open-index/open-npm huggingface dataset:
npm package: @seekdb/amazon-bedrock
Description: Amazon Bedrock embedding function for SeekDB.
Latest version: 1.2.0

# @seekdb/amazon-bedrock

Amazon Bedrock embedding function for SeekDB.

Amazon Bedrock is a managed foundation model platform on AWS. seekdb provides an `AmazonBedrockEmbeddingFunction` wrapper so you can generate embeddings via Bedrock and use them with seekdb collections.

## Dependencies and authentication

- An AWS account with Bedrock enabled and permission to invoke the target embedding model

## Installation

```bash
npm i seekdb @seekdb/amazon-bedrock
```

## Usage

```typescript
import { AmazonBedrockEmbeddingFunction } from "@seekdb/amazon-bedrock";

const ef = new AmazonBedrockEmbeddingFunction({
  region: "us-east-1",
  modelName: "amazon.titan-embed-text-v2",
});
```

## Configuration

- **apiKey**: API key (optional; can be provided via env var)
- **apiKeyEnv**: API key env var name (default: `"AMAZON_BEDROCK_API_KEY"`)
- **region**: AWS region (required, e.g. `"us-east-1"`)
- **modelName**: model name (default: `"amazon.titan-embed-text-v2"`)
npm package: @seekdb/cohere
Description: Cohere embedding function for SeekDB.
Latest version: 1.2.0

# @seekdb/cohere

Cohere embedding function for SeekDB.

Cohere provides embedding models for semantic search, clustering, and recommendation. seekdb provides a `CohereEmbeddingFunction` wrapper (powered by LiteLLM) to generate Cohere embeddings and use them with seekdb collections.

## Dependencies and authentication

`CohereEmbeddingFunction` calls the Cohere embedding API via LiteLLM. In practice, you typically need:

- A Cohere API key with access to the embedding models you plan to use
- Authentication is usually provided via environment variables (by default, `COHERE_API_KEY`). If you use a different environment variable name, set `api_key_env`.

## Installation

```bash
npm i seekdb @seekdb/cohere
```

## Usage

```typescript
import { CohereEmbeddingFunction } from "@seekdb/cohere";

const ef = new CohereEmbeddingFunction({
  modelName: "embed-english-v3.0",
  inputType: "search_document",
});
```

## Configuration

- **apiKey**: API key (optional; can be provided via env var)
- **apiKeyEnvVar**: API key env var name (default: `"COHERE_API_KEY"`)
- **modelName**: model name (default: `"embed-english-v3.0"`)
- **inputType**: `"search_document" | "search_query" | "classification" | "clustering" | "image"` (default: `"search_document"`)
- **truncate**: `"NONE" | "START" | "END"` (optional)
- **embeddingType**: `"float" | "int8" | "uint8" | "binary" | "ubinary"` (optional)
npm package: @seekdb/default-embed
Description: This package provides a default embedding function using Hugging Face Transformers.js. It runs locally in Node.js without requiring external API calls or API keys.
Latest version: 1.2.0

# @seekdb/amazon-bedrock

This package provides a default embedding function using Hugging Face Transformers.js. It runs locally in Node.js without requiring external API calls or API keys.

## Installation

The seekdb SDK includes this package by default - no installation needed for basic usage.

For manual installation:

```bash
npm install @seekdb/default-embed
```

## Usage

### Quick Start (Zero Configuration)

The SDK automatically uses this embedding function when no custom function is specified:

```typescript
const collection = await client.createCollection({
  name: "local_embed_collection",
});
```

For manual import of the built-in model:

```typescript
import { DefaultEmbeddingFunction } from "@seekdb/default-embed";

const defaultEmbed = new DefaultEmbeddingFunction({
  // If you encounter download issues, try switching the region to 'intl', defaults to 'cn'
  region: "cn",
});

const collection = await client.createCollection({
  name: "local_embed_collection",
  embeddingFunction: defaultEmbed,
});
```

## Configuration Options

- **region**: Model download region (`'cn'` or `'intl'`, default: `'cn'`)
  - Use `'cn'` for faster downloads in mainland China
  - Use `'intl'` for international regions

## Features

- **No API Key Required**: Runs completely locally without external dependencies
- **Zero Configuration**: Works out of the box with seekdb SDK
- **China-Optimized**: Built-in support for faster model downloads in China

The default model is suitable for most general-purpose semantic search and RAG applications.
npm package: @seekdb/google-vertex
Description: Google Vertex AI embedding function for SeekDB.
Latest version: 1.2.0

# @seekdb/google-vertex

Google Vertex AI embedding function for SeekDB.

Google Vertex AI provides text embedding models. seekdb provides a `GoogleVertexEmbeddingFunction` wrapper so you can generate embeddings via Vertex AI and use them with seekdb collections.

## Authentication (ADC)

`GoogleVertexEmbeddingFunction` calls the Vertex AI embedding API via the Google Cloud SDK. In practice, you typically need:

- A Google Cloud project with Vertex AI enabled and permission to invoke embedding models
- Google Application Default Credentials (ADC) configured for your environment

## Installation

```bash
npm i seekdb @seekdb/google-vertex
```

## Usage

```typescript
import { GoogleVertexEmbeddingFunction } from "@seekdb/google-vertex";

const ef = new GoogleVertexEmbeddingFunction({
  projectId: "your-gcp-project-id",
  modelName: "textembedding-gecko",
});
```

## Configuration

- **projectId**: GCP project id (required)
- **location**: Vertex AI location (default: `"us-central1"`)
- **modelName**: model name (default: `"textembedding-gecko"`)
npm package: @seekdb/jina
Description: Jina embedding function for SeekDB.
Latest version: 1.2.0

# @seekdb/jina

Jina embedding function for SeekDB.

Jina AI provides embedding models for semantic search and related retrieval tasks. seekdb provides a `JinaEmbeddingFunction` wrapper (powered by LiteLLM) to generate Jina embeddings and use them with seekdb collections.

## Dependencies and authentication

`JinaEmbeddingFunction` calls the Jina embedding API via LiteLLM. In practice, you typically need:

- A Jina API key with access to the embedding models you plan to use
- Authentication is usually provided via environment variables (by default, `JINA_API_KEY`). If you use a different environment variable name, set `api_key_env`.

## Installation

```bash
npm i seekdb @seekdb/jina
```

## Usage

```typescript
import { JinaEmbeddingFunction } from "@seekdb/jina";

const ef = new JinaEmbeddingFunction({
  modelName: "jina-clip-v2",
  // apiKeyEnvVar: "JINA_API_KEY",
});
```

## Configuration

- **apiKey**: API key (optional; can be provided via env var)
- **apiKeyEnvVar**: API key env var name (default: `"JINA_API_KEY"`)
- **modelName**: model name (default: `"jina-clip-v2"`)
npm package: @seekdb/ollama
Description: Ollama embedding function for SeekDB.
Latest version: 1.2.0

# @seekdb/ollama

Ollama embedding function for SeekDB.

Ollama can run embedding models locally and exposes OpenAI-compatible endpoints for embedding generation. seekdb provides an `OllamaEmbeddingFunction` wrapper that uses the OpenAI-compatible API to generate embeddings from an Ollama server.

## Dependencies and environment

In practice, you typically need:

- Ollama installed and running
- The embedding model pulled locally, for example:
  ```shell
  ollama pull nomic-embed-text
  ```

## Installation

```bash
npm i seekdb @seekdb/ollama
```

## Usage

```typescript
import { OllamaEmbeddingFunction } from "@seekdb/ollama";

const ef = new OllamaEmbeddingFunction({
  url: "http://localhost:11434/v1",
  modelName: "nomic-embed-text",
  // apiKeyEnv: "OLLAMA_API_KEY",
});
```

## Configuration

- **url**: Ollama base URL (default: `"http://localhost:11434/v1"`)
- **modelName**: model name (default: `"nomic-embed-text"`)
- **apiKeyEnv**: API key env var name (default: `"OLLAMA_API_KEY"`, optional)

## Notes

- Make sure Ollama is running and the model is available locally (e.g. `ollama pull nomic-embed-text`).
npm package: @seekdb/openai
Description: OpenAI embedding function for SeekDB.
Latest version: 1.2.0

# @seekdb/openai

OpenAI embedding function for SeekDB.

OpenAI embedding models are a core tool in natural language processing, efficiently converting text (words, sentences, paragraphs) into high-dimensional vectors (embeddings). These vectors are generated by high-performance pre-trained models (such as text-embedding-3-large), capturing deep semantic information without requiring users to perform any additional training. The key idea is to map raw text into a dense vector space, where semantic similarity between texts can be conveniently measured by computing distances between vectors (e.g., cosine similarity). This technology is widely used in semantic search, intelligent Q&A, text clustering and classification, enhanced recommender systems, and retrieval-augmented generation (RAG) scenarios.

## Installation

```bash
npm i seekdb @seekdb/openai
```

## Usage

```typescript
import { OpenAIEmbeddingFunction } from "@seekdb/openai";

const ef = new OpenAIEmbeddingFunction({
  modelName: "text-embedding-3-small",
  // baseURL: "https://api.openai.com/v1",
});
```

## Configuration

- **apiKey**: API key (optional; can be provided via env var)
- **apiKeyEnvVar**: API key env var name (default: `"OPENAI_API_KEY"`)
- **modelName**: model name (default: `"text-embedding-3-small"`)
- **dimensions**: output dimensions (optional)
- **organizationId**: organization id (optional; default: `process.env.OPENAI_ORG_ID`)
- **baseURL**: custom base URL (optional)