docs: Document custom endpoint support in Vercel AI SDK

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Adds a new "Using custom endpoints" section to the Vercel AI SDK documentation. Users can now pass their own Serverless endpoint IDs or Console URLs directly as model identifiers, enabling the SDK to work with custom-deployed models.

Trigger Events

runpod/ai-sdk-provider PR #61: feat(image): accept aiApiId as valid model ID

Promptless Research (4 files, 1 GitHub PR)

• Reviewed runpod/ai-sdk-provider#61
• Viewed .long_term_context/product_knowledge/product_overview.md
• Viewed .long_term_context/doc_workflow/client_instructions.md
• Viewed public-endpoints/ai-sdk.mdx
• Viewed .long_term_context/style/client_style_guide.md

Agent Response

I've reviewed PR #61 in the runpod/ai-sdk-provider repository and created a documentation suggestion.

Summary

PR #61 fixes createImageModel to properly handle custom endpoint IDs and Console URLs. Previously, unrecognized model IDs for image models incorrectly had /openai/v1 appended. Now all model types (chat, image, speech, transcription, video) use the same fallback pattern, resolving endpoint IDs to https://api.runpod.ai/v2/{endpointId}.

Documentation created

I created a new suggestion that adds a "Using custom endpoints" section to the Vercel AI SDK documentation. This section explains:

1. Using endpoint IDs - Pass your Serverless endpoint ID directly as the model identifier
2. Using Console URLs - Copy an endpoint URL from the Runpod Console and use it as-is (the SDK extracts the endpoint ID automatically)

This is valuable for users who have deployed custom models to their own Serverless endpoints and want to use them with the AI SDK.

Suggestion: Document custom endpoint support in Vercel AI SDK

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Citation: PR #61 fixes createImageModel to use the same fallback pattern as other model types. Changes in src/runpod-provider.ts show the new logic that supports endpoint IDs and Console URLs via parseRunpodConsoleEndpointId.
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