* 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
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Co-authored-by: Kenny Van de Maele <kenny@kvandemaele.be>
c3fcaf15b7