backstage release v0.129.0

Make AI chat sampling parameters configurable per installation. The plugin previously called streamText() without temperature, topP, topK, seed, minP, or maxOutputTokens, so the server’s defaults applied – which for vLLM means temperature=1.0, top_p=1.0, top_k=-1, seed=null. That is far too loose for a tool-using agent backed by a reasoning model and was the dominant cause of token-cost variance in production agent loops (same prompt, fresh chat, observed total-token spread of 22k / 607k / 22k across three runs against the same Qwen3 endpoint). Config now accepts an aiChat.sampling block with temperature, topP, topK, minP, seed, and maxOutputTokens; all fields are optional and default behaviour with no sampling: block is unchanged. temperature, topP, topK, seed, and maxOutputTokens are forwarded through the AI SDK to every provider that supports them. minP is spliced into the request body via the OpenAI-compatible provider’s transformRequestBody hook, since vLLM accepts it as a top-level field but it is not part of the AI SDK call settings. The ai-chat-backend README documents recommended values per model family (Qwen3 thinking/non-thinking, Qwen3-Coder, GPT-4 / GPT-4o, Anthropic Claude). See ./docs/releases/v0.129.0-changelog.md for more information.