided QA during warm start.'
+    )
+    parser.add_argument(
+        '--warmstart_max_turn_per_experts',
+        type=int,
+        default=2,
+        help='Max number of turns per perspective during warm start.'
+    )
+    parser.add_argument(
+        '--warmstart_max_thread',
+        type=int,
+        default=3,
+        help='Max number of threads for parallel perspective-guided QA during warm start.'
+    )
+    parser.add_argument(
+        '--max_thread_num',
+        type=int,
+        default=10,
+        help=("Maximum number of threads to use. "
+              "Consider reducing it if you keep getting 'Exceed rate limit' errors when calling the LM API.")
+    )
+    parser.add_argument(
+        '--max_num_round_table_experts',
+        type=int,
+        default=2,
+        help='Max number of active experts in round table discussion.'
+    )
+    parser.add_argument(
+        '--moderator_override_N_consecutive_answering_turn',
+        type=int,
+        default=3,
+        help=('Number of consecutive expert answering turns before the moderator overrides the conversation.')
+    )
+    parser.add_argument(
+        '--node_expansion_trigger_count',
+        type=int,
+        default=10,
+        help='Trigger node expansion for nodes that contain more than N snippets.'
+    )
+
+    # Boolean flags
+    parser.add_argument(
+        '--enable_log_print',
+        action='store_true',
+        help='If set, enable console log print.'
+    )
+
+    main(parser.parse_args())

examples/README.md → examples/storm_examples/README.md

@@ -11,7 +11,7 @@ We host a number of example scripts for various customization of STORM (e.g., us
 2. Run the following command under the root directory of the repository:
 
    ```
-    python examples/run_storm_wiki_mistral.py \
+    python examples/storm_examples/run_storm_wiki_mistral.py \
        --url $URL \
        --port $PORT \
        --output-dir $OUTPUT_DIR \
@@ -50,7 +50,7 @@ By default, STORM is grounded on the Internet using the search engine, but it ca
    To create the vector store offline, run
 
    ```
-   python examples/run_storm_wiki_gpt_with_VectorRM.py \
+   python examples/storm_examples/run_storm_wiki_gpt_with_VectorRM.py \
        --output-dir $OUTPUT_DIR \
        --vector-db-mode offline \
        --offline-vector-db-dir $OFFLINE_VECTOR_DB_DIR \
@@ -65,7 +65,7 @@ By default, STORM is grounded on the Internet using the search engine, but it ca
    To create the vector store online on a Qdrant server, run
 
    ```
-   python examples/run_storm_wiki_gpt_with_VectorRM.py \
+   python examples/storm_examples/run_storm_wiki_gpt_with_VectorRM.py \
        --output-dir $OUTPUT_DIR \
        --vector-db-mode online \
        --online-vector-db-url $ONLINE_VECTOR_DB_URL \
@@ -83,12 +83,12 @@ By default, STORM is grounded on the Internet using the search engine, but it ca
    - Run the following command under the root directory to downsample the dataset by filtering papers with terms `[cs.CV]` and get a csv file that match the format mentioned above.
 
      ```
-     python examples/helper/process_kaggle_arxiv_abstract_dataset.py --input-path $PATH_TO_THE_DOWNLOADED_FILE --output-path $PATH_TO_THE_PROCESSED_CSV
+     python examples/storm_examples/helper/process_kaggle_arxiv_abstract_dataset.py --input-path $PATH_TO_THE_DOWNLOADED_FILE --output-path $PATH_TO_THE_PROCESSED_CSV
      ```
    - Run the following command to run STORM grounding on the processed dataset. You can input a topic related to computer vision (e.g., "The progress of multimodal models in computer vision") to see the generated article. (Note that the generated article may not include enough details since the quick test only use the abstracts of arxiv papers.)
 
      ```
-     python examples/run_storm_wiki_gpt_with_VectorRM.py \
+     python examples/storm_examples/run_storm_wiki_gpt_with_VectorRM.py \
          --output-dir $OUTPUT_DIR \
          --vector-db-mode offline \
          --offline-vector-db-dir $OFFLINE_VECTOR_DB_DIR \
@@ -102,7 +102,7 @@ By default, STORM is grounded on the Internet using the search engine, but it ca
    - For a quicker run, you can also download the pre-embedded vector store directly from [here](https://drive.google.com/file/d/1bijFkw5BKU7bqcmXMhO-5hg2fdKAL9bf/view?usp=share_link).
 
      ```
-     python examples/run_storm_wiki_gpt_with_VectorRM.py \
+     python examples/storm_examples/run_storm_wiki_gpt_with_VectorRM.py \
          --output-dir $OUTPUT_DIR \
          --vector-db-mode offline \
          --offline-vector-db-dir $DOWNLOADED_VECTOR_DB_DR \

examples/run_storm_wiki_gpt_with_VectorRM.py → examples/storm_examples/run_storm_wiki_gpt_with_VectorRM.py

@@ -27,10 +27,8 @@
 """
 
 import os
-import sys
 from argparse import ArgumentParser
 
-sys.path.append('./')
 from knowledge_storm import STORMWikiRunnerArguments, STORMWikiRunner, STORMWikiLMConfigs
 from knowledge_storm.rm import VectorRM
 from knowledge_storm.lm import OpenAIModel, AzureOpenAIModel

examples/run_storm_wiki_groq.py → examples/storm_examples/run_storm_wiki_groq.py

@@ -18,17 +18,10 @@
 """
 
 import os
-import sys
 import re
-import logging
 from argparse import ArgumentParser
 
 from knowledge_storm import STORMWikiRunnerArguments, STORMWikiRunner, STORMWikiLMConfigs
-# Get the absolute path to the directory containing lm.py
-lm_path = os.path.abspath(os.path.join(os.path.dirname(__file__), '..', 'knowledge_storm'))
-
-# Add this path to sys.path
-sys.path.insert(0, lm_path)
 
 # Now import lm directly
 import lm

examples/run_storm_wiki_ollama.py → examples/storm_examples/run_storm_wiki_ollama.py

@@ -21,7 +21,6 @@
 
 from dspy import Example
 
-sys.path.append('./src')
 from knowledge_storm.lm import OllamaClient
 from knowledge_storm.rm import YouRM, BingSearch, BraveRM, SerperRM, DuckDuckGoSearchRM, TavilySearchRM, SearXNG
 from knowledge_storm import STORMWikiRunnerArguments, STORMWikiRunner, STORMWikiLMConfigs

knowledge_storm/__init__.py

@@ -1,7 +1,10 @@
-from .storm_wiki.engine import (
-    STORMWikiLMConfigs,
-    STORMWikiRunnerArguments,
-    STORMWikiRunner,
-)
+from .storm_wiki import *
+from .collaborative_storm import *
+from .encoder import *
+from .interface import *
+from .lm import *
+from .rm import *
+from .utils import *
+from .dataclass import *
 
-__version__ = "0.2.8"
+__version__ = "1.0.0"

knowledge_storm/collaborative_storm/__init__.py

@@ -0,0 +1,2 @@
+from .modules import *
+from .engine import *