src/DeepThink/modules/mindmap.py

import concurrent.futures
import logging
import os
import re
import json
import numpy as np
import networkx as nx
import matplotlib.pyplot as plt
from concurrent.futures import as_completed
from typing import Union, List, Tuple, Optional, Dict
from sentence_transformers import SentenceTransformer
from sklearn.metrics.pairwise import cosine_similarity
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
# sys.path.append('./src')

# from utils import ArticleTextProcessing
import dspy
from http import HTTPStatus

import dashscope


try:
    from streamlit.runtime.scriptrunner import add_script_run_ctx
    streamlit_connection = True
except ImportError as err:
    streamlit_connection = False

script_dir = os.path.dirname(os.path.abspath(__file__))




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 ConceptGenerator(dspy.Module):
    """Extract information and generate a list of concepts."""
    def __init__(self, lm: Union[dspy.dsp.LM, dspy.dsp.HFModel]):
        super().__init__()
        self.lm = lm
        self.concept_generator = dspy.Predict(GenConcept)

    def forward(self, infos: List[Dict]):
        snippets_list = []
        for info in infos:
            snippet = info.get('snippets', [])
            snippets_list.extend(snippet)
        
        snippets_list_str = "\n".join(f"{index + 1}. {snippet}" for index, snippet in enumerate(snippets_list))
        snippets_list_str = ArticleTextProcessing.limit_word_count_preserve_newline(snippets_list_str, 3000)

        with dspy.settings.context(lm=self.lm):
            concepts = self.concept_generator(info=snippets_list_str).concepts

        pattern = r"\d+\.\s*(.*)"
        matches = re.findall(pattern, concepts)
        concept_list = [match.strip() for match in matches]

        return concept_list

class ExtendConcept(dspy.Signature):
    """
    You are an analytical robot. I will provide you with a subject, the information I have searched about it, and our preliminary concept of it. I need you to generate a detailed, in-depth, and insightful report based on it, further exploring our initial ideas. 

    First, break down the subject into several broad categories, then create corresponding search engine keywords for each category. 

    Note: The new categories should not repeat the previous ones. 

    Your output format should be as follows:  
    -[Category 1]  
    --{Keyword 1}  
    --{Keyword 2}  
    -[Category 2]  
    --{Keyword 1}  
    --{Keyword 2}  
    The number of categories should be less than 5, and the number of keywords for each category should be less than 3.
    """
    info = dspy.InputField(prefix='The information you have collected from the webpage:', format=str)
    concept = dspy.InputField(prefix='The summary of the previous concepts:', format=str)
    category = dspy.InputField(prefix='The broader categories you need to further expand:', format=str)
    keywords = dspy.OutputField(format=str)


class GenConcept(dspy.Signature):
    """
    Please analyze, summarize, and evaluate the following webpage information. 
    Think like a person, distill the core point of each piece of information, and synthesize them into a comprehensive opinion. 
    Present your comprehensive opinion in the format of 1. 2. ...
    """
    info = dspy.InputField(prefix='The webpage information you have collected:', format=str)
    concepts = dspy.OutputField(format=str)


class MindPoint():
    def __init__(self, retriever, lm: Union[dspy.dsp.LM, dspy.dsp.HFModel], root: bool = False,
                 children: Optional[List['MindPoint']] = None, concept: str = '',
                 info: Optional[List[Dict]] = None, category: str = ''):
        self.root = root
        self.category = category
        self.children = children if children is not None else {}
        self.concept = concept
        self.info = info if info is not None else []
        self.lm = lm
        self.retriever = retriever
        self.concept_generator = ConceptGenerator(lm=lm)
    
    def extend(self):
        extend_concept = dspy.Predict(ExtendConcept)
        with dspy.settings.context(lm=self.lm):
            info='\n'.join([str(i) for i in self.info])
            keywords = extend_concept(info='\n'.join([str(i) for i in self.info]), concept=self.concept, category = self.category).keywords
        print(keywords)
        print('-----keywords------')
        categories = {}
        current_category = None
        for line in keywords.split('\n'):
            line = line.strip()
            if (line.startswith('-[') and line.endswith(']')) or (line.startswith('- [') and line.endswith(']')):
                current_category = line[2:-1]
                categories[current_category] = []
            elif (line.startswith('--{') and current_category) or (line.startswith('-- {') and current_category) or (line.startswith('--') and current_category):
                keyword = line[3:-1].strip()
                if keyword:
                    categories[current_category].append(keyword)

        for category, keywords_list in categories.items():
            new_info = self.retriever(keywords_list)
            new_concept = self.concept_generator.forward(new_info)
            new_node = MindPoint(concept=new_concept, info=new_info, lm=self.lm, retriever=self.retriever, category=category)
            self.children[category] = new_node


class MindMap():
    def __init__(self, 
                 retriever, 
                 gen_concept_lm: Union[dspy.dsp.LM, dspy.dsp.HFModel],
                 gen_concept_lm2: Union[dspy.dsp.LM, dspy.dsp.HFModel],
                 search_top_k: int ,
                 depth: int
                 ):
        self.retriever = retriever
        self.gen_concept_lm = gen_concept_lm
        self.gen_concept_lm2 = gen_concept_lm2
        self.search_top_k = search_top_k
        self.depth = depth
        self.concept_generator = ConceptGenerator(lm=self.gen_concept_lm)
        self.root = None

    def build_map(self, topic: str):
        root_info = self.retriever(topic)
        root_concept = self.concept_generator(root_info)
        root = MindPoint(root=True, info=root_info, concept=root_concept, lm=self.gen_concept_lm2, retriever=self.retriever, category=topic)
        self.root = root
        
        current_level = [root]
        
        for count in range(self.depth):
            next_level = []

            yield current_level
            if count == self.depth - 1:  # Check if it's the last layer
                break
            
            with concurrent.futures.ThreadPoolExecutor() as executor:
                futures = {executor.submit(node.extend): node for node in current_level}
                
                for future in concurrent.futures.as_completed(futures):
                    node = futures[future]
                    # Assuming extend populates children.
                    next_level.extend(node.children.values())
            
            yield current_level
            current_level = next_level
    
    def recursive_extend(self, node: MindPoint, count: int):
        if count >= self.depth:
            return
        node.extend()
        count += 1
        
        with concurrent.futures.ThreadPoolExecutor() as executor:
            futures = [executor.submit(self.recursive_extend, child, count + 1) for child in node.children.values()]

    def save_map(self, root: MindPoint, filename: str):
        def serialize_node(node: MindPoint):
            return {
                'category': node.category,
                'concept': node.concept,
                'children': {k: serialize_node(v) for k, v in node.children.items()},
                'info':node.info,
            }
        
        mind_map_dict = serialize_node(root)
        with open(filename, 'w', encoding='utf-8') as f:
            json.dump(mind_map_dict, f, ensure_ascii=False, indent=2)

    def load_map(self, filename: str):
        def deserialize_node(node_data):
            category = node_data['category']
            concept = node_data['concept']
            info = node_data['info']
            children_data = node_data['children']

            node = MindPoint(concept=concept, info=info, lm=self.gen_concept_lm, retriever=self.retriever, category=category)
            node.children = {k: deserialize_node(v) for k, v in children_data.items()}
            return node
        
        with open(filename, 'r', encoding='utf-8') as f:
            mind_map_dict = json.load(f)
        
        self.root = deserialize_node(mind_map_dict)
        return self.root

    def export_categories_and_concepts(self) -> str:
        root = self.root
        output = []

        def traverse(node: MindPoint, indent=0):
            output.append(" " * indent + node.category)
            for concept in node.concept:
                output.append(" " * (indent + 2) + concept)
            for child in node.children.values():
                traverse(child, indent + 2)

        traverse(root)
        return "\n".join(output)

    def get_all_infos(self) -> List[Dict[str, any]]:
        """
        Get all unique info from the MindMap, ensuring unique URLs.
        """
        all_infos = []
        seen_urls = set()

        def traverse(node: MindPoint):
            if node.info:
                for info in node.info:
                    url = info.get('url')
                    if url and url not in seen_urls:
                        seen_urls.add(url)
                        all_infos.append(info)
            for child in node.children.values():
                traverse(child)

        traverse(self.root)
        self.all_infos = all_infos
        return all_infos


    # def encoder(self, inputs):
    #     print('the length of encoder', len(inputs))
    #     if not isinstance(inputs, list):
    #         inputs = [inputs]

    #     split_list = lambda lst, n=20: [lst[i:i + n] for i in range(0, len(lst), n)]
    #     double_list = split_list(inputs)
    #     for data in double_list:
    #         resp = dashscope.TextEmbedding.call(
    #             model=dashscope.TextEmbedding.Models.text_embedding_v1,
    #             input=data
    #         )
    #         embeddings = []
    #         if resp.status_code == HTTPStatus.OK:
    #             for emb in resp['output']['embeddings']:
    #                 embeddings.append(emb['embedding'])
    
    def get_web_number(self):
        self.collected_urls = []
        self.collected_snippets = []
        seen_urls = set()

        for info in self.get_all_infos():
            url = info.get('url')
            snippets = info.get('snippets', [])
            if url and url not in seen_urls:
                seen_urls.add(url)
                for snippet in snippets:
                    self.collected_urls.append(url)
                    self.collected_snippets.append(snippet)

        return len(self.collected_snippets)

    def prepare_table_for_retrieval(self):
        """
        Prepare collected snippets and URLs for retrieval by encoding the snippets using paraphrase-MiniLM-L6-v2.
        collected_urls and collected_snippets have corresponding indices.
        """
        self.encoder = SentenceTransformer('./model/paraphrase-MiniLM-L6-v2')
        self.collected_urls = []
        self.collected_snippets = []
        seen_urls = set()

        for info in self.get_all_infos():
            url = info.get('url')
            snippets = info.get('snippets', [])
            if url and url not in seen_urls:
                seen_urls.add(url)
                for snippet in snippets:
                    self.collected_urls.append(url)
                    self.collected_snippets.append(snippet)

        self.encoded_snippets = self.encoder.encode(self.collected_snippets, show_progress_bar=True)

    def retrieve_information(self, queries: Union[List[str], str], search_top_k) -> List[Dict[str, any]]:
        """
        Retrieve relevant information based on the given queries.
        Returns a list of dictionaries containing 'url' and 'snippets'.
        """
        selected_urls = []
        selected_snippets = []
        if type(queries) is str:
            queries = [queries]
        for query in queries:
            encoded_query = self.encoder.encode(query, show_progress_bar=True)
            sim = cosine_similarity([encoded_query], self.encoded_snippets)[0]
            sorted_indices = np.argsort(sim)
            for i in sorted_indices[-search_top_k:][::-1]:
                selected_urls.append(self.collected_urls[i])
                selected_snippets.append(self.collected_snippets[i])

        url_to_snippets = {}
        for url, snippet in zip(selected_urls, selected_snippets):
            if url not in url_to_snippets:
                url_to_snippets[url] = set()
            url_to_snippets[url].add(snippet)

        result = []
        for url, snippets in url_to_snippets.items():
            result.append({
                'url': url,
                'snippets': list(snippets)
            })

        return result

    def visualize_map(self, root: MindPoint):
        G = nx.DiGraph()

        def add_edges(node: MindPoint, parent=None):
            if parent is not None:
                G.add_edge(parent, node.category)
            for child in node.children.values():
                add_edges(child, node.category)
        
        add_edges(root)

        plt.figure(figsize=(12, 8))
        pos = nx.spring_layout(G)
        nx.draw(G, pos, with_labels=True, node_size=3000, node_color='skyblue', font_size=10, font_weight='bold', arrows=True)
        plt.title("MindMap Visualization", fontsize=15)
        plt.show()


if __name__ == "__main__":
    import sys
    sys.path.append('/mnt/nas-alinlp/xizekun/project/DeepThink/src')

    from lm import OpenAIModel, OpenAIModel_New
    from rm import BingSearch, BingSearchAli
    from utils import load_api_key

    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,
    )
    
    root = mind_map.build_map('Taylor Hawkins')
    mind_map.save_map(root, '/mnt/nas-alinlp/xizekun/project/DeepThink/src/DeepThink/modules/Taylor.json')
    # a = mind_map.load_map('/mnt/nas-alinlp/xizekun/project/DeepThink/src/DeepThink/modules/mind_map_人工智能的发展趋势.json')
    b = mind_map.get_all_infos()

    
    # print(len(b))
    # mind_map.prepare_table_for_retrieval()
    # c = mind_map.retrieve_information('政府',5)
    # print(c)