EntityIdentification

EntityIdentification

By u3588064 GitHub

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

Overview

What is EntityIdentification?

EntityIdentification is a Model Context Protocol (MCP) server designed to determine whether two sets of data originate from the same entity.

How to use EntityIdentification?

To use EntityIdentification, install the necessary dependencies using pip and utilize the provided functions to compare data sets.

Key features of EntityIdentification?

  • Text Normalization: Standardizes text by converting it to lowercase, removing punctuation, and normalizing whitespace.
  • Value Comparison: Compares values both exactly and semantically, ignoring order for lists.
  • JSON Traversal: Iterates through JSON objects to compare corresponding values.
  • Language Model Integration: Uses a generative language model to assess semantic similarity and provide a final judgment.

Use cases of EntityIdentification?

  1. Identifying duplicate records in databases.
  2. Merging datasets from different sources.
  3. Validating data integrity in data pipelines.

FAQ from EntityIdentification?

  • Can EntityIdentification handle large datasets?

Yes! It is designed to efficiently compare large sets of data.

  • Is EntityIdentification free to use?

Yes! The project is open-source and free to use.

  • How accurate is the comparison?

The accuracy depends on the quality of the input data and the effectiveness of the normalization process.

Content

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:

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

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:

Wechat qrcode_for_gh_643efb7db5bc_344(1)

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