Google Gemini ↗

    # import
    import chromadb.utils.embedding_functions as embedding_functions

    # The GoogleGenaiEmbeddingFunction expects the API key in the GOOGLE_API_KEY environment variable.
    google_ef  = embedding_functions.GoogleGenaiEmbeddingFunction(model_name="models/gemini-embedding-001")
    google_ef(["document1","document2"])

    # pass documents to query for .add and .query
    collection = client.create_collection(name="name", embedding_function=google_ef)
    collection = client.get_collection(name="name", embedding_function=google_ef)
    ```

You can view a more [complete example](https://github.com/chroma-core/chroma/tree/main/examples/gemini) chatting over documents with Gemini embedding and langauge models.

For more info - please visit the [official Google docs](https://ai.google.dev/gemini-api/docs/embeddings).
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```typescript
    // npm install @chroma-core/google-gemini

    import { ChromaClient } from "chromadb";
    import { GoogleGeminiEmbeddingFunction } from "@chroma-core/google-gemini";

    const embedder = new GoogleGeminiEmbeddingFunction({
      apiKey: "<YOUR API KEY>",
      modelName: "gemini-embedding-001",
    });

    // use directly
    const embeddings = await embedder.generate(["document1", "document2"]);

    // pass documents to query for .add and .query
    const collection = await client.createCollection({
      name: "name",
      embeddingFunction: embedder,
    });
    const collectionGet = await client.getCollection({
      name: "name",
      embeddingFunction: embedder,
    });
    ```

You can view a more [complete example using Node](https://github.com/chroma-core/chroma/blob/main/clients/js/examples/node/app.js).

For more info - please visit the [official Google docs](https://ai.google.dev/gemini-api/docs/embeddings).
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