import concurrent.futures import copy import logging from concurrent.futures import as_completed from typing import List, Union import random import dspy import sys import concurrent.futures import json import os import pickle import re import sys from typing import List, Dict import httpx import toml from langchain_text_splitters import RecursiveCharacterTextSplitter from trafilatura import extract class ArticleTextProcessing: @staticmethod def limit_word_count_preserve_newline(input_string, max_word_count): """ Limit the word count of an input string to a specified maximum, while preserving the integrity of complete lines. The function truncates the input string at the nearest word that does not exceed the maximum word count, ensuring that no partial lines are included in the output. Words are defined as text separated by spaces, and lines are defined as text separated by newline characters. Args: input_string (str): The string to be truncated. This string may contain multiple lines. max_word_count (int): The maximum number of words allowed in the truncated string. Returns: str: The truncated string with word count limited to `max_word_count`, preserving complete lines. """ word_count = 0 limited_string = '' for word in input_string.split('\n'): line_words = word.split() for lw in line_words: if word_count < max_word_count: limited_string += lw + ' ' word_count += 1 else: break if word_count >= max_word_count: break limited_string = limited_string.strip() + '\n' return limited_string.strip() @staticmethod def remove_citations(s): """ Removes all citations from a given string. Citations are assumed to be in the format of numbers enclosed in square brackets, such as [1], [2], or [1, 2], etc. This function searches for all occurrences of such patterns and removes them, returning the cleaned string. Args: s (str): The string from which citations are to be removed. Returns: str: The string with all citation patterns removed. """ return re.sub(r'\[\d+(?:,\s*\d+)*\]', '', s) @staticmethod def get_first_section_dict_and_list(s): """ """ text = s sections = text.strip().split('\n# ') titles = [] content_dict = {} for section in sections: if section: lines = section.split('\n', 1) title = lines[0].strip() content = lines[1].strip() if len(lines) > 1 else "" titles.append(title) content_dict[title] = content return content_dict, titles @staticmethod def parse_citation_indices(s): """ Extracts citation indexes from the provided content string and returns them as a list of integers. Args: content (str): The content string containing citations in the format [number]. Returns: List[int]: A list of unique citation indexes extracted from the content, in the order they appear. """ matches = re.findall(r'\[\d+\]', s) return [int(index[1:-1]) for index in matches] @staticmethod def remove_uncompleted_sentences_with_citations(text): """ Removes uncompleted sentences and standalone citations from the input text. Sentences are identified by their ending punctuation (.!?), optionally followed by a citation in square brackets (e.g., "[1]"). Grouped citations (e.g., "[1, 2]") are split into individual ones (e.g., "[1] [2]"). Only text up to and including the last complete sentence and its citation is retained. Args: text (str): The input text from which uncompleted sentences and their citations are to be removed. Returns: str: The processed string with uncompleted sentences and standalone citations removed, leaving only complete sentences and their associated citations if present. """ # Convert citations like [1, 2, 3] to [1][2][3]. def replace_with_individual_brackets(match): numbers = match.group(1).split(', ') return ' '.join(f'[{n}]' for n in numbers) # Deduplicate and sort individual groups of citations. def deduplicate_group(match): citations = match.group(0) unique_citations = list(set(re.findall(r'\[\d+\]', citations))) sorted_citations = sorted(unique_citations, key=lambda x: int(x.strip('[]'))) # Return the sorted unique citations as a string return ''.join(sorted_citations) text = re.sub(r'\[([0-9, ]+)\]', replace_with_individual_brackets, text) text = re.sub(r'(\[\d+\])+', deduplicate_group, text) # Deprecated: Remove sentence without proper ending punctuation and citations. # Split the text into sentences (including citations). # sentences_with_trailing = re.findall(r'([^.!?]*[.!?].*?)(?=[^.!?]*[.!?]|$)', text) # Filter sentences to ensure they end with a punctuation mark and properly formatted citations # complete_sentences = [] # for sentence in sentences_with_trailing: # # Check if the sentence ends with properly formatted citations # if re.search(r'[.!?]( \[\d+\])*$|^[^.!?]*[.!?]$', sentence.strip()): # complete_sentences.append(sentence.strip()) # combined_sentences = ' '.join(complete_sentences) # Check for and append any complete citations that follow the last sentence # trailing_citations = re.findall(r'(\[\d+\]) ', text[text.rfind(combined_sentences) + len(combined_sentences):]) # if trailing_citations: # combined_sentences += ' '.join(trailing_citations) # Regex pattern to match sentence endings, including optional citation markers. eos_pattern = r'([.!?])\s*(\[\d+\])?\s*' matches = list(re.finditer(eos_pattern, text)) if matches: last_match = matches[-1] text = text[:last_match.end()].strip() return text @staticmethod def clean_up_citation(conv): for turn in conv.dlg_history: turn.agent_utterance = turn.agent_utterance[:turn.agent_utterance.find('References:')] turn.agent_utterance = turn.agent_utterance[:turn.agent_utterance.find('Sources:')] turn.agent_utterance = turn.agent_utterance.replace('Answer:', '').strip() try: max_ref_num = max([int(x) for x in re.findall(r'\[(\d+)\]', turn.agent_utterance)]) except Exception as e: max_ref_num = 0 if max_ref_num > len(turn.search_results): for i in range(len(turn.search_results), max_ref_num + 1): turn.agent_utterance = turn.agent_utterance.replace(f'[{i}]', '') turn.agent_utterance = ArticleTextProcessing.remove_uncompleted_sentences_with_citations( turn.agent_utterance) return conv @staticmethod def clean_up_outline(outline, topic=""): output_lines = [] current_level = 0 # To track the current section level for line in outline.split('\n'): stripped_line = line.strip() if topic != "" and f"# {topic.lower()}" in stripped_line.lower(): output_lines = [] # Check if the line is a section header if stripped_line.startswith('#') and stripped_line != '#': current_level = stripped_line.count('#') output_lines.append(stripped_line) # Check if the line is a bullet point # elif stripped_line.startswith('-'): # subsection_header = '#' * (current_level + 1) + ' ' + stripped_line[1:].strip() # output_lines.append(subsection_header) # Preserve lines with @ elif stripped_line.startswith('@'): output_lines.append(stripped_line) outline = '\n'.join(output_lines) # Remove references. outline = re.sub(r"#[#]? See also.*?(?=##|$)", '', outline, flags=re.DOTALL) outline = re.sub(r"#[#]? See Also.*?(?=##|$)", '', outline, flags=re.DOTALL) outline = re.sub(r"#[#]? Notes.*?(?=##|$)", '', outline, flags=re.DOTALL) outline = re.sub(r"#[#]? References.*?(?=##|$)", '', outline, flags=re.DOTALL) outline = re.sub(r"#[#]? External links.*?(?=##|$)", '', outline, flags=re.DOTALL) outline = re.sub(r"#[#]? External Links.*?(?=##|$)", '', outline, flags=re.DOTALL) outline = re.sub(r"#[#]? Bibliography.*?(?=##|$)", '', outline, flags=re.DOTALL) outline = re.sub(r"#[#]? Further reading*?(?=##|$)", '', outline, flags=re.DOTALL) outline = re.sub(r"#[#]? Further Reading*?(?=##|$)", '', outline, flags=re.DOTALL) outline = re.sub(r"#[#]? Summary.*?(?=##|$)", '', outline, flags=re.DOTALL) outline = re.sub(r"#[#]? Appendices.*?(?=##|$)", '', outline, flags=re.DOTALL) outline = re.sub(r"#[#]? Appendix.*?(?=##|$)", '', outline, flags=re.DOTALL) return outline @staticmethod def clean_up_section(text): """Clean up a section: 1. Remove uncompleted sentences (usually due to output token limitation). 2. Deduplicate individual groups of citations. 3. Remove unnecessary summary.""" paragraphs = text.split('\n') output_paragraphs = [] summary_sec_flag = False for p in paragraphs: p = p.strip() if len(p) == 0: continue if not p.startswith('#'): p = ArticleTextProcessing.remove_uncompleted_sentences_with_citations(p) if summary_sec_flag: if p.startswith('#'): summary_sec_flag = False else: continue if p.startswith('Overall') or p.startswith('In summary') or p.startswith('In conclusion'): continue if "# Summary" in p or '# Conclusion' in p: summary_sec_flag = True continue output_paragraphs.append(p) return '\n\n'.join(output_paragraphs) # Join with '\n\n' for markdown format. @staticmethod def update_citation_index(s, citation_map): """Update citation index in the string based on the citation map.""" for original_citation in citation_map: s = s.replace(f"[{original_citation}]", f"__PLACEHOLDER_{original_citation}__") for original_citation, unify_citation in citation_map.items(): s = s.replace(f"__PLACEHOLDER_{original_citation}__", f"[{unify_citation}]") return s @staticmethod def parse_article_into_dict(input_string): """ Parses a structured text into a nested dictionary. The structure of the text is defined by markdown-like headers (using '#' symbols) to denote sections and subsections. Each section can contain content and further nested subsections. The resulting dictionary captures the hierarchical structure of sections, where each section is represented as a key (the section's title) mapping to a value that is another dictionary. This dictionary contains two keys: - 'content': content of the section - 'subsections': a list of dictionaries, each representing a nested subsection following the same structure. Args: input_string (str): A string containing the structured text to parse. Returns: A dictionary representing contains the section title as the key, and another dictionary as the value, which includes the 'content' and 'subsections' keys as described above. """ lines = input_string.split('\n') lines = [line for line in lines if line.strip()] root = {'content': '', 'subsections': {}} current_path = [(root, -1)] # (current_dict, level) for line in lines: if line.startswith('#'): level = line.count('#') title = line.strip('# ').strip() new_section = {'content': '', 'subsections': {}} # Pop from stack until find the parent level while current_path and current_path[-1][1] >= level: current_path.pop() # Append new section to the nearest upper level's subsections current_path[-1][0]['subsections'][title] = new_section current_path.append((new_section, level)) else: current_path[-1][0]['content'] += line + '\n' return root['subsections'] class FileIOHelper: @staticmethod def dump_json(obj, file_name, encoding="utf-8"): with open(file_name, 'w', encoding=encoding) as fw: json.dump(obj, fw, default=FileIOHelper.handle_non_serializable, ensure_ascii=False) @staticmethod def handle_non_serializable(obj): return "non-serializable contents" # mark the non-serializable part @staticmethod def load_json(file_name, encoding="utf-8"): with open(file_name, 'r', encoding=encoding) as fr: return json.load(fr) @staticmethod def write_str(s, path): with open(path, 'w') as f: f.write(s) @staticmethod def load_str(path): with open(path, 'r') as f: return '\n'.join(f.readlines()) @staticmethod def dump_pickle(obj, path): with open(path, 'wb') as f: pickle.dump(obj, f) @staticmethod def load_pickle(path): with open(path, 'rb') as f: return pickle.load(f) class ArticleGenerationModule(): """ The interface for article generation stage. Given topic, collected information from knowledge curation stage, generated outline from outline generation stage, """ def __init__(self, retriever, article_gen_lm=Union[dspy.dsp.LM, dspy.dsp.HFModel], retrieve_top_k: int = 10, max_thread_num: int = 10, ): super().__init__() self.retrieve_top_k = retrieve_top_k self.article_gen_lm = article_gen_lm self.max_thread_num = max_thread_num self.retriever = retriever self.section_gen = ConvToSection(engine=self.article_gen_lm) def generate_section(self, topic, section_name, mindmap, section_query, section_outline): collected_info = mindmap.retrieve_information(queries=section_query, search_top_k=self.retrieve_top_k) output = self.section_gen( topic=topic, outline=section_outline, section=section_name, collected_info=collected_info, ) return {"section_name": section_name, "section_content": output.section, "collected_info": collected_info} def generate_article(self, topic: str, mindmap, article_with_outline, ): """ Generate article for the topic based on the information table and article outline. """ mindmap.prepare_table_for_retrieval() sections_to_write = article_with_outline.get_first_level_section_names() section_output_dict_collection = [] with concurrent.futures.ThreadPoolExecutor(max_workers=self.max_thread_num) as executor: future_to_sec_title = {} for section_title in sections_to_write: section_query = article_with_outline.get_outline_as_list( root_section_name=section_title, add_hashtags=False ) queries_with_hashtags = article_with_outline.get_outline_as_list( root_section_name=section_title, add_hashtags=True ) section_outline = "\n".join(queries_with_hashtags) future_to_sec_title[ executor.submit(self.generate_section, topic, section_title, mindmap, section_query,section_outline) ] = section_title for future in concurrent.futures.as_completed(future_to_sec_title): section_output_dict_collection.append(future.result()) article = copy.deepcopy(article_with_outline) for section_output_dict in section_output_dict_collection: article.update_section(parent_section_name=topic, current_section_content=section_output_dict["section_content"], current_section_info_list=section_output_dict["collected_info"], ) article.post_processing() return article class ConvToSection(dspy.Module): """Use the information collected from the information-seeking conversation to write a section.""" #给你传入的都是所有的section的对应的url，但是这个地方我们的目标是指根据一个来生成，这个地方需要完善，因为他的outline没有用到 def __init__(self, engine: Union[dspy.dsp.LM, dspy.dsp.HFModel]): super().__init__() self.write_section = dspy.Predict(WriteSection) self.engine = engine def forward(self, topic: str, outline:str, section: str, collected_info: List): all_info = '' for idx, info in enumerate(collected_info): all_info += f'[{idx + 1}]\n' + '\n'.join(info['snippets']) all_info += '\n\n' all_info = ArticleTextProcessing.limit_word_count_preserve_newline(all_info, 1500) with dspy.settings.context(lm=self.engine): section = ArticleTextProcessing.clean_up_section( self.write_section(topic=topic, info=info, section=section).output) section = section.replace('\[','[').replace('\]',']') return dspy.Prediction(section=section) class WriteSection(dspy.Signature): """Write a Wikipedia section based on the collected information. Here is the format of your writing: 1. Use "#" Title" to indicate section title, "##" Title" to indicate subsection title, "###" Title" to indicate subsubsection title, and so on. 2. Use [1], [2], ..., [n] in line (for example, "The capital of the United States is Washington, D.C.[1][3]."). You DO NOT need to include a References or Sources section to list the sources at the end. 3. The language style should resemble that of Wikipedia: concise yet informative, formal yet accessible. """ # """ # Write a detailed, Wikipedia-style report section based on the collected information. # Here is the format of your writing: # 1. Use "#" Title" to indicate section title, "##" Title" to indicate subsection title, "###" Title" to indicate subsubsection title, and so on. # 2. Use [1], [2], ..., [n] in line (for example, "The capital of the United States is Washington, D.C.[1][3]."). You DO NOT need to include a References or Sources section to list the sources at the end. # 3. The language style should resemble that of Wikipedia: concise yet informative, formal yet accessible. # """ info = dspy.InputField(prefix="The Collected information:\n", format=str) topic = dspy.InputField(prefix="The topic of the page: ", format=str) section = dspy.InputField(prefix="The section you need to write: ", format=str) output = dspy.OutputField( prefix="Write the section with proper inline citations (Start your writing with # section title. Don't include the page title or try to write other sections):\n", format=str) if __name__ == "__main__": import sys from mindmap import MindMap from outline_generation import OutlineGenerationModule sys.path.append('/mnt/nas-alinlp/xizekun/project/DeepThink/src') from storm_dataclass import Article from lm import OpenAIModel, OpenAIModel_New from rm import BingSearch, BingSearchAli from utils import load_api_key import os load_api_key(toml_file_path='/mnt/nas-alinlp/xizekun/project/DeepThink/secrets.toml') 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'), } lm = OpenAIModel(model='gpt-4-1106-preview', max_tokens=5000, **openai_kwargs) rm = BingSearchAli(ydc_api_key=os.getenv('BING_SEARCH_ALI_API_KEY'), k=3) retriever = rm gen_concept_lm = lm mind_map = MindMap( retriever=retriever, gen_concept_lm=lm, search_top_k=3, deepth = 3 ) a = mind_map.load_map('/mnt/nas-alinlp/xizekun/project/DeepThink/src/DeepThink/modules/Taylor.json') ag = ArticleGenerationModule( retriever = retriever, article_gen_lm = lm, retrieve_top_k = 5, max_thread_num = 10) module = OutlineGenerationModule(lm) outline = module.generate_outline(topic= 'Taylor Hawkins',mindmap = mind_map) print(outline) print('~~~~~~') article_with_outline = Article.from_outline_str(topic='Taylor Hawkins', outline_str=outline) a = ag.generate_article(topic = 'Taylor Hawkins', mindmap = mind_map, article_with_outline = article_with_outline) print(a.to_string())