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Maruf Hasan
c3fcaf15b7
* feat: add NVIDIA as an AI provider (integrate.api.nvidia.com) * feat: add NVIDIA option to provider settings dropdown and aliases * test: add NVIDIA provider detection and endpoint tests * Add NVIDIA to _HOST_TO_CURATED and expand non-chat model filtering - nvidia.com -> 'nvidia' curated key for proper provider routing - _NON_CHAT_PREFIXES: bge, snowflake/arctic-embed, nvidia/nv-embed - _NON_CHAT_CONTAINS: content-safety, -safety, -reward, nvclip, kosmos, fuyu, deplot, vila, neva, gliner, riva, -parse, -embedqa, -nemoretriever * Expand non-chat model filtering for NVIDIA embedding/guard/video models Add _NON_CHAT_PREFIXES: embed, recurrent Add _NON_CHAT_CONTAINS: topic-control, guard, calibration, ai-synthetic-video, cosmos-reason2 Catches remaining unfiltered non-chat models from NVIDIA catalog: embedding (llama-nemotron-embed, embed-qa), guard (llama-guard, nemoguard-topic-control), calibration (ising-calibration), video (ai-synthetic-video-detector, cosmos-reason2), recurrent (recurrentgemma-2b) * Filter non-chat models in _probe_endpoint via _is_chat_model() Previously _is_chat_model() was only used in the per-model probe and _first_chat_model(), so non-chat models still appeared in the model picker even though they were filtered in those specific paths. Applying the filter at _probe_endpoint() return ensures non-chat models (embeddings, safety guards, reward, calibration, video detectors, CLIP, VLM, translation, parsing, recurrent, etc.) never enter cached_models and never appear in the picker. * Fix _NON_CHAT_CONTAINS to catch org-prefixed embedding models Prefix checks (mid.startswith) miss models with org prefixes like baai/bge-m3, nvidia/embed-qa-4, google/recurrentgemma-2b, etc. Adding the same terms to _NON_CHAT_CONTAINS ensures they are caught regardless of the org prefix. Adds: embed, bge, recurrent, starcoder, gemma-2b * fix(model-routes): drop collision-prone substrings from global non-chat filter The NVIDIA PR added several substrings to the shared _NON_CHAT_PREFIXES and _NON_CHAT_CONTAINS tuples. These are intended to filter out embedding, retrieval, safety, and vision models from NVIDIA's catalog that are not chat-completions-capable. However, four of the added substrings collide with legitimate chat models served by other providers: - gemma-2b matches google/gemma-2b-it (instruct chat model) - starcoder matches bigcode/starcoder2-15b (code completion model) - recurrent matches google/recurrentgemma-2b (language model) - guard matches meta-llama/Llama-Guard-3-8B (safety classifier) Removing these four from the global tuples keeps the NVIDIA-specific filtering intact (safety, embedding, retrieval, and vision models are still caught by other tokens such as content-safety, -safety, -reward, embed, bge, -embedqa, -nemoretriever, nvclip, deplot, etc.) while preventing false negatives for instruct/code models on other providers. Tests added for gemma-2b-it, google/gemma-2b-it, and bigcode/starcoder2-15b-instruct asserting they are recognized as chat models. Co-authored-by: Kenny Van de Maele <kenny@kvandemaele.be> * fix(nvidia): remove duplicate bge/embed tokens from _NON_CHAT_CONTAINS Tokens already present in _NON_CHAT_PREFIXES, making the CONTAINS entries redundant since the prefix check runs first. Co-authored-by: Kenny Van de Maele <kenny@kvandemaele.be> * fix(nvidia): move bge to CONTAINS, add llama-guard, remove stray blanks Co-authored-by: Kenny Van de Maele <kenny@kvandemaele.be> * style: fix indentation of groq and xai test cases in test_provider_endpoints.py --------- Co-authored-by: Kenny Van de Maele <kenny@kvandemaele.be>