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odysseus/tests/test_provider_classification.py

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"""Provider classification from a base URL (REAL src.llm_core).
ROADMAP "Backend → more tests around ... provider setup" and "Provider
setup/probing audit for Anthropic, Gemini, Groq, xAI, OpenRouter, OpenAI, and
DeepSeek". `test_provider_endpoints.py` already pins URL/header *building*; this
module pins the two pieces of provider setup that decide WHICH provider an
endpoint is:
* `_detect_provider` — host-based provider identification (drives payload
shape, auth headers, and the /v1 collapse). The look-alike-host and
domain-in-path cases guard the hostname (not substring) matching.
* `_provider_label` — the human name shown in degraded-state messages.
Upstream-error formatting lives in `test_provider_classification_errors.py` and
the token-param quirk in `test_provider_classification_token_params.py`.
conftest.py stubs the heavy deps (sqlalchemy, src.database), so importing the
real module is side-effect free.
"""
import pytest
from src.llm_core import (
_detect_provider,
_provider_label,
)
# ── _detect_provider ──
# Matches on hostname (exact or subdomain), never substring, and falls back to
# the OpenAI-compatible default for everything it doesn't special-case.
class TestDetectProvider:
@pytest.mark.parametrize("url,expected", [
("https://api.anthropic.com", "anthropic"),
("https://api.anthropic.com/v1", "anthropic"),
("https://anthropic.com/v1", "anthropic"),
("https://openrouter.ai/api/v1", "openrouter"),
("https://api.groq.com/openai/v1", "groq"),
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>
2026-06-09 15:06:12 +06:00
("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
# special-cased — they speak the OpenAI dialect, so the generic
# "openai" path is correct, not a missed provider.
("https://api.openai.com/v1", "openai"),
("https://api.x.ai/v1", "openai"),
("https://api.deepseek.com", "openai"),
("https://generativelanguage.googleapis.com/v1beta/openai", "openai"),
# Ollama's OpenAI-compatible /v1 surface is generic, not native ollama.
("http://localhost:11434/v1", "openai"),
])
def test_known_providers(self, url, expected):
assert _detect_provider(url) == expected
def test_lookalike_host_is_not_matched(self):
# Host merely *starts* with the provider domain as a label — a classic
# substring-match trap (anthropic.com.evil.example is not Anthropic).
assert _detect_provider("https://anthropic.com.evil.example/v1") == "openai"
def test_provider_domain_in_path_is_not_matched(self):
# The provider domain appears only in the path, not the host.
assert _detect_provider("https://proxy.example.com/anthropic.com/v1") == "openai"
def test_trailing_dot_host_still_matches(self):
# A fully-qualified host with a trailing dot is still that host.
assert _detect_provider("https://api.anthropic.com./v1") == "anthropic"
@pytest.mark.parametrize("url", ["", None, "not a url", "://broken"])
def test_unidentifiable_falls_back_to_openai(self, url):
assert _detect_provider(url) == "openai"
# ── _provider_label ──
# Human-friendly name used in error/degraded-state messages.
class TestProviderLabel:
@pytest.mark.parametrize("url,expected", [
("https://api.anthropic.com/v1", "Anthropic"),
("https://ollama.com", "Ollama Cloud"),
("https://api.x.ai/v1", "xAI"),
("https://api.openai.com/v1", "OpenAI"),
("https://openrouter.ai/api/v1", "OpenRouter"),
("https://api.groq.com/openai/v1", "Groq"),
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>
2026-06-09 15:06:12 +06:00
("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"),
("https://api.together.xyz/v1", "Together"),
("https://api.together.ai/v1", "Together"),
("https://api.fireworks.ai/inference/v1", "Fireworks"),
("http://localhost:11434/api", "Ollama"),
])
def test_known_labels(self, url, expected):
assert _provider_label(url) == expected
@pytest.mark.parametrize("url", [
"http://localhost:8080/v1",
"http://127.0.0.1:8080/v1",
"http://localhost:8000/v1",
"http://localhost:1234/v1",
"http://localhost:9999/v1",
])
def test_local_non_ollama_endpoint(self, url):
# The serving tool is NOT inferred from the port: vLLM, SGLang, llama.cpp
# and plain OpenAI-compatible servers all share 8000/8080, so a port-only
# label would mislabel real setups. The tool is identified by /props
# fingerprinting during discovery; this helper stays neutral.
assert _provider_label(url) == "local endpoint"
def test_unknown_host_returns_host(self):
assert _provider_label("https://api.unknown-llm.example/v1") == "api.unknown-llm.example"
@pytest.mark.parametrize("url", ["", None])
def test_empty_returns_generic(self, url):
assert _provider_label(url) == "provider"