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| import json | |
| import os.path | |
| from utils.references import References | |
| from utils.knowledge import Knowledge | |
| from utils.file_operations import hash_name, make_archive, copy_templates | |
| from utils.tex_processing import create_copies | |
| from section_generator import section_generation # figures_generation, section_generation_bg, keywords_generation, | |
| from utils.prompts import generate_paper_prompts | |
| import logging | |
| import time | |
| from langchain.vectorstores import FAISS | |
| from utils.gpt_interaction import GPTModel | |
| from utils.prompts import SYSTEM | |
| from models import EMBEDDINGS | |
| TOTAL_TOKENS = 0 | |
| TOTAL_PROMPTS_TOKENS = 0 | |
| TOTAL_COMPLETION_TOKENS = 0 | |
| def log_usage(usage, generating_target, print_out=True): | |
| global TOTAL_TOKENS | |
| global TOTAL_PROMPTS_TOKENS | |
| global TOTAL_COMPLETION_TOKENS | |
| prompts_tokens = usage['prompt_tokens'] | |
| completion_tokens = usage['completion_tokens'] | |
| total_tokens = usage['total_tokens'] | |
| TOTAL_TOKENS += total_tokens | |
| TOTAL_PROMPTS_TOKENS += prompts_tokens | |
| TOTAL_COMPLETION_TOKENS += completion_tokens | |
| message = f">>USAGE>> For generating {generating_target}, {total_tokens} tokens have been used " \ | |
| f"({prompts_tokens} for prompts; {completion_tokens} for completion). " \ | |
| f"{TOTAL_TOKENS} tokens have been used in total." | |
| if print_out: | |
| print(message) | |
| logging.info(message) | |
| def _generation_setup(title, description="", template="ICLR2022", | |
| tldr=False, max_kw_refs=10, bib_refs=None, max_tokens_ref=2048, # generating references | |
| knowledge_database=None, max_tokens_kd=2048, query_counts=10, # querying from knowledge database | |
| debug=True): | |
| """ | |
| This function handles the setup process for paper generation; it contains three folds | |
| 1. Copy the template to the outputs folder. Create the log file `generation.log` | |
| 2. Collect references based on the given `title` and `description` | |
| 3. Generate the basic `paper` object (a dictionary) | |
| Parameters: | |
| title (str): The title of the paper. | |
| description (str, optional): A short description or abstract for the paper. Defaults to an empty string. | |
| template (str, optional): The template to be used for paper generation. Defaults to "ICLR2022". | |
| tldr (bool, optional): A flag indicating whether a TL;DR (Too Long; Didn't Read) summary should be used | |
| for the collected papers. Defaults to False. | |
| max_kw_refs (int, optional): The maximum number of references that can be associated with each keyword. | |
| Defaults to 10. | |
| bib_refs (path to a bibtex file, optional). | |
| Returns: | |
| tuple: A tuple containing the following elements: | |
| - paper (dict): A dictionary containing the generated paper information. | |
| - destination_folder (str): The path to the destination folder where the generation log is saved. | |
| - all_paper_ids (list): A list of all paper IDs collected for the references. | |
| """ | |
| # print("Generation setup...") | |
| # paper = {} | |
| # paper_body = {} | |
| llm = GPTModel(model="gpt-3.5-turbo") | |
| # Create a copy in the outputs folder. | |
| bibtex_path, destination_folder = copy_templates(template, title) | |
| logging.basicConfig(level=logging.INFO, filename=os.path.join(destination_folder, "generation.log")) | |
| ################################################################################################################### | |
| # Generate contributions | |
| ################################################################################################################### | |
| if description: | |
| contributions = description | |
| else: | |
| try: | |
| contributions, usage = llm(systems=SYSTEM["contributions"], prompts=title, return_json=True) | |
| contributions = [f"Contribution {idx}: {contributions[contribution]['statement']}\n" \ | |
| f"Novelty of Contribution {idx}: {contributions[contribution]['reason']}\n" | |
| for idx, contribution in enumerate(contributions)] | |
| contributions = "".join(contributions) | |
| log_usage(usage, "contributions") | |
| except RuntimeError: | |
| if debug: | |
| raise RuntimeError("Failed to generate contributions.") | |
| else: | |
| print("Failed to generate contributions. Use empty contributions.") | |
| contributions = "" | |
| print("Contributions:\n{}".format(contributions)) | |
| ################################################################################################################### | |
| # Generate references | |
| ################################################################################################################### | |
| # input_dict = {"title": title, "description": description} | |
| # keywords, usage = keywords_generation(input_dict) | |
| # log_usage(usage, "keywords") | |
| try: | |
| keywords, usage = llm(systems=SYSTEM["keywords"], prompts=title, return_json=True) | |
| log_usage(usage, "keywords") | |
| keywords = {keyword: max_kw_refs for keyword in keywords} | |
| except RuntimeError: | |
| if debug: | |
| raise RuntimeError("Failed to generate keywords.") | |
| else: | |
| print("Failed to generate keywords. Use default keywords.") | |
| keywords = {"machine learning": max_kw_refs, "artificial intelligence": max_kw_refs} # DEFAULT KEYWORDS | |
| # generate keywords dictionary | |
| # keywords = {keyword: max_kw_refs for keyword in keywords} | |
| print("Keywords: \n", keywords) | |
| # todo: in some rare situations, collected papers will be an empty list. handle this issue | |
| ref = References(title, bib_refs) | |
| ref.collect_papers(keywords, tldr=tldr) | |
| references = ref.to_prompts(max_tokens=max_tokens_ref) | |
| all_paper_ids = ref.to_bibtex(bibtex_path) | |
| ################################################################################################################### | |
| # Generate domain knowledge | |
| ################################################################################################################### | |
| prompts = f"Title: {title}\n Contributions: {contributions}" | |
| preliminaries_kw, _ = llm(systems=SYSTEM["preliminaries"], prompts=prompts) | |
| # check if the database exists or not | |
| db_path = f"knowledge_databases/{knowledge_database}" | |
| db_config_path = os.path.join(db_path, "db_meta.json") | |
| db_index_path = os.path.join(db_path, "faiss_index") | |
| if os.path.isdir(db_path): | |
| try: | |
| # load configuration file | |
| with open(db_config_path, "r", encoding="utf-8") as f: | |
| db_config = json.load(f) | |
| model_name = db_config["embedding_model"] | |
| embeddings = EMBEDDINGS[model_name] | |
| db = FAISS.load_local(db_index_path, embeddings) | |
| knowledge = Knowledge(db=db) | |
| knowledge.collect_knowledge(preliminaries_kw, max_query=query_counts) | |
| domain_knowledge = knowledge.to_prompts(max_tokens_kd) | |
| except Exception as e: | |
| if debug: | |
| raise RuntimeError(f"Failed to query from FAISS. Error {e}.") | |
| else: | |
| print(f"Failed to query from FAISS. Error {e}. Use empty domain knowledge instead.") | |
| domain_knowledge = "" | |
| else: | |
| print("Selected database doesn't exist or no database is selected.") | |
| domain_knowledge = "" | |
| ################################################################################################################### | |
| # Generate necessary media | |
| ################################################################################################################### | |
| prompts = f"Title: {title}\n Contributions: {contributions}" | |
| try: | |
| components, usage = llm(systems=SYSTEM["components"], prompts=prompts, return_json=True) | |
| log_usage(usage, "media") | |
| except RuntimeError: | |
| if debug: | |
| raise RuntimeError("Failed to generate media.") | |
| else: | |
| print("Failed to generate media. Use default media.") | |
| components = {} | |
| print(f"The paper information has been initialized. References are saved to {bibtex_path}.") | |
| paper = {} | |
| paper_body = {} | |
| paper["title"] = title | |
| paper["description"] = contributions | |
| paper["references"] = references | |
| paper["body"] = paper_body | |
| paper["bibtex"] = bibtex_path | |
| paper["domain_knowledge"] = domain_knowledge | |
| paper["components"] = components | |
| # print(json.dumps(paper, indent=4)) | |
| return paper, destination_folder, all_paper_ids | |
| # todo: use `all_paper_ids` to check if all citations are in this list | |
| def generate_backgrounds(title, description="", template="ICLR2022", model="gpt-4"): | |
| # todo: to match the current generation setup | |
| paper, destination_folder, _ = _generation_setup(title, description, template, model) | |
| for section in ["introduction", "related works", "backgrounds"]: | |
| try: | |
| usage = section_generation_bg(paper, section, destination_folder, model=model) | |
| log_usage(usage, section) | |
| except Exception as e: | |
| message = f"Failed to generate {section}. {type(e).__name__} was raised: {e}" | |
| print(message) | |
| logging.info(message) | |
| print(f"The paper '{title}' has been generated. Saved to {destination_folder}.") | |
| input_dict = {"title": title, "description": description, "generator": "generate_backgrounds"} | |
| filename = hash_name(input_dict) + ".zip" | |
| return make_archive(destination_folder, filename) | |
| def generate_draft(title, description="", # main input | |
| tldr=True, max_kw_refs=10, bib_refs=None, max_tokens_ref=2048, # references | |
| knowledge_database=None, max_tokens_kd=2048, query_counts=10, # domain knowledge | |
| sections=None, model="gpt-4", template="ICLR2022", prompts_mode=False, # outputs parameters | |
| ): | |
| """ | |
| This function generates a draft paper using the provided information; it contains three steps: 1. Pre-processing: | |
| Initializes the setup for paper generation and filters the sections to be included in the paper. 2. Processing: | |
| Generates each section of the paper. 3. Post-processing: Creates backup copies of the paper and returns the paper | |
| in a zipped format. | |
| Parameters: | |
| title (str): The title of the paper. | |
| description (str, optional): A short description or abstract for the paper. Defaults to an empty string. | |
| template (str, optional): The template to be used for paper generation. Defaults to "ICLR2022". | |
| tldr (bool, optional): A flag indicating whether a TL;DR (Too Long; Didn't Read) summary should be used | |
| for the collected papers. Defaults to True. | |
| max_kw_refs (int, optional): The maximum number of references that can be associated with each keyword. | |
| Defaults to 10. | |
| sections (list, optional): The sections to be included in the paper. If not provided, all the standard | |
| sections are included. | |
| bib_refs (path to a bibtex file, optional). | |
| model (str, optional): The language model to be used for paper generation. Defaults to "gpt-4". | |
| Returns: | |
| str: The path to the zipped file containing the generated paper and associated files. | |
| Note: The function also handles errors that occur during section generation and retries a maximum of 4 times | |
| before proceeding. | |
| """ | |
| def _filter_sections(sections): | |
| ordered_sections = ["introduction", "related works", "backgrounds", "methodology", "experiments", "conclusion", | |
| "abstract"] | |
| return [section for section in ordered_sections if section in sections] | |
| # pre-processing `sections` parameter; | |
| print("================START================") | |
| print(f"Generating the paper '{title}'.") | |
| print("================PRE-PROCESSING================") | |
| # make `sections` in a correct order | |
| if sections is None: | |
| sections = ["introduction", "related works", "backgrounds", "methodology", "experiments", "conclusion", | |
| "abstract"] | |
| else: | |
| sections = _filter_sections(sections) | |
| paper, destination_folder, _ = _generation_setup(title, description, template, tldr, max_kw_refs, bib_refs, | |
| max_tokens_ref=max_tokens_ref, max_tokens_kd=max_tokens_kd, | |
| query_counts=query_counts, | |
| knowledge_database=knowledge_database) | |
| # main components | |
| prompts_dict = {} | |
| print(f"================PROCESSING================") | |
| for section in sections: | |
| if prompts_mode: | |
| prompts = generate_paper_prompts(paper, section) | |
| prompts_dict[section] = prompts | |
| continue | |
| print(f"Generate {section} part...") | |
| max_attempts = 4 | |
| attempts_count = 0 | |
| while attempts_count < max_attempts: | |
| try: | |
| usage = section_generation(paper, section, destination_folder, model=model) | |
| print(f"{section} part has been generated. ") | |
| log_usage(usage, section) | |
| break | |
| except Exception as e: | |
| message = f"Failed to generate {section}. {type(e).__name__} was raised: {e}\n" | |
| print(message) | |
| logging.info(message) | |
| attempts_count += 1 | |
| time.sleep(15) | |
| # post-processing | |
| print("================POST-PROCESSING================") | |
| create_copies(destination_folder) | |
| input_dict = {"title": title, "description": description, "generator": "generate_draft"} | |
| filename = hash_name(input_dict) + ".zip" | |
| print("\nMission completed.\n") | |
| if prompts_mode: | |
| filename = hash_name(input_dict) + ".json" | |
| with open(filename, "w") as f: | |
| json.dump(prompts_dict, f) | |
| return filename | |
| else: | |
| return make_archive(destination_folder, filename) | |
| if __name__ == "__main__": | |
| import openai | |
| openai.api_key = os.getenv("OPENAI_API_KEY") | |
| openai.api_base = os.getenv("OPENAI_API_BASE") | |
| target_title = "Playing Atari with Decentralized Reinforcement Learning" | |
| output = generate_draft(target_title, knowledge_database="ml_textbook_test") | |
| print(output) | |