Merge remote-tracking branch 'origin/main' into TamiTakamiya/tool-param-definition-update

tests/external/kaze.yaml

@@ -1,6 +1,5 @@
-adapter:
-  adapter_type: kaze
-  pip_packages: ["tests/external/llama-stack-provider-kaze"]
-  config_class: llama_stack_provider_kaze.config.KazeProviderConfig
-  module: llama_stack_provider_kaze
+adapter_type: kaze
+pip_packages: ["tests/external/llama-stack-provider-kaze"]
+config_class: llama_stack_provider_kaze.config.KazeProviderConfig
+module: llama_stack_provider_kaze
 optional_api_dependencies: []

tests/external/llama-stack-api-weather/src/llama_stack_api_weather/weather.py

@@ -6,7 +6,8 @@
 
 from typing import Protocol
 
-from llama_stack.providers.datatypes import AdapterSpec, Api, ProviderSpec, RemoteProviderSpec
+from llama_stack.apis.version import LLAMA_STACK_API_V1
+from llama_stack.providers.datatypes import Api, ProviderSpec, RemoteProviderSpec
 from llama_stack.schema_utils import webmethod
 
 
@@ -16,12 +17,9 @@ def available_providers() -> list[ProviderSpec]:
             api=Api.weather,
             provider_type="remote::kaze",
             config_class="llama_stack_provider_kaze.KazeProviderConfig",
-            adapter=AdapterSpec(
-                adapter_type="kaze",
-                module="llama_stack_provider_kaze",
-                pip_packages=["llama_stack_provider_kaze"],
-                config_class="llama_stack_provider_kaze.KazeProviderConfig",
-            ),
+            adapter_type="kaze",
+            module="llama_stack_provider_kaze",
+            pip_packages=["llama_stack_provider_kaze"],
         ),
     ]
 
@@ -31,7 +29,7 @@ class WeatherProvider(Protocol):
     A protocol for the Weather API.
     """
 
-    @webmethod(route="/weather/locations", method="GET")
+    @webmethod(route="/weather/locations", method="GET", level=LLAMA_STACK_API_V1)
     async def get_available_locations() -> dict[str, list[str]]:
         """
         Get the available locations.

tests/integration/inference/test_batch_inference.py

@@ -1,76 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the terms described in the LICENSE file in
-# the root directory of this source tree.
-
-
-import pytest
-
-from ..test_cases.test_case import TestCase
-
-
-def skip_if_provider_doesnt_support_batch_inference(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
-    providers = {p.provider_id: p for p in client_with_models.providers.list()}
-    provider = providers[provider_id]
-    if provider.provider_type not in ("inline::meta-reference",):
-        pytest.skip(f"Model {model_id} hosted by {provider.provider_type} doesn't support batch inference")
-
-
-@pytest.mark.parametrize(
-    "test_case",
-    [
-        "inference:completion:batch_completion",
-    ],
-)
-def test_batch_completion_non_streaming(client_with_models, text_model_id, test_case):
-    skip_if_provider_doesnt_support_batch_inference(client_with_models, text_model_id)
-    tc = TestCase(test_case)
-
-    content_batch = tc["contents"]
-    response = client_with_models.inference.batch_completion(
-        content_batch=content_batch,
-        model_id=text_model_id,
-        sampling_params={
-            "max_tokens": 50,
-        },
-    )
-    assert len(response.batch) == len(content_batch)
-    for i, r in enumerate(response.batch):
-        print(f"response {i}: {r.content}")
-        assert len(r.content) > 10
-
-
-@pytest.mark.parametrize(
-    "test_case",
-    [
-        "inference:chat_completion:batch_completion",
-    ],
-)
-def test_batch_chat_completion_non_streaming(client_with_models, text_model_id, test_case):
-    skip_if_provider_doesnt_support_batch_inference(client_with_models, text_model_id)
-    tc = TestCase(test_case)
-    qa_pairs = tc["qa_pairs"]
-
-    message_batch = [
-        [
-            {
-                "role": "user",
-                "content": qa["question"],
-            }
-        ]
-        for qa in qa_pairs
-    ]
-
-    response = client_with_models.inference.batch_chat_completion(
-        messages_batch=message_batch,
-        model_id=text_model_id,
-    )
-    assert len(response.batch) == len(qa_pairs)
-    for i, r in enumerate(response.batch):
-        print(f"response {i}: {r.completion_message.content}")
-        assert len(r.completion_message.content) > 0
-        assert qa_pairs[i]["answer"].lower() in r.completion_message.content.lower()

tests/integration/inference/test_openai_completion.py

@@ -40,7 +40,6 @@ def skip_if_model_doesnt_support_openai_completion(client_with_models, model_id)
         "inline::sentence-transformers",
         "inline::vllm",
         "remote::bedrock",
-        "remote::cerebras",
         "remote::databricks",
         # Technically Nvidia does support OpenAI completions, but none of their hosted models
         # support both completions and chat completions endpoint and all the Llama models are
@@ -98,6 +97,9 @@ def skip_if_doesnt_support_n(client_with_models, model_id):
         #  the entered value was 2. Update the candidateCount value and try again.', 'status': 'INVALID_ARGUMENT'}
         "remote::tgi",  # TGI ignores n param silently
         "remote::together",  # `n` > 1 is not supported when streaming tokens. Please disable `stream`
+        # Error code 400 - {'message': '"n" > 1 is not currently supported', 'type': 'invalid_request_error', 'param': 'n', 'code': 'wrong_api_format'}
+        "remote::cerebras",
+        "remote::databricks",  # Bad request: parameter "n" must be equal to 1 for streaming mode
     ):
         pytest.skip(f"Model {model_id} hosted by {provider.provider_type} doesn't support n param.")
 
@@ -109,8 +111,8 @@ def skip_if_model_doesnt_support_openai_chat_completion(client_with_models, mode
         "inline::sentence-transformers",
         "inline::vllm",
         "remote::bedrock",
-        "remote::cerebras",
         "remote::databricks",
+        "remote::cerebras",
         "remote::runpod",
         "remote::watsonx",  # watsonx returns 404 when hitting the /openai/v1 endpoint
     ):

tests/integration/inference/test_openai_embeddings.py

@@ -33,6 +33,7 @@ def skip_if_model_doesnt_support_user_param(client, model_id):
     provider = provider_from_model(client, model_id)
     if provider.provider_type in (
         "remote::together",  # service returns 400
+        "remote::fireworks",  # service returns 400 malformed input
     ):
         pytest.skip(f"Model {model_id} hosted by {provider.provider_type} does not support user param.")
 
@@ -40,7 +41,9 @@ def skip_if_model_doesnt_support_user_param(client, model_id):
 def skip_if_model_doesnt_support_encoding_format_base64(client, model_id):
     provider = provider_from_model(client, model_id)
     if provider.provider_type in (
-        "remote::together",  # param silently ignored, always returns floats
+        "remote::databricks",  # param silently ignored, always returns floats
+        "remote::fireworks",  # param silently ignored, always returns list of floats
+        "remote::ollama",  # param silently ignored, always returns list of floats
     ):
         pytest.skip(f"Model {model_id} hosted by {provider.provider_type} does not support encoding_format='base64'.")
 
@@ -50,6 +53,8 @@ def skip_if_model_doesnt_support_variable_dimensions(client_with_models, model_i
     if provider.provider_type in (
         "remote::together",  # returns 400
         "inline::sentence-transformers",
+        # Error code: 400 - {'error_code': 'BAD_REQUEST', 'message': 'Bad request: json: unknown field "dimensions"\n'}
+        "remote::databricks",
     ):
         pytest.skip(
             f"Model {model_id} hosted by {provider.provider_type} does not support variable output embedding dimensions."
@@ -73,7 +78,6 @@ def skip_if_model_doesnt_support_openai_embeddings(client, model_id):
         "inline::meta-reference",
         "remote::bedrock",
         "remote::cerebras",
-        "remote::databricks",
         "remote::runpod",
         "remote::sambanova",
         "remote::tgi",
@@ -287,7 +291,6 @@ def test_openai_embeddings_base64_batch_processing(compat_client, client_with_mo
         input=input_texts,
         encoding_format="base64",
     )
-
     # Validate response structure
     assert response.object == "list"
     assert response.model == embedding_model_id

tests/integration/inference/test_openai_vision_inference.py

@@ -0,0 +1,77 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.
+
+
+import base64
+import pathlib
+
+import pytest
+
+
+@pytest.fixture
+def image_path():
+    return pathlib.Path(__file__).parent / "dog.png"
+
+
+@pytest.fixture
+def base64_image_data(image_path):
+    return base64.b64encode(image_path.read_bytes()).decode("utf-8")
+
+
+async def test_openai_chat_completion_image_url(openai_client, vision_model_id):
+    message = {
+        "role": "user",
+        "content": [
+            {
+                "type": "image_url",
+                "image_url": {
+                    "url": "https://raw.githubusercontent.com/meta-llama/llama-stack/main/tests/integration/inference/dog.png"
+                },
+            },
+            {
+                "type": "text",
+                "text": "Describe what is in this image.",
+            },
+        ],
+    }
+
+    response = openai_client.chat.completions.create(
+        model=vision_model_id,
+        messages=[message],
+        stream=False,
+    )
+
+    message_content = response.choices[0].message.content.lower().strip()
+    assert len(message_content) > 0
+    assert any(expected in message_content for expected in {"dog", "puppy", "pup"})
+
+
+async def test_openai_chat_completion_image_data(openai_client, vision_model_id, base64_image_data):
+    message = {
+        "role": "user",
+        "content": [
+            {
+                "type": "image_url",
+                "image_url": {
+                    "url": f"data:image/png;base64,{base64_image_data}",
+                },
+            },
+            {
+                "type": "text",
+                "text": "Describe what is in this image.",
+            },
+        ],
+    }
+
+    response = openai_client.chat.completions.create(
+        model=vision_model_id,
+        messages=[message],
+        stream=False,
+    )
+
+    message_content = response.choices[0].message.content.lower().strip()
+    assert len(message_content) > 0
+    assert any(expected in message_content for expected in {"dog", "puppy", "pup"})

tests/integration/providers/utils/sqlstore/test_authorized_sqlstore.py

@@ -57,7 +57,7 @@ def authorized_store(backend_config):
     config = config_func()
 
     base_sqlstore = sqlstore_impl(config)
-    authorized_store = AuthorizedSqlStore(base_sqlstore)
+    authorized_store = AuthorizedSqlStore(base_sqlstore, default_policy())
 
     yield authorized_store
 
@@ -106,7 +106,7 @@ async def test_authorized_store_attributes(mock_get_authenticated_user, authoriz
         await authorized_store.insert(table_name, {"id": "1", "data": "public_data"})
 
         # Test fetching with no user - should not error on JSON comparison
-        result = await authorized_store.fetch_all(table_name, policy=default_policy())
+        result = await authorized_store.fetch_all(table_name)
         assert len(result.data) == 1
         assert result.data[0]["id"] == "1"
         assert result.data[0]["access_attributes"] is None
@@ -119,7 +119,7 @@ async def test_authorized_store_attributes(mock_get_authenticated_user, authoriz
         await authorized_store.insert(table_name, {"id": "2", "data": "admin_data"})
 
         # Fetch all - admin should see both
-        result = await authorized_store.fetch_all(table_name, policy=default_policy())
+        result = await authorized_store.fetch_all(table_name)
         assert len(result.data) == 2
 
         # Test with non-admin user
@@ -127,7 +127,7 @@ async def test_authorized_store_attributes(mock_get_authenticated_user, authoriz
         mock_get_authenticated_user.return_value = regular_user
 
         # Should only see public record
-        result = await authorized_store.fetch_all(table_name, policy=default_policy())
+        result = await authorized_store.fetch_all(table_name)
         assert len(result.data) == 1
         assert result.data[0]["id"] == "1"
 
@@ -156,7 +156,7 @@ async def test_authorized_store_attributes(mock_get_authenticated_user, authoriz
 
         # Now test with the multi-user who has both roles=admin and teams=dev
         mock_get_authenticated_user.return_value = multi_user
-        result = await authorized_store.fetch_all(table_name, policy=default_policy())
+        result = await authorized_store.fetch_all(table_name)
 
         # Should see:
         # - public record (1) - no access_attributes
@@ -217,21 +217,24 @@ async def test_user_ownership_policy(mock_get_authenticated_user, authorized_sto
             ),
         ]
 
+        # Create a new authorized store with the owner-only policy
+        owner_only_store = AuthorizedSqlStore(authorized_store.sql_store, owner_only_policy)
+
         # Test user1 access - should only see their own record
         mock_get_authenticated_user.return_value = user1
-        result = await authorized_store.fetch_all(table_name, policy=owner_only_policy)
+        result = await owner_only_store.fetch_all(table_name)
         assert len(result.data) == 1, f"Expected user1 to see 1 record, got {len(result.data)}"
         assert result.data[0]["id"] == "1", f"Expected user1's record, got {result.data[0]['id']}"
 
         # Test user2 access - should only see their own record
         mock_get_authenticated_user.return_value = user2
-        result = await authorized_store.fetch_all(table_name, policy=owner_only_policy)
+        result = await owner_only_store.fetch_all(table_name)
         assert len(result.data) == 1, f"Expected user2 to see 1 record, got {len(result.data)}"
         assert result.data[0]["id"] == "2", f"Expected user2's record, got {result.data[0]['id']}"
 
         # Test with anonymous user - should see no records
         mock_get_authenticated_user.return_value = None
-        result = await authorized_store.fetch_all(table_name, policy=owner_only_policy)
+        result = await owner_only_store.fetch_all(table_name)
         assert len(result.data) == 0, f"Expected anonymous user to see 0 records, got {len(result.data)}"
 
     finally:

tests/integration/recordings/responses/0547d0909f24.json

@@ -0,0 +1,53 @@
+{
+  "request": {
+    "method": "POST",
+    "url": "https://api.cerebras.ai/v1/v1/completions",
+    "headers": {},
+    "body": {
+      "model": "llama-3.3-70b",
+      "prompt": "Respond to this question and explain your answer. Complete the sentence using one word: Roses are red, violets are ",
+      "stream": false,
+      "extra_body": {}
+    },
+    "endpoint": "/v1/completions",
+    "model": "llama-3.3-70b"
+  },
+  "response": {
+    "body": {
+      "__type__": "openai.types.completion.Completion",
+      "__data__": {
+        "id": "chatcmpl-6438a448-bbbd-4da1-af88-19390676b0e9",
+        "choices": [
+          {
+            "finish_reason": "stop",
+            "index": 0,
+            "logprobs": null,
+            "text": " blue, sugar is white, but my heart is ________________________.\nA) black\nB) pink\nC) blank\nD) broken\nMy answer is D) broken. This is because the traditional romantic poem has a positive tone until it comes to the heart, which represents the speaker's emotional state. The word \"broken\" shows that the speaker is hurting, which adds a element of sadness to the poem. This is a typical way to express sorrow or longing in poetry.\nThe best answer is D.<|eot_id|>"
+          }
+        ],
+        "created": 1758191351,
+        "model": "llama-3.3-70b",
+        "object": "text_completion",
+        "system_fingerprint": "fp_c5ec625e72d41732d8fd",
+        "usage": {
+          "completion_tokens": 105,
+          "prompt_tokens": 26,
+          "total_tokens": 131,
+          "completion_tokens_details": null,
+          "prompt_tokens_details": {
+            "audio_tokens": null,
+            "cached_tokens": 0
+          }
+        },
+        "time_info": {
+          "queue_time": 0.00016155,
+          "prompt_time": 0.001595551,
+          "completion_time": 0.107480394,
+          "total_time": 0.11038637161254883,
+          "created": 1758191351
+        }
+      }
+    },
+    "is_streaming": false
+  }
+}

tests/integration/recordings/responses/0648374e43e7.json

@@ -0,0 +1,146 @@
+{
+  "request": {
+    "method": "POST",
+    "url": "https://api.cerebras.ai/v1/v1/chat/completions",
+    "headers": {},
+    "body": {
+      "model": "llama-3.3-70b",
+      "messages": [
+        {
+          "role": "user",
+          "content": "What's the weather in Tokyo? Use the get_weather function to get the weather."
+        }
+      ],
+      "stream": true,
+      "tools": [
+        {
+          "type": "function",
+          "function": {
+            "name": "get_weather",
+            "description": "Get the weather in a given city",
+            "parameters": {
+              "type": "object",
+              "properties": {
+                "city": {
+                  "type": "string",
+                  "description": "The city to get the weather for"
+                }
+              }
+            }
+          }
+        }
+      ]
+    },
+    "endpoint": "/v1/chat/completions",
+    "model": "llama-3.3-70b"
+  },
+  "response": {
+    "body": [
+      {
+        "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk",
+        "__data__": {
+          "id": "chatcmpl-8b6a9499-1a5f-46dc-96b7-3d2b71eecd99",
+          "choices": [
+            {
+              "delta": {
+                "content": null,
+                "function_call": null,
+                "refusal": null,
+                "role": "assistant",
+                "tool_calls": null
+              },
+              "finish_reason": null,
+              "index": 0,
+              "logprobs": null
+            }
+          ],
+          "created": 1758191362,
+          "model": "llama-3.3-70b",
+          "object": "chat.completion.chunk",
+          "service_tier": null,
+          "system_fingerprint": "fp_c5ec625e72d41732d8fd",
+          "usage": null
+        }
+      },
+      {
+        "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk",
+        "__data__": {
+          "id": "chatcmpl-8b6a9499-1a5f-46dc-96b7-3d2b71eecd99",
+          "choices": [
+            {
+              "delta": {
+                "content": null,
+                "function_call": null,
+                "refusal": null,
+                "role": null,
+                "tool_calls": [
+                  {
+                    "index": 0,
+                    "id": "439c86fe5",
+                    "function": {
+                      "arguments": "{\"city\": \"Tokyo\"}",
+                      "name": "get_weather"
+                    },
+                    "type": "function"
+                  }
+                ]
+              },
+              "finish_reason": null,
+              "index": 0,
+              "logprobs": null
+            }
+          ],
+          "created": 1758191362,
+          "model": "llama-3.3-70b",
+          "object": "chat.completion.chunk",
+          "service_tier": null,
+          "system_fingerprint": "fp_c5ec625e72d41732d8fd",
+          "usage": null
+        }
+      },
+      {
+        "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk",
+        "__data__": {
+          "id": "chatcmpl-8b6a9499-1a5f-46dc-96b7-3d2b71eecd99",
+          "choices": [
+            {
+              "delta": {
+                "content": null,
+                "function_call": null,
+                "refusal": null,
+                "role": null,
+                "tool_calls": null
+              },
+              "finish_reason": "tool_calls",
+              "index": 0,
+              "logprobs": null
+            }
+          ],
+          "created": 1758191362,
+          "model": "llama-3.3-70b",
+          "object": "chat.completion.chunk",
+          "service_tier": null,
+          "system_fingerprint": "fp_c5ec625e72d41732d8fd",
+          "usage": {
+            "completion_tokens": 12,
+            "prompt_tokens": 248,
+            "total_tokens": 260,
+            "completion_tokens_details": null,
+            "prompt_tokens_details": {
+              "audio_tokens": null,
+              "cached_tokens": 0
+            }
+          },
+          "time_info": {
+            "queue_time": 0.00016941,
+            "prompt_time": 0.007276727,
+            "completion_time": 0.00388514,
+            "total_time": 0.013146162033081055,
+            "created": 1758191362
+          }
+        }
+      }
+    ],
+    "is_streaming": true
+  }
+}

tests/integration/recordings/responses/0d3290adae1d.json

@@ -0,0 +1,93 @@
+{
+  "request": {
+    "method": "POST",
+    "url": "https://api.cerebras.ai/v1/v1/chat/completions",
+    "headers": {},
+    "body": {
+      "model": "llama-3.3-70b",
+      "messages": [
+        {
+          "role": "user",
+          "content": "What's the weather in Tokyo? Use the get_weather function to get the weather."
+        }
+      ],
+      "stream": false,
+      "tools": [
+        {
+          "type": "function",
+          "function": {
+            "name": "get_weather",
+            "description": "Get the weather in a given city",
+            "parameters": {
+              "type": "object",
+              "properties": {
+                "city": {
+                  "type": "string",
+                  "description": "The city to get the weather for"
+                }
+              }
+            }
+          }
+        }
+      ]
+    },
+    "endpoint": "/v1/chat/completions",
+    "model": "llama-3.3-70b"
+  },
+  "response": {
+    "body": {
+      "__type__": "openai.types.chat.chat_completion.ChatCompletion",
+      "__data__": {
+        "id": "chatcmpl-6228def9-c13d-4d7a-9029-e2c638a16f1b",
+        "choices": [
+          {
+            "finish_reason": "tool_calls",
+            "index": 0,
+            "logprobs": null,
+            "message": {
+              "content": null,
+              "refusal": null,
+              "role": "assistant",
+              "annotations": null,
+              "audio": null,
+              "function_call": null,
+              "tool_calls": [
+                {
+                  "id": "1c40cbc30",
+                  "function": {
+                    "arguments": "{\"city\": \"Tokyo\"}",
+                    "name": "get_weather"
+                  },
+                  "type": "function"
+                }
+              ]
+            }
+          }
+        ],
+        "created": 1758191364,
+        "model": "llama-3.3-70b",
+        "object": "chat.completion",
+        "service_tier": null,
+        "system_fingerprint": "fp_c5ec625e72d41732d8fd",
+        "usage": {
+          "completion_tokens": 12,
+          "prompt_tokens": 248,
+          "total_tokens": 260,
+          "completion_tokens_details": null,
+          "prompt_tokens_details": {
+            "audio_tokens": null,
+            "cached_tokens": 0
+          }
+        },
+        "time_info": {
+          "queue_time": 0.00041449,
+          "prompt_time": 0.007237483,
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tests/integration/recordings/responses/121a72d1c4cf.json

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+}

tests/integration/recordings/responses/225b4d2263a7.json

@@ -0,0 +1,802 @@
+{
+  "request": {
+    "method": "POST",
+    "url": "https://api.together.xyz/v1/v1/embeddings",
+    "headers": {},
+    "body": {
+      "model": "togethercomputer/m2-bert-80M-32k-retrieval",
+      "input": [
+        "What is the capital of France?"
+      ]
+    },
+    "endpoint": "/v1/embeddings",
+    "model": "togethercomputer/m2-bert-80M-32k-retrieval"
+  },
+  "response": {
+    "body": {
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tests/integration/recordings/responses/249b7f0ddde6.json

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tests/integration/recordings/responses/28648cf8d421.json

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tests/integration/recordings/responses/29585e055e6f.json

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tests/integration/recordings/responses/2983cc1d79f0.json

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tests/integration/recordings/responses/2c55f483cea8.json

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tests/integration/recordings/responses/33b71fb85bfb.json

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tests/integration/recordings/responses/3a81146f2afa.json

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+          "model": "llama3.2:3b-instruct-fp16",
+          "object": "text_completion",
+          "system_fingerprint": "fp_ollama",
+          "usage": null
+        }
+      },
+      {
+        "__type__": "openai.types.completion.Completion",
+        "__data__": {
+          "id": "cmpl-439",
+          "choices": [
+            {
+              "finish_reason": null,
+              "index": 0,
+              "logprobs": null,
+              "text": "vio"
+            }
+          ],
+          "created": 1757857132,
+          "model": "llama3.2:3b-instruct-fp16",
+          "object": "text_completion",
+          "system_fingerprint": "fp_ollama",
+          "usage": null
+        }
+      },
+      {
+        "__type__": "openai.types.completion.Completion",
+        "__data__": {
+          "id": "cmpl-439",
+          "choices": [
+            {
+              "finish_reason": "length",
+              "index": 0,
+              "logprobs": null,
+              "text": ""
+            }
+          ],
+          "created": 1757857132,
+          "model": "llama3.2:3b-instruct-fp16",
+          "object": "text_completion",
+          "system_fingerprint": "fp_ollama",
+          "usage": null
+        }
+      }
+    ],
+    "is_streaming": true
+  }
+}

tests/integration/recordings/responses/3cdb5cab6ce6.json

@@ -0,0 +1,66 @@
+{
+  "request": {
+    "method": "POST",
+    "url": "https://api.cerebras.ai/v1/v1/chat/completions",
+    "headers": {},
+    "body": {
+      "model": "llama-3.3-70b",
+      "messages": [
+        {
+          "role": "user",
+          "content": "Which planet do humans live on?"
+        }
+      ],
+      "stream": false
+    },
+    "endpoint": "/v1/chat/completions",
+    "model": "llama-3.3-70b"
+  },
+  "response": {
+    "body": {
+      "__type__": "openai.types.chat.chat_completion.ChatCompletion",
+      "__data__": {
+        "id": "chatcmpl-839aab91-21a7-4ed9-b224-d22e524eda37",
+        "choices": [
+          {
+            "finish_reason": "stop",
+            "index": 0,
+            "logprobs": null,
+            "message": {
+              "content": "Humans live on Earth.",
+              "refusal": null,
+              "role": "assistant",
+              "annotations": null,
+              "audio": null,
+              "function_call": null,
+              "tool_calls": null
+            }
+          }
+        ],
+        "created": 1758191360,
+        "model": "llama-3.3-70b",
+        "object": "chat.completion",
+        "service_tier": null,
+        "system_fingerprint": "fp_c5ec625e72d41732d8fd",
+        "usage": {
+          "completion_tokens": 6,
+          "prompt_tokens": 42,
+          "total_tokens": 48,
+          "completion_tokens_details": null,
+          "prompt_tokens_details": {
+            "audio_tokens": null,
+            "cached_tokens": 0
+          }
+        },
+        "time_info": {
+          "queue_time": 0.00028033,
+          "prompt_time": 0.001467015,
+          "completion_time": 0.007069593,
+          "total_time": 0.010509490966796875,
+          "created": 1758191360
+        }
+      }
+    },
+    "is_streaming": false
+  }
+}

tests/integration/recordings/responses/3d89a56a76b1.json

@@ -0,0 +1,46 @@
+{
+  "request": {
+    "method": "POST",
+    "url": "https://api.together.xyz/v1/v1/embeddings",
+    "headers": {},
+    "body": {
+      "model": "togethercomputer/m2-bert-80M-32k-retrieval",
+      "input": [
+        "First text for base64",
+        "Second text for base64",
+        "Third text for base64"
+      ],
+      "encoding_format": "base64"
+    },
+    "endpoint": "/v1/embeddings",
+    "model": "togethercomputer/m2-bert-80M-32k-retrieval"
+  },
+  "response": {
+    "body": {
+      "__type__": "openai.types.create_embedding_response.CreateEmbeddingResponse",
+      "__data__": {
+        "data": [
+          {
+            "embedding": "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",
+            "index": 0,
+            "object": "embedding"
+          },
+          {
+            "embedding": "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",
+            "index": 1,
+            "object": "embedding"
+          },
+          {
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+      }
+    },
+    "is_streaming": false
+  }
+}

tests/integration/recordings/responses/3ef0f9aab128.json

@@ -0,0 +1,344 @@
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+    },
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+}

tests/integration/recordings/responses/41ace09e5dba.json

@@ -0,0 +1,59 @@
+{
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+    "url": "https://api.together.xyz/v1/v1/chat/completions",
+    "headers": {},
+    "body": {
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+      "messages": [
+        {
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+          "content": "Test trace openai 2"
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+    },
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+  }
+}

tests/integration/recordings/responses/4d438c6bbaed.json

@@ -0,0 +1,422 @@
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+    "headers": {},
+    "body": {
+      "model": "all-minilm:l6-v2",
+      "input": [
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+      ],
+      "encoding_format": "float"
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+    "endpoint": "/v1/embeddings",
+    "model": "all-minilm:l6-v2"
+  },
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+}

tests/integration/recordings/responses/517505777888.json

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+}

tests/integration/recordings/responses/52b4e16b7289.json

@@ -0,0 +1,181 @@
+{
+  "request": {
+    "method": "POST",
+    "url": "https://api.together.xyz/v1/v1/chat/completions",
+    "headers": {},
+    "body": {
+      "model": "meta-llama/Llama-3.3-70B-Instruct-Turbo-Free",
+      "messages": [
+        {
+          "role": "user",
+          "content": "What's the weather in Tokyo? YOU MUST USE THE get_weather function to get the weather."
+        }
+      ],
+      "response_format": {
+        "type": "text"
+      },
+      "stream": true,
+      "tools": [
+        {
+          "type": "function",
+          "function": {
+            "type": "function",
+            "name": "get_weather",
+            "description": "Get the weather in a given city",
+            "parameters": {
+              "type": "object",
+              "properties": {
+                "city": {
+                  "type": "string",
+                  "description": "The city to get the weather for"
+                }
+              }
+            },
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+        }
+      ]
+    },
+    "endpoint": "/v1/chat/completions",
+    "model": "meta-llama/Llama-3.3-70B-Instruct-Turbo-Free"
+  },
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+    ],
+    "is_streaming": true
+  }
+}

tests/integration/recordings/responses/53365c6ae29c.json

@@ -0,0 +1,59 @@
+{
+  "request": {
+    "method": "POST",
+    "url": "https://api.together.xyz/v1/v1/chat/completions",
+    "headers": {},
+    "body": {
+      "model": "meta-llama/Llama-3.3-70B-Instruct-Turbo-Free",
+      "messages": [
+        {
+          "role": "user",
+          "content": "Test OpenAI telemetry creation"
+        }
+      ],
+      "stream": false
+    },
+    "endpoint": "/v1/chat/completions",
+    "model": "meta-llama/Llama-3.3-70B-Instruct-Turbo-Free"
+  },
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+      "__type__": "openai.types.chat.chat_completion.ChatCompletion",
+      "__data__": {
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+          {
+            "finish_reason": "stop",
+            "index": 0,
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+            "message": {
+              "content": "To test OpenAI telemetry creation, let's break down the process into steps that can help ensure telemetry data is correctly created and utilized. Telemetry in the context of AI, such as OpenAI, typically refers to the automated process of collecting, transmitting, and analyzing data from remote sources, in this case, user interactions with AI models. Here\u2019s how you might approach testing telemetry creation:\n\n### 1. **Define Telemetry Requirements**\n- **Identify Data Points:** Determine what data points are crucial for your analysis. This could include user input, model responses, interaction timestamps, user IDs, etc.\n- **Purpose of Telemetry:** Understand why you are collecting this data. Is it for model improvement, user experience enhancement, or security monitoring?\n\n### 2. **Implement Data Collection**\n- **API Integration:** If you're using OpenAI's API, ensure you have the necessary permissions and access to collect the required data. OpenAI provides APIs for interacting with their models, and you can collect telemetry data through these interactions.\n- **Logging Mechanisms:** Implement logging mechanisms in your application to capture relevant data. This could involve server-side logging for API calls or client-side logging for user interactions.\n\n### 3. **Data Transmission**\n- **Secure Data Transfer:** Ensure that the data collected is transmitted securely to your analytics or storage system. HTTPS is a standard for secure data transmission over the internet.\n- **Data Format:** Decide on a data format for transmission. JSON is commonly used due to its readability and ease of parsing.\n\n### 4. **Data Analysis**\n- **Analytics Tools:** Utilize appropriate analytics tools or platforms to process and analyze the collected data. This could range from simple statistical analysis to complex machine learning models.\n- **Visualization:** Use data visualization techniques to represent the insights gained from the telemetry data. This helps in understanding trends, patterns, and areas for improvement.\n\n### 5. **Testing the Telemetry System**\n- **Mock Data:** Test your telemetry system with mock data to ensure it can collect, transmit, and analyze data correctly without affecting real users.\n- **Real-World Testing:** Once the system seems to work with mock data, gradually introduce it to real-world scenarios, starting with a small user base to identify and fix any issues that arise.\n- **Feedback Loop:** Implement a feedback loop to continuously monitor the telemetry system's performance and the insights it provides, making adjustments as necessary.\n\n### Example of Testing with Mock Data\nIf you're testing an OpenAI model's interaction telemetry, you might simulate user queries and model responses, then verify that this data is correctly logged, transmitted, and analyzed. For instance, in Python, you might use a mock library to simulate API calls and then assert that the expected data is collected and processed.\n\n```python\nimport unittest\nfrom unittest.mock import Mock\nimport json\n\nclass TestTelemetry(unittest.TestCase):\n\n    def test_telemetry_data_collection(self):\n        # Mock user interaction\n        user_input = \"Hello, how are you?\"\n        model_response = \"I'm doing well, thanks.\"\n\n        # Mock the API call\n        api_call_mock = Mock(return_value=model_response)\n\n        # Simulate the interaction\n        response = api_call_mock(user_input)\n\n        # Verify telemetry data is collected correctly\n        expected_data = {\n            'user_input': user_input,\n            'model_response': model_response,\n            # Other relevant data points\n        }\n\n        # Assuming `collect_telemetry_data` is your function to collect telemetry\n        collected_data = collect_telemetry_data(user_input, response)\n\n        self.assertEqual(collected_data, expected_data)\n\nif __name__ == '__main__':\n    unittest.main()\n```\n\nThis example simplifies the process and focuses on the concept. Actual implementation details may vary based on your specific requirements, the technologies you're using, and the OpenAI APIs or models you're interacting with.",
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+          }
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+        "object": "chat.completion",
+        "service_tier": null,
+        "system_fingerprint": null,
+        "usage": {
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+          "prompt_tokens": 40,
+          "total_tokens": 831,
+          "completion_tokens_details": null,
+          "prompt_tokens_details": null,
+          "cached_tokens": 0
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+        "prompt": []
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+    },
+    "is_streaming": false
+  }
+}

tests/integration/recordings/responses/546dc9533c84.json

@@ -0,0 +1,801 @@
+{
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+    "method": "POST",
+    "url": "https://api.together.xyz/v1/v1/embeddings",
+    "headers": {},
+    "body": {
+      "model": "togethercomputer/m2-bert-80M-32k-retrieval",
+      "input": "This is the first text",
+      "encoding_format": "float"
+    },
+    "endpoint": "/v1/embeddings",
+    "model": "togethercomputer/m2-bert-80M-32k-retrieval"
+  },
+  "response": {
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tests/integration/recordings/responses/58c8091104ff.json

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tests/integration/recordings/responses/590d43ed64b8.json

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tests/integration/recordings/responses/6412295819a1.json

@@ -0,0 +1,43 @@
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+    "url": "http://0.0.0.0:11434/v1/v1/completions",
+    "headers": {},
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+      "prompt": "Respond to this question and explain your answer. Complete the sentence using one word: Roses are red, violets are ",
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+}

tests/integration/recordings/responses/6841bb14fa8d.json

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+{
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+}

tests/integration/recordings/responses/68e59155a09f.json

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+}

tests/integration/recordings/responses/69464dfd3a06.json

@@ -0,0 +1,59 @@
+{
+  "request": {
+    "method": "POST",
+    "url": "https://api.together.xyz/v1/v1/chat/completions",
+    "headers": {},
+    "body": {
+      "model": "meta-llama/Llama-3.3-70B-Instruct-Turbo-Free",
+      "messages": [
+        {
+          "role": "user",
+          "content": "Test trace openai 0"
+        }
+      ],
+      "stream": false
+    },
+    "endpoint": "/v1/chat/completions",
+    "model": "meta-llama/Llama-3.3-70B-Instruct-Turbo-Free"
+  },
+  "response": {
+    "body": {
+      "__type__": "openai.types.chat.chat_completion.ChatCompletion",
+      "__data__": {
+        "id": "oCfx5VY-4Yz4kd-984c2a91a8fd8f78",
+        "choices": [
+          {
+            "finish_reason": "stop",
+            "index": 0,
+            "logprobs": null,
+            "message": {
+              "content": "This conversation has just begun. I'm happy to chat with you, but I don't have any prior context or information to work with. What would you like to talk about?",
+              "refusal": null,
+              "role": "assistant",
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tests/integration/recordings/responses/72dc126ecb92.json

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tests/integration/recordings/responses/7550dd0d24bc.json

@@ -0,0 +1,166 @@
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tests/integration/recordings/responses/7f53b458dad9.json

@@ -0,0 +1,83 @@
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tests/integration/recordings/responses/8ce928ad0b85.json

@@ -0,0 +1,421 @@
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+      "input": "Test user parameter",
+      "encoding_format": "float",
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tests/integration/recordings/responses/a0ec01643fa2.json

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tests/integration/recordings/responses/a1c5bf09ea53.json

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tests/integration/recordings/responses/a1ea41fbf9a2.json

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tests/integration/recordings/responses/ae82d694f34c.json

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tests/integration/recordings/responses/b459f403a5ae.json

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tests/integration/recordings/responses/b734171a0872.json

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tests/integration/recordings/responses/c48eb1cb6e1c.json

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+}

tests/integration/recordings/responses/c8a59b661fd5.json

@@ -0,0 +1,46 @@
+{
+  "request": {
+    "method": "POST",
+    "url": "https://api.together.xyz/v1/v1/completions",
+    "headers": {},
+    "body": {
+      "model": "meta-llama/Llama-3.3-70B-Instruct-Turbo-Free",
+      "prompt": "Say completions",
+      "max_tokens": 20,
+      "extra_body": {}
+    },
+    "endpoint": "/v1/completions",
+    "model": "meta-llama/Llama-3.3-70B-Instruct-Turbo-Free"
+  },
+  "response": {
+    "body": {
+      "__type__": "openai.types.completion.Completion",
+      "__data__": {
+        "id": "oCfwct8-4Yz4kd-984c2861287d4cc3",
+        "choices": [
+          {
+            "finish_reason": "length",
+            "index": 0,
+            "logprobs": null,
+            "text": " of the following sentences:\n* The _most_ important thing in life is...\n* The _least",
+            "seed": 12050749903881546000
+          }
+        ],
+        "created": 1758820465,
+        "model": "meta-llama/Llama-3.3-70B-Instruct-Turbo-Free",
+        "object": "text.completion",
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+        },
+        "prompt": []
+      }
+    },
+    "is_streaming": false
+  }
+}

tests/integration/recordings/responses/d2e057a81717.json

@@ -0,0 +1,61 @@
+{
+  "request": {
+    "method": "POST",
+    "url": "https://api.together.xyz/v1/v1/chat/completions",
+    "headers": {},
+    "body": {
+      "model": "meta-llama/Llama-3.3-70B-Instruct-Turbo-Free",
+      "messages": [
+        {
+          "role": "user",
+          "content": "Test trace openai with temperature 1"
+        }
+      ],
+      "max_tokens": 100,
+      "stream": false,
+      "temperature": 0.7
+    },
+    "endpoint": "/v1/chat/completions",
+    "model": "meta-llama/Llama-3.3-70B-Instruct-Turbo-Free"
+  },
+  "response": {
+    "body": {
+      "__type__": "openai.types.chat.chat_completion.ChatCompletion",
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+        "choices": [
+          {
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+            "index": 0,
+            "logprobs": null,
+            "message": {
+              "content": "I'll do my best to generate a response with a temperature of 1. \n\nIn the context of language models like myself, temperature is a parameter that controls the level of randomness or creativity in the generated text. A temperature of 1 is relatively high, which means the model will produce more diverse and potentially less coherent text.\n\nHere's a response with a temperature of 1:\n\n\"The stars shone brightly in the midnight sky, like diamonds scattered across the velvet expanse. The world was bath",
+              "refusal": null,
+              "role": "assistant",
+              "annotations": null,
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+            },
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+          }
+        ],
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+        "model": "meta-llama/Llama-3.3-70B-Instruct-Turbo-Free",
+        "object": "chat.completion",
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+          "cached_tokens": 0
+        },
+        "prompt": []
+      }
+    },
+    "is_streaming": false
+  }
+}

tests/integration/recordings/responses/d5971b8fdb94.json

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+{
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+    "headers": {},
+    "body": {
+      "model": "togethercomputer/m2-bert-80M-32k-retrieval",
+      "input": "Hello, world!",
+      "encoding_format": "float"
+    },
+    "endpoint": "/v1/embeddings",
+    "model": "togethercomputer/m2-bert-80M-32k-retrieval"
+  },
+  "response": {
+    "body": {
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+}

tests/integration/recordings/responses/dc978ebb7159.json

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tests/integration/recordings/responses/e509387fc329.json

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tests/integration/recordings/responses/e8b427b3d631.json

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+}

tests/integration/recordings/responses/ecae140151d1.json

@@ -0,0 +1,43 @@
+{
+  "request": {
+    "method": "POST",
+    "url": "http://0.0.0.0:11434/v1/v1/completions",
+    "headers": {},
+    "body": {
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+      "prompt": "Say completions",
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+      "extra_body": {}
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+    "model": "llama3.2:3b-instruct-fp16"
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+        "choices": [
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+            "logprobs": null,
+            "text": "Sure, I'd be happy to provide some definitions and examples of related words or phrases.\n\nTo better"
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+        "created": 1757857133,
+        "model": "llama3.2:3b-instruct-fp16",
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+    },
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+}

tests/integration/recordings/responses/ecf6f0c51485.json

@@ -0,0 +1,536 @@
+{
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+    "headers": {},
+    "body": {
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+      "messages": [
+        {
+          "role": "user",
+          "content": "What is the name of the US captial?"
+        }
+      ],
+      "stream": true
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+    "endpoint": "/v1/chat/completions",
+    "model": "databricks-meta-llama-3-3-70b-instruct"
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+        "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk",
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tests/integration/recordings/responses/ef4d211b38bf.json

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tests/integration/recordings/responses/f23defea82ec.json

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tests/integration/recordings/responses/f4c5ae637cd1.json

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tests/integration/recordings/responses/fbc7b626714d.json

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+        "__data__": {
+          "id": "llama-guard3:1b",
+          "created": 1756671473,
+          "object": "model",
+          "owned_by": "library"
+        }
+      },
+      {
+        "__type__": "openai.types.model.Model",
+        "__data__": {
+          "id": "all-minilm:l6-v2",
+          "created": 1756655274,
+          "object": "model",
+          "owned_by": "library"
+        }
+      },
+      {
+        "__type__": "openai.types.model.Model",
+        "__data__": {
+          "id": "all-minilm:latest",
+          "created": 1747317111,
+          "object": "model",
+          "owned_by": "library"
+        }
+      },
+      {
+        "__type__": "openai.types.model.Model",
+        "__data__": {
+          "id": "llama3.2:3b-instruct-fp16",
+          "created": 1744974677,
+          "object": "model",
+          "owned_by": "library"
+        }
+      },
+      {
+        "__type__": "openai.types.model.Model",
+        "__data__": {
+          "id": "llama3.2:3b",
+          "created": 1743536220,
+          "object": "model",
+          "owned_by": "library"
+        }
+      }
+    ],
+    "is_streaming": false
+  }
+}

tests/integration/recordings/responses/models-bd032f995f2a-af43cc69.json

@@ -0,0 +1,96 @@
+{
+  "request": {
+    "method": "POST",
+    "url": "https://api.cerebras.ai/v1/v1/models",
+    "headers": {},
+    "body": {},
+    "endpoint": "/v1/models",
+    "model": ""
+  },
+  "response": {
+    "body": [
+      {
+        "__type__": "openai.types.model.Model",
+        "__data__": {
+          "id": "llama-4-maverick-17b-128e-instruct",
+          "created": 0,
+          "object": "model",
+          "owned_by": "Cerebras"
+        }
+      },
+      {
+        "__type__": "openai.types.model.Model",
+        "__data__": {
+          "id": "llama-4-scout-17b-16e-instruct",
+          "created": 0,
+          "object": "model",
+          "owned_by": "Cerebras"
+        }
+      },
+      {
+        "__type__": "openai.types.model.Model",
+        "__data__": {
+          "id": "qwen-3-235b-a22b-instruct-2507",
+          "created": 0,
+          "object": "model",
+          "owned_by": "Cerebras"
+        }
+      },
+      {
+        "__type__": "openai.types.model.Model",
+        "__data__": {
+          "id": "llama3.1-8b",
+          "created": 0,
+          "object": "model",
+          "owned_by": "Cerebras"
+        }
+      },
+      {
+        "__type__": "openai.types.model.Model",
+        "__data__": {
+          "id": "qwen-3-32b",
+          "created": 0,
+          "object": "model",
+          "owned_by": "Cerebras"
+        }
+      },
+      {
+        "__type__": "openai.types.model.Model",
+        "__data__": {
+          "id": "gpt-oss-120b",
+          "created": 0,
+          "object": "model",
+          "owned_by": "Cerebras"
+        }
+      },
+      {
+        "__type__": "openai.types.model.Model",
+        "__data__": {
+          "id": "qwen-3-235b-a22b-thinking-2507",
+          "created": 0,
+          "object": "model",
+          "owned_by": "Cerebras"
+        }
+      },
+      {
+        "__type__": "openai.types.model.Model",
+        "__data__": {
+          "id": "llama-3.3-70b",
+          "created": 0,
+          "object": "model",
+          "owned_by": "Cerebras"
+        }
+      },
+      {
+        "__type__": "openai.types.model.Model",
+        "__data__": {
+          "id": "qwen-3-coder-480b",
+          "created": 0,
+          "object": "model",
+          "owned_by": "Cerebras"
+        }
+      }
+    ],
+    "is_streaming": false
+  }
+}

tests/integration/suites.py

@@ -55,7 +55,7 @@ SETUP_DEFINITIONS: dict[str, Setup] = {
         },
         defaults={
             "text_model": "ollama/llama3.2:3b-instruct-fp16",
-            "embedding_model": "sentence-transformers/all-MiniLM-L6-v2",
+            "embedding_model": "ollama/all-minilm:l6-v2",
             "safety_model": "ollama/llama-guard3:1b",
             "safety_shield": "llama-guard",
         },
@@ -68,7 +68,7 @@ SETUP_DEFINITIONS: dict[str, Setup] = {
         },
         defaults={
             "vision_model": "ollama/llama3.2-vision:11b",
-            "embedding_model": "sentence-transformers/all-MiniLM-L6-v2",
+            "embedding_model": "ollama/all-minilm:l6-v2",
         },
     ),
     "vllm": Setup(
@@ -87,7 +87,7 @@ SETUP_DEFINITIONS: dict[str, Setup] = {
         description="OpenAI GPT models for high-quality responses and tool calling",
         defaults={
             "text_model": "openai/gpt-4o",
-            "embedding_model": "sentence-transformers/all-MiniLM-L6-v2",
+            "embedding_model": "openai/text-embedding-3-small",
         },
     ),
     "tgi": Setup(
@@ -108,6 +108,30 @@ SETUP_DEFINITIONS: dict[str, Setup] = {
             "embedding_model": "together/togethercomputer/m2-bert-80M-32k-retrieval",
         },
     ),
+    "cerebras": Setup(
+        name="cerebras",
+        description="Cerebras models",
+        defaults={
+            "text_model": "cerebras/llama-3.3-70b",
+        },
+    ),
+    "databricks": Setup(
+        name="databricks",
+        description="Databricks models",
+        defaults={
+            "text_model": "databricks/databricks-meta-llama-3-3-70b-instruct",
+            "embedding_model": "databricks/databricks-bge-large-en",
+        },
+    ),
+    "fireworks": Setup(
+        name="fireworks",
+        description="Fireworks provider with a text model",
+        defaults={
+            "text_model": "accounts/fireworks/models/llama-v3p1-8b-instruct",
+            "vision_model": "accounts/fireworks/models/llama-v3p2-90b-vision-instruct",
+            "embedding_model": "nomic-ai/nomic-embed-text-v1.5",
+        },
+    ),
 }
 
 

tests/unit/distribution/routers/test_routing_tables.py

@@ -10,6 +10,7 @@ from unittest.mock import AsyncMock
 
 import pytest
 
+from llama_stack.apis.common.content_types import URL
 from llama_stack.apis.common.type_system import NumberType
 from llama_stack.apis.datasets.datasets import Dataset, DatasetPurpose, URIDataSource
 from llama_stack.apis.datatypes import Api
@@ -645,3 +646,25 @@ async def test_models_source_interaction_cleanup_provider_models(cached_disk_dis
 
     # Cleanup
     await table.shutdown()
+
+
+async def test_tool_groups_routing_table_exception_handling(cached_disk_dist_registry):
+    """Test that the tool group routing table handles exceptions when listing tools, like if an MCP server is unreachable."""
+
+    exception_throwing_tool_groups_impl = ToolGroupsImpl()
+    exception_throwing_tool_groups_impl.list_runtime_tools = AsyncMock(side_effect=Exception("Test exception"))
+
+    table = ToolGroupsRoutingTable(
+        {"test_provider": exception_throwing_tool_groups_impl}, cached_disk_dist_registry, {}
+    )
+    await table.initialize()
+
+    await table.register_tool_group(
+        toolgroup_id="test-toolgroup-exceptions",
+        provider_id="test_provider",
+        mcp_endpoint=URL(uri="http://localhost:8479/foo/bar"),
+    )
+
+    tools = await table.list_tools(toolgroup_id="test-toolgroup-exceptions")
+
+    assert len(tools.data) == 0

tests/unit/distribution/test_distribution.py

@@ -12,7 +12,7 @@ import yaml
 from pydantic import BaseModel, Field, ValidationError
 
 from llama_stack.core.datatypes import Api, Provider, StackRunConfig
-from llama_stack.core.distribution import get_provider_registry
+from llama_stack.core.distribution import INTERNAL_APIS, get_provider_registry, providable_apis
 from llama_stack.providers.datatypes import ProviderSpec
 
 
@@ -66,10 +66,9 @@ def base_config(tmp_path):
 def provider_spec_yaml():
     """Common provider spec YAML for testing."""
     return """
-adapter:
-  adapter_type: test_provider
-  config_class: test_provider.config.TestProviderConfig
-  module: test_provider
+adapter_type: test_provider
+config_class: test_provider.config.TestProviderConfig
+module: test_provider
 api_dependencies:
   - safety
 """
@@ -152,6 +151,24 @@ class TestProviderRegistry:
         assert registry[Api.inference]["test_provider"].provider_type == "test_provider"
         assert registry[Api.inference]["test_provider"].api == Api.inference
 
+    def test_internal_apis_excluded(self):
+        """Test that internal APIs are excluded and APIs without provider registries are marked as internal."""
+        import importlib
+
+        apis = providable_apis()
+
+        for internal_api in INTERNAL_APIS:
+            assert internal_api not in apis, f"Internal API {internal_api} should not be in providable_apis"
+
+        for api in apis:
+            module_name = f"llama_stack.providers.registry.{api.name.lower()}"
+            try:
+                importlib.import_module(module_name)
+            except ImportError as err:
+                raise AssertionError(
+                    f"API {api} is in providable_apis but has no provider registry module ({module_name})"
+                ) from err
+
     def test_external_remote_providers(self, api_directories, mock_providers, base_config, provider_spec_yaml):
         """Test loading external remote providers from YAML files."""
         remote_dir, _ = api_directories
@@ -164,9 +181,9 @@ class TestProviderRegistry:
         assert Api.inference in registry
         assert "remote::test_provider" in registry[Api.inference]
         provider = registry[Api.inference]["remote::test_provider"]
-        assert provider.adapter.adapter_type == "test_provider"
-        assert provider.adapter.module == "test_provider"
-        assert provider.adapter.config_class == "test_provider.config.TestProviderConfig"
+        assert provider.adapter_type == "test_provider"
+        assert provider.module == "test_provider"
+        assert provider.config_class == "test_provider.config.TestProviderConfig"
         assert Api.safety in provider.api_dependencies
 
     def test_external_inline_providers(self, api_directories, mock_providers, base_config, inline_provider_spec_yaml):
@@ -228,8 +245,7 @@ class TestProviderRegistry:
         """Test handling of malformed remote provider spec (missing required fields)."""
         remote_dir, _ = api_directories
         malformed_spec = """
-adapter:
-  adapter_type: test_provider
+adapter_type: test_provider
   # Missing required fields
 api_dependencies:
   - safety
@@ -252,7 +268,7 @@ pip_packages:
         with open(inline_dir / "malformed.yaml", "w") as f:
             f.write(malformed_spec)
 
-        with pytest.raises(KeyError) as exc_info:
+        with pytest.raises(ValidationError) as exc_info:
             get_provider_registry(base_config)
         assert "config_class" in str(exc_info.value)
 

tests/unit/distribution/test_library_client_initialization.py

@@ -27,13 +27,17 @@ class TestLlamaStackAsLibraryClientAutoInitialization:
         mock_impls = {}
         mock_route_impls = RouteImpls({})
 
-        async def mock_construct_stack(config, custom_provider_registry):
-            return mock_impls
+        class MockStack:
+            def __init__(self, config, custom_provider_registry=None):
+                self.impls = mock_impls
+
+            async def initialize(self):
+                pass
 
         def mock_initialize_route_impls(impls):
             return mock_route_impls
 
-        monkeypatch.setattr("llama_stack.core.library_client.construct_stack", mock_construct_stack)
+        monkeypatch.setattr("llama_stack.core.library_client.Stack", MockStack)
         monkeypatch.setattr("llama_stack.core.library_client.initialize_route_impls", mock_initialize_route_impls)
 
         client = LlamaStackAsLibraryClient("ci-tests")
@@ -46,13 +50,17 @@ class TestLlamaStackAsLibraryClientAutoInitialization:
         mock_impls = {}
         mock_route_impls = RouteImpls({})
 
-        async def mock_construct_stack(config, custom_provider_registry):
-            return mock_impls
+        class MockStack:
+            def __init__(self, config, custom_provider_registry=None):
+                self.impls = mock_impls
+
+            async def initialize(self):
+                pass
 
         def mock_initialize_route_impls(impls):
             return mock_route_impls
 
-        monkeypatch.setattr("llama_stack.core.library_client.construct_stack", mock_construct_stack)
+        monkeypatch.setattr("llama_stack.core.library_client.Stack", MockStack)
         monkeypatch.setattr("llama_stack.core.library_client.initialize_route_impls", mock_initialize_route_impls)
 
         client = AsyncLlamaStackAsLibraryClient("ci-tests")
@@ -68,13 +76,17 @@ class TestLlamaStackAsLibraryClientAutoInitialization:
         mock_impls = {}
         mock_route_impls = RouteImpls({})
 
-        async def mock_construct_stack(config, custom_provider_registry):
-            return mock_impls
+        class MockStack:
+            def __init__(self, config, custom_provider_registry=None):
+                self.impls = mock_impls
+
+            async def initialize(self):
+                pass
 
         def mock_initialize_route_impls(impls):
             return mock_route_impls
 
-        monkeypatch.setattr("llama_stack.core.library_client.construct_stack", mock_construct_stack)
+        monkeypatch.setattr("llama_stack.core.library_client.Stack", MockStack)
         monkeypatch.setattr("llama_stack.core.library_client.initialize_route_impls", mock_initialize_route_impls)
 
         client = LlamaStackAsLibraryClient("ci-tests")
@@ -90,13 +102,17 @@ class TestLlamaStackAsLibraryClientAutoInitialization:
         mock_impls = {}
         mock_route_impls = RouteImpls({})
 
-        async def mock_construct_stack(config, custom_provider_registry):
-            return mock_impls
+        class MockStack:
+            def __init__(self, config, custom_provider_registry=None):
+                self.impls = mock_impls
+
+            async def initialize(self):
+                pass
 
         def mock_initialize_route_impls(impls):
             return mock_route_impls
 
-        monkeypatch.setattr("llama_stack.core.library_client.construct_stack", mock_construct_stack)
+        monkeypatch.setattr("llama_stack.core.library_client.Stack", MockStack)
         monkeypatch.setattr("llama_stack.core.library_client.initialize_route_impls", mock_initialize_route_impls)
 
         client = AsyncLlamaStackAsLibraryClient("ci-tests")
@@ -112,13 +128,17 @@ class TestLlamaStackAsLibraryClientAutoInitialization:
         mock_impls = {}
         mock_route_impls = RouteImpls({})
 
-        async def mock_construct_stack(config, custom_provider_registry):
-            return mock_impls
+        class MockStack:
+            def __init__(self, config, custom_provider_registry=None):
+                self.impls = mock_impls
+
+            async def initialize(self):
+                pass
 
         def mock_initialize_route_impls(impls):
             return mock_route_impls
 
-        monkeypatch.setattr("llama_stack.core.library_client.construct_stack", mock_construct_stack)
+        monkeypatch.setattr("llama_stack.core.library_client.Stack", MockStack)
         monkeypatch.setattr("llama_stack.core.library_client.initialize_route_impls", mock_initialize_route_impls)
 
         sync_client = LlamaStackAsLibraryClient("ci-tests")

tests/unit/providers/inference/test_litellm_openai_mixin.py

@@ -26,7 +26,6 @@ class TestProviderDataValidator(BaseModel):
 class TestLiteLLMAdapter(LiteLLMOpenAIMixin):
     def __init__(self, config: TestConfig):
         super().__init__(
-            model_entries=[],
             litellm_provider_name="test",
             api_key_from_config=config.api_key,
             provider_data_api_key_field="test_api_key",

tests/unit/providers/inference/test_openai_base_url_config.py

@@ -5,7 +5,7 @@
 # the root directory of this source tree.
 
 import os
-from unittest.mock import AsyncMock, MagicMock, patch
+from unittest.mock import MagicMock, patch
 
 from llama_stack.core.stack import replace_env_vars
 from llama_stack.providers.remote.inference.openai.config import OpenAIConfig
@@ -80,11 +80,22 @@ class TestOpenAIBaseURLConfig:
         # Mock the get_api_key method
         adapter.get_api_key = MagicMock(return_value="test-key")
 
-        # Mock the AsyncOpenAI client and its models.retrieve method
+        # Mock a model object that will be returned by models.list()
+        mock_model = MagicMock()
+        mock_model.id = "gpt-4"
+
+        # Create an async iterator that yields our mock model
+        async def mock_async_iterator():
+            yield mock_model
+
+        # Mock the AsyncOpenAI client and its models.list method
         mock_client = MagicMock()
-        mock_client.models.retrieve = AsyncMock(return_value=MagicMock())
+        mock_client.models.list = MagicMock(return_value=mock_async_iterator())
         mock_openai_class.return_value = mock_client
 
+        # Set the __provider_id__ attribute that's expected by list_models
+        adapter.__provider_id__ = "openai"
+
         # Call check_model_availability and verify it returns True
         assert await adapter.check_model_availability("gpt-4")
 
@@ -94,8 +105,8 @@ class TestOpenAIBaseURLConfig:
             base_url=custom_url,
         )
 
-        # Verify the method was called and returned True
-        mock_client.models.retrieve.assert_called_once_with("gpt-4")
+        # Verify the models.list method was called
+        mock_client.models.list.assert_called_once()
 
     @patch.dict(os.environ, {"OPENAI_BASE_URL": "https://proxy.openai.com/v1"})
     @patch("llama_stack.providers.utils.inference.openai_mixin.AsyncOpenAI")
@@ -110,11 +121,22 @@ class TestOpenAIBaseURLConfig:
         # Mock the get_api_key method
         adapter.get_api_key = MagicMock(return_value="test-key")
 
-        # Mock the AsyncOpenAI client
+        # Mock a model object that will be returned by models.list()
+        mock_model = MagicMock()
+        mock_model.id = "gpt-4"
+
+        # Create an async iterator that yields our mock model
+        async def mock_async_iterator():
+            yield mock_model
+
+        # Mock the AsyncOpenAI client and its models.list method
         mock_client = MagicMock()
-        mock_client.models.retrieve = AsyncMock(return_value=MagicMock())
+        mock_client.models.list = MagicMock(return_value=mock_async_iterator())
         mock_openai_class.return_value = mock_client
 
+        # Set the __provider_id__ attribute that's expected by list_models
+        adapter.__provider_id__ = "openai"
+
         # Call check_model_availability and verify it returns True
         assert await adapter.check_model_availability("gpt-4")
 

tests/unit/providers/inference/test_remote_vllm.py

@@ -62,15 +62,19 @@ from llama_stack.providers.remote.inference.vllm.vllm import (
 
 @pytest.fixture(scope="module")
 def mock_openai_models_list():
-    with patch("openai.resources.models.AsyncModels.list", new_callable=AsyncMock) as mock_list:
+    with patch("openai.resources.models.AsyncModels.list") as mock_list:
         yield mock_list
 
 
-@pytest.fixture(scope="module")
+@pytest.fixture(scope="function")
 async def vllm_inference_adapter():
     config = VLLMInferenceAdapterConfig(url="http://mocked.localhost:12345")
     inference_adapter = VLLMInferenceAdapter(config)
     inference_adapter.model_store = AsyncMock()
+    # Mock the __provider_spec__ attribute that would normally be set by the resolver
+    inference_adapter.__provider_spec__ = MagicMock()
+    inference_adapter.__provider_spec__.provider_type = "vllm-inference"
+    inference_adapter.__provider_spec__.provider_data_validator = MagicMock()
     await inference_adapter.initialize()
     return inference_adapter
 
@@ -120,6 +124,10 @@ async def test_tool_call_response(vllm_inference_adapter):
         mock_client.chat.completions.create = AsyncMock()
         mock_create_client.return_value = mock_client
 
+        # Mock the model to return a proper provider_resource_id
+        mock_model = Model(identifier="mock-model", provider_resource_id="mock-model", provider_id="vllm-inference")
+        vllm_inference_adapter.model_store.get_model.return_value = mock_model
+
         messages = [
             SystemMessage(content="You are a helpful assistant"),
             UserMessage(content="How many?"),
@@ -555,31 +563,29 @@ async def test_health_status_success(vllm_inference_adapter):
     """
     Test the health method of VLLM InferenceAdapter when the connection is successful.
 
-    This test verifies that the health method returns a HealthResponse with status OK, only
-    when the connection to the vLLM server is successful.
+    This test verifies that the health method returns a HealthResponse with status OK
+    when the /health endpoint responds successfully.
     """
-    with patch.object(VLLMInferenceAdapter, "client", new_callable=PropertyMock) as mock_create_client:
-        # Create mock client and models
-        mock_client = MagicMock()
-        mock_models = MagicMock()
+    with patch("httpx.AsyncClient") as mock_client_class:
+        # Create mock response
+        mock_response = MagicMock()
+        mock_response.raise_for_status.return_value = None
 
-        # Create a mock async iterator that yields a model when iterated
-        async def mock_list():
-            for model in [MagicMock()]:
-                yield model
-
-        # Set up the models.list to return our mock async iterator
-        mock_models.list.return_value = mock_list()
-        mock_client.models = mock_models
-        mock_create_client.return_value = mock_client
+        # Create mock client instance
+        mock_client_instance = MagicMock()
+        mock_client_instance.get = AsyncMock(return_value=mock_response)
+        mock_client_class.return_value.__aenter__.return_value = mock_client_instance
 
         # Call the health method
         health_response = await vllm_inference_adapter.health()
+
         # Verify the response
         assert health_response["status"] == HealthStatus.OK
 
-        # Verify that models.list was called
-        mock_models.list.assert_called_once()
+        # Verify that the health endpoint was called
+        mock_client_instance.get.assert_called_once()
+        call_args = mock_client_instance.get.call_args[0]
+        assert call_args[0].endswith("/health")
 
 
 async def test_health_status_failure(vllm_inference_adapter):
@@ -589,28 +595,42 @@ async def test_health_status_failure(vllm_inference_adapter):
     This test verifies that the health method returns a HealthResponse with status ERROR
     and an appropriate error message when the connection to the vLLM server fails.
     """
-    with patch.object(VLLMInferenceAdapter, "client", new_callable=PropertyMock) as mock_create_client:
-        # Create mock client and models
-        mock_client = MagicMock()
-        mock_models = MagicMock()
-
-        # Create a mock async iterator that raises an exception when iterated
-        async def mock_list():
-            raise Exception("Connection failed")
-            yield  # Unreachable code
-
-        # Set up the models.list to return our mock async iterator
-        mock_models.list.return_value = mock_list()
-        mock_client.models = mock_models
-        mock_create_client.return_value = mock_client
+    with patch("httpx.AsyncClient") as mock_client_class:
+        # Create mock client instance that raises an exception
+        mock_client_instance = MagicMock()
+        mock_client_instance.get.side_effect = Exception("Connection failed")
+        mock_client_class.return_value.__aenter__.return_value = mock_client_instance
 
         # Call the health method
         health_response = await vllm_inference_adapter.health()
+
         # Verify the response
         assert health_response["status"] == HealthStatus.ERROR
         assert "Health check failed: Connection failed" in health_response["message"]
 
-        mock_models.list.assert_called_once()
+
+async def test_health_status_no_static_api_key(vllm_inference_adapter):
+    """
+    Test the health method of VLLM InferenceAdapter when no static API key is provided.
+
+    This test verifies that the health method returns a HealthResponse with status OK
+    when the /health endpoint responds successfully, regardless of API token configuration.
+    """
+    with patch("httpx.AsyncClient") as mock_client_class:
+        # Create mock response
+        mock_response = MagicMock()
+        mock_response.raise_for_status.return_value = None
+
+        # Create mock client instance
+        mock_client_instance = MagicMock()
+        mock_client_instance.get = AsyncMock(return_value=mock_response)
+        mock_client_class.return_value.__aenter__.return_value = mock_client_instance
+
+        # Call the health method
+        health_response = await vllm_inference_adapter.health()
+
+        # Verify the response
+        assert health_response["status"] == HealthStatus.OK
 
 
 async def test_openai_chat_completion_is_async(vllm_inference_adapter):
@@ -656,3 +676,109 @@ async def test_openai_chat_completion_is_async(vllm_inference_adapter):
 
         assert mock_create_client.call_count == 4  # no cheating
         assert total_time < (sleep_time * 2), f"Total time taken: {total_time}s exceeded expected max"
+
+
+async def test_should_refresh_models():
+    """
+    Test the should_refresh_models method with different refresh_models configurations.
+
+    This test verifies that:
+    1. When refresh_models is True, should_refresh_models returns True regardless of api_token
+    2. When refresh_models is False, should_refresh_models returns False regardless of api_token
+    """
+
+    # Test case 1: refresh_models is True, api_token is None
+    config1 = VLLMInferenceAdapterConfig(url="http://test.localhost", api_token=None, refresh_models=True)
+    adapter1 = VLLMInferenceAdapter(config1)
+    result1 = await adapter1.should_refresh_models()
+    assert result1 is True, "should_refresh_models should return True when refresh_models is True"
+
+    # Test case 2: refresh_models is True, api_token is empty string
+    config2 = VLLMInferenceAdapterConfig(url="http://test.localhost", api_token="", refresh_models=True)
+    adapter2 = VLLMInferenceAdapter(config2)
+    result2 = await adapter2.should_refresh_models()
+    assert result2 is True, "should_refresh_models should return True when refresh_models is True"
+
+    # Test case 3: refresh_models is True, api_token is "fake" (default)
+    config3 = VLLMInferenceAdapterConfig(url="http://test.localhost", api_token="fake", refresh_models=True)
+    adapter3 = VLLMInferenceAdapter(config3)
+    result3 = await adapter3.should_refresh_models()
+    assert result3 is True, "should_refresh_models should return True when refresh_models is True"
+
+    # Test case 4: refresh_models is True, api_token is real token
+    config4 = VLLMInferenceAdapterConfig(url="http://test.localhost", api_token="real-token-123", refresh_models=True)
+    adapter4 = VLLMInferenceAdapter(config4)
+    result4 = await adapter4.should_refresh_models()
+    assert result4 is True, "should_refresh_models should return True when refresh_models is True"
+
+    # Test case 5: refresh_models is False, api_token is real token
+    config5 = VLLMInferenceAdapterConfig(url="http://test.localhost", api_token="real-token-456", refresh_models=False)
+    adapter5 = VLLMInferenceAdapter(config5)
+    result5 = await adapter5.should_refresh_models()
+    assert result5 is False, "should_refresh_models should return False when refresh_models is False"
+
+
+async def test_provider_data_var_context_propagation(vllm_inference_adapter):
+    """
+    Test that PROVIDER_DATA_VAR context is properly propagated through the vLLM inference adapter.
+    This ensures that dynamic provider data (like API tokens) can be passed through context.
+    Note: The base URL is always taken from config.url, not from provider data.
+    """
+    # Mock the AsyncOpenAI class to capture provider data
+    with (
+        patch("llama_stack.providers.utils.inference.openai_mixin.AsyncOpenAI") as mock_openai_class,
+        patch.object(vllm_inference_adapter, "get_request_provider_data") as mock_get_provider_data,
+    ):
+        mock_client = AsyncMock()
+        mock_client.chat.completions.create = AsyncMock()
+        mock_openai_class.return_value = mock_client
+
+        # Mock provider data to return test data
+        mock_provider_data = MagicMock()
+        mock_provider_data.vllm_api_token = "test-token-123"
+        mock_provider_data.vllm_url = "http://test-server:8000/v1"
+        mock_get_provider_data.return_value = mock_provider_data
+
+        # Mock the model
+        mock_model = Model(identifier="test-model", provider_resource_id="test-model", provider_id="vllm-inference")
+        vllm_inference_adapter.model_store.get_model.return_value = mock_model
+
+        try:
+            # Execute chat completion
+            await vllm_inference_adapter.chat_completion(
+                "test-model",
+                [UserMessage(content="Hello")],
+                stream=False,
+                tools=None,
+                tool_config=ToolConfig(tool_choice=ToolChoice.auto),
+            )
+
+            # Verify that ALL client calls were made with the correct parameters
+            calls = mock_openai_class.call_args_list
+            incorrect_calls = []
+
+            for i, call in enumerate(calls):
+                api_key = call[1]["api_key"]
+                base_url = call[1]["base_url"]
+
+                if api_key != "test-token-123" or base_url != "http://mocked.localhost:12345":
+                    incorrect_calls.append({"call_index": i, "api_key": api_key, "base_url": base_url})
+
+            if incorrect_calls:
+                error_msg = (
+                    f"Found {len(incorrect_calls)} calls with incorrect parameters out of {len(calls)} total calls:\n"
+                )
+                for incorrect_call in incorrect_calls:
+                    error_msg += f"  Call {incorrect_call['call_index']}: api_key='{incorrect_call['api_key']}', base_url='{incorrect_call['base_url']}'\n"
+                error_msg += "Expected: api_key='test-token-123', base_url='http://mocked.localhost:12345'"
+                raise AssertionError(error_msg)
+
+            # Ensure at least one call was made
+            assert len(calls) >= 1, "No AsyncOpenAI client calls were made"
+
+            # Verify that chat completion was called
+            mock_client.chat.completions.create.assert_called_once()
+
+        finally:
+            # Clean up context
+            pass

tests/unit/providers/nvidia/test_eval.py

@@ -150,7 +150,7 @@ class TestNVIDIAEvalImpl(unittest.TestCase):
         self._assert_request_body(
             {
                 "config": f"nvidia/{MOCK_BENCHMARK_ID}",
-                "target": {"type": "model", "model": "meta/llama-3.1-8b-instruct"},
+                "target": {"type": "model", "model": "Llama3.1-8B-Instruct"},
             }
         )
 

tests/unit/providers/utils/inference/test_openai_mixin.py

@@ -0,0 +1,368 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.
+
+from unittest.mock import AsyncMock, MagicMock, PropertyMock, patch
+
+import pytest
+
+from llama_stack.apis.inference import Model, OpenAIUserMessageParam
+from llama_stack.apis.models import ModelType
+from llama_stack.providers.utils.inference.openai_mixin import OpenAIMixin
+
+
+class OpenAIMixinImpl(OpenAIMixin):
+    def __init__(self):
+        self.__provider_id__ = "test-provider"
+
+    def get_api_key(self) -> str:
+        raise NotImplementedError("This method should be mocked in tests")
+
+    def get_base_url(self) -> str:
+        raise NotImplementedError("This method should be mocked in tests")
+
+
+class OpenAIMixinWithEmbeddingsImpl(OpenAIMixin):
+    """Test implementation with embedding model metadata"""
+
+    embedding_model_metadata = {
+        "text-embedding-3-small": {"embedding_dimension": 1536, "context_length": 8192},
+        "text-embedding-ada-002": {"embedding_dimension": 1536, "context_length": 8192},
+    }
+
+    __provider_id__ = "test-provider"
+
+    def get_api_key(self) -> str:
+        raise NotImplementedError("This method should be mocked in tests")
+
+    def get_base_url(self) -> str:
+        raise NotImplementedError("This method should be mocked in tests")
+
+
+@pytest.fixture
+def mixin():
+    """Create a test instance of OpenAIMixin with mocked model_store"""
+    mixin_instance = OpenAIMixinImpl()
+
+    # just enough to satisfy _get_provider_model_id calls
+    mock_model_store = MagicMock()
+    mock_model = MagicMock()
+    mock_model.provider_resource_id = "test-provider-resource-id"
+    mock_model_store.get_model = AsyncMock(return_value=mock_model)
+    mixin_instance.model_store = mock_model_store
+
+    return mixin_instance
+
+
+@pytest.fixture
+def mixin_with_embeddings():
+    """Create a test instance of OpenAIMixin with embedding model metadata"""
+    return OpenAIMixinWithEmbeddingsImpl()
+
+
+@pytest.fixture
+def mock_models():
+    """Create multiple mock OpenAI model objects"""
+    models = [MagicMock(id=id) for id in ["some-mock-model-id", "another-mock-model-id", "final-mock-model-id"]]
+    return models
+
+
+@pytest.fixture
+def mock_client_with_models(mock_models):
+    """Create a mock client with models.list() set up to return mock_models"""
+    mock_client = MagicMock()
+
+    async def mock_models_list():
+        for model in mock_models:
+            yield model
+
+    mock_client.models.list.return_value = mock_models_list()
+    return mock_client
+
+
+@pytest.fixture
+def mock_client_with_empty_models():
+    """Create a mock client with models.list() set up to return empty list"""
+    mock_client = MagicMock()
+
+    async def mock_empty_models_list():
+        return
+        yield  # Make it an async generator but don't yield anything
+
+    mock_client.models.list.return_value = mock_empty_models_list()
+    return mock_client
+
+
+@pytest.fixture
+def mock_client_with_exception():
+    """Create a mock client with models.list() set up to raise an exception"""
+    mock_client = MagicMock()
+    mock_client.models.list.side_effect = Exception("API Error")
+    return mock_client
+
+
+@pytest.fixture
+def mock_client_context():
+    """Fixture that provides a context manager for mocking the OpenAI client"""
+
+    def _mock_client_context(mixin, mock_client):
+        return patch.object(type(mixin), "client", new_callable=PropertyMock, return_value=mock_client)
+
+    return _mock_client_context
+
+
+class TestOpenAIMixinListModels:
+    """Test cases for the list_models method"""
+
+    async def test_list_models_success(self, mixin, mock_client_with_models, mock_client_context):
+        """Test successful model listing"""
+        assert len(mixin._model_cache) == 0
+
+        with mock_client_context(mixin, mock_client_with_models):
+            result = await mixin.list_models()
+
+            assert result is not None
+            assert len(result) == 3
+
+            model_ids = [model.identifier for model in result]
+            assert "some-mock-model-id" in model_ids
+            assert "another-mock-model-id" in model_ids
+            assert "final-mock-model-id" in model_ids
+
+            for model in result:
+                assert model.provider_id == "test-provider"
+                assert model.model_type == ModelType.llm
+                assert model.provider_resource_id == model.identifier
+
+            assert len(mixin._model_cache) == 3
+            for model_id in ["some-mock-model-id", "another-mock-model-id", "final-mock-model-id"]:
+                assert model_id in mixin._model_cache
+                cached_model = mixin._model_cache[model_id]
+                assert cached_model.identifier == model_id
+                assert cached_model.provider_resource_id == model_id
+
+    async def test_list_models_empty_response(self, mixin, mock_client_with_empty_models, mock_client_context):
+        """Test handling of empty model list"""
+        with mock_client_context(mixin, mock_client_with_empty_models):
+            result = await mixin.list_models()
+
+            assert result is not None
+            assert len(result) == 0
+            assert len(mixin._model_cache) == 0
+
+
+class TestOpenAIMixinCheckModelAvailability:
+    """Test cases for the check_model_availability method"""
+
+    async def test_check_model_availability_with_cache(self, mixin, mock_client_with_models, mock_client_context):
+        """Test model availability check when cache is populated"""
+        with mock_client_context(mixin, mock_client_with_models):
+            mock_client_with_models.models.list.assert_not_called()
+            await mixin.list_models()
+            mock_client_with_models.models.list.assert_called_once()
+
+            assert await mixin.check_model_availability("some-mock-model-id")
+            assert await mixin.check_model_availability("another-mock-model-id")
+            assert await mixin.check_model_availability("final-mock-model-id")
+            assert not await mixin.check_model_availability("non-existent-model")
+            mock_client_with_models.models.list.assert_called_once()
+
+    async def test_check_model_availability_without_cache(self, mixin, mock_client_with_models, mock_client_context):
+        """Test model availability check when cache is empty (calls list_models)"""
+        assert len(mixin._model_cache) == 0
+
+        with mock_client_context(mixin, mock_client_with_models):
+            mock_client_with_models.models.list.assert_not_called()
+            assert await mixin.check_model_availability("some-mock-model-id")
+            mock_client_with_models.models.list.assert_called_once()
+
+            assert len(mixin._model_cache) == 3
+            assert "some-mock-model-id" in mixin._model_cache
+
+    async def test_check_model_availability_model_not_found(self, mixin, mock_client_with_models, mock_client_context):
+        """Test model availability check for non-existent model"""
+        with mock_client_context(mixin, mock_client_with_models):
+            mock_client_with_models.models.list.assert_not_called()
+            assert not await mixin.check_model_availability("non-existent-model")
+            mock_client_with_models.models.list.assert_called_once()
+
+            assert len(mixin._model_cache) == 3
+
+
+class TestOpenAIMixinCacheBehavior:
+    """Test cases for cache behavior and edge cases"""
+
+    async def test_cache_overwrites_on_list_models_call(self, mixin, mock_client_with_models, mock_client_context):
+        """Test that calling list_models overwrites existing cache"""
+        initial_model = Model(
+            provider_id="test-provider",
+            provider_resource_id="old-model",
+            identifier="old-model",
+            model_type=ModelType.llm,
+        )
+        mixin._model_cache = {"old-model": initial_model}
+
+        with mock_client_context(mixin, mock_client_with_models):
+            await mixin.list_models()
+
+            assert len(mixin._model_cache) == 3
+            assert "old-model" not in mixin._model_cache
+            assert "some-mock-model-id" in mixin._model_cache
+            assert "another-mock-model-id" in mixin._model_cache
+            assert "final-mock-model-id" in mixin._model_cache
+
+
+class TestOpenAIMixinImagePreprocessing:
+    """Test cases for image preprocessing functionality"""
+
+    async def test_openai_chat_completion_with_image_preprocessing_enabled(self, mixin):
+        """Test that image URLs are converted to base64 when download_images is True"""
+        mixin.download_images = True
+
+        message = OpenAIUserMessageParam(
+            role="user",
+            content=[
+                {"type": "text", "text": "What's in this image?"},
+                {"type": "image_url", "image_url": {"url": "http://example.com/image.jpg"}},
+            ],
+        )
+
+        mock_client = MagicMock()
+        mock_response = MagicMock()
+        mock_client.chat.completions.create = AsyncMock(return_value=mock_response)
+
+        with patch.object(type(mixin), "client", new_callable=PropertyMock, return_value=mock_client):
+            with patch("llama_stack.providers.utils.inference.openai_mixin.localize_image_content") as mock_localize:
+                mock_localize.return_value = (b"fake_image_data", "jpeg")
+
+                await mixin.openai_chat_completion(model="test-model", messages=[message])
+
+            mock_localize.assert_called_once_with("http://example.com/image.jpg")
+
+            mock_client.chat.completions.create.assert_called_once()
+            call_args = mock_client.chat.completions.create.call_args
+            processed_messages = call_args[1]["messages"]
+            assert len(processed_messages) == 1
+            content = processed_messages[0]["content"]
+            assert len(content) == 2
+            assert content[0]["type"] == "text"
+            assert content[1]["type"] == "image_url"
+            assert content[1]["image_url"]["url"] == "data:image/jpeg;base64,ZmFrZV9pbWFnZV9kYXRh"
+
+    async def test_openai_chat_completion_with_image_preprocessing_disabled(self, mixin):
+        """Test that image URLs are not modified when download_images is False"""
+        mixin.download_images = False  # explicitly set to False
+
+        message = OpenAIUserMessageParam(
+            role="user",
+            content=[
+                {"type": "text", "text": "What's in this image?"},
+                {"type": "image_url", "image_url": {"url": "http://example.com/image.jpg"}},
+            ],
+        )
+
+        mock_client = MagicMock()
+        mock_response = MagicMock()
+        mock_client.chat.completions.create = AsyncMock(return_value=mock_response)
+
+        with patch.object(type(mixin), "client", new_callable=PropertyMock, return_value=mock_client):
+            with patch("llama_stack.providers.utils.inference.openai_mixin.localize_image_content") as mock_localize:
+                await mixin.openai_chat_completion(model="test-model", messages=[message])
+
+            mock_localize.assert_not_called()
+
+            mock_client.chat.completions.create.assert_called_once()
+            call_args = mock_client.chat.completions.create.call_args
+            processed_messages = call_args[1]["messages"]
+            assert len(processed_messages) == 1
+            content = processed_messages[0]["content"]
+            assert len(content) == 2
+            assert content[1]["image_url"]["url"] == "http://example.com/image.jpg"
+
+
+class TestOpenAIMixinEmbeddingModelMetadata:
+    """Test cases for embedding_model_metadata attribute functionality"""
+
+    async def test_embedding_model_identified_and_augmented(self, mixin_with_embeddings, mock_client_context):
+        """Test that models in embedding_model_metadata are correctly identified as embeddings with metadata"""
+        # Create mock models: 1 embedding model and 1 LLM, while there are 2 known embedding models
+        mock_embedding_model = MagicMock(id="text-embedding-3-small")
+        mock_llm_model = MagicMock(id="gpt-4")
+        mock_models = [mock_embedding_model, mock_llm_model]
+
+        mock_client = MagicMock()
+
+        async def mock_models_list():
+            for model in mock_models:
+                yield model
+
+        mock_client.models.list.return_value = mock_models_list()
+
+        with mock_client_context(mixin_with_embeddings, mock_client):
+            result = await mixin_with_embeddings.list_models()
+
+            assert result is not None
+            assert len(result) == 2
+
+            # Find the models in the result
+            embedding_model = next(m for m in result if m.identifier == "text-embedding-3-small")
+            llm_model = next(m for m in result if m.identifier == "gpt-4")
+
+            # Check embedding model
+            assert embedding_model.model_type == ModelType.embedding
+            assert embedding_model.metadata == {"embedding_dimension": 1536, "context_length": 8192}
+            assert embedding_model.provider_id == "test-provider"
+            assert embedding_model.provider_resource_id == "text-embedding-3-small"
+
+            # Check LLM model
+            assert llm_model.model_type == ModelType.llm
+            assert llm_model.metadata == {}  # No metadata for LLMs
+            assert llm_model.provider_id == "test-provider"
+            assert llm_model.provider_resource_id == "gpt-4"
+
+
+class TestOpenAIMixinAllowedModels:
+    """Test cases for allowed_models filtering functionality"""
+
+    async def test_list_models_with_allowed_models_filter(self, mixin, mock_client_with_models, mock_client_context):
+        """Test that list_models filters models based on allowed_models set"""
+        mixin.allowed_models = {"some-mock-model-id", "another-mock-model-id"}
+
+        with mock_client_context(mixin, mock_client_with_models):
+            result = await mixin.list_models()
+
+            assert result is not None
+            assert len(result) == 2
+
+            model_ids = [model.identifier for model in result]
+            assert "some-mock-model-id" in model_ids
+            assert "another-mock-model-id" in model_ids
+            assert "final-mock-model-id" not in model_ids
+
+    async def test_list_models_with_empty_allowed_models(self, mixin, mock_client_with_models, mock_client_context):
+        """Test that empty allowed_models set allows all models"""
+        assert len(mixin.allowed_models) == 0
+
+        with mock_client_context(mixin, mock_client_with_models):
+            result = await mixin.list_models()
+
+            assert result is not None
+            assert len(result) == 3  # All models should be included
+
+            model_ids = [model.identifier for model in result]
+            assert "some-mock-model-id" in model_ids
+            assert "another-mock-model-id" in model_ids
+            assert "final-mock-model-id" in model_ids
+
+    async def test_check_model_availability_with_allowed_models(
+        self, mixin, mock_client_with_models, mock_client_context
+    ):
+        """Test that check_model_availability respects allowed_models"""
+        mixin.allowed_models = {"final-mock-model-id"}
+
+        with mock_client_context(mixin, mock_client_with_models):
+            assert await mixin.check_model_availability("final-mock-model-id")
+            assert not await mixin.check_model_availability("some-mock-model-id")
+            assert not await mixin.check_model_availability("another-mock-model-id")

tests/unit/providers/utils/test_model_registry.py

@@ -84,14 +84,14 @@ def unknown_model() -> Model:
 
 @pytest.fixture
 def helper(known_provider_model: ProviderModelEntry, known_provider_model2: ProviderModelEntry) -> ModelRegistryHelper:
-    return ModelRegistryHelper([known_provider_model, known_provider_model2])
+    return ModelRegistryHelper(model_entries=[known_provider_model, known_provider_model2])
 
 
 class MockModelRegistryHelperWithDynamicModels(ModelRegistryHelper):
     """Test helper that simulates a provider with dynamically available models."""
 
     def __init__(self, model_entries: list[ProviderModelEntry], available_models: list[str]):
-        super().__init__(model_entries)
+        super().__init__(model_entries=model_entries)
         self._available_models = available_models
 
     async def check_model_availability(self, model: str) -> bool:

tests/unit/utils/test_authorized_sqlstore.py

@@ -26,7 +26,7 @@ async def test_authorized_fetch_with_where_sql_access_control(mock_get_authentic
                 db_path=tmp_dir + "/" + db_name,
             )
         )
-        sqlstore = AuthorizedSqlStore(base_sqlstore)
+        sqlstore = AuthorizedSqlStore(base_sqlstore, default_policy())
 
         # Create table with access control
         await sqlstore.create_table(
@@ -56,24 +56,24 @@ async def test_authorized_fetch_with_where_sql_access_control(mock_get_authentic
         mock_get_authenticated_user.return_value = admin_user
 
         # Admin should see both documents
-        result = await sqlstore.fetch_all("documents", policy=default_policy(), where={"id": 1})
+        result = await sqlstore.fetch_all("documents", where={"id": 1})
         assert len(result.data) == 1
         assert result.data[0]["title"] == "Admin Document"
 
         # User should only see their document
         mock_get_authenticated_user.return_value = regular_user
 
-        result = await sqlstore.fetch_all("documents", policy=default_policy(), where={"id": 1})
+        result = await sqlstore.fetch_all("documents", where={"id": 1})
         assert len(result.data) == 0
 
-        result = await sqlstore.fetch_all("documents", policy=default_policy(), where={"id": 2})
+        result = await sqlstore.fetch_all("documents", where={"id": 2})
         assert len(result.data) == 1
         assert result.data[0]["title"] == "User Document"
 
-        row = await sqlstore.fetch_one("documents", policy=default_policy(), where={"id": 1})
+        row = await sqlstore.fetch_one("documents", where={"id": 1})
         assert row is None
 
-        row = await sqlstore.fetch_one("documents", policy=default_policy(), where={"id": 2})
+        row = await sqlstore.fetch_one("documents", where={"id": 2})
         assert row is not None
         assert row["title"] == "User Document"
 
@@ -88,7 +88,7 @@ async def test_sql_policy_consistency(mock_get_authenticated_user):
                 db_path=tmp_dir + "/" + db_name,
             )
         )
-        sqlstore = AuthorizedSqlStore(base_sqlstore)
+        sqlstore = AuthorizedSqlStore(base_sqlstore, default_policy())
 
         await sqlstore.create_table(
             table="resources",
@@ -144,7 +144,7 @@ async def test_sql_policy_consistency(mock_get_authenticated_user):
             user = User(principal=user_data["principal"], attributes=user_data["attributes"])
             mock_get_authenticated_user.return_value = user
 
-            sql_results = await sqlstore.fetch_all("resources", policy=policy)
+            sql_results = await sqlstore.fetch_all("resources")
             sql_ids = {row["id"] for row in sql_results.data}
             policy_ids = set()
             for scenario in test_scenarios:
@@ -174,7 +174,7 @@ async def test_authorized_store_user_attribute_capture(mock_get_authenticated_us
                 db_path=tmp_dir + "/" + db_name,
             )
         )
-        authorized_store = AuthorizedSqlStore(base_sqlstore)
+        authorized_store = AuthorizedSqlStore(base_sqlstore, default_policy())
 
         await authorized_store.create_table(
             table="user_data",