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97 lines
3.5 KiB
Markdown
97 lines
3.5 KiB
Markdown
---
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title: "Gemini Embedding"
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type: docs
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weight: 1
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description: >
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Use Google's Gemini models to generate high-performance text embeddings for
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vector databases.
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---
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## About
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Google Gemini provides state-of-the-art embedding models that convert text into
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high-dimensional vectors.
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### Authentication
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Toolbox supports two authentication modes:
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1. **Google AI (API Key):** Used if you
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provide `apiKey` (or set `GOOGLE_API_KEY`/`GEMINI_API_KEY` environment
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variables). This uses the [Google AI Studio][ai-studio] backend.
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2. **Vertex AI (ADC):** Used if provided `project` and `location` (or set
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`GOOGLE_CLOUD_PROJECT`/`GOOGLE_CLOUD_LOCATION` environment variables). This uses [Application
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Default Credentials (ADC)][adc].
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We recommend using an API key for quick testing and using Vertex AI with ADC for
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production environments.
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[adc]: https://cloud.google.com/docs/authentication#adc
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[api-key]: https://ai.google.dev/gemini-api/docs/api-key#api-keys
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[ai-studio]: https://aistudio.google.com/app/apikey
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## Behavior
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### Automatic Vectorization
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When a tool parameter is configured with `embeddedBy: <your-gemini-model-name>`,
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the Toolbox intercepts the raw text input from the client and sends it to the
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Gemini API. The resulting numerical array is then formatted before being passed
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to your database source.
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### Dimension Matching
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The `dimension` field must match the expected size of your database column
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(e.g., a `vector(768)` column in PostgreSQL). This setting is supported by newer
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models since 2024 only. You cannot set this value if using the earlier model
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(`models/embedding-001`). Check out [available Gemini models][modellist] for
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more information.
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[modellist]:
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https://docs.cloud.google.com/vertex-ai/generative-ai/docs/embeddings/get-text-embeddings#supported-models
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## Example
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### Using Google AI
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Google AI uses API Key for authentication. You can get an API key from [Google
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AI Studio][ai-studio].
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```yaml
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kind: embeddingModel
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name: gemini-model
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type: gemini
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model: gemini-embedding-001
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apiKey: ${GOOGLE_API_KEY}
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dimension: 768
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```
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### Using Vertex AI
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Vertex AI uses Application Default Credentials (ADC) for authentication. Learn
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how to set up ADC [here][adc].
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```yaml
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kind: embeddingModel
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name: gemini-model
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type: gemini
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model: gemini-embedding-001
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project: ${GOOGLE_CLOUD_PROJECT}
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location: us-central1
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dimension: 768
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```
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[adc]: https://docs.cloud.google.com/docs/authentication/provide-credentials-adc
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{{< notice tip >}} Use environment variable replacement with the format
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${ENV_NAME} instead of hardcoding your secrets into the configuration file.
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{{< /notice >}}
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## Reference
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| **field** | **type** | **required** | **description** |
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| ----------- | :------: | :----------: | ---------------------------------------------------------------------------------------------------------------------------------------------------- |
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| type | string | true | Must be `gemini`. |
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| model | string | true | The Gemini model ID to use (e.g., `gemini-embedding-001`). |
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| dimension | integer | false | The number of dimensions in the output vector (e.g., `768`). |
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