1259 commits

Author	SHA1	Message	Date
Sébastien Han	9c8be89fb6	chore: bump python supported version to 3.12 (#2475) ... # What does this PR do? The project now supports Python >= 3.12 Signed-off-by: Sébastien Han <seb@redhat.com>	2025-06-24 09:22:04 +05:30
Rohan Awhad	d797f9aec1	fix: #2495 FileNotFound Err in container image (#2498) ... # What does this PR do? Closes #2495 Changes: - Delay the `COPY run.yaml` into docker image step until after external provider handling - Split the check for `external_providers_dir` into “non-empty” and “directory exists" ## Test Plan 0. Create and Activate venv 1. Create a `simple_build.yaml` ```yaml version: '2' distribution_spec: providers: inference: - remote::openai image_type: container image_name: openai-stack ``` 2. Run llama stack build: ```bash llama stack build --config simple_build.yaml ``` 3. Run the docker container: ```bash docker run \ -p 8321:8321 \ -e OPENAI_API_KEY=$OPENAI_API_KEY \ openai_stack:0.2.12 ``` This should show server is running. ``` INFO 2025-06-23 19:07:57,832 llama_stack.distribution.distribution:151 core: Loading external providers from /.llama/providers.d INFO 2025-06-23 19:07:59,324 __main__:572 server: Listening on ['::', '0.0.0.0']:8321 INFO: Started server process [1] INFO: Waiting for application startup. INFO 2025-06-23 19:07:59,336 __main__:156 server: Starting up INFO: Application startup complete. INFO: Uvicorn running on http://['::', '0.0.0.0']:8321 (Press CTRL+C to quit) ``` Notice the first line: ``` Loading external providers from /.llama/providers.d ``` This is expected behaviour. Co-authored-by: Rohan Awhad <rawhad@redhat.com>	2025-06-24 09:08:08 +05:30
ehhuang	23b7dc7b37	fix: stack build (#2485) ... # What does this PR do? probably related to 3.11 upgrade ^^^^ File "/opt/homebrew/Caskroom/miniconda/base/envs/myenv/lib/python3.11/site-packages/termcolor/termcolor.py", line 147, in colored text = fmt_str % (COLORS[color], text) ~~~~~~^^^^^^^ KeyError: 'light_blue' ## Test Plan	2025-06-20 15:15:43 -07:00
github-actions[bot]	d70573bd47	build: Bump version to 0.2.12	2025-06-20 21:06:17 +00:00
ehhuang	d3b60507d7	feat: support auth attributes in inference/responses stores (#2389) ... # What does this PR do? Inference/Response stores now store user attributes when inserting, and respects them when fetching. ## Test Plan pytest tests/unit/utils/test_sqlstore.py	2025-06-20 10:24:45 -07:00
ehhuang	6832e8a658	feat: remove score_threshold constraint (#2479) ... # What does this PR do? See inline comment. fixes test _ test_openai_vector_store_search_with_high_score_filter[llama_stack_client-meta-llama/Llama-3.3-70B-Instruct-meta-llama/Llama-4-Scout-17B-16E-Instruct-all-MiniLM-L6-v2-None-None] _ llama-stack/llama_stack/distribution/library_client.py:98: in convert_to_pydantic return TypeAdapter(annotation).validate_python(value) .venv/lib/python3.10/site-packages/pydantic/type_adapter.py:421: in validate_python return self.validator.validate_python( E pydantic_core._pydantic_core.ValidationError: 1 validation error for nullable[SearchRankingOptions] E score_threshold E Input should be less than or equal to 1 [type=less_than_equal, input_value=1.3458905661753127, input_type=float] E For further information visit https://errors.pydantic.dev/2.11/v/less_than_equal The above exception was the direct cause of the following exception: llama-stack/tests/integration/vector_io/test_openai_vector_stores.py:376: in test_openai_vector_store_search_with_high_score_filter search_response = compat_client.vector_stores.search( .venv/lib/python3.10/site-packages/llama_stack_client/resources/vector_stores/vector_stores.py:356: in search return self._post( .venv/lib/python3.10/site-packages/llama_stack_client/_base_client.py:1232: in post return cast(ResponseT, self.request(cast_to, opts, stream=stream, stream_cls=stream_cls)) llama-stack/llama_stack/distribution/library_client.py:177: in request result = loop.run_until_complete(self.async_client.request(*args, **kwargs)) /opt/hostedtoolcache/Python/3.10.18/x64/lib/python3.10/asyncio/base_events.py:649: in run_until_complete return future.result() llama-stack/llama_stack/distribution/library_client.py:292: in request response = await self._call_non_streaming( llama-stack/llama_stack/distribution/library_client.py:313: in _call_non_streaming body = self._convert_body(path, options.method, body) llama-stack/llama_stack/distribution/library_client.py:425: in _convert_body converted_body[param_name] = convert_to_pydantic(param.annotation, value) llama-stack/llama_stack/distribution/library_client.py:112: in convert_to_pydantic raise ValueError(f"Failed to convert parameter {value} into {annotation}: {e}") from e E ValueError: Failed to convert parameter {'score_threshold': 1.3458905661753127} into llama_stack.apis.vector_io.vector_io.SearchRankingOptions | None: 1 validation error for nullable[SearchRankingOptions] E score_threshold E Input should be less than or equal to 1 [type=less_than_equal, input_value=1.3458905661753127, input_type=float] E For further information visit https://errors.pydantic.dev/2.11/v/less_than_equal ## Test Plan	2025-06-20 09:17:42 +05:30
Eran Cohen	747e594680	feat: expand set of known gemini models (#2471) ... feat: Add Gemini 2.0 and 2.5 models This commit expands the set of known Gemini models by introducing: - `gemini/gemini-2.0-flash` - `gemini/gemini-2.5-flash` - `gemini/gemini-2.5-pro` These new models are added to `LLM_MODEL_IDS` for broader compatibility and updated in `run.yaml` to allow for their immediate use in starter configurations. Signed-off-by: Eran Cohen <eranco@redhat.com>	2025-06-19 12:19:37 -04:00
Ben Browning	f394c7f2d9	feat: Add missing Vector Store Files API surface (#2468) ... # What does this PR do? This adds the ability to list, retrieve, update, and delete Vector Store Files. It implements these new APIs for the faiss and sqlite-vec providers, since those are the two that also have the rest of the vector store files implementation. Closes #2445 ## Test Plan ### test_openai_vector_stores Integration Tests There are a number of new integration tests added, which I ran for each provider as outlined below. faiss (from ollama distro): ``` INFERENCE_MODEL="meta-llama/Llama-3.2-3B-Instruct" \ llama stack run llama_stack/templates/ollama/run.yaml LLAMA_STACK_CONFIG=http://localhost:8321 \ pytest -sv tests/integration/vector_io/test_openai_vector_stores.py \ --embedding-model=all-MiniLM-L6-v2 ``` sqlite-vec (from starter distro): ``` llama stack run llama_stack/templates/starter/run.yaml LLAMA_STACK_CONFIG=http://localhost:8321 \ pytest -sv tests/integration/vector_io/test_openai_vector_stores.py \ --embedding-model=all-MiniLM-L6-v2 ``` ### file_search verification tests I also ensured the file_search verification tests continue to work, both for faiss and sqlite-vec. faiss (ollama distro): ``` INFERENCE_MODEL="meta-llama/Llama-3.2-3B-Instruct" \ llama stack run llama_stack/templates/ollama/run.yaml pytest -sv tests/verifications/openai_api/test_responses.py \ -k'file_search' \ --base-url=http://localhost:8321/v1/openai/v1 \ --model=meta-llama/Llama-3.2-3B-Instruct ``` sqlite-vec (starter distro): ``` llama stack run llama_stack/templates/starter/run.yaml pytest -sv tests/verifications/openai_api/test_responses.py \ -k'file_search' \ --base-url=http://localhost:8321/v1/openai/v1 \ --model=together/meta-llama/Llama-3.2-3B-Instruct-Turbo ``` --------- Signed-off-by: Ben Browning <bbrownin@redhat.com>	2025-06-19 11:08:24 -04:00
Ihar Hrachyshka	a2f054607d	fix: cancel scheduler tasks on shutdown (#2130) ... # What does this PR do? Scheduler: cancel tasks on shutdown. Otherwise the currently running tasks will never exit (before they actually complete), which means the process can't be properly shut down (only with SIGKILL). Ideally, we let tasks know that they are about to shutdown and give them some time to do so; but in the lack of the mechanism, it's better to cancel than linger forever. [//]: # (If resolving an issue, uncomment and update the line below) [//]: # (Closes #[issue-number]) ## Test Plan Start a long running task (e.g. torchtune or external kfp-provider training). Ctr-C the process in TTY. Confirm it exits in reasonable time. ``` ^CINFO: Shutting down INFO: Waiting for application shutdown. 13:32:26.187 - INFO - Shutting down 13:32:26.187 - INFO - Shutting down DatasetsRoutingTable 13:32:26.187 - INFO - Shutting down DatasetIORouter 13:32:26.187 - INFO - Shutting down TorchtuneKFPPostTrainingImpl Traceback (most recent call last): File "/opt/homebrew/Cellar/python@3.12/3.12.4/Frameworks/Python.framework/Versions/3.12/lib/python3.12/asyncio/runners.py", line 118, in run return self._loop.run_until_complete(task) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/opt/homebrew/Cellar/python@3.12/3.12.4/Frameworks/Python.framework/Versions/3.12/lib/python3.12/asyncio/base_events.py", line 687, in run_until_complete return future.result() ^^^^^^^^^^^^^^^ asyncio.exceptions.CancelledError During handling of the above exception, another exception occurred: Traceback (most recent call last): File "<frozen runpy>", line 198, in _run_module_as_main File "<frozen runpy>", line 88, in _run_code File "/Users/ihrachys/src/llama-stack-provider-kfp-trainer/.venv/lib/python3.12/site-packages/kfp/dsl/executor_main.py", line 109, in <module> executor_main() File "/Users/ihrachys/src/llama-stack-provider-kfp-trainer/.venv/lib/python3.12/site-packages/kfp/dsl/executor_main.py", line 101, in executor_main output_file = executor.execute() ^^^^^^^^^^^^^^^^^^ File "/Users/ihrachys/src/llama-stack-provider-kfp-trainer/.venv/lib/python3.12/site-packages/kfp/dsl/executor.py", line 361, in execute result = self.func(**func_kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^ File "/var/folders/45/1q1rx6cn7jbcn2ty852w0g_r0000gn/T/tmp.RKpPrvTWDD/ephemeral_component.py", line 118, in component asyncio.run(recipe.setup()) File "/opt/homebrew/Cellar/python@3.12/3.12.4/Frameworks/Python.framework/Versions/3.12/lib/python3.12/asyncio/runners.py", line 194, in run return runner.run(main) ^^^^^^^^^^^^^^^^ File "/opt/homebrew/Cellar/python@3.12/3.12.4/Frameworks/Python.framework/Versions/3.12/lib/python3.12/asyncio/runners.py", line 123, in run raise KeyboardInterrupt() KeyboardInterrupt 13:32:31.219 - ERROR - Task 'component' finished with status FAILURE ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ INFO 2025-05-09 13:32:31,221 llama_stack.providers.utils.scheduler:221 scheduler: Job test-jobc3c2e1e4-859c-4852-a41d-ef29e55e3efa: Pipeline [1m[95m'test-jobc3c2e1e4-859c-4852-a41d-ef29e55e3efa'[1m[0m finished with status [1m[91mFAILURE[1m[0m. Inner task failed: [1m[96m'component'[1m[0m. ERROR 2025-05-09 13:32:31,223 llama_stack_provider_kfp_trainer.scheduler:54 scheduler: Job test-jobc3c2e1e4-859c-4852-a41d-ef29e55e3efa failed. ╭───────────────────────────────────── Traceback (most recent call last) ─────────────────────────────────────╮ │ /Users/ihrachys/src/llama-stack-provider-kfp-trainer/src/llama_stack_provider_kfp_trainer/scheduler.py:45 │ │ in do │ │ │ │ 42 │ │ │ │ │ 43 │ │ │ job.status = JobStatus.running │ │ 44 │ │ │ try: │ │ ❱ 45 │ │ │ │ artifacts = self._to_artifacts(job.handler().output) │ │ 46 │ │ │ │ for artifact in artifacts: │ │ 47 │ │ │ │ │ on_artifact_collected_cb(artifact) │ │ 48 │ │ │ │ /Users/ihrachys/src/llama-stack-provider-kfp-trainer/.venv/lib/python3.12/site-packages/kfp/dsl/base_compon │ │ ent.py:101 in __call__ │ │ │ │ 98 │ │ │ │ f'{self.name}() missing {len(missing_arguments)} required ' │ │ 99 │ │ │ │ f'{argument_or_arguments}: {arguments}.') │ │ 100 │ │ │ │ ❱ 101 │ │ return pipeline_task.PipelineTask( │ │ 102 │ │ │ component_spec=self.component_spec, │ │ 103 │ │ │ args=task_inputs, │ │ 104 │ │ │ execute_locally=pipeline_context.Pipeline.get_default_pipeline() is │ │ │ │ /Users/ihrachys/src/llama-stack-provider-kfp-trainer/.venv/lib/python3.12/site-packages/kfp/dsl/pipeline_ta │ │ sk.py:187 in __init__ │ │ │ │ 184 │ │ ]) │ │ 185 │ │ │ │ 186 │ │ if execute_locally: │ │ ❱ 187 │ │ │ self._execute_locally(args=args) │ │ 188 │ │ │ 189 │ def _execute_locally(self, args: Dict[str, Any]) -> None: │ │ 190 │ │ """Execute the pipeline task locally. │ │ │ │ /Users/ihrachys/src/llama-stack-provider-kfp-trainer/.venv/lib/python3.12/site-packages/kfp/dsl/pipeline_ta │ │ sk.py:197 in _execute_locally │ │ │ │ 194 │ │ from kfp.local import task_dispatcher │ │ 195 │ │ │ │ 196 │ │ if self.pipeline_spec is not None: │ │ ❱ 197 │ │ │ self._outputs = pipeline_orchestrator.run_local_pipeline( │ │ 198 │ │ │ │ pipeline_spec=self.pipeline_spec, │ │ 199 │ │ │ │ arguments=args, │ │ 200 │ │ │ ) │ │ │ │ /Users/ihrachys/src/llama-stack-provider-kfp-trainer/.venv/lib/python3.12/site-packages/kfp/local/pipeline_ │ │ orchestrator.py:43 in run_local_pipeline │ │ │ │ 40 │ │ │ 41 │ # validate and access all global state in this function, not downstream │ │ 42 │ config.LocalExecutionConfig.validate() │ │ ❱ 43 │ return _run_local_pipeline_implementation( │ │ 44 │ │ pipeline_spec=pipeline_spec, │ │ 45 │ │ arguments=arguments, │ │ 46 │ │ raise_on_error=config.LocalExecutionConfig.instance.raise_on_error, │ │ │ │ /Users/ihrachys/src/llama-stack-provider-kfp-trainer/.venv/lib/python3.12/site-packages/kfp/local/pipeline_ │ │ orchestrator.py:108 in _run_local_pipeline_implementation │ │ │ │ 105 │ │ │ ) │ │ 106 │ │ return outputs │ │ 107 │ elif dag_status == status.Status.FAILURE: │ │ ❱ 108 │ │ log_and_maybe_raise_for_failure( │ │ 109 │ │ │ pipeline_name=pipeline_name, │ │ 110 │ │ │ fail_stack=fail_stack, │ │ 111 │ │ │ raise_on_error=raise_on_error, │ │ │ │ /Users/ihrachys/src/llama-stack-provider-kfp-trainer/.venv/lib/python3.12/site-packages/kfp/local/pipeline_ │ │ orchestrator.py:137 in log_and_maybe_raise_for_failure │ │ │ │ 134 │ │ logging_utils.format_task_name(task_name) for task_name in fail_stack) │ │ 135 │ msg = f'Pipeline {pipeline_name_with_color} finished with status │ │ {status_with_color}. Inner task failed: {task_chain_with_color}.' │ │ 136 │ if raise_on_error: │ │ ❱ 137 │ │ raise RuntimeError(msg) │ │ 138 │ with logging_utils.local_logger_context(): │ │ 139 │ │ logging.error(msg) │ │ 140 │ ╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────╯ RuntimeError: Pipeline [1m[95m'test-jobc3c2e1e4-859c-4852-a41d-ef29e55e3efa'[1m[0m finished with status [1m[91mFAILURE[1m[0m. Inner task failed: [1m[96m'component'[1m[0m. INFO 2025-05-09 13:32:31,266 llama_stack.distribution.server.server:136 server: Shutting down DistributionInspectImpl INFO 2025-05-09 13:32:31,266 llama_stack.distribution.server.server:136 server: Shutting down ProviderImpl INFO: Application shutdown complete. INFO: Finished server process [26648] ``` [//]: # (## Documentation) Signed-off-by: Ihar Hrachyshka <ihar.hrachyshka@gmail.com>	2025-06-19 17:01:33 +02:00
Sébastien Han	c20388c424	ci: add python package build test (#2457) ... # What does this PR do? We now test a package build on every PRs. Closes: https://github.com/meta-llama/llama-stack/issues/2406 Signed-off-by: Sébastien Han <seb@redhat.com>	2025-06-19 18:57:32 +05:30
Sébastien Han	fa1d986f72	fix: remove asyncio.TimeoutError since Python update (#2476) ... # What does this PR do? Since we now support Pythong starting from 3.11, this is not needed anymore. Signed-off-by: Sébastien Han <seb@redhat.com>	2025-06-19 18:52:41 +05:30
Charlie Doern	d12f195f56	feat: drop python 3.10 support (#2469) ... # What does this PR do? dropped python3.10, updated pyproject and dependencies, and also removed some blocks of code with special handling for enum.StrEnum Closes #2458 Signed-off-by: Charlie Doern <cdoern@redhat.com>	2025-06-19 12:07:14 +05:30
ehhuang	db2cd9e8f3	feat: support filters in file search (#2472) ... # What does this PR do? Move to use vector_stores.search for file search tool in Responses, which supports filters. closes #2435 ## Test Plan Added e2e test with fitlers. myenv ❯ llama stack run llama_stack/templates/fireworks/run.yaml pytest -sv tests/verifications/openai_api/test_responses.py \ -k 'file_search and filters' \ --base-url=http://localhost:8321/v1/openai/v1 \ --model=meta-llama/Llama-3.3-70B-Instruct	2025-06-18 21:50:55 -07:00
ehhuang	e6bfc717cb	feat(ui): add infinite scroll pagination to chat completions/responses logs table (#2466) ... ## Summary: This commit adds infinite scroll pagination to the chat completions and responses tables. ## Test Plan: 1. Run unit tests: npm run test 2. Manual testing: Navigate to chat completions/responses pages 3. Verify infinite scroll triggers when approaching bottom 4. Added playwright tests: npm run test:e2e	2025-06-18 15:28:39 -07:00
Sumit Jaiswal	90d03552d4	feat: To add health check for faiss inline vector_io provider (#2319) ... # What does this PR do? <!-- Provide a short summary of what this PR does and why. Link to relevant issues if applicable. --> To add health check for faiss inline vector_io provider. I tried adding `async def health(self) -> HealthResponse:` like in inference provider, but it didn't worked for `inline->vector_io->faiss` provider. And via debug logs, I understood the critical issue, that the health responses are being stored with the API name as the key, not as a nested dictionary with provider IDs. This means that all providers of the same API type (e.g., "vector_io") will share the same health response, and only the last one processed will be visible in the API response. I've created a patch file that fixes this issue by: - Storing the original get_providers_health method - Creating a patched version that correctly maps health responses to providers - Applying the patch to the `ProviderImpl` class Not an expert, so please let me know, if there can be any other workaround using which I can get the health status updated directly from `faiss.py`. <!-- If resolving an issue, uncomment and update the line below --> <!-- Closes #[issue-number] --> ## Test Plan <!-- Describe the tests you ran to verify your changes with result summaries. *Provide clear instructions so the plan can be easily re-executed.* --> Added unit tests to test the provider patch implementation in the PR. Adding a screenshot with the FAISS inline vector_io health status as "OK" 	2025-06-18 17:56:25 +02:00
github-actions[bot]	7d812e3bf0	build: Bump version to 0.2.11	2025-06-17 19:08:17 +00:00
Hardik Shah	822307e6d5	fix: Do not throw when listing vector stores (#2460) ... When trying to `list` vector_stores , if we cannot retrieve one, log an error and return all the ones that are valid. ### Test Plan ``` pytest -sv --stack-config=http://localhost:8321 tests/integration/vector_io/test_openai_vector_stores.py --embedding-model all-MiniLM-L6-v2 ``` Also tested for `--stack-config fireworks`	2025-06-17 11:19:43 -07:00
Ben Browning	94fcfb5674	fix: broken links on nvidia distro docs when rendered (#2446) ... # What does this PR do? The Nvidia distribution docs had some broken links when viewing the rendered docs site, where the deep links they were attempting into our code on GitHub weren't actually getting users to the intended destination. This updates those links to use the `{repopath}` helper we use elsewhere to generate valid deep links into the Llama Stack repository. ## Test Plan I generated the site locally after this change and ensured the links now resolve to their intended destination. Signed-off-by: Ben Browning <bbrownin@redhat.com>	2025-06-17 13:02:13 +05:30
ehhuang	15f630e5da	feat: support pagination in inference/responses stores (#2397) ... # What does this PR do? ## Test Plan added unit tests	2025-06-16 22:43:35 -07:00
Varsha	6f1a935365	chore: Add OpenAI compatiblity for vLLM embeddings (#2448) ... # What does this PR do? - Implement OpenAI-compatible embeddings endpoint in vLLM provider - Support both float and base64 encoding formats - Add proper error handling and response formatting <!-- If resolving an issue, uncomment and update the line below --> Closes #2447 ## Test Plan <!-- Describe the tests you ran to verify your changes with result summaries. *Provide clear instructions so the plan can be easily re-executed.* --> Signed-off-by: Varsha Prasad Narsing <varshaprasad96@gmail.com>	2025-06-16 19:06:05 -04:00
Jash Gulabrai	40e2c97915	feat: Add Nvidia e2e beginner notebook and tool calling notebook (#1964) ... # What does this PR do? This PR contains two sets of notebooks that serve as reference material for developers getting started with Llama Stack using the NVIDIA Provider. Developers should be able to execute these notebooks end-to-end, pointing to their NeMo Microservices deployment. 1. `beginner_e2e/`: Notebook that walks through a beginner end-to-end workflow that covers creating datasets, running inference, customizing and evaluating models, and running safety checks. 2. `tool_calling/`: Notebook that is ported over from the [Data Flywheel & Tool Calling notebook](https://github.com/NVIDIA/GenerativeAIExamples/tree/main/nemo/data-flywheel) that is referenced in the NeMo Microservices docs. I updated the notebook to use the Llama Stack client wherever possible, and added relevant instructions. [//]: # (If resolving an issue, uncomment and update the line below) [//]: # (Closes #[issue-number]) ## Test Plan - Both notebook folders contain READMEs with pre-requisites. To manually test these notebooks, you'll need to have a deployment of the NeMo Microservices Platform and update the `config.py` file with your deployment's information. - I've run through these notebooks manually end-to-end to verify each step works. [//]: # (## Documentation) --------- Co-authored-by: Jash Gulabrai <jgulabrai@nvidia.com>	2025-06-16 11:29:01 -04:00
Rohan Awhad	436c7aa751	feat: Add url field to PaginatedResponse and populate it using route … (#2419) ... …path # What does this PR do? Closes #1847 Changes: - llama_stack/apis/common/responses.py: adds optional `url` field to PaginatedResponse - llama_stack/distribution/server/server.py: automatically populate the URL field with route path ## Test Plan - Built and ran llama stack server using the following cmds: ```bash export INFERENCE_MODEL=llama3.1:8b llama stack build --run --template ollama --image-type container llama stack run llama_stack/templates/ollama/run.yaml ``` - Ran `curl` to test if we are seeing the `url` param in response: ```bash curl -X 'GET' \ 'http://localhost:8321/v1/agents' \ -H 'accept: application/json' ``` - Expected and Received Output: `{"data":[],"has_more":false,"url":"/v1/agents"}` --------- Co-authored-by: Rohan Awhad <rawhad@redhat.com>	2025-06-16 11:19:48 +02:00
Hardik Shah	985d0b156c	feat: Add suffix to openai_completions (#2449) ... For code completion apps need "fill in the middle" capabilities. Added option of `suffix` to `openai_completion` to enable this. Updated ollama provider to showcase the same. ### Test Plan ``` pytest -sv --stack-config="inference=ollama" tests/integration/inference/test_openai_completion.py --text-model qwen2.5-coder:1.5b -k test_openai_completion_non_streaming_suffix ``` ### OpenAI Sample script ``` from openai import OpenAI client = OpenAI(base_url="http://localhost:8321/v1/openai/v1") response = client.completions.create( model="qwen2.5-coder:1.5b", prompt="The capital of ", suffix="is Paris.", max_tokens=10, ) print(response.choices[0].text) ``` ### Output ``` France is ____. To answer this question, we ```	2025-06-13 16:06:06 -07:00
Varsha	2e8054bede	feat: Implement hybrid search in SQLite-vec (#2312) ... # What does this PR do? Add support for hybrid search mode in SQLite-vec provider, which combines keyword and vector search for better results. The implementation: - Adds hybrid search mode as a new option alongside vector and keyword search - Implements query_hybrid method in SQLiteVecIndex that: - First performs keyword search to get candidate matches - Then applies vector similarity search on those candidates - Updates documentation to reflect the new search mode This change improves search quality by leveraging both semantic similarity and keyword matching, while maintaining backward compatibility with existing vector and keyword search modes. ## Test Plan ``` pytest tests/unit/providers/vector_io/test_sqlite_vec.py -v -s --tb=short /Users/vnarsing/miniconda3/envs/stack-client/lib/python3.10/site-packages/pytest_asyncio/plugin.py:217: PytestDeprecationWarning: The configuration option "asyncio_default_fixture_loop_scope" is unset. The event loop scope for asynchronous fixtures will default to the fixture caching scope. Future versions of pytest-asyncio will default the loop scope for asynchronous fixtures to function scope. Set the default fixture loop scope explicitly in order to avoid unexpected behavior in the future. Valid fixture loop scopes are: "function", "class", "module", "package", "session" warnings.warn(PytestDeprecationWarning(_DEFAULT_FIXTURE_LOOP_SCOPE_UNSET)) =============================================================================================== test session starts =============================================================================================== platform darwin -- Python 3.10.16, pytest-8.3.5, pluggy-1.5.0 -- /Users/vnarsing/miniconda3/envs/stack-client/bin/python cachedir: .pytest_cache metadata: {'Python': '3.10.16', 'Platform': 'macOS-14.7.6-arm64-arm-64bit', 'Packages': {'pytest': '8.3.5', 'pluggy': '1.5.0'}, 'Plugins': {'html': '4.1.1', 'json-report': '1.5.0', 'timeout': '2.4.0', 'metadata': '3.1.1', 'anyio': '4.8.0', 'asyncio': '0.26.0', 'nbval': '0.11.0', 'cov': '6.1.1'}} rootdir: /Users/vnarsing/go/src/github/meta-llama/llama-stack configfile: pyproject.toml plugins: html-4.1.1, json-report-1.5.0, timeout-2.4.0, metadata-3.1.1, anyio-4.8.0, asyncio-0.26.0, nbval-0.11.0, cov-6.1.1 asyncio: mode=strict, asyncio_default_fixture_loop_scope=None, asyncio_default_test_loop_scope=function collected 10 items tests/unit/providers/vector_io/test_sqlite_vec.py::test_add_chunks PASSED tests/unit/providers/vector_io/test_sqlite_vec.py::test_query_chunks_vector PASSED tests/unit/providers/vector_io/test_sqlite_vec.py::test_query_chunks_full_text_search PASSED tests/unit/providers/vector_io/test_sqlite_vec.py::test_query_chunks_hybrid PASSED tests/unit/providers/vector_io/test_sqlite_vec.py::test_query_chunks_full_text_search_k_greater_than_results PASSED tests/unit/providers/vector_io/test_sqlite_vec.py::test_chunk_id_conflict PASSED tests/unit/providers/vector_io/test_sqlite_vec.py::test_generate_chunk_id PASSED tests/unit/providers/vector_io/test_sqlite_vec.py::test_query_chunks_hybrid_no_keyword_matches PASSED tests/unit/providers/vector_io/test_sqlite_vec.py::test_query_chunks_hybrid_score_threshold PASSED tests/unit/providers/vector_io/test_sqlite_vec.py::test_query_chunks_hybrid_different_embedding PASSED ``` --------- Signed-off-by: Varsha Prasad Narsing <varshaprasad96@gmail.com>	2025-06-13 15:54:06 -04:00
Ben Browning	941f505eb0	feat: File search tool for Responses API (#2426) ... # What does this PR do? This is an initial working prototype of wiring up the `file_search` builtin tool for the Responses API to our existing rag knowledge search tool. This is me seeing what I could pull together on top of the bits we already have merged. This may not be the ideal way to implement this, and things like how I shuffle the vector store ids from the original response API tool request to the actual tool execution feel a bit hacky (grep for `tool_kwargs["vector_db_ids"]` in `_execute_tool_call` to see what I mean). ## Test Plan I stubbed in some new tests to exercise this using text and pdf documents. Note that this is currently under tests/verification only because it sometimes flakes with tool calling of the small Llama-3.2-3B model we run in CI (and that I use as an example below). We'd want to make the test a bit more robust in some way if we moved this over to tests/integration and ran it in CI. ### OpenAI SaaS (to verify test correctness) ``` pytest -sv tests/verifications/openai_api/test_responses.py \ -k 'file_search' \ --base-url=https://api.openai.com/v1 \ --model=gpt-4o ``` ### Fireworks with faiss vector store ``` llama stack run llama_stack/templates/fireworks/run.yaml pytest -sv tests/verifications/openai_api/test_responses.py \ -k 'file_search' \ --base-url=http://localhost:8321/v1/openai/v1 \ --model=meta-llama/Llama-3.3-70B-Instruct ``` ### Ollama with faiss vector store This sometimes flakes on Ollama because the quantized small model doesn't always choose to call the tool to answer the user's question. But, it often works. ``` ollama run llama3.2:3b INFERENCE_MODEL="meta-llama/Llama-3.2-3B-Instruct" \ llama stack run ./llama_stack/templates/ollama/run.yaml \ --image-type venv \ --env OLLAMA_URL="http://0.0.0.0:11434" pytest -sv tests/verifications/openai_api/test_responses.py \ -k'file_search' \ --base-url=http://localhost:8321/v1/openai/v1 \ --model=meta-llama/Llama-3.2-3B-Instruct ``` ### OpenAI provider with sqlite-vec vector store ``` llama stack run ./llama_stack/templates/starter/run.yaml --image-type venv pytest -sv tests/verifications/openai_api/test_responses.py \ -k 'file_search' \ --base-url=http://localhost:8321/v1/openai/v1 \ --model=openai/gpt-4o-mini ``` ### Ensure existing vector store integration tests still pass ``` ollama run llama3.2:3b INFERENCE_MODEL="meta-llama/Llama-3.2-3B-Instruct" \ llama stack run ./llama_stack/templates/ollama/run.yaml \ --image-type venv \ --env OLLAMA_URL="http://0.0.0.0:11434" LLAMA_STACK_CONFIG=http://localhost:8321 \ pytest -sv tests/integration/vector_io \ --text-model "meta-llama/Llama-3.2-3B-Instruct" \ --embedding-model=all-MiniLM-L6-v2 ``` --------- Signed-off-by: Ben Browning <bbrownin@redhat.com>	2025-06-13 14:32:48 -04:00
Francisco Arceo	554ada57b0	chore: Add OpenAI compatibility for Ollama embeddings (#2440) ... # What does this PR do? This PR adds OpenAI compatibility for Ollama embeddings. Closes https://github.com/meta-llama/llama-stack/issues/2428 Summary of changes: - `llama_stack/providers/remote/inference/ollama/ollama.py` - Implements the OpenAI embeddings endpoint for Ollama, replacing the NotImplementedError with a full function that validates the model, prepares parameters, calls the client, encodes embedding data (optionally in base64), and returns a correctly structured response. - Updates import statements to include the new embedding response utilities. - `llama_stack/providers/utils/inference/litellm_openai_mixin.py` - Refactors the embedding data encoding logic to use a new shared utility (`b64_encode_openai_embeddings_response`) instead of inline base64 encoding and packing logic. - Cleans up imports accordingly. - `llama_stack/providers/utils/inference/openai_compat.py` - Adds `b64_encode_openai_embeddings_response` to handle encoding OpenAI embedding outputs (including base64 support) in a reusable way. - Adds `prepare_openai_embeddings_params` utility for standardizing embedding parameter preparation. - Updates imports to include the new embedding data class. - `tests/integration/inference/test_openai_embeddings.py` - Removes `"remote::ollama"` from the list of providers that skip OpenAI embeddings tests, since support is now implemented. ## Note There was one minor issue, which required me to override the `OpenAIEmbeddingsResponse.model` name with `self._get_model(model).identifier` name, which is very unsatisfying. ## Test Plan Unit Tests and integration tests --------- Signed-off-by: Francisco Javier Arceo <farceo@redhat.com>	2025-06-13 14:28:51 -04:00
grs	e2e15ebb6c	feat(auth): allow token to be provided for use against jwks endpoint (#2394) ... Though the jwks endpoint does not usually require authentication, it does in a kubernetes cluster. While the cluster can be configured to allow anonymous access to that endpoint, this avoids the need to do so.	2025-06-13 10:13:41 +02:00
Hardik Shah	fef670b024	feat: update openai tests to work with both clients (#2442) ... https://github.com/meta-llama/llama-stack-client-python/pull/238 updated llama-stack-client to also support Open AI endpoints for embeddings, files, vector-stores. This updates the test to test all configs -- openai sdk, llama stack sdk and library-as-client.	2025-06-12 16:30:23 -07:00
Hardik Shah	0bc1747ed8	feat: update search for vector_stores (#2441) ... Updated the `search` functionality return response to match openai. ## Test Plan ``` pytest -sv --stack-config=http://localhost:8321 tests/integration/vector_io/test_openai_vector_stores.py --embedding-model all-MiniLM-L6-v2 ```	2025-06-12 15:34:22 -07:00
Ibrahim Haroon	35c2817d0a	fix(weaviate): handle case where distance is 0 by setting score to infinity (#2415) ... # What does this PR do? Fixes provider weaviate `query_vector` function for when the distance between the query embedding and an embedding within the vector db is 0 (identical vectors). Catches `ZeroDivisionError` and then sets `score` to infinity, which represent maximum similarity. <!-- If resolving an issue, uncomment and update the line below --> Closes [#2381] ## Test Plan Checkout this PR Execute this code and there will no longer be a `ZeroDivisionError` exception ``` from llama_stack_client import LlamaStackClient base_url = "http://localhost:8321" client = LlamaStackClient(base_url=base_url) models = client.models.list() embedding_model = ( em := next(m for m in models if m.model_type == "embedding") ).identifier embedding_dimension = 384 _ = client.vector_dbs.register( vector_db_id="foo_db", embedding_model=embedding_model, embedding_dimension=embedding_dimension, provider_id="weaviate", ) chunk = { "content": "foo", "mime_type": "text/plain", "metadata": { "document_id": "foo-id" } } client.vector_io.insert(vector_db_id="foo_db", chunks=[chunk]) client.vector_io.query(vector_db_id="foo_db", query="foo") ```	2025-06-12 11:23:59 -04:00
Sébastien Han	eb04731750	ci: fix external provider test (#2438) ... # What does this PR do? The test wasn't using the correct virtual environment. Also augment the console width for logs. Signed-off-by: Sébastien Han <seb@redhat.com>	2025-06-12 16:14:32 +02:00
Hardik Shah	de37a04c3e	fix: set appropriate defaults for params (#2434) ... Setting defaults to be `| None` else they get marked as required params in open-api spec.	2025-06-11 17:30:34 -07:00
Hardik Shah	d55100d9b7	feat: OpenAIVectorIOMixin for vector_stores common logic (#2427) ... Extracts common OpenAI vector-store code into its own mixin so that all providers can share the same core logic. This also makes it easy for Llama Stack to support both vector-stores and Llama Stack APIs in the interim so that both share the same underlying vector-dbs. Each provider contains storage specific logic to `create / edit / delete / list` vector dbs while the plumbing logic is standardized in the common code. Ensured that this works well with both faiss and sqllite-vec. ### Test Plan ``` llama stack run starter pytest -sv --stack-config http://localhost:8321 tests/integration/vector_io/test_openai_vector_stores.py --embedding-model all-MiniLM-L6-v2 ```	2025-06-11 15:40:57 -07:00
Rohan Awhad	4e37b49cdc	fix: #1867 InferenceRouter has no attribute formatter (#2422) ... # What does this PR do? Closes #1867 [Steps to reproduce the bug](https://github.com/meta-llama/llama-stack/issues/1867#issuecomment-2956819381) The change was designed to minimize code changes. Open to option of skipping `metrics` field entirely when `telemetry` is disabled. ## Test Plan 1. Build llama-stack remote-vllm container ```bash llama stack build --template remote-vllm --image-type container ``` 2. Create a small run.yaml ```yaml version: '2' image_name: remote-vllm apis: - inference providers: inference: - provider_id: vllm-inference provider_type: remote::vllm config: url: ${env.VLLM_URL:http://localhost:8000/v1} max_tokens: ${env.VLLM_MAX_TOKENS:4096} api_token: ${env.VLLM_API_TOKEN:fake} tls_verify: ${env.VLLM_TLS_VERIFY:true} metadata_store: type: sqlite db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/remote-vllm}/registry.db inference_store: type: sqlite db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/remote-vllm}/inference_store.db models: - metadata: {} model_id: ${env.INFERENCE_MODEL} provider_id: vllm-inference model_type: llm shields: [] vector_dbs: [] datasets: [] scoring_fns: [] benchmarks: [] server: port: 8321 ``` 3. Run the llama-stack server ```bash export VLLM_URL="http://localhost:8000/v1" export INFERENCE_MODEL="meta-llama/Llama-3.2-3B-Instruct" llama stack run run.yaml ``` 4. Then perform a curl ```bash curl -X 'POST' \ 'http://localhost:8321/v1/inference/completion' \ -H 'accept: application/json' \ -H 'Content-Type: application/json' \ -d '{ "model_id": "meta-llama/Llama-3.2-3B-Instruct", "content": "string", "sampling_params": { "strategy": { "type": "greedy" }, "max_tokens": 10, "repetition_penalty": 1, "stop": [ "string" ] }, "stream": false, "logprobs": { "top_k": 0 } }' ``` 5. You should receive a 200 response with metric values set to 0, similar to one below: ``` { "metrics": [ { "metric": "prompt_tokens", "value": 0, "unit": null }, { "metric": "completion_tokens", "value": 0, "unit": null }, { "metric": "total_tokens", "value": 0, "unit": null } ], [...] } ``` Co-authored-by: Rohan Awhad <rawhad@redhat.com>	2025-06-11 18:14:41 +02:00
Hardik Shah	5ac43268e8	feat: Add OpenAI compat /v1/vector_store APIs (#2423) ... Adding OpenAI compat `/v1/vector-store` apis. This PR implements the `faiss` provider with followup PRs coming up for other providers. Added routes to create, update, delete, list vector stores. Also added route to search a vector store Inserting into vector stores is missing and will be a follow up diff. ### Test Plan - Added new integration test for testing the faiss provider ``` pytest -sv --stack-config http://localhost:8321 tests/integration/vector_io/test_openai_vector_stores.py --embedding-model all-MiniLM-L6-v2 ```	2025-06-10 13:07:39 -07:00
Ibrahim Haroon	28ca00d0d9	fix(pgvector): handle case where distance is 0 by setting score to infinity (#2416) ... # What does this PR do? Fixes provider pgvector `query_vector` function for when the distance between the query embedding and an embedding within the vector db is 0 (identical vectors). Catches `ZeroDivisionError` and then sets `score` to infinity, which represent maximum similarity. <!-- If resolving an issue, uncomment and update the line below --> Closes [#2381] ## Test Plan Checkout this PR Execute this code and there will no longer be a `ZeroDivisionError` exception ``` from llama_stack_client import LlamaStackClient base_url = "http://localhost:8321" client = LlamaStackClient(base_url=base_url) models = client.models.list() embedding_model = ( em := next(m for m in models if m.model_type == "embedding") ).identifier embedding_dimension = 384 _ = client.vector_dbs.register( vector_db_id="foo_db", embedding_model=embedding_model, embedding_dimension=embedding_dimension, provider_id="pgvector", ) chunk = { "content": "foo", "mime_type": "text/plain", "metadata": { "document_id": "foo-id" } } client.vector_io.insert(vector_db_id="foo_db", chunks=[chunk]) client.vector_io.query(vector_db_id="foo_db", query="foo") ```	2025-06-07 16:31:30 -04:00
Ibrahim Haroon	a34cef925b	fix(faiss): handle case where distance is 0 by setting d to minimum positive… (#2387) ... # What does this PR do? Adds try-catch to faiss `query_vector` function for when the distance between the query embedding and an embedding within the vector db is 0 (identical vectors). Catches `ZeroDivisionError` and then appends `(1.0 / sys.float_info.min)` to `scores` to represent maximum similarity. <!-- If resolving an issue, uncomment and update the line below --> Closes [#2381] ## Test Plan Checkout this PR Execute this code and there will no longer be a `ZeroDivisionError` exception ``` from llama_stack_client import LlamaStackClient base_url = "http://localhost:8321" client = LlamaStackClient(base_url=base_url) models = client.models.list() embedding_model = ( em := next(m for m in models if m.model_type == "embedding") ).identifier embedding_dimension = 384 _ = client.vector_dbs.register( vector_db_id="foo_db", embedding_model=embedding_model, embedding_dimension=embedding_dimension, provider_id="faiss", ) chunk = { "content": "foo", "mime_type": "text/plain", "metadata": { "document_id": "foo-id" } } client.vector_io.insert(vector_db_id="foo_db", chunks=[chunk]) client.vector_io.query(vector_db_id="foo_db", query="foo") ``` ### Running unit tests `uv run pytest tests/unit/rag/test_rag_query.py -v` --------- Signed-off-by: Ben Browning <bbrownin@redhat.com> Co-authored-by: Ben Browning <bbrownin@redhat.com>	2025-06-07 16:09:46 -04:00
Sumit Jaiswal	33ecefd284	feat: To add health status check for remote VLLM (#2303) ... # What does this PR do? <!-- Provide a short summary of what this PR does and why. Link to relevant issues if applicable. --> To add health status check for remote VLLM <!-- If resolving an issue, uncomment and update the line below --> <!-- Closes #[issue-number] --> ## Test Plan <!-- Describe the tests you ran to verify your changes with result summaries. *Provide clear instructions so the plan can be easily re-executed.* --> PR includes the unit test to test the added health check implementation feature.	2025-06-06 15:33:12 -04:00
Hardik Shah	1f48577a02	fix: ChromaDB provider (#2413) ... fixes the remote::chromaDB provider for vector_io by updating the method definition appropriately. Fixed impl to use score_threshold properly. ### Test Plan ``` # Start Chroma Docker docker run --rm \ --name chromadb \ -p 8800:8000 \ -v ~/chroma:/chroma/chroma \ -e IS_PERSISTENT=TRUE \ -e ANONYMIZED_TELEMETRY=FALSE \ chromadb/chroma:latest # run pytest CHROMADB_URL="http://localhost:8800" pytest -sv tests/integration/vector_io/test_vector_io.py --stack-config vector_io=remote::chromadb,inference=fireworks --embedding-model nomic-ai/nomic-embed-text-v1.5 ```	2025-06-06 11:25:58 -07:00
github-actions[bot]	692709cd45	build: Bump version to 0.2.10	2025-06-05 22:56:39 +00:00
ehhuang	446893f791	feat: add deps dynamically based on metastore config (#2405) ... # What does this PR do? ## Test Plan changed metastore in one of the templates, rerun distro gen, observe change in build.yaml	2025-06-05 14:07:25 -07:00
Ashwin Bharambe	3251b44d8a	refactor: unify stream and non-stream impls for responses (#2388) ... The non-streaming version is just a small layer on top of the streaming version - just pluck off the final `response.completed` event and return that as the response! This PR also includes a couple other changes which I ended up making while working on it on a flight: - changes to `ollama` so it does not pull embedding models unconditionally - a small fix to library client to make the stream and non-stream cases a bit more symmetric	2025-06-05 17:48:09 +02:00
Jose Angel Morena Simon	ef885d2147	fix(server): Add missing OpenTelemetry dependencies to resolve telemetry import errors (#2391) ... This PR fixes a runtime import error caused by missing OpenTelemetry dependencies during `llama stack run`. Specifically, the following imports fail if `opentelemetry-sdk` and `opentelemetry-exporter-otlp-proto-http` are not present in the environment: ```python from opentelemetry import metrics, trace from opentelemetry.exporter.otlp.proto.http.metric_exporter import OTLPMetricExporter ``` See [llama\_stack/providers/inline/telemetry/meta\_reference/telemetry.py#L10-L19](https://github.com/meta-llama/llama-stack/blob/main/llama_stack/providers/inline/telemetry/meta_reference/telemetry.py#L10-L19) This PR resolves the issue by adding both packages to the `SERVER_DEPENDENCIES` list: ```python "opentelemetry-sdk", "opentelemetry-exporter-otlp-proto-http", ``` ### Reproduction Steps ```bash llama stack build --config llama.yaml --image-type venv --image-name fun-with-lamas llama stack run ~/.llama/distributions/fun-with-lamas/fun-with-lamas-run.yaml ``` Results in: ``` ModuleNotFoundError: No module named 'opentelemetry' ``` or ``` ModuleNotFoundError: No module named 'opentelemetry.exporter' ``` Signed-off-by: Jose Angel Morena <jmorenas@redhat.com> Co-authored-by: raghotham <rsm@meta.com>	2025-06-05 09:34:46 +02:00
ehhuang	a58c0639d5	chore: update postgres_demo distro config (#2396) ... # What does this PR do? ## Test Plan	2025-06-04 17:41:27 -07:00
Sébastien Han	c8c742ba45	fix: vllm starter name (#2392) ... Signed-off-by: Sébastien Han <seb@redhat.com>	2025-06-04 16:21:36 +02:00
grs	0de9536717	fix: remove debug print accidentally merged (#2393) ... I accidentally left a debug print in a PR that was merged. This removes that.	2025-06-04 15:14:14 +02:00
Ashwin Bharambe	ed69c1b3cc	feat(responses): add more streaming response types (#2375)	2025-06-03 15:48:41 -07:00
ehhuang	d96f6ec763	chore(ui): use proxy server for backend API calls; simplified k8s deployment (#2350) ... # What does this PR do? - no more CORS middleware needed ## Test Plan ### Local test llama stack run starter --image-type conda npm run dev verify UI works in browser ### Deploy to k8s temporarily change ui-k8s.yaml.template to load from PR commit <img width="604" alt="image" src="https://github.com/user-attachments/assets/87fa2e52-1e93-4e32-9e0f-5b283b7a37b3" /> sh ./apply.sh $ kubectl get services go to external_ip:8322 and play around with UI <img width="1690" alt="image" src="https://github.com/user-attachments/assets/5b7ec827-4302-4435-a9eb-df423676d873" />	2025-06-03 14:57:10 -07:00
grs	7c1998db25	feat: fine grained access control policy (#2264) ... This allows a set of rules to be defined for determining access to resources. The rules are (loosely) based on the cedar policy format. A rule defines a list of action either to permit or to forbid. It may specify a principal or a resource that must match for the rule to take effect. It may also specify a condition, either a 'when' or an 'unless', with additional constraints as to where the rule applies. A list of rules is held for each type to be protected and tried in order to find a match. If a match is found, the request is permitted or forbidden depening on the type of rule. If no match is found, the request is denied. If no rules are specified for a given type, a rule that allows any action as long as the resource attributes match the user attributes is added (i.e. the previous behaviour is the default. Some examples in yaml: ``` model: - permit: principal: user-1 actions: [create, read, delete] comment: user-1 has full access to all models - permit: principal: user-2 actions: [read] resource: model-1 comment: user-2 has read access to model-1 only - permit: actions: [read] when: user_in: resource.namespaces comment: any user has read access to models with matching attributes vector_db: - forbid: actions: [create, read, delete] unless: user_in: role::admin comment: only user with admin role can use vector_db resources ``` --------- Signed-off-by: Gordon Sim <gsim@redhat.com>	2025-06-03 14:51:12 -07:00
Ben Browning	8bee2954be	feat: Structured output for Responses API (#2324) ... # What does this PR do? This adds the missing `text` parameter to the Responses API that is how users control structured outputs. All we do with that parameter is map it to the corresponding chat completion response_format. ## Test Plan The new unit tests exercise the various permutations allowed for this property, while a couple of new verification tests actually use it for real to verify the model outputs are following the format as expected. Unit tests: `python -m pytest -s -v tests/unit/providers/agents/meta_reference/test_openai_responses.py` Verification tests: ``` llama stack run llama_stack/templates/together/run.yaml pytest -s -vv 'tests/verifications/openai_api/test_responses.py' \ --base-url=http://localhost:8321/v1/openai/v1 \ --model meta-llama/Llama-4-Scout-17B-16E-Instruct ``` Note that the verification tests can only be run with a real Llama Stack server (as opposed to using the library client via `--provider=stack:together`) because the Llama Stack python client is not yet updated to accept this text field. Signed-off-by: Ben Browning <bbrownin@redhat.com>	2025-06-03 14:43:00 -07:00