Merge branch 'main' into hide-non-openai-inference-apis

tests/README.md

@@ -38,26 +38,15 @@ For running integration tests, you must provide a few things:
   - a distribution name (e.g., `starter`) or a path to a `run.yaml` file
   - a comma-separated list of api=provider pairs, e.g. `inference=fireworks,safety=llama-guard,agents=meta-reference`. This is most useful for testing a single API surface.
 
-- Whether you are using replay or live mode for inference. This is specified with the LLAMA_STACK_TEST_INFERENCE_MODE environment variable. The default mode currently is "live" -- that is certainly surprising, but we will fix this soon.
-
 - Any API keys you need to use should be set in the environment, or can be passed in with the --env option.
 
 You can run the integration tests in replay mode with:
 ```bash
 # Run all tests with existing recordings
-LLAMA_STACK_TEST_INFERENCE_MODE=replay \
-  LLAMA_STACK_TEST_RECORDING_DIR=tests/integration/recordings \
   uv run --group test \
   pytest -sv tests/integration/ --stack-config=starter
 ```
 
-If you don't specify LLAMA_STACK_TEST_INFERENCE_MODE, by default it will be in "live" mode -- that is, it will make real API calls.
-
-```bash
-# Test against live APIs
-FIREWORKS_API_KEY=your_key pytest -sv tests/integration/inference --stack-config=starter
-```
-
 ### Re-recording tests
 
 #### Local Re-recording (Manual Setup Required)
@@ -66,7 +55,6 @@ If you want to re-record tests locally, you can do so with:
 
 ```bash
 LLAMA_STACK_TEST_INFERENCE_MODE=record \
-  LLAMA_STACK_TEST_RECORDING_DIR=tests/integration/recordings \
   uv run --group test \
   pytest -sv tests/integration/ --stack-config=starter -k "<appropriate test name>"
 ```
@@ -89,7 +77,7 @@ You must be careful when re-recording. CI workflows assume a specific setup for
 ./scripts/github/schedule-record-workflow.sh --test-subdirs "agents,inference"
 
 # Record with vision tests enabled
-./scripts/github/schedule-record-workflow.sh --test-subdirs "inference" --run-vision-tests
+./scripts/github/schedule-record-workflow.sh --test-suite vision
 
 # Record with specific provider
 ./scripts/github/schedule-record-workflow.sh --test-subdirs "agents" --test-provider vllm

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/README.md

@@ -6,9 +6,7 @@ Integration tests verify complete workflows across different providers using Lla
 
 ```bash
 # Run all integration tests with existing recordings
-LLAMA_STACK_TEST_INFERENCE_MODE=replay \
-  LLAMA_STACK_TEST_RECORDING_DIR=tests/integration/recordings \
-  uv run --group test \
+uv run --group test \
   pytest -sv tests/integration/ --stack-config=starter
 ```
 
@@ -42,6 +40,37 @@ Model parameters can be influenced by the following options:
 Each of these are comma-separated lists and can be used to generate multiple parameter combinations. Note that tests will be skipped
 if no model is specified.
 
+### Suites and Setups
+
+- `--suite`: single named suite that narrows which tests are collected.
+- Available suites:
+  - `base`: collects most tests (excludes responses and post_training)
+  - `responses`: collects tests under `tests/integration/responses` (needs strong tool-calling models)
+  - `vision`: collects only `tests/integration/inference/test_vision_inference.py`
+- `--setup`: global configuration that can be used with any suite. Setups prefill model/env defaults; explicit CLI flags always win.
+  - Available setups:
+    - `ollama`: Local Ollama provider with lightweight models (sets OLLAMA_URL, uses llama3.2:3b-instruct-fp16)
+    - `vllm`: VLLM provider for efficient local inference (sets VLLM_URL, uses Llama-3.2-1B-Instruct)
+    - `gpt`: OpenAI GPT models for high-quality responses (uses gpt-4o)
+    - `claude`: Anthropic Claude models for high-quality responses (uses claude-3-5-sonnet)
+
+Examples
+
+```bash
+# Fast responses run with a strong tool-calling model
+pytest -s -v tests/integration --stack-config=server:starter --suite=responses --setup=gpt
+
+# Fast single-file vision run with Ollama defaults
+pytest -s -v tests/integration --stack-config=server:starter --suite=vision --setup=ollama
+
+# Base suite with VLLM for performance
+pytest -s -v tests/integration --stack-config=server:starter --suite=base --setup=vllm
+
+# Override a default from setup
+pytest -s -v tests/integration --stack-config=server:starter \
+  --suite=responses --setup=gpt --embedding-model=text-embedding-3-small
+```
+
 ## Examples
 
 ### Testing against a Server
@@ -98,29 +127,24 @@ pytest -s -v tests/integration/vector_io/ \
 
 The testing system supports three modes controlled by environment variables:
 
-### LIVE Mode (Default)
-Tests make real API calls:
+### REPLAY Mode (Default)
+Uses cached responses instead of making API calls:
 ```bash
-LLAMA_STACK_TEST_INFERENCE_MODE=live pytest tests/integration/
+pytest tests/integration/
 ```
-
 ### RECORD Mode
 Captures API interactions for later replay:
 ```bash
-LLAMA_STACK_TEST_INFERENCE_MODE=record \
-LLAMA_STACK_TEST_RECORDING_DIR=tests/integration/recordings \
-pytest tests/integration/inference/test_new_feature.py
+pytest tests/integration/inference/test_new_feature.py --inference-mode=record
 ```
 
-### REPLAY Mode
-Uses cached responses instead of making API calls:
+### LIVE Mode
+Tests make real API calls (but not recorded):
 ```bash
-LLAMA_STACK_TEST_INFERENCE_MODE=replay \
-LLAMA_STACK_TEST_RECORDING_DIR=tests/integration/recordings \
-pytest tests/integration/
+pytest tests/integration/ --inference-mode=live
 ```
 
-Note that right now you must specify the recording directory. This is because different tests use different recording directories and we don't (yet) have a fool-proof way to map a test to a recording directory. We are working on this.
+By default, the recording directory is `tests/integration/recordings`. You can override this by setting the `LLAMA_STACK_TEST_RECORDING_DIR` environment variable.
 
 ## Managing Recordings
 
@@ -138,16 +162,14 @@ cat recordings/responses/abc123.json | jq '.'
 #### Remote Re-recording (Recommended)
 Use the automated workflow script for easier re-recording:
 ```bash
-./scripts/github/schedule-record-workflow.sh --test-subdirs "inference,agents"
+./scripts/github/schedule-record-workflow.sh --subdirs "inference,agents"
 ```
 See the [main testing guide](../README.md#remote-re-recording-recommended) for full details.
 
 #### Local Re-recording
 ```bash
 # Re-record specific tests
-LLAMA_STACK_TEST_INFERENCE_MODE=record \
-LLAMA_STACK_TEST_RECORDING_DIR=tests/integration/recordings \
-pytest -s -v --stack-config=server:starter tests/integration/inference/test_modified.py
+pytest -s -v --stack-config=server:starter tests/integration/inference/test_modified.py --inference-mode=record
 ```
 
 Note that when re-recording tests, you must use a Stack pointing to a server (i.e., `server:starter`). This subtlety exists because the set of tests run in server are a superset of the set of tests run in the library client.

tests/integration/batches/test_batches.py

@@ -268,3 +268,58 @@ class TestBatchesIntegration:
 
         deleted_error_file = openai_client.files.delete(final_batch.error_file_id)
         assert deleted_error_file.deleted, f"Error file {final_batch.error_file_id} was not deleted successfully"
+
+    def test_batch_e2e_completions(self, openai_client, batch_helper, text_model_id):
+        """Run an end-to-end batch with a single successful text completion request."""
+        request_body = {"model": text_model_id, "prompt": "Say completions", "max_tokens": 20}
+
+        batch_requests = [
+            {
+                "custom_id": "success-1",
+                "method": "POST",
+                "url": "/v1/completions",
+                "body": request_body,
+            }
+        ]
+
+        with batch_helper.create_file(batch_requests) as uploaded_file:
+            batch = openai_client.batches.create(
+                input_file_id=uploaded_file.id,
+                endpoint="/v1/completions",
+                completion_window="24h",
+                metadata={"test": "e2e_completions_success"},
+            )
+
+            final_batch = batch_helper.wait_for(
+                batch.id,
+                max_wait_time=3 * 60,
+                expected_statuses={"completed"},
+                timeout_action="skip",
+            )
+
+        assert final_batch.status == "completed"
+        assert final_batch.request_counts is not None
+        assert final_batch.request_counts.total == 1
+        assert final_batch.request_counts.completed == 1
+        assert final_batch.output_file_id is not None
+
+        output_content = openai_client.files.content(final_batch.output_file_id)
+        if isinstance(output_content, str):
+            output_text = output_content
+        else:
+            output_text = output_content.content.decode("utf-8")
+
+        output_lines = output_text.strip().split("\n")
+        assert len(output_lines) == 1
+
+        result = json.loads(output_lines[0])
+        assert result["custom_id"] == "success-1"
+        assert "response" in result
+        assert result["response"]["status_code"] == 200
+
+        deleted_output_file = openai_client.files.delete(final_batch.output_file_id)
+        assert deleted_output_file.deleted
+
+        if final_batch.error_file_id is not None:
+            deleted_error_file = openai_client.files.delete(final_batch.error_file_id)
+            assert deleted_error_file.deleted

tests/integration/conftest.py

@@ -6,15 +6,17 @@
 import inspect
 import itertools
 import os
-import platform
 import textwrap
 import time
+from pathlib import Path
 
 import pytest
 from dotenv import load_dotenv
 
 from llama_stack.log import get_logger
 
+from .suites import SETUP_DEFINITIONS, SUITE_DEFINITIONS
+
 logger = get_logger(__name__, category="tests")
 
 
@@ -30,6 +32,8 @@ def pytest_runtest_makereport(item, call):
 def pytest_sessionstart(session):
     # stop macOS from complaining about duplicate OpenMP libraries
     os.environ["KMP_DUPLICATE_LIB_OK"] = "TRUE"
+    if "LLAMA_STACK_TEST_INFERENCE_MODE" not in os.environ:
+        os.environ["LLAMA_STACK_TEST_INFERENCE_MODE"] = "replay"
 
 
 def pytest_runtest_teardown(item):
@@ -59,9 +63,36 @@ def pytest_configure(config):
         key, value = env_var.split("=", 1)
         os.environ[key] = value
 
-    if platform.system() == "Darwin":  # Darwin is the system name for macOS
-        os.environ["DISABLE_CODE_SANDBOX"] = "1"
-        logger.info("Setting DISABLE_CODE_SANDBOX=1 for macOS")
+    inference_mode = config.getoption("--inference-mode")
+    os.environ["LLAMA_STACK_TEST_INFERENCE_MODE"] = inference_mode
+
+    suite = config.getoption("--suite")
+    if suite:
+        if suite not in SUITE_DEFINITIONS:
+            raise pytest.UsageError(f"Unknown suite: {suite}. Available: {', '.join(sorted(SUITE_DEFINITIONS.keys()))}")
+
+    # Apply setups (global parameterizations): env + defaults
+    setup = config.getoption("--setup")
+    if suite and not setup:
+        setup = SUITE_DEFINITIONS[suite].default_setup
+
+    if setup:
+        if setup not in SETUP_DEFINITIONS:
+            raise pytest.UsageError(
+                f"Unknown setup '{setup}'. Available: {', '.join(sorted(SETUP_DEFINITIONS.keys()))}"
+            )
+
+        setup_obj = SETUP_DEFINITIONS[setup]
+        logger.info(f"Applying setup '{setup}'{' for suite ' + suite if suite else ''}")
+        # Apply env first
+        for k, v in setup_obj.env.items():
+            if k not in os.environ:
+                os.environ[k] = str(v)
+        # Apply defaults if not provided explicitly
+        for dest, value in setup_obj.defaults.items():
+            current = getattr(config.option, dest, None)
+            if not current:
+                setattr(config.option, dest, value)
 
 
 def pytest_addoption(parser):
@@ -103,16 +134,32 @@ def pytest_addoption(parser):
         default=384,
         help="Output dimensionality of the embedding model to use for testing. Default: 384",
     )
+
     parser.addoption(
-        "--record-responses",
-        action="store_true",
-        help="Record new API responses instead of using cached ones.",
+        "--inference-mode",
+        help="Inference mode: { record, replay, live } (default: replay)",
+        choices=["record", "replay", "live"],
+        default="replay",
     )
     parser.addoption(
         "--report",
         help="Path where the test report should be written, e.g. --report=/path/to/report.md",
     )
 
+    available_suites = ", ".join(sorted(SUITE_DEFINITIONS.keys()))
+    suite_help = (
+        f"Single test suite to run (narrows collection). Available: {available_suites}. Example: --suite=responses"
+    )
+    parser.addoption("--suite", help=suite_help)
+
+    # Global setups for any suite
+    available_setups = ", ".join(sorted(SETUP_DEFINITIONS.keys()))
+    setup_help = (
+        f"Global test setup configuration. Available: {available_setups}. "
+        "Can be used with any suite. Example: --setup=ollama"
+    )
+    parser.addoption("--setup", help=setup_help)
+
 
 MODEL_SHORT_IDS = {
     "meta-llama/Llama-3.2-3B-Instruct": "3B",
@@ -195,3 +242,36 @@ def pytest_generate_tests(metafunc):
 
 
 pytest_plugins = ["tests.integration.fixtures.common"]
+
+
+def pytest_ignore_collect(path: str, config: pytest.Config) -> bool:
+    """Skip collecting paths outside the selected suite roots for speed."""
+    suite = config.getoption("--suite")
+    if not suite:
+        return False
+
+    sobj = SUITE_DEFINITIONS.get(suite)
+    roots: list[str] = sobj.get("roots", []) if isinstance(sobj, dict) else getattr(sobj, "roots", [])
+    if not roots:
+        return False
+
+    p = Path(str(path)).resolve()
+
+    # Only constrain within tests/integration to avoid ignoring unrelated tests
+    integration_root = (Path(str(config.rootpath)) / "tests" / "integration").resolve()
+    if not p.is_relative_to(integration_root):
+        return False
+
+    for r in roots:
+        rp = (Path(str(config.rootpath)) / r).resolve()
+        if rp.is_file():
+            # Allow the exact file and any ancestor directories so pytest can walk into it.
+            if p == rp:
+                return False
+            if p.is_dir() and rp.is_relative_to(p):
+                return False
+        else:
+            # Allow anything inside an allowed directory
+            if p.is_relative_to(rp):
+                return False
+    return True

tests/integration/inference/test_openai_completion.py

@@ -5,11 +5,26 @@
 # the root directory of this source tree.
 
 
+import time
+import unicodedata
+
 import pytest
 
 from ..test_cases.test_case import TestCase
 
 
+def _normalize_text(text: str) -> str:
+    """
+    Normalize Unicode text by removing diacritical marks for comparison.
+
+    The test case streaming_01 expects the answer "Sol" for the question "What's the name of the Sun
+    in latin?", but the model is returning "sōl" (with a macron over the 'o'), which is the correct
+    Latin spelling. The test is failing because it's doing a simple case-insensitive string search
+    for "sol" but the actual response contains the diacritical mark.
+    """
+    return unicodedata.normalize("NFD", text).encode("ascii", "ignore").decode("ascii").lower()
+
+
 def provider_from_model(client_with_models, model_id):
     models = {m.identifier: m for m in client_with_models.models.list()}
     models.update({m.provider_resource_id: m for m in client_with_models.models.list()})
@@ -25,7 +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
@@ -33,8 +47,17 @@ def skip_if_model_doesnt_support_openai_completion(client_with_models, model_id)
         "remote::nvidia",
         "remote::runpod",
         "remote::sambanova",
-        "remote::tgi",
         "remote::vertexai",
+        # {"error":{"message":"Unknown request URL: GET /openai/v1/completions. Please check the URL for typos,
+        # or see the docs at https://console.groq.com/docs/","type":"invalid_request_error","code":"unknown_url"}}
+        "remote::groq",
+        "remote::gemini",  # https://generativelanguage.googleapis.com/v1beta/openai/completions -> 404
+        "remote::anthropic",  # at least claude-3-{5,7}-{haiku,sonnet}-* / claude-{sonnet,opus}-4-* are not supported
+        "remote::azure",  # {'error': {'code': 'OperationNotSupported', 'message': 'The completion operation
+        #  does not work with the specified model, gpt-5-mini. Please choose different model and try
+        #  again. You can learn more about which models can be used with each operation here:
+        #  https://go.microsoft.com/fwlink/?linkid=2197993.'}}"}
+        "remote::watsonx",  # return 404 when hitting the /openai/v1 endpoint
     ):
         pytest.skip(f"Model {model_id} hosted by {provider.provider_type} doesn't support OpenAI completions.")
 
@@ -56,6 +79,31 @@ def skip_if_model_doesnt_support_suffix(client_with_models, model_id):
         pytest.skip(f"Provider {provider.provider_type} doesn't support suffix.")
 
 
+def skip_if_doesnt_support_n(client_with_models, model_id):
+    provider = provider_from_model(client_with_models, model_id)
+    if provider.provider_type in (
+        "remote::sambanova",
+        "remote::ollama",
+        # https://console.groq.com/docs/openai#currently-unsupported-openai-features
+        # -> Error code: 400 - {'error': {'message': "'n' : number must be at most 1", 'type': 'invalid_request_error'}}
+        "remote::groq",
+        # Error code: 400 - [{'error': {'code': 400, 'message': 'Only one candidate can be specified in the
+        # current model', 'status': 'INVALID_ARGUMENT'}}]
+        "remote::gemini",
+        # https://docs.anthropic.com/en/api/openai-sdk#simple-fields
+        "remote::anthropic",
+        "remote::vertexai",
+        #  Error code: 400 - [{'error': {'code': 400, 'message': 'Unable to submit request because candidateCount must be 1 but
+        #  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.")
+
+
 def skip_if_model_doesnt_support_openai_chat_completion(client_with_models, model_id):
     provider = provider_from_model(client_with_models, model_id)
     if provider.provider_type in (
@@ -63,10 +111,10 @@ 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::tgi",
+        "remote::watsonx",  # watsonx returns 404 when hitting the /openai/v1 endpoint
     ):
         pytest.skip(f"Model {model_id} hosted by {provider.provider_type} doesn't support OpenAI chat completions.")
 
@@ -130,7 +178,8 @@ def test_openai_completion_non_streaming_suffix(llama_stack_client, client_with_
     assert len(response.choices) > 0
     choice = response.choices[0]
     assert len(choice.text) > 5
-    assert "france" in choice.text.lower()
+    normalized_text = _normalize_text(choice.text)
+    assert "france" in normalized_text
 
 
 @pytest.mark.parametrize(
@@ -221,7 +270,9 @@ def test_openai_chat_completion_non_streaming(compat_client, client_with_models,
     )
     message_content = response.choices[0].message.content.lower().strip()
     assert len(message_content) > 0
-    assert expected.lower() in message_content
+    normalized_expected = _normalize_text(expected)
+    normalized_content = _normalize_text(message_content)
+    assert normalized_expected in normalized_content
 
 
 @pytest.mark.parametrize(
@@ -245,10 +296,13 @@ def test_openai_chat_completion_streaming(compat_client, client_with_models, tex
     )
     streamed_content = []
     for chunk in response:
-        if chunk.choices[0].delta.content:
+        # On some providers like Azure, the choices are empty on the first chunk, so we need to check for that
+        if chunk.choices and len(chunk.choices) > 0 and chunk.choices[0].delta.content:
             streamed_content.append(chunk.choices[0].delta.content.lower().strip())
     assert len(streamed_content) > 0
-    assert expected.lower() in "".join(streamed_content)
+    normalized_expected = _normalize_text(expected)
+    normalized_content = _normalize_text("".join(streamed_content))
+    assert normalized_expected in normalized_content
 
 
 @pytest.mark.parametrize(
@@ -260,10 +314,7 @@ def test_openai_chat_completion_streaming(compat_client, client_with_models, tex
 )
 def test_openai_chat_completion_streaming_with_n(compat_client, client_with_models, text_model_id, test_case):
     skip_if_model_doesnt_support_openai_chat_completion(client_with_models, text_model_id)
-
-    provider = provider_from_model(client_with_models, text_model_id)
-    if provider.provider_type == "remote::ollama":
-        pytest.skip(f"Model {text_model_id} hosted by {provider.provider_type} doesn't support n > 1.")
+    skip_if_doesnt_support_n(client_with_models, text_model_id)
 
     tc = TestCase(test_case)
     question = tc["question"]
@@ -284,8 +335,12 @@ def test_openai_chat_completion_streaming_with_n(compat_client, client_with_mode
                     streamed_content.get(choice.index, "") + choice.delta.content.lower().strip()
                 )
     assert len(streamed_content) == 2
+    normalized_expected = _normalize_text(expected)
     for i, content in streamed_content.items():
-        assert expected.lower() in content, f"Choice {i}: Expected {expected.lower()} in {content}"
+        normalized_content = _normalize_text(content)
+        assert normalized_expected in normalized_content, (
+            f"Choice {i}: Expected {normalized_expected} in {normalized_content}"
+        )
 
 
 @pytest.mark.parametrize(
@@ -315,16 +370,23 @@ def test_inference_store(compat_client, client_with_models, text_model_id, strea
         content = ""
         response_id = None
         for chunk in response:
-            if response_id is None:
+            if response_id is None and chunk.id:
                 response_id = chunk.id
-            if chunk.choices[0].delta.content:
+            if chunk.choices and len(chunk.choices) > 0 and chunk.choices[0].delta.content:
                 content += chunk.choices[0].delta.content
     else:
         response_id = response.id
         content = response.choices[0].message.content
 
-    responses = client.chat.completions.list(limit=1000)
-    assert response_id in [r.id for r in responses.data]
+    tries = 0
+    while tries < 10:
+        responses = client.chat.completions.list(limit=1000)
+        if response_id in [r.id for r in responses.data]:
+            break
+        else:
+            tries += 1
+            time.sleep(0.1)
+    assert tries < 10, f"Response {response_id} not found after 1 second"
 
     retrieved_response = client.chat.completions.retrieve(response_id)
     assert retrieved_response.id == response_id
@@ -379,15 +441,28 @@ def test_inference_store_tool_calls(compat_client, client_with_models, text_mode
         content = ""
         response_id = None
         for chunk in response:
-            if response_id is None:
+            if response_id is None and chunk.id:
                 response_id = chunk.id
-            if delta := chunk.choices[0].delta:
-                if delta.content:
-                    content += delta.content
+            if chunk.choices and len(chunk.choices) > 0:
+                if delta := chunk.choices[0].delta:
+                    if delta.content:
+                        content += delta.content
     else:
         response_id = response.id
         content = response.choices[0].message.content
 
+    # wait for the response to be stored
+    tries = 0
+    while tries < 10:
+        responses = client.chat.completions.list(limit=1000)
+        if response_id in [r.id for r in responses.data]:
+            break
+        else:
+            tries += 1
+            time.sleep(0.1)
+
+    assert tries < 10, f"Response {response_id} not found after 1 second"
+
     responses = client.chat.completions.list(limit=1000)
     assert response_id in [r.id for r in responses.data]
 
@@ -441,4 +516,5 @@ def test_openai_chat_completion_non_streaming_with_file(openai_client, client_wi
         stream=False,
     )
     message_content = response.choices[0].message.content.lower().strip()
-    assert "hello world" in message_content
+    normalized_content = _normalize_text(message_content)
+    assert "hello world" in normalized_content

tests/integration/inference/test_openai_embeddings.py

@@ -29,9 +29,40 @@ def provider_from_model(client_with_models, model_id):
     return providers[provider_id]
 
 
-def skip_if_model_doesnt_support_variable_dimensions(model_id):
-    if "text-embedding-3" not in model_id:
-        pytest.skip("{model_id} does not support variable output embedding dimensions")
+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.")
+
+
+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::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'.")
+
+
+def skip_if_model_doesnt_support_variable_dimensions(client_with_models, model_id):
+    provider = provider_from_model(client_with_models, model_id)
+    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."
+        )
+    if provider.provider_type == "remote::openai" and "text-embedding-3" not in model_id:
+        pytest.skip(
+            f"Model {model_id} hosted by {provider.provider_type} does not support variable output embedding dimensions."
+        )
 
 
 @pytest.fixture(params=["openai_client", "llama_stack_client"])
@@ -47,7 +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",
@@ -92,6 +122,7 @@ def test_openai_embeddings_multiple_strings(compat_client, client_with_models, e
     response = compat_client.embeddings.create(
         model=embedding_model_id,
         input=input_texts,
+        encoding_format="float",
     )
 
     assert response.object == "list"
@@ -127,7 +158,7 @@ def test_openai_embeddings_with_encoding_format_float(compat_client, client_with
 def test_openai_embeddings_with_dimensions(compat_client, client_with_models, embedding_model_id):
     """Test OpenAI embeddings endpoint with custom dimensions parameter."""
     skip_if_model_doesnt_support_openai_embeddings(client_with_models, embedding_model_id)
-    skip_if_model_doesnt_support_variable_dimensions(embedding_model_id)
+    skip_if_model_doesnt_support_variable_dimensions(client_with_models, embedding_model_id)
 
     input_text = "Test dimensions parameter"
     dimensions = 16
@@ -148,6 +179,7 @@ def test_openai_embeddings_with_dimensions(compat_client, client_with_models, em
 def test_openai_embeddings_with_user_parameter(compat_client, client_with_models, embedding_model_id):
     """Test OpenAI embeddings endpoint with user parameter."""
     skip_if_model_doesnt_support_openai_embeddings(client_with_models, embedding_model_id)
+    skip_if_model_doesnt_support_user_param(client_with_models, embedding_model_id)
 
     input_text = "Test user parameter"
     user_id = "test-user-123"
@@ -196,11 +228,13 @@ def test_openai_embeddings_different_inputs_different_outputs(compat_client, cli
     response1 = compat_client.embeddings.create(
         model=embedding_model_id,
         input=input_text1,
+        encoding_format="float",
     )
 
     response2 = compat_client.embeddings.create(
         model=embedding_model_id,
         input=input_text2,
+        encoding_format="float",
     )
 
     embedding1 = response1.data[0].embedding
@@ -214,7 +248,8 @@ def test_openai_embeddings_different_inputs_different_outputs(compat_client, cli
 def test_openai_embeddings_with_encoding_format_base64(compat_client, client_with_models, embedding_model_id):
     """Test OpenAI embeddings endpoint with base64 encoding format."""
     skip_if_model_doesnt_support_openai_embeddings(client_with_models, embedding_model_id)
-    skip_if_model_doesnt_support_variable_dimensions(embedding_model_id)
+    skip_if_model_doesnt_support_encoding_format_base64(client_with_models, embedding_model_id)
+    skip_if_model_doesnt_support_variable_dimensions(client_with_models, embedding_model_id)
 
     input_text = "Test base64 encoding format"
     dimensions = 12
@@ -247,6 +282,7 @@ def test_openai_embeddings_with_encoding_format_base64(compat_client, client_wit
 def test_openai_embeddings_base64_batch_processing(compat_client, client_with_models, embedding_model_id):
     """Test OpenAI embeddings endpoint with base64 encoding for batch processing."""
     skip_if_model_doesnt_support_openai_embeddings(client_with_models, embedding_model_id)
+    skip_if_model_doesnt_support_encoding_format_base64(client_with_models, embedding_model_id)
 
     input_texts = ["First text for base64", "Second text for base64", "Third text for base64"]
 
@@ -255,7 +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/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/00ba04f74a96.json

@@ -20,15 +20,15 @@
       "__type__": "ollama._types.GenerateResponse",
       "__data__": {
         "model": "llama-guard3:1b",
-        "created_at": "2025-08-01T23:12:53.860911Z",
+        "created_at": "2025-09-03T17:37:35.23084Z",
         "done": true,
         "done_reason": "stop",
-        "total_duration": 249137667,
-        "load_duration": 152509542,
+        "total_duration": 195981375,
+        "load_duration": 110522917,
         "prompt_eval_count": 216,
-        "prompt_eval_duration": 71000000,
+        "prompt_eval_duration": 72393958,
         "eval_count": 2,
-        "eval_duration": 24000000,
+        "eval_duration": 11843000,
         "response": "safe",
         "thinking": null,
         "context": null

tests/integration/recordings/responses/04172112ffbb.json

@@ -21,7 +21,7 @@
         "__type__": "ollama._types.GenerateResponse",
         "__data__": {
           "model": "llama3.2:3b-instruct-fp16",
-          "created_at": "2025-07-31T17:59:18.033900164Z",
+          "created_at": "2025-09-03T17:41:43.950283Z",
           "done": false,
           "done_reason": null,
           "total_duration": null,
@@ -39,7 +39,7 @@
         "__type__": "ollama._types.GenerateResponse",
         "__data__": {
           "model": "llama3.2:3b-instruct-fp16",
-          "created_at": "2025-07-31T17:59:18.213371151Z",
+          "created_at": "2025-09-03T17:41:43.991122Z",
           "done": false,
           "done_reason": null,
           "total_duration": null,
@@ -57,7 +57,7 @@
         "__type__": "ollama._types.GenerateResponse",
         "__data__": {
           "model": "llama3.2:3b-instruct-fp16",
-          "created_at": "2025-07-31T17:59:18.387513976Z",
+          "created_at": "2025-09-03T17:41:44.031378Z",
           "done": false,
           "done_reason": null,
           "total_duration": null,
@@ -75,7 +75,7 @@
         "__type__": "ollama._types.GenerateResponse",
         "__data__": {
           "model": "llama3.2:3b-instruct-fp16",
-          "created_at": "2025-07-31T17:59:18.564344287Z",
+          "created_at": "2025-09-03T17:41:44.073098Z",
           "done": false,
           "done_reason": null,
           "total_duration": null,
@@ -93,7 +93,7 @@
         "__type__": "ollama._types.GenerateResponse",
         "__data__": {
           "model": "llama3.2:3b-instruct-fp16",
-          "created_at": "2025-07-31T17:59:18.746579415Z",
+          "created_at": "2025-09-03T17:41:44.115961Z",
           "done": false,
           "done_reason": null,
           "total_duration": null,
@@ -111,7 +111,7 @@
         "__type__": "ollama._types.GenerateResponse",
         "__data__": {
           "model": "llama3.2:3b-instruct-fp16",
-          "created_at": "2025-07-31T17:59:18.923276047Z",
+          "created_at": "2025-09-03T17:41:44.156517Z",
           "done": false,
           "done_reason": null,
           "total_duration": null,
@@ -129,7 +129,7 @@
         "__type__": "ollama._types.GenerateResponse",
         "__data__": {
           "model": "llama3.2:3b-instruct-fp16",
-          "created_at": "2025-07-31T17:59:19.099961963Z",
+          "created_at": "2025-09-03T17:41:44.197079Z",
           "done": false,
           "done_reason": null,
           "total_duration": null,
@@ -147,7 +147,7 @@
         "__type__": "ollama._types.GenerateResponse",
         "__data__": {
           "model": "llama3.2:3b-instruct-fp16",
-          "created_at": "2025-07-31T17:59:19.275621884Z",
+          "created_at": "2025-09-03T17:41:44.237565Z",
           "done": false,
           "done_reason": null,
           "total_duration": null,
@@ -165,7 +165,7 @@
         "__type__": "ollama._types.GenerateResponse",
         "__data__": {
           "model": "llama3.2:3b-instruct-fp16",
-          "created_at": "2025-07-31T17:59:19.452204196Z",
+          "created_at": "2025-09-03T17:41:44.277755Z",
           "done": false,
           "done_reason": null,
           "total_duration": null,
@@ -183,7 +183,7 @@
         "__type__": "ollama._types.GenerateResponse",
         "__data__": {
           "model": "llama3.2:3b-instruct-fp16",
-          "created_at": "2025-07-31T17:59:19.626937514Z",
+          "created_at": "2025-09-03T17:41:44.318476Z",
           "done": false,
           "done_reason": null,
           "total_duration": null,
@@ -201,7 +201,7 @@
         "__type__": "ollama._types.GenerateResponse",
         "__data__": {
           "model": "llama3.2:3b-instruct-fp16",
-          "created_at": "2025-07-31T17:59:19.805566767Z",
+          "created_at": "2025-09-03T17:41:44.358628Z",
           "done": false,
           "done_reason": null,
           "total_duration": null,
@@ -219,7 +219,7 @@
         "__type__": "ollama._types.GenerateResponse",
         "__data__": {
           "model": "llama3.2:3b-instruct-fp16",
-          "created_at": "2025-07-31T17:59:19.985987477Z",
+          "created_at": "2025-09-03T17:41:44.398984Z",
           "done": false,
           "done_reason": null,
           "total_duration": null,
@@ -237,7 +237,7 @@
         "__type__": "ollama._types.GenerateResponse",
         "__data__": {
           "model": "llama3.2:3b-instruct-fp16",
-          "created_at": "2025-07-31T17:59:20.166458601Z",
+          "created_at": "2025-09-03T17:41:44.439232Z",
           "done": false,
           "done_reason": null,
           "total_duration": null,
@@ -255,7 +255,7 @@
         "__type__": "ollama._types.GenerateResponse",
         "__data__": {
           "model": "llama3.2:3b-instruct-fp16",
-          "created_at": "2025-07-31T17:59:20.343346795Z",
+          "created_at": "2025-09-03T17:41:44.479478Z",
           "done": false,
           "done_reason": null,
           "total_duration": null,
@@ -273,7 +273,7 @@
         "__type__": "ollama._types.GenerateResponse",
         "__data__": {
           "model": "llama3.2:3b-instruct-fp16",
-          "created_at": "2025-07-31T17:59:20.525008091Z",
+          "created_at": "2025-09-03T17:41:44.520202Z",
           "done": false,
           "done_reason": null,
           "total_duration": null,
@@ -291,7 +291,7 @@
         "__type__": "ollama._types.GenerateResponse",
         "__data__": {
           "model": "llama3.2:3b-instruct-fp16",
-          "created_at": "2025-07-31T17:59:20.709087695Z",
+          "created_at": "2025-09-03T17:41:44.560517Z",
           "done": false,
           "done_reason": null,
           "total_duration": null,
@@ -309,7 +309,7 @@
         "__type__": "ollama._types.GenerateResponse",
         "__data__": {
           "model": "llama3.2:3b-instruct-fp16",
-          "created_at": "2025-07-31T17:59:20.887074305Z",
+          "created_at": "2025-09-03T17:41:44.601592Z",
           "done": false,
           "done_reason": null,
           "total_duration": null,
@@ -327,15 +327,15 @@
         "__type__": "ollama._types.GenerateResponse",
         "__data__": {
           "model": "llama3.2:3b-instruct-fp16",
-          "created_at": "2025-07-31T17:59:21.065244925Z",
+          "created_at": "2025-09-03T17:41:44.642064Z",
           "done": true,
           "done_reason": "stop",
-          "total_duration": 4373531496,
-          "load_duration": 44438132,
+          "total_duration": 887142667,
+          "load_duration": 119331417,
           "prompt_eval_count": 56,
-          "prompt_eval_duration": 1296273199,
+          "prompt_eval_duration": 74294709,
           "eval_count": 18,
-          "eval_duration": 3032321735,
+          "eval_duration": 692842791,
           "response": "",
           "thinking": null,
           "context": null

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": {
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+}

tests/integration/recordings/responses/0b27fd737699.json

@@ -20,15 +20,15 @@
       "__type__": "ollama._types.GenerateResponse",
       "__data__": {
         "model": "llama-guard3:1b",
-        "created_at": "2025-08-01T23:13:57.556416Z",
+        "created_at": "2025-09-03T17:37:47.461886Z",
         "done": true,
         "done_reason": "stop",
-        "total_duration": 432363250,
-        "load_duration": 159296417,
+        "total_duration": 338927833,
+        "load_duration": 100895125,
         "prompt_eval_count": 223,
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+        "prompt_eval_duration": 221583042,
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-        "eval_duration": 14000000,
+        "eval_duration": 12341416,
         "response": "safe",
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tests/integration/recordings/responses/0b3f2e4754ff.json

@@ -24,7 +24,7 @@
       {
         "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk",
         "__data__": {
-          "id": "chatcmpl-29",
+          "id": "chatcmpl-414",
           "choices": [
             {
               "delta": {
@@ -39,7 +39,7 @@
               "logprobs": null
             }
           ],
-          "created": 1754090031,
+          "created": 1756921333,
           "model": "llama3.2:3b-instruct-fp16",
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           "service_tier": null,
@@ -50,7 +50,7 @@
       {
         "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk",
         "__data__": {
-          "id": "chatcmpl-29",
+          "id": "chatcmpl-414",
           "choices": [
             {
               "delta": {
@@ -65,7 +65,7 @@
               "logprobs": null
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           ],
-          "created": 1754090031,
+          "created": 1756921333,
           "model": "llama3.2:3b-instruct-fp16",
           "object": "chat.completion.chunk",
           "service_tier": null,
@@ -76,7 +76,7 @@
       {
         "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk",
         "__data__": {
-          "id": "chatcmpl-29",
+          "id": "chatcmpl-414",
           "choices": [
             {
               "delta": {
@@ -91,7 +91,7 @@
               "logprobs": null
             }
           ],
-          "created": 1754090031,
+          "created": 1756921333,
           "model": "llama3.2:3b-instruct-fp16",
           "object": "chat.completion.chunk",
           "service_tier": null,
@@ -102,7 +102,7 @@
       {
         "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk",
         "__data__": {
-          "id": "chatcmpl-29",
+          "id": "chatcmpl-414",
           "choices": [
             {
               "delta": {
@@ -117,7 +117,7 @@
               "logprobs": null
             }
           ],
-          "created": 1754090031,
+          "created": 1756921333,
           "model": "llama3.2:3b-instruct-fp16",
           "object": "chat.completion.chunk",
           "service_tier": null,
@@ -128,7 +128,7 @@
       {
         "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk",
         "__data__": {
-          "id": "chatcmpl-29",
+          "id": "chatcmpl-414",
           "choices": [
             {
               "delta": {
@@ -143,7 +143,7 @@
               "logprobs": null
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           ],
-          "created": 1754090031,
+          "created": 1756921334,
           "model": "llama3.2:3b-instruct-fp16",
           "object": "chat.completion.chunk",
           "service_tier": null,
@@ -154,7 +154,7 @@
       {
         "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk",
         "__data__": {
-          "id": "chatcmpl-29",
+          "id": "chatcmpl-414",
           "choices": [
             {
               "delta": {
@@ -169,7 +169,7 @@
               "logprobs": null
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           ],
-          "created": 1754090031,
+          "created": 1756921334,
           "model": "llama3.2:3b-instruct-fp16",
           "object": "chat.completion.chunk",
           "service_tier": null,
@@ -180,7 +180,7 @@
       {
         "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk",
         "__data__": {
-          "id": "chatcmpl-29",
+          "id": "chatcmpl-414",
           "choices": [
             {
               "delta": {
@@ -195,7 +195,7 @@
               "logprobs": null
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           ],
-          "created": 1754090031,
+          "created": 1756921334,
           "model": "llama3.2:3b-instruct-fp16",
           "object": "chat.completion.chunk",
           "service_tier": null,
@@ -206,7 +206,7 @@
       {
         "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk",
         "__data__": {
-          "id": "chatcmpl-29",
+          "id": "chatcmpl-414",
           "choices": [
             {
               "delta": {
@@ -221,7 +221,7 @@
               "logprobs": null
             }
           ],
-          "created": 1754090031,
+          "created": 1756921334,
           "model": "llama3.2:3b-instruct-fp16",
           "object": "chat.completion.chunk",
           "service_tier": null,

tests/integration/recordings/responses/0c1f45455d3b.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": "Hello, world!"
+        }
+      ],
+      "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": "oBUtgGr-4Yz4kd-9801a2f00b2b42e8",
+        "choices": [
+          {
+            "finish_reason": "stop",
+            "index": 0,
+            "logprobs": null,
+            "message": {
+              "content": "Hello! It's nice to meet you. Is there something I can help you with or would you like to chat?",
+              "refusal": null,
+              "role": "assistant",
+              "annotations": null,
+              "audio": null,
+              "function_call": null,
+              "tool_calls": []
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+        "model": "meta-llama/Llama-3.3-70B-Instruct-Turbo-Free",
+        "object": "chat.completion",
+        "service_tier": null,
+        "system_fingerprint": null,
+        "usage": {
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+          "prompt_tokens": 39,
+          "total_tokens": 64,
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+          "prompt_tokens_details": null,
+          "cached_tokens": 0
+        },
+        "prompt": []
+      }
+    },
+    "is_streaming": false
+  }
+}

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": [
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+              "role": "assistant",
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+              "function_call": null,
+              "tool_calls": [
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+                  "id": "1c40cbc30",
+                  "function": {
+                    "arguments": "{\"city\": \"Tokyo\"}",
+                    "name": "get_weather"
+                  },
+                  "type": "function"
+                }
+              ]
+            }
+          }
+        ],
+        "created": 1758191364,
+        "model": "llama-3.3-70b",
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+        "system_fingerprint": "fp_c5ec625e72d41732d8fd",
+        "usage": {
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+          "total_tokens": 260,
+          "completion_tokens_details": null,
+          "prompt_tokens_details": {
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+            "cached_tokens": 0
+          }
+        },
+        "time_info": {
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+          "prompt_time": 0.007237483,
+          "completion_time": 0.003803105,
+          "total_time": 0.013348102569580078,
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+        }
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+    "is_streaming": false
+  }
+}

tests/integration/recordings/responses/0e8f2b001dd9.json

@@ -1,7 +1,7 @@
 {
   "request": {
     "method": "POST",
-    "url": "http://localhost:11434/v1/v1/chat/completions",
+    "url": "http://0.0.0.0:11434/v1/v1/chat/completions",
     "headers": {},
     "body": {
       "model": "llama3.2:3b-instruct-fp16",
@@ -20,14 +20,14 @@
     "body": {
       "__type__": "openai.types.chat.chat_completion.ChatCompletion",
       "__data__": {
-        "id": "chatcmpl-368",
+        "id": "chatcmpl-161",
         "choices": [
           {
             "finish_reason": "stop",
             "index": 0,
             "logprobs": null,
             "message": {
-              "content": "Saturn is known for its extensive ring system.",
+              "content": "The answer is Saturn.",
               "refusal": null,
               "role": "assistant",
               "annotations": null,
@@ -37,15 +37,15 @@
             }
           }
         ],
-        "created": 1754081853,
+        "created": 1756921364,
         "model": "llama3.2:3b-instruct-fp16",
         "object": "chat.completion",
         "service_tier": null,
         "system_fingerprint": "fp_ollama",
         "usage": {
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           "prompt_tokens": 39,
-          "total_tokens": 50,
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           "prompt_tokens_details": null
         }

tests/integration/recordings/responses/0fda25b9241c.json

@@ -0,0 +1,71 @@
+{
+  "request": {
+    "method": "POST",
+    "url": "https://shan-mfbb618r-eastus2.cognitiveservices.azure.com/openai/v1/v1/chat/completions",
+    "headers": {},
+    "body": {
+      "model": "gpt-5-mini",
+      "messages": [
+        {
+          "role": "user",
+          "content": "Which planet do humans live on?"
+        }
+      ],
+      "stream": false
+    },
+    "endpoint": "/v1/chat/completions",
+    "model": "gpt-5-mini"
+  },
+  "response": {
+    "body": {
+      "__type__": "openai.types.chat.chat_completion.ChatCompletion",
+      "__data__": {
+        "id": "chatcmpl-CECIXqfvjuluKkZtG3q2QJoSQhBU0",
+        "choices": [
+          {
+            "finish_reason": "stop",
+            "index": 0,
+            "logprobs": null,
+            "message": {
+              "content": "Humans live on Earth \u2014 the third planet from the Sun. It's the only known planet that naturally supports life, with a breathable atmosphere, liquid water, and temperatures suitable for living organisms.",
+              "refusal": null,
+              "role": "assistant",
+              "annotations": [],
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+            "content_filter_results": {}
+          }
+        ],
+        "created": 1757499901,
+        "model": "gpt-5-mini-2025-08-07",
+        "object": "chat.completion",
+        "service_tier": null,
+        "system_fingerprint": null,
+        "usage": {
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+          "prompt_tokens": 13,
+          "total_tokens": 125,
+          "completion_tokens_details": {
+            "accepted_prediction_tokens": 0,
+            "audio_tokens": 0,
+            "reasoning_tokens": 64,
+            "rejected_prediction_tokens": 0
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+          "prompt_tokens_details": {
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+          }
+        },
+        "prompt_filter_results": [
+          {
+            "prompt_index": 0,
+            "content_filter_results": {}
+          }
+        ]
+      }
+    },
+    "is_streaming": false
+  }
+}

tests/integration/recordings/responses/10eea8c15ddc.json

@@ -20,15 +20,15 @@
       "__type__": "ollama._types.GenerateResponse",
       "__data__": {
         "model": "llama-guard3:1b",
-        "created_at": "2025-08-01T23:12:51.682357Z",
+        "created_at": "2025-09-03T17:37:33.473237Z",
         "done": true,
         "done_reason": "stop",
-        "total_duration": 238161000,
-        "load_duration": 72494750,
+        "total_duration": 279025042,
+        "load_duration": 162673250,
         "prompt_eval_count": 212,
-        "prompt_eval_duration": 87000000,
+        "prompt_eval_duration": 73595834,
         "eval_count": 5,
-        "eval_duration": 74000000,
+        "eval_duration": 41950291,
         "response": "unsafe\nS8",
         "thinking": null,
         "context": null

tests/integration/recordings/responses/121a72d1c4cf.json

@@ -0,0 +1,728 @@
+{
+  "request": {
+    "method": "POST",
+    "url": "__databricks__/serving-endpoints/v1/chat/completions",
+    "headers": {},
+    "body": {
+      "model": "databricks-meta-llama-3-3-70b-instruct",
+      "messages": [
+        {
+          "role": "user",
+          "content": "Hello, world!"
+        }
+      ],
+      "stream": true
+    },
+    "endpoint": "/v1/chat/completions",
+    "model": "databricks-meta-llama-3-3-70b-instruct"
+  },
+  "response": {
+    "body": [
+      {
+        "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk",
+        "__data__": {
+          "id": "chatcmpl_7268e4ee-3b8e-461e-80dc-608e76f3801d",
+          "choices": [
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+        }
+      },
+      {
+        "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk",
+        "__data__": {
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+}

tests/integration/recordings/responses/140187e305dc.json

@@ -1,7 +1,7 @@
 {
   "request": {
     "method": "POST",
-    "url": "http://localhost:11434/v1/v1/chat/completions",
+    "url": "http://0.0.0.0:11434/v1/v1/chat/completions",
     "headers": {},
     "body": {
       "model": "llama3.2:3b-instruct-fp16",
@@ -20,14 +20,14 @@
     "body": {
       "__type__": "openai.types.chat.chat_completion.ChatCompletion",
       "__data__": {
-        "id": "chatcmpl-876",
+        "id": "chatcmpl-974",
         "choices": [
           {
             "finish_reason": "stop",
             "index": 0,
             "logprobs": null,
             "message": {
-              "content": "I'm afraid I don't have a built-in ability to directly interface with or \"test\" OpenAI models, including the original GPT-1 model. However, I can explain how you might approach this task:\n\nThe OpenAI GPT-1 is a large transformer-based language model that was trained on a massive dataset of text and achieved state-of-the-art results in various natural language processing tasks.\n\nTo test or evaluate the performance of a model like GPT-1, you would typically follow these steps:\n\n1. **Get access to the OpenAI API**: The OpenAI API provides a way for developers to interact with the GPT-1 model programmatically. You can sign up for an API key on the OpenAI website.\n2. **Choose a testing platform or environment**: You'll need a compute platform that supports the necessary algorithms and data structures to run inference on the GPT-1 model. Some popular options include AWS, Google Cloud, or Azure Compute Virtual Machines.\n3. **Prepare your test input data**: This will involve creating text inputs in the format expected by the OpenAI API (i.e., a JSON object containing the text to be processed).\n4. **Use the OpenAI Python library or SDK**: The OpenAI Python library provides an easy-to-use interface for interacting with the GPT-1 model through the API.\n\nHere's some example code that demonstrates how you might use the OpenAI Flask API to test a single input:\n\n```python\nfrom flask import Flask, request, jsonify\nimport json\n\napp = Flask(__name__)\n\n@ app . route ( '/ /gpt-en ', ' Text ', methods = ['POST'])\ndef gpt_en () -> Json :\n    data = request . get_json ()\n    if not data or \"message\" in ( data ):\n        return None , 400 , { ' error' : \"Input must be a text string.\" }\n    response = []\n    while True:\n        message = \"\"\n        for token in data [\"input\"]:\n            response_text = f\"{data['prompt']} {token}\"\n            data[\"input\"] = [response_text]\n            new_response = gpt_en()(data)\n            if all([not item or not isinstance(item, dict) for item in new_response]):\n             break\n\n        message = json . dumps ({}\"text\": response_text})\n        response.append(message)\n\n    return jsonify ({\"output\": response}), 200 , {}\n\nif __name__ == \"__main__\":\n   app.run(debug=True)\n```\n\n5. **Evaluate the output**: Once you have processed your test input data using the GPT-1 model, you can evaluate the accuracy of the generated responses.\n\nKeep in mind that this is just a basic example to illustrate how you might approach testing the OpenAI GPT-1 model.",
+              "content": "I'm happy to help you test the OpenAI API, however I can not access the API.\n\nInstead why don't we follow these steps:\n\n*   Check documentation\n*   Contact support\n*   Reach out to their community forum. \n\nLet me know if I can be of any additional assistance",
               "refusal": null,
               "role": "assistant",
               "annotations": null,
@@ -37,15 +37,15 @@
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+}

tests/integration/recordings/responses/17253d7cc667.json

@@ -20,15 +20,15 @@
       "__type__": "ollama._types.GenerateResponse",
       "__data__": {
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-        "created_at": "2025-08-01T23:12:52.919624Z",
+        "created_at": "2025-09-03T17:37:34.308033Z",
         "done": true,
         "done_reason": "stop",
-        "total_duration": 201956834,
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         "response": "safe",
         "thinking": null,
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tests/integration/recordings/responses/173ecb3aab28.json

@@ -40,7 +40,7 @@
       {
         "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk",
         "__data__": {
-          "id": "chatcmpl-457",
+          "id": "chatcmpl-921",
           "choices": [
             {
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@@ -55,7 +55,7 @@
               "logprobs": null
             }
           ],
-          "created": 1754090032,
+          "created": 1756920971,
           "model": "llama3.2:3b-instruct-fp16",
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@@ -66,7 +66,7 @@
       {
         "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk",
         "__data__": {
-          "id": "chatcmpl-457",
+          "id": "chatcmpl-921",
           "choices": [
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@@ -81,7 +81,7 @@
               "logprobs": null
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-          "created": 1754090032,
+          "created": 1756920971,
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@@ -92,7 +92,7 @@
       {
         "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk",
         "__data__": {
-          "id": "chatcmpl-457",
+          "id": "chatcmpl-921",
           "choices": [
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               "delta": {
@@ -107,7 +107,7 @@
               "logprobs": null
             }
           ],
-          "created": 1754090032,
+          "created": 1756920971,
           "model": "llama3.2:3b-instruct-fp16",
           "object": "chat.completion.chunk",
           "service_tier": null,
@@ -118,7 +118,7 @@
       {
         "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk",
         "__data__": {
-          "id": "chatcmpl-457",
+          "id": "chatcmpl-921",
           "choices": [
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@@ -133,7 +133,7 @@
               "logprobs": null
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           ],
-          "created": 1754090032,
+          "created": 1756920971,
           "model": "llama3.2:3b-instruct-fp16",
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@@ -144,7 +144,7 @@
       {
         "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk",
         "__data__": {
-          "id": "chatcmpl-457",
+          "id": "chatcmpl-921",
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@@ -159,7 +159,7 @@
               "logprobs": null
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           ],
-          "created": 1754090032,
+          "created": 1756920971,
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           "service_tier": null,
@@ -170,7 +170,7 @@
       {
         "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk",
         "__data__": {
-          "id": "chatcmpl-457",
+          "id": "chatcmpl-921",
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@@ -185,7 +185,7 @@
               "logprobs": null
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-          "created": 1754090032,
+          "created": 1756920971,
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@@ -196,7 +196,7 @@
       {
         "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk",
         "__data__": {
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+          "id": "chatcmpl-921",
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@@ -211,7 +211,7 @@
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@@ -222,7 +222,7 @@
       {
         "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk",
         "__data__": {
-          "id": "chatcmpl-457",
+          "id": "chatcmpl-921",
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@@ -237,7 +237,7 @@
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-          "created": 1754090032,
+          "created": 1756920971,
           "model": "llama3.2:3b-instruct-fp16",
           "object": "chat.completion.chunk",
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tests/integration/recordings/responses/174458ad71b2.json

@@ -20,15 +20,15 @@
       "__type__": "ollama._types.GenerateResponse",
       "__data__": {
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-        "created_at": "2025-08-01T23:12:53.580806Z",
+        "created_at": "2025-09-03T17:37:34.994704Z",
         "done": true,
         "done_reason": "stop",
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         "response": "safe",
         "thinking": null,
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tests/integration/recordings/responses/178016edef0e.json

@@ -20,15 +20,15 @@
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       "__data__": {
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-        "created_at": "2025-08-01T23:12:52.354566Z",
+        "created_at": "2025-09-03T17:37:33.769233Z",
         "done": true,
         "done_reason": "stop",
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-        "eval_duration": 84000000,
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         "response": "unsafe\nS12",
         "thinking": null,
         "context": null

tests/integration/recordings/responses/197228e26971.json

@@ -20,15 +20,15 @@
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       "__data__": {
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-        "created_at": "2025-08-01T23:12:52.686478Z",
+        "created_at": "2025-09-03T17:37:34.074233Z",
         "done": true,
         "done_reason": "stop",
-        "total_duration": 304136208,
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         "response": "unsafe\nS2",
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tests/integration/recordings/responses/198ef7208389.json

@@ -20,15 +20,15 @@
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       "__data__": {
         "model": "llama-guard3:1b",
-        "created_at": "2025-08-01T23:12:51.186501Z",
+        "created_at": "2025-09-03T17:37:32.84197Z",
         "done": true,
         "done_reason": "stop",
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         "response": "unsafe\nS1",
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tests/integration/recordings/responses/1adfaa0e062e.json

@@ -20,15 +20,15 @@
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       "__data__": {
         "model": "llama-guard3:1b",
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+        "created_at": "2025-09-03T17:37:34.607413Z",
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tests/integration/recordings/responses/1b8394f90636.json

@@ -22,15 +22,15 @@
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       "__data__": {
         "model": "llama3.2:3b-instruct-fp16",
-        "created_at": "2025-08-04T22:55:05.685988Z",
+        "created_at": "2025-09-03T17:36:13.821929Z",
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         "response": " _______.\n\nThe best answer is blue. The traditional nursery rhyme goes like this:\n\nRoses are red,\nViolets are blue,\nSugar is sweet,\nAnd so are you! (Or something similar.)",
         "thinking": null,
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tests/integration/recordings/responses/1b92be674e2a.json

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         "response": "Mark Zuckerberg is the founder, chairman and CEO of Meta, which he originally founded as Facebook in 2004.",
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tests/integration/recordings/responses/211b1562d4e6.json

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         "response": "Humans live on Earth.",
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tests/integration/recordings/responses/225b4d2263a7.json

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tests/integration/recordings/responses/249b7f0ddde6.json

@@ -1,35 +1,33 @@
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@@ -39,15 +37,15 @@
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tests/integration/recordings/responses/27463384d1a3.json

@@ -0,0 +1,56 @@
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tests/integration/recordings/responses/28648cf8d421.json

@@ -0,0 +1,56 @@
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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/2afe3b38ca01.json

@@ -22,7 +22,7 @@
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@@ -238,15 +238,15 @@
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tests/integration/recordings/responses/2b2ad549510d.json

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+    "endpoint": "/v1/chat/completions",
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+}

tests/integration/recordings/responses/2c55f483cea8.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": "Quick test"
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+      "max_tokens": 5
+    },
+    "endpoint": "/v1/chat/completions",
+    "model": "meta-llama/Llama-3.3-70B-Instruct-Turbo-Free"
+  },
+  "response": {
+    "body": {
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+      "__data__": {
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+        "choices": [
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+        "usage": {
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+          "total_tokens": 42,
+          "completion_tokens_details": null,
+          "prompt_tokens_details": null,
+          "cached_tokens": 0
+        },
+        "prompt": []
+      }
+    },
+    "is_streaming": false
+  }
+}

tests/integration/recordings/responses/2d187a11704c.json

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@@ -472,7 +472,7 @@
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tests/integration/recordings/responses/382c2f22274c.json

@@ -1,7 +1,7 @@
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-    "url": "http://localhost:11434/v1/v1/chat/completions",
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tests/integration/recordings/responses/3a81146f2afa.json

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+      {
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+        "__data__": {
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+}

tests/integration/recordings/responses/3c0bf9ba81b2.json

@@ -20,14 +20,14 @@
     "body": {
       "__type__": "openai.types.chat.chat_completion.ChatCompletion",
       "__data__": {
-        "id": "chatcmpl-651",
+        "id": "chatcmpl-334",
         "choices": [
           {
             "finish_reason": "length",
             "index": 0,
             "logprobs": null,
             "message": {
-              "content": "I'm ready to help",
+              "content": "It looks like we've",
               "refusal": null,
               "role": "assistant",
               "annotations": null,
@@ -37,7 +37,7 @@
             }
           }
         ],
-        "created": 1755294941,
+        "created": 1756921086,
         "model": "llama3.2:3b-instruct-fp16",
         "object": "chat.completion",
         "service_tier": null,

tests/integration/recordings/responses/3c3f13cb7794.json

@@ -21,7 +21,7 @@
         "__type__": "ollama._types.GenerateResponse",
         "__data__": {
           "model": "llama3.2:3b-instruct-fp16",
-          "created_at": "2025-08-04T22:55:11.338232Z",
+          "created_at": "2025-09-03T17:36:18.136699Z",
           "done": false,
           "done_reason": null,
           "total_duration": null,
@@ -39,7 +39,7 @@
         "__type__": "ollama._types.GenerateResponse",
         "__data__": {
           "model": "llama3.2:3b-instruct-fp16",
-          "created_at": "2025-08-04T22:55:11.39419Z",
+          "created_at": "2025-09-03T17:36:18.177622Z",
           "done": false,
           "done_reason": null,
           "total_duration": null,
@@ -57,7 +57,7 @@
         "__type__": "ollama._types.GenerateResponse",
         "__data__": {
           "model": "llama3.2:3b-instruct-fp16",
-          "created_at": "2025-08-04T22:55:11.445346Z",
+          "created_at": "2025-09-03T17:36:18.218104Z",
           "done": false,
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@@ -75,7 +75,7 @@
         "__type__": "ollama._types.GenerateResponse",
         "__data__": {
           "model": "llama3.2:3b-instruct-fp16",
-          "created_at": "2025-08-04T22:55:11.496701Z",
+          "created_at": "2025-09-03T17:36:18.258837Z",
           "done": false,
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@@ -93,7 +93,7 @@
         "__type__": "ollama._types.GenerateResponse",
         "__data__": {
           "model": "llama3.2:3b-instruct-fp16",
-          "created_at": "2025-08-04T22:55:11.546804Z",
+          "created_at": "2025-09-03T17:36:18.299715Z",
           "done": false,
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@@ -111,7 +111,7 @@
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-          "created_at": "2025-08-04T22:55:11.601009Z",
+          "created_at": "2025-09-03T17:36:18.341602Z",
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@@ -129,7 +129,7 @@
         "__type__": "ollama._types.GenerateResponse",
         "__data__": {
           "model": "llama3.2:3b-instruct-fp16",
-          "created_at": "2025-08-04T22:55:11.652788Z",
+          "created_at": "2025-09-03T17:36:18.385504Z",
           "done": false,
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@@ -147,7 +147,7 @@
         "__type__": "ollama._types.GenerateResponse",
         "__data__": {
           "model": "llama3.2:3b-instruct-fp16",
-          "created_at": "2025-08-04T22:55:11.703325Z",
+          "created_at": "2025-09-03T17:36:18.429427Z",
           "done": false,
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@@ -165,7 +165,7 @@
         "__type__": "ollama._types.GenerateResponse",
         "__data__": {
           "model": "llama3.2:3b-instruct-fp16",
-          "created_at": "2025-08-04T22:55:11.754033Z",
+          "created_at": "2025-09-03T17:36:18.473547Z",
           "done": false,
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           "total_duration": null,
@@ -183,7 +183,7 @@
         "__type__": "ollama._types.GenerateResponse",
         "__data__": {
           "model": "llama3.2:3b-instruct-fp16",
-          "created_at": "2025-08-04T22:55:11.804654Z",
+          "created_at": "2025-09-03T17:36:18.516327Z",
           "done": false,
           "done_reason": null,
           "total_duration": null,
@@ -201,15 +201,15 @@
         "__type__": "ollama._types.GenerateResponse",
         "__data__": {
           "model": "llama3.2:3b-instruct-fp16",
-          "created_at": "2025-08-04T22:55:11.854841Z",
+          "created_at": "2025-09-03T17:36:18.559332Z",
           "done": true,
           "done_reason": "stop",
-          "total_duration": 652371000,
-          "load_duration": 42086042,
+          "total_duration": 628034000,
+          "load_duration": 116384417,
           "prompt_eval_count": 26,
-          "prompt_eval_duration": 78000000,
+          "prompt_eval_duration": 87798792,
           "eval_count": 11,
-          "eval_duration": 531000000,
+          "eval_duration": 423189583,
           "response": "",
           "thinking": null,
           "context": null

tests/integration/recordings/responses/3ca695048bee.json

@@ -1,7 +1,7 @@
 {
   "request": {
     "method": "POST",
-    "url": "http://localhost:11434/v1/v1/chat/completions",
+    "url": "http://0.0.0.0:11434/v1/v1/chat/completions",
     "headers": {},
     "body": {
       "model": "llama3.2:3b-instruct-fp16",
@@ -39,7 +39,7 @@
       {
         "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk",
         "__data__": {
-          "id": "chatcmpl-490",
+          "id": "chatcmpl-3",
           "choices": [
             {
               "delta": {
@@ -50,7 +50,7 @@
                 "tool_calls": [
                   {
                     "index": 0,
-                    "id": "call_rolv1ozt",
+                    "id": "call_3kigugt3",
                     "function": {
                       "arguments": "{\"city\":\"Tokyo\"}",
                       "name": "get_weather"
@@ -64,7 +64,7 @@
               "logprobs": null
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           ],
-          "created": 1754081852,
+          "created": 1756921361,
           "model": "llama3.2:3b-instruct-fp16",
           "object": "chat.completion.chunk",
           "service_tier": null,
@@ -75,7 +75,7 @@
       {
         "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk",
         "__data__": {
-          "id": "chatcmpl-490",
+          "id": "chatcmpl-3",
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             {
               "delta": {
@@ -85,12 +85,12 @@
                 "role": "assistant",
                 "tool_calls": null
               },
-              "finish_reason": "stop",
+              "finish_reason": "tool_calls",
               "index": 0,
               "logprobs": null
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-          "created": 1754081852,
+          "created": 1756921361,
           "model": "llama3.2:3b-instruct-fp16",
           "object": "chat.completion.chunk",
           "service_tier": null,

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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+            "index": 2,
+            "object": "embedding"
+          }
+        ],
+        "model": "togethercomputer/m2-bert-80M-32k-retrieval",
+        "object": "list",
+        "usage": null
+      }
+    },
+    "is_streaming": false
+  }
+}

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tests/integration/recordings/responses/41ace09e5dba.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 2"
+        }
+      ],
+      "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": "oCfxBri-4Yz4kd-984c2b177fb74ce3",
+        "choices": [
+          {
+            "finish_reason": "stop",
+            "index": 0,
+            "logprobs": null,
+            "message": {
+              "content": "This conversation has just begun. What would you like to talk about? I can summarize our conversation at the end, if you like.",
+              "refusal": null,
+              "role": "assistant",
+              "annotations": null,
+              "audio": null,
+              "function_call": null,
+              "tool_calls": []
+            },
+            "seed": 7149743687991911000
+          }
+        ],
+        "created": 1758820576,
+        "model": "meta-llama/Llama-3.3-70B-Instruct-Turbo-Free",
+        "object": "chat.completion",
+        "service_tier": null,
+        "system_fingerprint": null,
+        "usage": {
+          "completion_tokens": 28,
+          "prompt_tokens": 41,
+          "total_tokens": 69,
+          "completion_tokens_details": null,
+          "prompt_tokens_details": null,
+          "cached_tokens": 0
+        },
+        "prompt": []
+      }
+    },
+    "is_streaming": false
+  }
+}

tests/integration/recordings/responses/41e27b9b5d09.json

@@ -0,0 +1,42 @@
+{
+  "request": {
+    "method": "POST",
+    "url": "http://0.0.0.0:11434/v1/v1/completions",
+    "headers": {},
+    "body": {
+      "model": "llama3.2:3b-instruct-fp16",
+      "prompt": "Say completions",
+      "max_tokens": 20
+    },
+    "endpoint": "/v1/completions",
+    "model": "llama3.2:3b-instruct-fp16"
+  },
+  "response": {
+    "body": {
+      "__type__": "openai.types.completion.Completion",
+      "__data__": {
+        "id": "cmpl-271",
+        "choices": [
+          {
+            "finish_reason": "length",
+            "index": 0,
+            "logprobs": null,
+            "text": "You want me to respond with a completion, but you didn't specify what I should complete. Could"
+          }
+        ],
+        "created": 1756846620,
+        "model": "llama3.2:3b-instruct-fp16",
+        "object": "text_completion",
+        "system_fingerprint": "fp_ollama",
+        "usage": {
+          "completion_tokens": 20,
+          "prompt_tokens": 28,
+          "total_tokens": 48,
+          "completion_tokens_details": null,
+          "prompt_tokens_details": null
+        }
+      }
+    },
+    "is_streaming": false
+  }
+}

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tests/integration/recordings/responses/44a1d9de0602.json

@@ -20,7 +20,7 @@
     "body": {
       "__type__": "openai.types.chat.chat_completion.ChatCompletion",
       "__data__": {
-        "id": "chatcmpl-987",
+        "id": "chatcmpl-507",
         "choices": [
           {
             "finish_reason": "length",
@@ -37,7 +37,7 @@
             }
           }
         ],
-        "created": 1755294921,
+        "created": 1756921150,
         "model": "llama3.2:3b-instruct-fp16",
         "object": "chat.completion",
         "service_tier": null,

tests/integration/recordings/responses/44fb9cf5875f.json

@@ -20,15 +20,15 @@
       "__type__": "ollama._types.GenerateResponse",
       "__data__": {
         "model": "llama3.2:3b-instruct-fp16",
-        "created_at": "2025-07-31T17:59:42.166585642Z",
+        "created_at": "2025-09-03T17:41:49.581065Z",
         "done": true,
         "done_reason": "stop",
-        "total_duration": 9490295253,
-        "load_duration": 42349084,
+        "total_duration": 2391571708,
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tests/integration/recordings/responses/48d2fb183a2a.json

@@ -67,15 +67,15 @@
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-        "created_at": "2025-08-04T22:55:40.583477Z",
+        "created_at": "2025-09-03T17:36:40.283084Z",
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         "thinking": null,
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tests/integration/recordings/responses/4ca6152a0eb8.json

@@ -0,0 +1,59 @@
+{
+  "request": {
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+    "headers": {},
+    "body": {
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+    "model": "meta-llama/Llama-3.3-70B-Instruct-Turbo-Free"
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tests/integration/recordings/responses/4d438c6bbaed.json

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tests/integration/recordings/responses/4ebcaf6c2aee.json

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tests/integration/recordings/responses/4f00cf740aba.json

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tests/integration/recordings/responses/50340cd4d253.json

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tests/integration/recordings/responses/50a8dc5b8ece.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"
+                }
+              }
+            },
+            "strict": null
+          }
+        }
+      ]
+    },
+    "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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+        "id": "oCfxQwj-4Yz4kd-984c2bd8ba58901d",
+        "choices": [
+          {
+            "finish_reason": "stop",
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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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+        "model": "meta-llama/Llama-3.3-70B-Instruct-Turbo-Free",
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+      }
+    },
+    "is_streaming": false
+  }
+}

tests/integration/recordings/responses/5370751803dc.json

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tests/integration/recordings/responses/545d86510a80.json

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@@ -238,15 +238,15 @@
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-          "load_duration": 63705209,
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tests/integration/recordings/responses/546dc9533c84.json

@@ -0,0 +1,801 @@
+{
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+    "url": "https://api.together.xyz/v1/v1/embeddings",
+    "headers": {},
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+    "model": "togethercomputer/m2-bert-80M-32k-retrieval"
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+}

tests/integration/recordings/responses/554de3cd986f.json

@@ -22,7 +22,7 @@
         "__type__": "ollama._types.GenerateResponse",
         "__data__": {
           "model": "llama3.2:3b-instruct-fp16",
-          "created_at": "2025-08-01T23:14:04.40585Z",
+          "created_at": "2025-09-03T17:37:51.805591Z",
           "done": false,
           "done_reason": null,
           "total_duration": null,
@@ -40,7 +40,7 @@
         "__type__": "ollama._types.GenerateResponse",
         "__data__": {
           "model": "llama3.2:3b-instruct-fp16",
-          "created_at": "2025-08-01T23:14:04.455647Z",
+          "created_at": "2025-09-03T17:37:51.850067Z",
           "done": false,
           "done_reason": null,
           "total_duration": null,
@@ -58,7 +58,7 @@
         "__type__": "ollama._types.GenerateResponse",
         "__data__": {
           "model": "llama3.2:3b-instruct-fp16",
-          "created_at": "2025-08-01T23:14:04.509581Z",
+          "created_at": "2025-09-03T17:37:51.892443Z",
           "done": false,
           "done_reason": null,
           "total_duration": null,
@@ -76,7 +76,7 @@
         "__type__": "ollama._types.GenerateResponse",
         "__data__": {
           "model": "llama3.2:3b-instruct-fp16",
-          "created_at": "2025-08-01T23:14:04.56592Z",
+          "created_at": "2025-09-03T17:37:51.934364Z",
           "done": false,
           "done_reason": null,
           "total_duration": null,
@@ -94,7 +94,7 @@
         "__type__": "ollama._types.GenerateResponse",
         "__data__": {
           "model": "llama3.2:3b-instruct-fp16",
-          "created_at": "2025-08-01T23:14:04.616979Z",
+          "created_at": "2025-09-03T17:37:51.978382Z",
           "done": false,
           "done_reason": null,
           "total_duration": null,
@@ -112,7 +112,7 @@
         "__type__": "ollama._types.GenerateResponse",
         "__data__": {
           "model": "llama3.2:3b-instruct-fp16",
-          "created_at": "2025-08-01T23:14:04.671413Z",
+          "created_at": "2025-09-03T17:37:52.019332Z",
           "done": false,
           "done_reason": null,
           "total_duration": null,
@@ -130,7 +130,7 @@
         "__type__": "ollama._types.GenerateResponse",
         "__data__": {
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-          "created_at": "2025-08-01T23:14:04.725494Z",
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@@ -148,7 +148,7 @@
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@@ -166,7 +166,7 @@
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@@ -220,7 +220,7 @@
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@@ -256,7 +256,7 @@
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@@ -292,7 +292,7 @@
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@@ -310,7 +310,7 @@
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         "__data__": {
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-          "created_at": "2025-08-01T23:14:05.257133Z",
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@@ -328,7 +328,7 @@
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@@ -346,15 +346,15 @@
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-          "created_at": "2025-08-01T23:14:05.370124Z",
+          "created_at": "2025-09-03T17:37:52.55904Z",
           "done": true,
           "done_reason": "stop",
-          "total_duration": 1532801458,
-          "load_duration": 213911041,
+          "total_duration": 971882292,
+          "load_duration": 116634209,
           "prompt_eval_count": 376,
-          "prompt_eval_duration": 350000000,
+          "prompt_eval_duration": 99382958,
           "eval_count": 19,
-          "eval_duration": 967000000,
+          "eval_duration": 755260750,
           "response": "",
           "thinking": null,
           "context": null

tests/integration/recordings/responses/561746e1c8de.json

@@ -21,7 +21,7 @@
         "__type__": "ollama._types.GenerateResponse",
         "__data__": {
           "model": "llama3.2:3b-instruct-fp16",
-          "created_at": "2025-08-15T20:24:49.18651486Z",
+          "created_at": "2025-09-03T17:36:20.465701Z",
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@@ -39,7 +39,7 @@
         "__type__": "ollama._types.GenerateResponse",
         "__data__": {
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-          "created_at": "2025-08-15T20:24:49.370611348Z",
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@@ -57,7 +57,7 @@
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-          "created_at": "2025-08-15T20:24:49.557000029Z",
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@@ -75,7 +75,7 @@
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-          "created_at": "2025-08-15T20:24:49.746777116Z",
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@@ -93,7 +93,7 @@
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-          "created_at": "2025-08-15T20:24:49.942233333Z",
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@@ -111,7 +111,7 @@
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-          "created_at": "2025-08-15T20:24:50.126788846Z",
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@@ -129,7 +129,7 @@
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@@ -147,7 +147,7 @@
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-          "created_at": "2025-08-15T20:24:50.501507173Z",
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@@ -165,7 +165,7 @@
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-          "created_at": "2025-08-15T20:24:50.692296777Z",
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@@ -183,7 +183,7 @@
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-          "created_at": "2025-08-15T20:24:50.878846539Z",
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@@ -201,15 +201,15 @@
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         "__data__": {
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-          "created_at": "2025-08-15T20:24:51.063200561Z",
+          "created_at": "2025-09-03T17:36:20.877784Z",
           "done": true,
           "done_reason": "stop",
-          "total_duration": 33982453650,
-          "load_duration": 2909001805,
+          "total_duration": 612057417,
+          "load_duration": 97443583,
           "prompt_eval_count": 341,
-          "prompt_eval_duration": 29194357307,
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-          "eval_duration": 1878247732,
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           "thinking": null,
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tests/integration/recordings/responses/563b994bb7d1.json

@@ -20,15 +20,15 @@
       "__type__": "ollama._types.GenerateResponse",
       "__data__": {
         "model": "llama3.2:3b-instruct-fp16",
-        "created_at": "2025-08-04T22:55:13.25248Z",
+        "created_at": "2025-09-03T17:36:19.594923Z",
         "done": true,
         "done_reason": "stop",
-        "total_duration": 1344654917,
-        "load_duration": 200585375,
+        "total_duration": 988472417,
+        "load_duration": 117976625,
         "prompt_eval_count": 326,
-        "prompt_eval_duration": 564000000,
+        "prompt_eval_duration": 451625542,
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-        "eval_duration": 578000000,
+        "eval_duration": 418313417,
         "response": "[get_weather(location=\"San Francisco, CA\")]",
         "thinking": null,
         "context": null

tests/integration/recordings/responses/565b1072cb9d.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": "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": "meta-llama/Llama-3.3-70B-Instruct-Turbo-Free"
+  },
+  "response": {
+    "body": {
+      "__type__": "openai.types.completion.Completion",
+      "__data__": {
+        "id": "oBUswCe-62bZhn-98019f663cac0f68",
+        "choices": [
+          {
+            "finish_reason": "stop",
+            "index": 0,
+            "logprobs": null,
+            "text": " _______________________. \n\n## Step 1: Identify the traditional completion of the sentence.\nThe traditional completion of the sentence \"Roses are red, violets are...\" is based on a well-known poem.\n\n## Step 2: Recall the poem.\nThe poem states, \"Roses are red, violets are blue...\"\n\n## Step 3: Determine the word that completes the sentence.\nBased on the poem, the word that completes the sentence is \"blue\".\n\nThe final answer is: $\\boxed{blue}$",
+            "seed": 4892505926413923000
+          }
+        ],
+        "created": 1758038908,
+        "model": "meta-llama/Llama-3.3-70B-Instruct-Turbo-Free",
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+          "total_tokens": 131,
+          "completion_tokens_details": null,
+          "prompt_tokens_details": null,
+          "cached_tokens": 0
+        },
+        "prompt": []
+      }
+    },
+    "is_streaming": false
+  }
+}

tests/integration/recordings/responses/57b67d1b1a36.json

@@ -0,0 +1,71 @@
+{
+  "request": {
+    "method": "POST",
+    "url": "https://shan-mfbb618r-eastus2.cognitiveservices.azure.com/openai/v1/v1/chat/completions",
+    "headers": {},
+    "body": {
+      "model": "gpt-5-mini",
+      "messages": [
+        {
+          "role": "user",
+          "content": "Which planet has rings around it with a name starting with letter S?"
+        }
+      ],
+      "stream": false
+    },
+    "endpoint": "/v1/chat/completions",
+    "model": "gpt-5-mini"
+  },
+  "response": {
+    "body": {
+      "__type__": "openai.types.chat.chat_completion.ChatCompletion",
+      "__data__": {
+        "id": "chatcmpl-CECIkT5cbqFazpungtewksVePcUNa",
+        "choices": [
+          {
+            "finish_reason": "stop",
+            "index": 0,
+            "logprobs": null,
+            "message": {
+              "content": "Saturn. It's the planet famous for its prominent ring system made of ice and rock.",
+              "refusal": null,
+              "role": "assistant",
+              "annotations": [],
+              "audio": null,
+              "function_call": null,
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+            "content_filter_results": {}
+          }
+        ],
+        "created": 1757499914,
+        "model": "gpt-5-mini-2025-08-07",
+        "object": "chat.completion",
+        "service_tier": null,
+        "system_fingerprint": null,
+        "usage": {
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+          "prompt_tokens": 20,
+          "total_tokens": 176,
+          "completion_tokens_details": {
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+            "audio_tokens": 0,
+            "reasoning_tokens": 128,
+            "rejected_prediction_tokens": 0
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+        "prompt_filter_results": [
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+            "content_filter_results": {}
+          }
+        ]
+      }
+    },
+    "is_streaming": false
+  }
+}

tests/integration/recordings/responses/58c8091104ff.json

@@ -1,33 +1,33 @@
 {
   "request": {
     "method": "POST",
-    "url": "http://localhost:11434/v1/v1/completions",
+    "url": "http://0.0.0.0:11434/v1/v1/chat/completions",
     "headers": {},
     "body": {
       "model": "llama3.2:3b-instruct-fp16",
       "messages": [
         {
           "role": "user",
-          "content": "Test trace openai 1"
+          "content": "Hello"
         }
       ],
-      "stream": false
+      "max_tokens": 10
     },
-    "endpoint": "/v1/completions",
+    "endpoint": "/v1/chat/completions",
     "model": "llama3.2:3b-instruct-fp16"
   },
   "response": {
     "body": {
       "__type__": "openai.types.chat.chat_completion.ChatCompletion",
       "__data__": {
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+        "id": "chatcmpl-981",
         "choices": [
           {
-            "finish_reason": "stop",
+            "finish_reason": "length",
             "index": 0,
             "logprobs": null,
             "message": {
-              "content": "I don't have information on testing \"OpenAI\" as a product has not been released.",
+              "content": "Hello! It's nice to meet you. Is",
               "refusal": null,
               "role": "assistant",
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@@ -37,15 +37,15 @@
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+          "prompt_tokens": 26,
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         }

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

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+{
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+    "url": "http://0.0.0.0:11434/v1/v1/embeddings",
+    "headers": {},
+    "body": {
+      "model": "all-minilm:l6-v2",
+      "input": "This is completely different content",
+      "encoding_format": "float"
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+    "endpoint": "/v1/embeddings",
+    "model": "all-minilm:l6-v2"
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+  "response": {
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tests/integration/recordings/responses/5f5d16afadb4.json

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

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tests/integration/recordings/responses/6730dcde0b73.json

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

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tests/integration/recordings/responses/6857b19d3f0a.json

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

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+}

tests/integration/recordings/responses/6906a6e71988.json

@@ -20,15 +20,15 @@
       "__type__": "ollama._types.GenerateResponse",
       "__data__": {
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+        "created_at": "2025-09-03T17:38:00.98692Z",
         "done": true,
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-        "total_duration": 488566500,
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+        "load_duration": 90611333,
         "prompt_eval_count": 317,
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         "eval_count": 2,
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         "response": "safe",
         "thinking": null,
         "context": null