wehub-resource-sync
88 lines
3.3 KiB
Plaintext
88 lines
3.3 KiB
Plaintext
---
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id: portkey
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title: Portkey
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sidebar_label: Portkey
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---
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`deepeval`'s integration with Portkey AI allows you to use the portkey gateway to connect to any model to power all of `deepeval`'s metrics.
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### Command Line
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To configure your Portkey model through the CLI, run the following command:
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```bash
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deepeval set-portkey \
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--model "your-model" \ # Ex: gpt-4.1
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--provider "your-provider" \ # Ex: openai
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--base-url "your-base-url" \
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--temperature=0
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```
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:::info
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The CLI command above sets Portkey as the default provider for all metrics, unless overridden in Python code. To use a different default model provider, you must first unset Portkey:
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```bash
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deepeval unset-portkey
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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 define `PortkeyModel` directly in python code:
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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 PortkeyModel
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from deepeval.metrics import AnswerRelevancyMetric
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model = PortkeyModel(
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model="gpt-4.1",
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provider="openai",
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api_key="your-api-key",
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base_url="your-base-url"
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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 Portkey model directly in `deepeval`, set the `USE_PORTKEY_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="gpt-4.1",
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)
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```
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You should also set the other necessary vars like `PORTKEY_API_KEY` to be able to use the Portkey 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 `PortkeyModel`:
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- [Optional] `model`: A string specifying the name of the Portkey model to use. Defaults to `PORTKEY_MODEL_NAME` if not passed; raises an error at runtime if unset.
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- [Optional] `api_key`: A string specifying your Portkey API key for authentication. Defaults to `PORTKEY_API_KEY` if not passed; raises an error at runtime if unset.
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- [Optional] `provider`: A string specifying the Portkey provider of your model. Defaults to `PORTKEY_PROVIDER_NAME` if not passed; raises an error at runtime if unset.
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- [Optional] `base_url`: A string specifying the base URL for the model API. Defaults to `PORTKEY_BASE_URL` 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] `generation_kwargs`: A dictionary of additional generation parameters forwarded to Portkey's `chat.completions.create(...)` call.
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Any additional `**kwargs` you would like to use for your `Portkey` client can be passed directly to `PortkeyModel(...)`. These are forwarded to the underlying OpenAI client constructor. We recommend double-checking the parameters supported by your chosen model and provider in the [official Portkey docs](https://portkey.ai/docs).
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:::tip
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Pass generation parameters such as `max_tokens` via `generation_kwargs` (they are forwarded to `chat.completions.create(...)`).
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:::
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