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fix: update tests for OpenAI-style models endpoint (#4053)

Browse source 
The llama-stack-client now uses /`v1/openai/v1/models` which returns
OpenAI-compatible model objects with 'id' and 'custom_metadata' fields
instead of the Resource-style 'identifier' field. Updated api_recorder
to handle the new endpoint and modified tests to access model metadata
appropriately. Deleted stale model recordings for re-recording.

**NOTE: CI will be red on this one since it is dependent on
https://github.com/llamastack/llama-stack-client-python/pull/291/files
landing. I verified locally that it is green.**
This commit is contained in:
Ashwin Bharambe 2025-11-03 17:30:08 -08:00 • committed by GitHub
parent 4a5ef65286
commit cb40da210f
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GPG key ID: B5690EEEBB952194
27 changed files with 852 additions and 6707 deletions
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31
docs/docs/getting_started/detailed_tutorial.mdx
View file

@ -239,8 +239,13 @@ client = LlamaStackClient(base_url="http://localhost:8321")
models = client.models.list()
# Select the first LLM
llm = next(m for m in models if m.model_type == "llm" and m.provider_id == "ollama")
model_id = llm.identifier
llm = next(
m for m in models
if m.custom_metadata
and m.custom_metadata.get("model_type") == "llm"
and m.custom_metadata.get("provider_id") == "ollama"
)
model_id = llm.id
print("Model:", model_id)
@ -279,8 +284,13 @@ import uuid
client = LlamaStackClient(base_url=f"http://localhost:8321")
models = client.models.list()
llm = next(m for m in models if m.model_type == "llm" and m.provider_id == "ollama")
model_id = llm.identifier
llm = next(
m for m in models
if m.custom_metadata
and m.custom_metadata.get("model_type") == "llm"
and m.custom_metadata.get("provider_id") == "ollama"
)
model_id = llm.id
agent = Agent(client, model=model_id, instructions="You are a helpful assistant.")
@ -450,8 +460,11 @@ import uuid
client = LlamaStackClient(base_url="http://localhost:8321")
# Create a vector database instance
embed_lm = next(m for m in client.models.list() if m.model_type == "embedding")
embedding_model = embed_lm.identifier
embed_lm = next(
m for m in client.models.list()
if m.custom_metadata and m.custom_metadata.get("model_type") == "embedding"
)
embedding_model = embed_lm.id
vector_db_id = f"v{uuid.uuid4().hex}"
# The VectorDB API is deprecated; the server now returns its own authoritative ID.
# We capture the correct ID from the response's .identifier attribute.
@ -489,9 +502,11 @@ client.tool_runtime.rag_tool.insert(
llm = next(
m
for m in client.models.list()
if m.model_type == "llm" and m.provider_id == "ollama"
if m.custom_metadata
and m.custom_metadata.get("model_type") == "llm"
and m.custom_metadata.get("provider_id") == "ollama"
)
model = llm.identifier
model = llm.id
# Create the RAG agent
rag_agent = Agent(

2
src/llama_stack/core/ui/page/distribution/models.py
View file

@ -12,7 +12,7 @@ from llama_stack.core.ui.modules.api import llama_stack_api
def models():
# Models Section
st.header("Models")
models_info = {m.identifier: m.to_dict() for m in llama_stack_api.client.models.list()}
models_info = {m.id: m.model_dump() for m in llama_stack_api.client.models.list()}
selected_model = st.selectbox("Select a model", list(models_info.keys()))
st.json(models_info[selected_model])

6
src/llama_stack/core/ui/page/playground/chat.py
View file

@ -12,7 +12,11 @@ from llama_stack.core.ui.modules.api import llama_stack_api
with st.sidebar:
st.header("Configuration")
available_models = llama_stack_api.client.models.list()
available_models = [model.identifier for model in available_models if model.model_type == "llm"]
available_models = [
model.id
for model in available_models
if model.custom_metadata and model.custom_metadata.get("model_type") == "llm"
]
selected_model = st.selectbox(
"Choose a model",
available_models,

17
src/llama_stack/testing/api_recorder.py
View file

@ -156,7 +156,7 @@ def normalize_inference_request(method: str, url: str, headers: dict[str, Any],
}
# Include test_id for isolation, except for shared infrastructure endpoints
if parsed.path not in ("/api/tags", "/v1/models"):
if parsed.path not in ("/api/tags", "/v1/models", "/v1/openai/v1/models"):
normalized["test_id"] = test_id
normalized_json = json.dumps(normalized, sort_keys=True)
@ -430,7 +430,7 @@ class ResponseStorage:
# For model-list endpoints, include digest in filename to distinguish different model sets
endpoint = request.get("endpoint")
if endpoint in ("/api/tags", "/v1/models"):
if endpoint in ("/api/tags", "/v1/models", "/v1/openai/v1/models"):
digest = _model_identifiers_digest(endpoint, response)
response_file = f"models-{request_hash}-{digest}.json"
@ -554,13 +554,14 @@ def _model_identifiers_digest(endpoint: str, response: dict[str, Any]) -> str:
Supported endpoints:
- '/api/tags' (Ollama): response body has 'models': [ { name/model/digest/id/... }, ... ]
- '/v1/models' (OpenAI): response body is: [ { id: ... }, ... ]
- '/v1/openai/v1/models' (OpenAI): response body is: [ { id: ... }, ... ]
Returns a list of unique identifiers or None if structure doesn't match.
"""
if "models" in response["body"]:
# ollama
items = response["body"]["models"]
else:
# openai
# openai or openai-style endpoints
items = response["body"]
idents = [m.model if endpoint == "/api/tags" else m.id for m in items]
return sorted(set(idents))
@ -581,7 +582,7 @@ def _combine_model_list_responses(endpoint: str, records: list[dict[str, Any]])
seen: dict[str, dict[str, Any]] = {}
for rec in records:
body = rec["response"]["body"]
if endpoint == "/v1/models":
if endpoint in ("/v1/models", "/v1/openai/v1/models"):
for m in body:
key = m.id
seen[key] = m
@ -665,7 +666,7 @@ async def _patched_inference_method(original_method, self, client_type, endpoint
logger.info(f" Test context: {get_test_context()}")
if mode == APIRecordingMode.LIVE or storage is None:
if endpoint == "/v1/models":
if endpoint in ("/v1/models", "/v1/openai/v1/models"):
return original_method(self, *args, **kwargs)
else:
return await original_method(self, *args, **kwargs)
@ -699,7 +700,7 @@ async def _patched_inference_method(original_method, self, client_type, endpoint
recording = None
if mode == APIRecordingMode.REPLAY or mode == APIRecordingMode.RECORD_IF_MISSING:
# Special handling for model-list endpoints: merge all recordings with this hash
if endpoint in ("/api/tags", "/v1/models"):
if endpoint in ("/api/tags", "/v1/models", "/v1/openai/v1/models"):
records = storage._model_list_responses(request_hash)
recording = _combine_model_list_responses(endpoint, records)
else:
@ -739,13 +740,13 @@ async def _patched_inference_method(original_method, self, client_type, endpoint
)
if mode == APIRecordingMode.RECORD or (mode == APIRecordingMode.RECORD_IF_MISSING and not recording):
if endpoint == "/v1/models":
if endpoint in ("/v1/models", "/v1/openai/v1/models"):
response = original_method(self, *args, **kwargs)
else:
response = await original_method(self, *args, **kwargs)
# we want to store the result of the iterator, not the iterator itself
if endpoint == "/v1/models":
if endpoint in ("/v1/models", "/v1/openai/v1/models"):
response = [m async for m in response]
request_data = {

916
tests/integration/common/recordings/models-64a2277c90f0f42576f60c1030e3a020403d34a95f56931b792d5939f4cebc57-329b4213.json generated
View file

@ -1,916 +0,0 @@
{
"test_id": null,
"request": {
"method": "POST",
"url": "https://api.openai.com/v1/v1/models",
"headers": {},
"body": {},
"endpoint": "/v1/models",
"model": ""
},
"response": {
"body": [
{
"__type__": "openai.types.model.Model",
"__data__": {
"id": "gpt-4-0613",
"created": 1686588896,
"object": "model",
"owned_by": "openai"
}
},
{
"__type__": "openai.types.model.Model",
"__data__": {
"id": "gpt-4",
"created": 1687882411,
"object": "model",
"owned_by": "openai"
}
},
{
"__type__": "openai.types.model.Model",
"__data__": {
"id": "gpt-3.5-turbo",
"created": 1677610602,
"object": "model",
"owned_by": "openai"
}
},
{
"__type__": "openai.types.model.Model",
"__data__": {
"id": "sora-2-pro",
"created": 1759708663,
"object": "model",
"owned_by": "system"
}
},
{
"__type__": "openai.types.model.Model",
"__data__": {
"id": "gpt-audio-mini-2025-10-06",
"created": 1759512137,
"object": "model",
"owned_by": "system"
}
},
{
"__type__": "openai.types.model.Model",
"__data__": {
"id": "gpt-realtime-mini",
"created": 1759517133,
"object": "model",
"owned_by": "system"
}
},
{
"__type__": "openai.types.model.Model",
"__data__": {
"id": "gpt-realtime-mini-2025-10-06",
"created": 1759517175,
"object": "model",
"owned_by": "system"
}
},
{
"__type__": "openai.types.model.Model",
"__data__": {
"id": "sora-2",
"created": 1759708615,
"object": "model",
"owned_by": "system"
}
},
{
"__type__": "openai.types.model.Model",
"__data__": {
"id": "davinci-002",
"created": 1692634301,
"object": "model",
"owned_by": "system"
}
},
{
"__type__": "openai.types.model.Model",
"__data__": {
"id": "babbage-002",
"created": 1692634615,
"object": "model",
"owned_by": "system"
}
},
{
"__type__": "openai.types.model.Model",
"__data__": {
"id": "gpt-3.5-turbo-instruct",
"created": 1692901427,
"object": "model",
"owned_by": "system"
}
},
{
"__type__": "openai.types.model.Model",
"__data__": {
"id": "gpt-3.5-turbo-instruct-0914",
"created": 1694122472,
"object": "model",
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}
},
{
"__type__": "openai.types.model.Model",
"__data__": {
"id": "dall-e-3",
"created": 1698785189,
"object": "model",
"owned_by": "system"
}
},
{
"__type__": "openai.types.model.Model",
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"created": 1698798177,
"object": "model",
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}
},
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}
},
{
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"object": "model",
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}
},
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"object": "model",
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}
},
{
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"object": "model",
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}
},
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}
},
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}
},
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}
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}
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}
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}
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}
},
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}
},
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"created": 1742068596,
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}
},
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}
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}
},
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"created": 1742403959,
"object": "model",
"owned_by": "system"
}
},
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}
},
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}
},
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}
},
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}
},
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"created": 1744315746,
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}
},
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"created": 1744316542,
"object": "model",
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}
},
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"created": 1744317547,
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}
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9
tests/integration/conftest.py
View file

@ -368,10 +368,8 @@ def vector_provider_wrapper(func):
return func(*args, **kwargs)
# For CI tests (replay/record), only use providers that are available in ci-tests environment
if os.environ.get("LLAMA_STACK_TEST_INFERENCE_MODE") in ("replay", "record"):
all_providers = ["faiss", "sqlite-vec"]
else:
inference_mode = os.environ.get("LLAMA_STACK_TEST_INFERENCE_MODE")
if inference_mode == "live":
# For live tests, try all providers (they'll skip if not available)
all_providers = [
"faiss",
@ -382,6 +380,9 @@ def vector_provider_wrapper(func):
"weaviate",
"qdrant",
]
else:
# For CI tests (replay/record), only use providers that are available in ci-tests environment
all_providers = ["faiss", "sqlite-vec"]
return pytest.mark.parametrize("vector_io_provider_id", all_providers)(wrapper)

2
tests/integration/fixtures/common.py
View file

@ -160,7 +160,7 @@ def client_with_models(
providers = [p for p in client.providers.list() if p.api == "inference"]
assert len(providers) > 0, "No inference providers found"
model_ids = {m.identifier for m in client.models.list()}
model_ids = {m.id for m in client.models.list()}
if text_model_id and text_model_id not in model_ids:
raise ValueError(f"text_model_id {text_model_id} not found")

8
tests/integration/inference/test_openai_completion.py
View file

@ -27,9 +27,11 @@ def _normalize_text(text: str) -> str:
def provider_from_model(client_with_models, model_id):
models = {m.identifier: m for m in client_with_models.models.list()}
models.update({m.provider_resource_id: m for m in client_with_models.models.list()})
provider_id = models[model_id].provider_id
models = {m.id: m for m in client_with_models.models.list()}
models.update(
{m.custom_metadata["provider_resource_id"]: m for m in client_with_models.models.list() if m.custom_metadata}
)
provider_id = models[model_id].custom_metadata["provider_id"]
providers = {p.provider_id: p for p in client_with_models.providers.list()}
return providers[provider_id]

8
tests/integration/inference/test_openai_embeddings.py
View file

@ -31,9 +31,11 @@ def decode_base64_to_floats(base64_string: str) -> list[float]:
def provider_from_model(client_with_models, model_id):
models = {m.identifier: m for m in client_with_models.models.list()}
models.update({m.provider_resource_id: m for m in client_with_models.models.list()})
provider_id = models[model_id].provider_id
models = {m.id: m for m in client_with_models.models.list()}
models.update(
{m.custom_metadata["provider_resource_id"]: m for m in client_with_models.models.list() if m.custom_metadata}
)
provider_id = models[model_id].custom_metadata["provider_id"]
providers = {p.provider_id: p for p in client_with_models.providers.list()}
return providers[provider_id]

2
tests/integration/inference/test_provider_data_routing.py
View file

@ -56,7 +56,7 @@ def test_unregistered_model_routing_with_provider_data(client_with_models):
test_model_id = "anthropic/claude-3-5-sonnet-20241022"
# First, verify the model is NOT registered
registered_models = {m.identifier for m in client.models.list()}
registered_models = {m.id for m in client.models.list()}
assert test_model_id not in registered_models, f"Model {test_model_id} should not be pre-registered for this test"
# Check if anthropic provider is available in ci-tests

527
tests/integration/recordings/responses/models-7d9446738fd7-d5d684a3.json generated
View file

@ -1,527 +0,0 @@
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69
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1500
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File diff suppressed because it is too large Load diff

42
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@ -52,9 +52,9 @@
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34
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4
tests/integration/safety/test_llama_guard.py
View file

@ -40,7 +40,7 @@ def text_model(request, client_with_models):
model_id = request.param
# Check if the model is available
available_models = [m.identifier for m in client_with_models.models.list()]
available_models = [m.id for m in client_with_models.models.list()]
if model_id not in available_models:
pytest.skip(
@ -76,7 +76,7 @@ def vision_model(request, client_with_models):
model_id = request.param
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available_models = [m.id for m in client_with_models.models.list()]
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