Google Gemini Embedding Function for Chroma

This package provides a Google Gemini embedding provider for Chroma using the Google Generative AI SDK.

Installation

``bash
npm install @chroma-core/google-gemini
`

Usage

`typescript
import { ChromaClient } from 'chromadb';
import { GoogleGeminiEmbeddingFunction } from '@chroma-core/google-gemini';

// Initialize the embedder
const embedder = new GoogleGeminiEmbeddingFunction({
apiKey: 'your-api-key', // Or set GEMINI_API_KEY env var
modelName: 'text-embedding-004', // Optional, defaults to latest model
taskType: 'RETRIEVAL_DOCUMENT', // Optional
});

// Create a new ChromaClient
const client = new ChromaClient({
path: 'http://localhost:8000',
});

// Create a collection with the embedder
const collection = await client.createCollection({
name: 'my-collection',
embeddingFunction: embedder,
});

// Add documents
await collection.add({
ids: ["1", "2", "3"],
documents: ["Document 1", "Document 2", "Document 3"],
});

// Query documents
const results = await collection.query({
queryTexts: ["Sample query"],
nResults: 2,
});
`

Configuration

Set your Google AI API key as an environment variable:

`bash
export GEMINI_API_KEY=your-api-key
`

Get your API key from the Google AI Studio.

Configuration Options

- apiKey: Your Google AI API key (or set via environment variable)
- apiKeyEnvVar: Environment variable name for API key (default: GEMINI_API_KEY)
- modelName: Model to use for embeddings
- taskType: Task type for the embedding request (e.g., RETRIEVAL_DOCUMENT, RETRIEVAL_QUERY)

Supported Models

- text-embedding-004 (latest)
- embedding-001`

Check the Google AI documentation for the most up-to-date list of available embedding models.