EntityIdentification
实体识别
识别两组数据是否来自同一实体。 识别数据是否来自同一主体
这是一个 MCP(模型上下文协议)服务器。 这是一个支持MCP协议的服务器。
数据比较工具
此工具提供了一种全面的方法,可以比较两组数据,评估其值的精确性和语义相等性。它利用文本规范化和语言模型来确定数据是否来自同一实体。
特征
文本规范化:将文本转换为小写,删除标点符号,并规范化空格。
值比较:直接和语义上比较值(忽略列表的顺序)。
JSON 遍历:遍历 JSON 对象中的每个键并比较相应的值。
语言模型集成:使用生成语言模型评估语义相似度,并对数据是否来自同一实体做出最终判断。
Related MCP server: Agentic RAG with MCP Server
安装
要使用此工具,请确保已安装必要的依赖项。您可以使用 pip 安装它们:
pip install genai用法
功能
规范化文本(文本) :
通过将输入文本转换为小写、删除标点符号和规范化空格来规范化输入文本。
比较值(val1,val2) :
精确且语义地比较两个值。
如果值是列表,它会忽略元素的顺序以进行语义比较。
比较json(json1,json2) :
按键比较两个 JSON 对象。
使用
compare_values来评估每个键的值。集成语言模型评估语义相似度并给出最终判断。
例子
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)贡献
欢迎贡献!请打开一个问题或提交一个拉取请求。
执照
本项目遵循 MIT 许可证。详情请参阅LICENSE文件。
接触
如果您有任何问题或建议,请联系我:
GitHub: u3588064@connect.hku.hk 。
微信
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
No tool schema history has been recorded yet.
This server cannot be installed
Maintenance
Resources
Unclaimed servers have limited discoverability.
Looking for Admin?
If you are the server author, to access and configure the admin panel.
Related MCP Connectors
MCP server providing access to the Scorecard API to evaluate and optimize LLM systems.
MCP server for VC pitch-deck scoring, thesis-fit matching, and deal-flow management.
MCP server unifying ERPs, CRMs, APIs and knowledge base for Claude, ChatGPT and Gemini.
Related MCP Servers
- FlicenseNot gradedqualityDmaintenanceThis MCP server extracts entities and relationships from text and stores them in Neo4j, supporting multiple isolated knowledge graph projects that share the same database.101-
- FlicenseNot gradedqualityDmaintenanceThe MCP server provides tools for entity extraction, query refinement, and relevance checking to enhance RAG applications by integrating with OpenAI and Gemini.-
- FlicenseNot gradedqualityDmaintenanceAn MCP server that implements a heavily typed knowledge graph memory system with AI-powered entity and relation extraction, enabling structured knowledge storage and retrieval from unstructured text using predefined or custom ontologies.9-
- FlicenseNot gradedqualityBmaintenanceThis MCP server enables human-in-the-loop semantic labeling by creating self-contained HTML forms for ambiguous data and securely retrieving labeled results.-
Latest Blog Posts
- Who's Calling? MCP Hosts Are an Identity Blind Spot (And the Spec Knows It)By Om-Shree-0709 on .mcpAgent IdentityOAuth 2.1
- Your AI Chatbot Just Exposed Your CEO's Salary to an InternBy Om-Shree-0709 on .Agent IdentityMCP SecurityOAuth Delegation
- Why MCP Servers Need Execution Sandboxing (And Why Your Current Stack Isn't Enough)By Om-Shree-0709 on .Agentic AiPrompt InjectionWebAssembly
MCP directory API
We provide all the information about MCP servers via our MCP API.
curl -X GET 'https://glama.ai/api/mcp/v1/servers/u3588064/Entity-Resolution'
If you have feedback or need assistance with the MCP directory API, please join our Discord server