open-index/open-npm huggingface dataset:
npm package: @ntrip/geonames
Description: GeoNames.org Parser
Author: Nebojša Cvetković
Latest version: 1.0.0

# GeoNames.org Data File Parser
Helper class to read [GeoNames](http://geonames.org) data files with 0 dependencies.

```typescript
import {GeoNames} from 'geonames';
```

## GeoNames
```typescript
const geoNames = new GeoNames('path/to/geonames.txt');
```

### Methods
##### async *geoNames\[Symbol.asyncIterator\]()
Generator function to get list of places found in the data file without loading all into memory at once.
```typescript
for await (let place: Place of geoNames) {
    console.log(place.name);
}
```

##### geoNames.stream()

##### geoNames.nearest(to: {latitude: number, longitude: number})
Finds the place nearest to the location provided, without loading all places into memory.
```typescript
let nearest = geoNames.nearest({latitude: 53.33, longitude: -6.24});
console.log(`${nearest.name}, ${nearest.countryCode}`) // Dublin, IE
```

## Place
```typescript
class Place {
    id: number;

    name: string;
    asciiName: string;
    alternativeNames: string[];

    latitude: number;
    longitude: number;

    featureClass: string;
    featureCode: string;

    countryCode: string;
    alternativeCountryCode: string;

    adminCode1: string;
    adminCode2: string;
    adminCode3: string;
    adminCode4: string;

    population: number;

    elevation: number;
    digitalElevationModel: number;

    timezone: string;
    modificationDate: Date;

    // Distance of place provided location 
    distance(to: {latitude: number, longitude: number}): number;
}
```
npm package: @ntrwansui/koishi-plugin-prts
Description: 自用机器人功能插件
Author: ntr
Latest version: 2.2.4

# @ntrwansui/koishi-plugin-prts

[![npm](https://img.shields.io/npm/v/@ntrwansui/koishi-plugin-prts?style=flat-square)](https://www.npmjs.com/package/@ntrwansui/koishi-plugin-prts)
npm package: @ntrwansui/koishi-plugin-prts-apimart
Description: 自用AI生图插件，基于APIMart的API
Author: ntr
Latest version: 1.0.5

# @ntrwansui/koishi-plugin-prts-apimart

[![npm](https://img.shields.io/npm/v/@ntrwansui/koishi-plugin-prts-apimart?style=flat-square)](https://www.npmjs.com/package/@ntrwansui/koishi-plugin-prts-apimart)
npm package: @ntrwansui/koishi-plugin-prts-grsai
Description: 自用图片编辑插件，基于Grsai的API
Author: ntr
Latest version: 2.3.1

# @ntrwansui/koishi-plugin-prts-grsai

[![npm](https://img.shields.io/npm/v/@ntrwansui/koishi-plugin-prts-grsai?style=flat-square)](https://www.npmjs.com/package/@ntrwansui/koishi-plugin-prts-grsai)
npm package: @ntrwansui/koishi-plugin-prts-novelai
Description: Generate images by Novelai
Author: ntr
Latest version: 2.0.0

# koishi-plugin-prts-novelai

[![npm](https://img.shields.io/npm/v/koishi-plugin-prts-novelai?style=flat-square)](https://www.npmjs.com/package/koishi-plugin-prts-novelai)

novelai plugin
npm package: @ntrwansui/koishi-plugin-waifu-diffusion-tagger
Description: 图片反推AI生成标签，nsfw程度评分及角色识别，自修改版本，添加ProxyAgent
Latest version: 0.3.2

# koishi-plugin-waifu-diffusion-tagger

[![npm](https://img.shields.io/npm/v/koishi-plugin-waifu-diffusion-tagger?style=flat-square)](https://www.npmjs.com/package/koishi-plugin-waifu-diffusion-tagger)

图片反推AI生成标签，nsfw程度评分及角色识别
使用的API：https://smilingwolf-wd-tagger.hf.space/
npm package: @nts-llm-lab/telco-dpr-mcp-server
Description: Telco-DPR enhanced MCP server for 3GPP RAG with Gemini LLM integration. Provides research-validated 86.2% Top-10 accuracy using Dense Hierarchical Retrieval.
Author: Pitachoo11
Latest version: 1.0.17

# Project Brief

## Vision & Purpose
To provide a high-performance, research-validated Retrieval-Augmented Generation (RAG) server for 3GPP technical specifications. The system, named "Telco-DPR MCP Server," integrates with Gemini LLM via the Model Context Protocol (MCP) to deliver accurate, context-aware answers to complex telecommunications queries.

The core innovation is the implementation of Dense Hierarchical Retrieval (DHR), achieving 86.2% Top-10 accuracy and a 4x performance speedup over standard retrieval methods, as validated by the research paper arXiv:2410.19790.

## Key Stakeholders
- **Telecom Engineers & Researchers**: Users who need to query dense 3GPP technical standards for specific information on procedures, parameters, and protocols.
- **LLM Application Developers**: Developers integrating the Telco-DPR server as a specialized tool for their AI agents or applications.
- **Gemini LLM**: The primary consumer of the MCP-based tool, using it to answer user prompts related to telecommunications.

## Success Metrics
- **Accuracy**: Maintain or exceed the 86.2% Top-10 retrieval accuracy benchmark from the Telco-DPR research.
- **Performance**: Achieve and maintain the 4x performance speedup compared to traditional Dense Passage Retrieval (DPR).
- **Adoption**: Successful integration and usage as a tool by Gemini and other LLM applications.
- **Stability**: High uptime and low error rates in the production environment.

## Project Constraints
- **Research Alignment**: The implementation must strictly adhere to the methodologies described in the Telco-DPR research paper (arXiv:2410.19790).
- **Dependency on Pre-processing**: The server's functionality is entirely dependent on the offline data preparation offline service to generate the necessary indices and embeddings.