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| description: Learn how Flowise integrates with Tuning Engines | ||
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| # Tuning Engines | ||
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| Use [Tuning Engines](https://tuningengines.com/) with Flowise as an OpenAI-compatible inference endpoint for governed model routing. | ||
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| Tuning Engines can sit between Flowise and your model providers so your team can use tenant-scoped inference keys, model aliases, policy controls, routing, fallback, usage tracking, and audit trails without changing Flowise nodes for every provider. | ||
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| ## How to use Tuning Engines with Flowise | ||
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| ### Step 1: Create an inference key | ||
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| In Tuning Engines, create an inference key with access to the model aliases you want Flowise to use. | ||
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| Keep the key in your Flowise credential store or environment variables. Do not paste provider credentials into Flowise when you are using a Tuning Engines inference key. | ||
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| ### Step 2: Configure an OpenAI-compatible chat model | ||
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| In Flowise, use the standard OpenAI-compatible chat model node. | ||
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| - Set `BasePath` to `https://api.tuningengines.com/v1` | ||
| - Set the API key to your Tuning Engines inference key | ||
| - Set the model name to a model alias enabled for that key, for example `gpt-4o-mini` | ||
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| ### Step 3: Test the flow | ||
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| Run a simple chatflow message and confirm that the request appears in Tuning Engines usage and trace views. | ||
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| If the model is denied, verify that the inference key's tenant, role, and model permissions allow the selected alias. | ||
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To improve clarity for Flowise users, it is recommended to use the exact labels found in the user interface. In the 'ChatOpenAI' or 'OpenAI Compatible' nodes, the fields are typically labeled Base Path and Model Name. Additionally, API keys are managed through the Connect Credential system rather than being entered directly into the node configuration.