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    Entity Resolution

    MCP (Model Context Protocol) server for identifying whether two sets of data are from the same entity. 识别两组数据是否来自同一主体的MCP服务器

    1 stars
    JavaScript
    Updated May 3, 2025

    Table of Contents

    • Features
    • Installation
    • Usage
    • Functions
    • Example
    • Contributing
    • License
    • Contact

    Table of Contents

    • Features
    • Installation
    • Usage
    • Functions
    • Example
    • Contributing
    • License
    • Contact

    Documentation

    EntityIdentification

    Identify whether two sets of data are from the same entity. 识别两组数据是否来自同一主体

    This is a MCP (Model Context Protocol) server. 这是一个支持MCP协议的服务器。

    Data Comparison Tool

    This tool provides a comprehensive way to compare two sets of data, evaluating both exact and semantic equality of their values. It leverages text normalization and a language model to determine if the data originates from the same entity.

    Features

    • Text Normalization: Converts text to lowercase, removes punctuation, and normalizes whitespace.
    • Value Comparison: Compares values directly and semantically (ignoring order for lists).
    • JSON Traversal: Iterates through each key in the JSON objects and compares corresponding values.
    • Language Model Integration: Uses a generative language model to assess semantic similarity and provide a final judgment on whether the data comes from the same entity.

    Installation

    To use this tool, ensure you have the necessary dependencies installed. You can install them using pip:

    bash
    pip install genai

    Usage

    Functions

    1. normalize_text(text):

    • Normalizes the input text by converting it to lowercase, removing punctuation, and normalizing whitespace.

    2. compare_values(val1, val2):

    • Compares two values both exactly and semantically.
    • If the values are lists, it ignores the order of elements for semantic comparison.

    3. compare_json(json1, json2):

    • Compares two JSON objects key by key.
    • Uses compare_values to evaluate each key's values.
    • Integrates a language model to assess semantic similarity and provides a final judgment.

    Example

    python
    import json
    import genai
    import re
    
    # Define your JSON objects
    json1 = {
        "name": "John Doe",
        "address": "123 Main St, Anytown, USA",
        "hobbies": ["reading", "hiking", "coding"]
    }
    
    json2 = {
        "name": "john doe",
        "address": "123 Main Street, Anytown, USA",
        "hobbies": ["coding", "hiking", "reading"]
    }
    
    # Compare the JSON objects
    comparison_results = compare_json(json1, json2)
    
    # Generate final matching result
    model1 = genai.GenerativeModel("gemini-2.0-flash-thinking-exp")
    result_matching = model1.generate_content("综合这些信息,你认为可以判断两个数据来自同一主体吗?"+json.dumps(comparison_results, ensure_ascii=False, indent=4))
    print(result_matching.text)

    Contributing

    Contributions are welcome! Please open an issue or submit a pull request.

    License

    This project is licensed under the MIT License. See the LICENSE file for details.

    Contact

    If you have any questions or suggestions, please contact me:

    • Email: u3588064@connect.hku.hk
    • GitHub: u3588064@connect.hku.hk。

    Wechat

    qrcode_for_gh_643efb7db5bc_344(1)

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