confident-ai--deepeval
wehub-resource-sync
105 行
4.1 KiB
Plaintext
105 行
4.1 KiB
Plaintext
---
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# id: gemini
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title: Gemini
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sidebar_label: Gemini
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---
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`deepeval` allows you to directly integrate Gemini models into all available LLM-based metrics, either through the command line or directly within your python code.
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### Command Line
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Run the following command in your terminal to configure your deepeval environment to use Gemini models for all metrics.
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```bash
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deepeval set-gemini \
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--model=<model> # e.g. "gemini-2.5-flash"
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```
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:::info
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The CLI command above sets Gemini as the default provider for all metrics, unless overridden in Python code. To use a different default model provider, you must first unset Gemini:
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```bash
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deepeval unset-gemini
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```
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:::
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:::tip[Persisting settings]
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You can persist CLI settings with the optional `--save` flag.
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See [Flags and Configs -> Persisting CLI settings](/docs/evaluation-flags-and-configs#persisting-cli-settings-with---save).
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:::
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### Python
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Alternatively, you can specify your model directly in code using `GeminiModel` from `deepeval`'s model collection. By default, `model` is set to `gemini-2.5-pro`.
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<Tabs items={["Python", "ENV"]}>
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<Tab value="Python">
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```python
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from deepeval.models import GeminiModel
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from deepeval.metrics import AnswerRelevancyMetric
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model = GeminiModel(
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model="gemini-2.5-pro",
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api_key="Your Gemini API Key",
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temperature=0,
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cost_per_input_token=0.00000125,
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cost_per_output_token=0.00000500
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)
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answer_relevancy = AnswerRelevancyMetric(model=model)
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```
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</Tab>
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<Tab value="ENV">
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To use any Gemini model directly in `deepeval`, set the `USE_GEMINI_MODEL=1` in your `env` and simply pass the name of your desired model in your metric initialization:
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```python
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from deepeval.metrics import AnswerRelevancyMetric
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answer_relevancy = AnswerRelevancyMetric(
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model="gemini-2.5-pro",
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)
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```
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You should also set the other necessary vars like `GOOGLE_API_KEY` to be able to use the Gemini models as shown above.
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</Tab>
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</Tabs>
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There are **ZERO** mandatory and **SIX** optional parameters when creating a `GeminiModel`:
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- [Optional] `model`: A string specifying the name of the Gemini model to use. Defaults to `GEMINI_MODEL_NAME` if not passed; raises an error at runtime if unset.
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- [Optional] `api_key`: A string specifying the Google API key for authentication. Defaults to `GOOGLE_API_KEY` if not passed; raises an error at runtime if unset.
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- [Optional] `temperature`: A float specifying the model temperature. Defaults to `TEMPERATURE` if not passed; falls back to `0.0` if unset.
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- [Optional] `cost_per_input_token`: A float specifying the cost per input token for the provided model. Defaults to `GEMINI_COST_PER_INPUT_TOKEN` if set; falls back to `deepeval`'s model cost registry, else `None`.
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- [Optional] `cost_per_output_token`: A float specifying the cost per output token for the provided model. Defaults to `GEMINI_COST_PER_OUTPUT_TOKEN` if set; falls back to `deepeval`'s model cost registry, else `None`.
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- [Optional] `generation_kwargs`: A dictionary of additional generation parameters forwarded to the Gemini API `generate_content(...)` call.
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Parameters may be explicitly passed to the model at initialization time, or configured with optional settings. The **mandatory** parameters are required at runtime, but you can provide them either explicitly as constructor arguments, **or** via `deepeval` settings / environment variables (constructor args take precedence). See [Environment variables and settings](/docs/evaluation-flags-and-configs#model-settings-gemini) for the Gemini-related environment variables.
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:::note
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At runtime, you must provide an API key (via `api_key` or `GOOGLE_API_KEY`) unless you’re using Vertex AI. See [Vertex AI](/docs/integrations/models/vertex-ai).
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:::
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### Available Gemini Models
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:::note
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This list only displays some of the available models. For a comprehensive list, refer to the Gemini's official documentation.
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:::
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Below is a list of commonly used Gemini models:
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`gemini-3-pro-preview`
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`gemini-3-flash-preview`
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`gemini-2.5-pro`
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`gemini-2.5-flash`
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`gemini-2.5-flash-lite`
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`gemini-2.0-flash`
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`gemini-2.0-flash-lite`
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`gemini-pro-latest`
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`gemini-flash-latest`
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`gemini-flash-lite-latest`
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