open-index/open-npm huggingface dataset:
npm package: @seekdb/qwen
Description: This package provides an embedding function using the Alibaba Cloud DashScope Qwen Embeddings API. Qwen offers high-quality text embedding models that can convert text into dense vector representations, with support for both China and international region
Latest version: 1.2.0

# Qwen Embedding Function for seekdb

This package provides an embedding function using the Alibaba Cloud DashScope Qwen Embeddings API. Qwen offers high-quality text embedding models that can convert text into dense vector representations, with support for both China and international regions.

## Installation

```bash
npm install @seekdb/qwen
```

## Usage

### Quick Start

```typescript
import { QwenEmbeddingFunction } from "@seekdb/qwen";

const qwenEmbed = new QwenEmbeddingFunction({
  modelName: "text-embedding-v4",
});
```

## Configuration Options

- **apiKey**: DashScope API key (optional, defaults to environment variable)
- **apiKeyEnvVar**: Environment variable name for the API key (default: `'DASHSCOPE_API_KEY'`)
- **modelName**: Model name to use (default: `'text-embedding-v4'`)
- **dimensions**: Embedding dimensions (default: `1024`)
- **region**: API region, either `'cn'` (China) or `'intl'` (International) (default: `'cn'`)
npm package: @seekdb/sentence-transformer
Description: Sentence Transformer embedding function for SeekDB.
Latest version: 1.2.0

# @seekdb/sentence-transformer

Sentence Transformer embedding function for SeekDB.

Sentence Transformer is a deep learning framework designed to convert sentences, phrases, or short passages into high-dimensional vectors (also called embeddings). Its core idea is that semantically similar sentences should be close to each other in the vector space, while semantically different sentences should be far apart, so you can measure semantic similarity by computing cosine similarity or Euclidean distance between vectors. The framework is built on powerful pre-trained Transformer models (such as BERT and RoBERTa) and uses pooling strategies (e.g., mean pooling, CLS pooling) to aggregate token vectors into a fixed-size sentence-level vector. It is widely used in semantic search, text clustering, sentence classification, information retrieval, and retrieval-augmented generation (RAG) scenarios.

## Authentication (ADC)

- The `sentence-transformer` library wraps core capabilities such as model loading and encoding.
- Sentence Transformer typically runs locally. When you use a specified model for the first time, its pre-trained weights are automatically downloaded from the Hugging Face Hub and cached locally, without requiring any additional API key authentication.

## Installation

```bash
npm i seekdb @seekdb/sentence-transformer
```

## Usage

```typescript
import { SentenceTransformerEmbeddingFunction } from "@seekdb/sentence-transformer";

const ef = new SentenceTransformerEmbeddingFunction({
  modelName: "Xenova/all-MiniLM-L6-v2",
  device: "cpu",
  normalizeEmbeddings: false,
});
```

## Configuration

- **modelName**: model name (default: `"Xenova/all-MiniLM-L6-v2"`)
- **device**: device (default: `"cpu"`)
- **normalizeEmbeddings**: normalize output vectors (default: `false`)
- **kwargs**: task pipeline options (optional; JSON-serializable)
npm package: @seekdb/siliconflow
Description: Siliconflow embedding function for SeekDB.
Latest version: 1.2.0

# @seekdb/siliconflow

Siliconflow embedding function for SeekDB.

SiliconFlow is a one-stop cloud service platform that brings together a wide range of leading open-source large models. Its Embedding service offers a suite of high-performance semantic vector models, widely used in retrieval-augmented generation (RAG) for LLMs, search, recommendations, and other scenarios. Models on the platform—such as the BGE family and the Qwen Embedding family—provide strong semantic representation capabilities, and some models (e.g., Qwen3-Embedding) also support flexible output dimensions to balance accuracy and storage cost.

## Installation

```bash
npm i seekdb @seekdb/siliconflow
```

## Usage

```typescript
import { SiliconFlowEmbeddingFunction } from "@seekdb/siliconflow";

const ef = new SiliconFlowEmbeddingFunction({
  modelName: "BAAI/bge-large-zh-v1.5",
});
```

## Configuration

- **api_key_env**：API Key 所在的环境变量名，默认是 `SILICONFLOW_API_KEY`。
- **modelName**: model name (default: `"BAAI/bge-large-zh-v1.5"`)
npm package: @seekdb/tencent-hunyuan
Description: Tencent Hunyuan embedding function for SeekDB.
Latest version: 1.2.0

# @seekdb/tencent-hunyuan

Tencent Hunyuan embedding function for SeekDB.

Tencent Hunyuan is a large language model fully developed in-house by Tencent across the entire stack, delivering excellent performance in high-quality content creation, mathematical and logical reasoning, code generation, and multi-turn conversations. Its Embedding API (GetEmbedding) is part of the Tencent Hunyuan model API suite, converting input text into high-quality 1024-dimensional vector representations and providing strong semantic understanding for building applications such as RAG systems and agent memory stores.

## Installation

```bash
npm i seekdb @seekdb/tencent-hunyuan
```

## Usage

```typescript
import { TencentHunyuanEmbeddingFunction } from "@seekdb/tencent-hunyuan";

const ef = new TencentHunyuanEmbeddingFunction({
  modelName: "hunyuan-embedding",
});
```

## Configuration

- **api_key_env**：API Key 所在的环境变量名，默认是 `HUNYUAN_API_KEY`。
- **modelName**: model name (default: `"hunyuan-embedding"`)
npm package: @seekdb/voyageai
Description: Voyage AI embedding function for SeekDB.
Latest version: 1.2.0

# @seekdb/voyageai

Voyage AI embedding function for SeekDB.

VoyageAI EmbeddingFunction is one of SeekDB’s embedding adapters designed to support a diverse AI ecosystem. As an AI-native hybrid search database, SeekDB’s core strength lies in providing unified storage and retrieval for vectors, full-text, structured, and semi-structured data. With embedding functions like this, developers can easily call external, high-quality embedding services (such as VoyageAI) to inject powerful semantic understanding into SeekDB, enabling more accurate RAG systems, agent memory stores, and related applications.

## Installation

```bash
npm i seekdb @seekdb/voyageai
```

## Usage

```typescript
import { VoyageAIEmbeddingFunction } from "@seekdb/voyageai";

const ef = new VoyageAIEmbeddingFunction({
  modelName: "voyage-4-large",
  // inputType: "document",
  // truncation: true,
});
```

## Configuration

- **apiKey**: API key (optional; can be provided via env var)
- **apiKeyEnvVar**: API key env var name (default: `"VOYAGE_API_KEY"`)
- **modelName**: model name (default: `"voyage-4-large"`)
- **inputType**: Voyage input type (optional)
- **truncation**: truncate inputs (optional)