feat(providers): add NVIDIA AI provider endpoint support (#3456)

* 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>

tests/test_model_routes.py

@@ -347,6 +347,8 @@ class TestIsChatModel:
         "gpt-4o", "gpt-4o-mini", "claude-sonnet-4", "llama-3.3-70b",
         "deepseek-chat", "gemini-2.0-flash", "o3",
         "llama-4-scout-17b-16e-instruct",
+        "gemma-2b-it", "google/gemma-2b-it",
+        "bigcode/starcoder2-15b-instruct",
     ])
     def test_chat_models(self, model_id):
         assert _is_chat_model(model_id) is True

tests/test_provider_classification.py

@@ -40,6 +40,7 @@ class TestDetectProvider:
         ("https://anthropic.com/v1", "anthropic"),
         ("https://openrouter.ai/api/v1", "openrouter"),
         ("https://api.groq.com/openai/v1", "groq"),
+        ("https://integrate.api.nvidia.com/v1", "nvidia"),
         ("http://localhost:11434/api", "ollama"),
         ("https://ollama.com", "ollama"),
         # xAI, DeepSeek and Gemini's OpenAI-compatible surface are NOT
@@ -84,6 +85,7 @@ class TestProviderLabel:
         ("https://api.openai.com/v1", "OpenAI"),
         ("https://openrouter.ai/api/v1", "OpenRouter"),
         ("https://api.groq.com/openai/v1", "Groq"),
+        ("https://integrate.api.nvidia.com/v1", "NVIDIA"),
         ("https://api.mistral.ai/v1", "Mistral"),
         ("https://api.deepseek.com", "DeepSeek"),
         ("https://generativelanguage.googleapis.com/v1beta/openai", "Google"),

tests/test_provider_endpoints.py

@@ -50,6 +50,9 @@ PROVIDER_CASES = [
     ("groq", "https://api.groq.com/openai/v1",
      "https://api.groq.com/openai/v1/chat/completions",
      "https://api.groq.com/openai/v1/models"),
+    ("nvidia", "https://integrate.api.nvidia.com/v1",
+     "https://integrate.api.nvidia.com/v1/chat/completions",
+     "https://integrate.api.nvidia.com/v1/models"),
     ("xai", "https://api.x.ai/v1",
      "https://api.x.ai/v1/chat/completions",
      "https://api.x.ai/v1/models"),
@@ -112,6 +115,7 @@ def test_headers_anthropic_without_key_still_sends_version():
     "https://api.x.ai/v1",
     "https://api.deepseek.com",
     "https://api.groq.com/openai/v1",
+    "https://integrate.api.nvidia.com/v1",
     "https://generativelanguage.googleapis.com/v1beta/openai",
 ])
 def test_headers_openai_style_use_bearer(base):