Added LiteLLM as a new LLM provider (#909)

Co-authored-by: Thorsten Sommer <SommerEngineering@users.noreply.github.com>

app/MindWork AI Studio/wwwroot/changelog/v26.8.2.md

@@ -5,6 +5,7 @@
 - Added the option for IT departments to enable assistant plugins they rolled out. Approving an assistant only stated that it is safe, so everybody still had to switch it on themselves. An approval can now also enable the assistant, either as a default which you may switch off again, or in a way your IT department keeps in place. The plugin page and the security card of the assistant tell you which of the two applies.
 - Added knowledge about the latest AI models. AI Studio now recognizes Qwen 3.8 Flash, GLM-5.3 Flash, Meta's Muse Glimmer, NVIDIA's Nemotron 3.5, Tencent's Hunyuan Hy3, Grok 4, Claude Opus 5 and Sonnet 5, and Gemini 3.6 and 3.7. It knows what each of them is capable of, so images, videos, tool usage, and reasoning are available right away instead of staying hidden.
 - Added the IONOS AI Model Hub as a provider for chats and embeddings. It runs open-source models in Germany, is subject to the GDPR, and IONOS states that your data is not used for training.
+- Added LiteLLM as a new LLM provider for chats, embeddings, and speech-to-text. LiteLLM is an AI gateway you run yourself in front of models from many providers. Because your gateway decides where your data goes, you set its trust level yourself. Thanks Prodman Devokadev (`prodmanpd`) for this first contribution.
 - Added speech-to-text for Helmholtz Blablador and GroqCloud, and embeddings for GWDG SAIA. These providers offer these services now, so you can select them when you dictate a message or when you set up a data source.
 - Improved the safety of plugin symbols: AI Studio now shows the symbol of a plugin in isolation, so nothing inside a symbol can reach the rest of the app.
 - Improved how much memory AI Studio needs. Working with large documents used to grow the app to several gigabytes, and on macOS that memory was never handed back. AI Studio now stays at a fraction of that and returns memory to your system. This matters most on devices with little memory, such as a Raspberry Pi.