# What does this PR do?
We want to bundle a bunch of (typically remote) providers in a distro
template and be able to configure them "on the fly" via environment
variables. So far, we have been able to do this with simple env var
replacements. However, sometimes you want to only conditionally enable
providers (because the relevant remote services may not be alive, or
relevant.) This was not possible until now.
To aid this, we add a simple (bash-like) env var replacement
enhancement: `${env.FOO+bar}` evaluates to `bar` if the variable is SET
and evaluates to empty string if it is not. On top of that, we update
our main resolver to ignore any provider whose ID is null.
This allows using the distro like this:
```bash
llama stack run dev --env CHROMADB_URL=http://localhost:6001 --env ENABLE_CHROMADB=1
```
when only Chroma is UP. This disables the other `pgvector` provider in
the run configuration.
## Test Plan
Hard code `chromadb` as the vector io provider inside
`test_vector_io.py` and run:
```bash
LLAMA_STACK_BASE_URL=http://localhost:8321 pytest -s -v tests/client-sdk/vector_io/ --embedding-model all-MiniLM-L6-v2
```
# What does this PR do?
This is to be consistent with OpenAI API and support vLLM <= v0.6.3
References:
*
https://platform.openai.com/docs/api-reference/chat/create#chat-create-tool_choice
* https://github.com/vllm-project/vllm/pull/10000
This fixes the error when running older versions of vLLM:
```
00:50:19.834 [START] /v1/inference/chat-completion
INFO 2025-02-28 00:50:20,203 httpx:1025: HTTP Request: POST https://api-xeai-granite-3-1-8b-instruct.apps.int.stc.ai.preprod.us-east-1.aws.paas.redhat.com/v1/chat/completions "HTTP/1.1 400 Bad Request"
Traceback (most recent call last):
File "/usr/local/lib/python3.10/site-packages/llama_stack/distribution/server/server.py", line 235, in endpoint
return await maybe_await(value)
File "/usr/local/lib/python3.10/site-packages/llama_stack/distribution/server/server.py", line 201, in maybe_await
return await value
File "/usr/local/lib/python3.10/site-packages/llama_stack/providers/utils/telemetry/trace_protocol.py", line 89, in async_wrapper
result = await method(self, *args, **kwargs)
File "/usr/local/lib/python3.10/site-packages/llama_stack/distribution/routers/routers.py", line 193, in chat_completion
return await provider.chat_completion(**params)
File "/usr/local/lib/python3.10/site-packages/llama_stack/providers/utils/telemetry/trace_protocol.py", line 89, in async_wrapper
result = await method(self, *args, **kwargs)
File "/usr/local/lib/python3.10/site-packages/llama_stack/providers/remote/inference/vllm/vllm.py", line 286, in chat_completion
return await self._nonstream_chat_completion(request, self.client)
File "/usr/local/lib/python3.10/site-packages/llama_stack/providers/remote/inference/vllm/vllm.py", line 292, in _nonstream_chat_completion
r = client.chat.completions.create(**params)
File "/usr/local/lib/python3.10/site-packages/openai/_utils/_utils.py", line 279, in wrapper
return func(*args, **kwargs)
File "/usr/local/lib/python3.10/site-packages/openai/resources/chat/completions/completions.py", line 879, in create
return self._post(
File "/usr/local/lib/python3.10/site-packages/openai/_base_client.py", line 1290, in post
return cast(ResponseT, self.request(cast_to, opts, stream=stream, stream_cls=stream_cls))
File "/usr/local/lib/python3.10/site-packages/openai/_base_client.py", line 967, in request
return self._request(
File "/usr/local/lib/python3.10/site-packages/openai/_base_client.py", line 1071, in _request
raise self._make_status_error_from_response(err.response) from None
openai.BadRequestError: Error code: 400 - {'object': 'error', 'message': "[{'type': 'value_error', 'loc': ('body',), 'msg': 'Value error, When using `tool_choice`, `tools` must be set.', 'input': {'messages': [{'role': 'user', 'content': [{'type': 'text', 'text': 'What model are you?'}]}], 'model': 'granite-3-1-8b-instruct', 'max_tokens': 4096, 'stream': False, 'temperature': 0.0, 'tools': None, 'tool_choice': 'auto'}, 'ctx': {'error': ValueError('When using `tool_choice`, `tools` must be set.')}}]", 'type': 'BadRequestError', 'param': None, 'code': 400}
INFO: 2600:1700:9d20:ac0::49:59736 - "POST /v1/inference/chat-completion HTTP/1.1" 500 Internal Server Error
00:50:20.266 [END] /v1/inference/chat-completion [StatusCode.OK] (431.99ms)
```
## Test Plan
All existing tests pass.
---------
Signed-off-by: Yuan Tang <terrytangyuan@gmail.com>
# What does this PR do?
updates nvidia inference provider's embedding implementation to use new
signature
add support for task_type, output_dimensions, text_truncation parameters
## Test Plan
`LLAMA_STACK_BASE_URL=http://localhost:8321 pytest -v
tests/client-sdk/inference/test_embedding.py --embedding-model
baai/bge-m3`
Each model known to the system has two identifiers:
- the `provider_resource_id` (what the provider calls it) -- e.g.,
`accounts/fireworks/models/llama-v3p1-8b-instruct`
- the `identifier` (`model_id`) under which it is registered and gets
routed to the appropriate provider.
We have so far used the HuggingFace repo alias as the standardized
identifier you can use to refer to the model. So in the above example,
we'd use `meta-llama/Llama-3.1-8B-Instruct` as the name under which it
gets registered. This makes it convenient for users to refer to these
models across providers.
However, we forgot to register the _actual_ provider model ID also. You
should be able to route via `provider_resource_id` also, of course.
This change fixes this (somewhat grave) omission.
*Note*: this change is additive -- more aliases work now compared to
before.
## Test Plan
Run the following for distro=(ollama fireworks together)
```
LLAMA_STACK_CONFIG=$distro \
pytest -s -v tests/client-sdk/inference/test_text_inference.py \
--inference-model=meta-llama/Llama-3.1-8B-Instruct --vision-inference-model=""
```
Groq has never supported raw completions anyhow. So this makes it easier
to switch it to LiteLLM. All our test suite passes.
I also updated all the openai-compat providers so they work with api
keys passed from headers. `provider_data`
## Test Plan
```bash
LLAMA_STACK_CONFIG=groq \
pytest -s -v tests/client-sdk/inference/test_text_inference.py \
--inference-model=groq/llama-3.3-70b-versatile --vision-inference-model=""
```
Also tested (openai, anthropic, gemini) providers. No regressions.
# What does this PR do?
This PR makes a couple of changes required to get the test
`tests/client-sdk/agents/test_agents.py::test_builtin_tool_web_search`
passing on the remote-vllm provider.
First, we adjust agent_instance to also pass in the description and
parameters of builtin tools. We need these parameters so we can pass the
tool's expected parameters into vLLM. The meta-reference implementations
may not have needed these for builtin tools, as they are able to take
advantage of the Llama-model specific support for certain builtin tools.
However, with vLLM, our server-side chat templates for tool calling
treat all tools the same and don't separate out Llama builtin vs custom
tools. So, we need to pass the full set of parameter definitions and
list of required parameters for builtin tools as well.
Next, we adjust the vllm streaming chat completion code to fix up some
edge cases where it was returning an extra ChatCompletionResponseEvent
with an empty ToolCall with empty string call_id, tool_name, and
arguments properties. This is a bug discovered after the above fix,
where after a successful tool invocation we were sending extra chunks
back to the client with these empty ToolCalls.
## Test Plan
With these changes, the following test that previously failed now
passes:
```
VLLM_URL="http://localhost:8000/v1" \
INFERENCE_MODEL="meta-llama/Llama-3.2-3B-Instruct" \
LLAMA_STACK_CONFIG=remote-vllm \
python -m pytest -v \
tests/client-sdk/agents/test_agents.py::test_builtin_tool_web_search \
--inference-model "meta-llama/Llama-3.2-3B-Instruct"
```
Additionally, I ran the remote-vllm client-sdk and provider inference
tests as below to ensure they all still passed with this change:
```
VLLM_URL="http://localhost:8000/v1" \
INFERENCE_MODEL="meta-llama/Llama-3.2-3B-Instruct" \
LLAMA_STACK_CONFIG=remote-vllm \
python -m pytest -v \
tests/client-sdk/inference/test_text_inference.py \
--inference-model "meta-llama/Llama-3.2-3B-Instruct"
```
```
VLLM_URL="http://localhost:8000/v1" \
python -m pytest -s -v \
llama_stack/providers/tests/inference/test_text_inference.py \
--providers "inference=vllm_remote"
```
[//]: # (## Documentation)
Signed-off-by: Ben Browning <bbrownin@redhat.com>
# What does this PR do?
This PR introduces more non-llama model support to llama stack.
Providers introduced: openai, anthropic and gemini. All of these
providers use essentially the same piece of code -- the implementation
works via the `litellm` library.
We will expose only specific models for providers we enable making sure
they all work well and pass tests. This setup (instead of automatically
enabling _all_ providers and models allowed by LiteLLM) ensures we can
also perform any needed prompt tuning on a per-model basis as needed
(just like we do it for llama models.)
## Test Plan
```bash
#!/bin/bash
args=("$@")
for model in openai/gpt-4o anthropic/claude-3-5-sonnet-latest gemini/gemini-1.5-flash; do
LLAMA_STACK_CONFIG=dev pytest -s -v tests/client-sdk/inference/test_text_inference.py \
--embedding-model=all-MiniLM-L6-v2 \
--vision-inference-model="" \
--inference-model=$model "${args[@]}"
done
```
# What does this PR do?
Create a distribution template using Groq as inference provider.
Link to issue: https://github.com/meta-llama/llama-stack/issues/958
## Test Plan
Run `python llama_stack/scripts/distro_codegen.py` to generate run.yaml
and build.yaml
Test the newly created template by running
`llama stack build --template <template-name>`
`llama stack run <template-name>`
# What does this PR do?
- Fixed type hinting and missing imports across multiple modules.
- Improved compatibility by using `TYPE_CHECKING` for conditional
imports.
- Updated `pyproject.toml` to enforce stricter linting.
Signed-off-by: Sébastien Han <seb@redhat.com>
Signed-off-by: Sébastien Han <seb@redhat.com>
This PR begins the process of supporting non-llama models within Llama
Stack. We start simple by adding support for this functionality within a
few existing providers: fireworks, together and ollama.
## Test Plan
```bash
LLAMA_STACK_CONFIG=fireworks pytest -s -v tests/client-sdk/inference/test_text_inference.py \
--inference-model accounts/fireworks/models/phi-3-vision-128k-instruct
```
^ this passes most of the tests but as expected fails the tool calling
related tests since they are very specific to Llama models
```
inference/test_text_inference.py::test_text_completion_streaming[accounts/fireworks/models/phi-3-vision-128k-instruct] PASSED
inference/test_text_inference.py::test_completion_log_probs_non_streaming[accounts/fireworks/models/phi-3-vision-128k-instruct] PASSED
inference/test_text_inference.py::test_completion_log_probs_streaming[accounts/fireworks/models/phi-3-vision-128k-instruct] PASSED
inference/test_text_inference.py::test_text_completion_structured_output[accounts/fireworks/models/phi-3-vision-128k-instruct-completion-01] PASSED
inference/test_text_inference.py::test_text_chat_completion_non_streaming[accounts/fireworks/models/phi-3-vision-128k-instruct-Which planet do humans live on?-Earth] PASSED
inference/test_text_inference.py::test_text_chat_completion_non_streaming[accounts/fireworks/models/phi-3-vision-128k-instruct-Which planet has rings around it with a name starting w
ith letter S?-Saturn] PASSED
inference/test_text_inference.py::test_text_chat_completion_streaming[accounts/fireworks/models/phi-3-vision-128k-instruct-What's the name of the Sun in latin?-Sol] PASSED
inference/test_text_inference.py::test_text_chat_completion_streaming[accounts/fireworks/models/phi-3-vision-128k-instruct-What is the name of the US captial?-Washington] PASSED
inference/test_text_inference.py::test_text_chat_completion_with_tool_calling_and_non_streaming[accounts/fireworks/models/phi-3-vision-128k-instruct] FAILED
inference/test_text_inference.py::test_text_chat_completion_with_tool_calling_and_streaming[accounts/fireworks/models/phi-3-vision-128k-instruct] FAILED
inference/test_text_inference.py::test_text_chat_completion_with_tool_choice_required[accounts/fireworks/models/phi-3-vision-128k-instruct] FAILED
inference/test_text_inference.py::test_text_chat_completion_with_tool_choice_none[accounts/fireworks/models/phi-3-vision-128k-instruct] PASSED
inference/test_text_inference.py::test_text_chat_completion_structured_output[accounts/fireworks/models/phi-3-vision-128k-instruct] ERROR
inference/test_text_inference.py::test_text_chat_completion_tool_calling_tools_not_in_request[accounts/fireworks/models/phi-3-vision-128k-instruct-True] PASSED
inference/test_text_inference.py::test_text_chat_completion_tool_calling_tools_not_in_request[accounts/fireworks/models/phi-3-vision-128k-instruct-False] PASSED
```
Embedding models are tiny and can be pulled on-demand. Let's do that so
the user doesn't have to do "yet another thing" to get themselves set
up.
Thanks @hardikjshah for the suggestion.
Also fixed a build dependency miss (TODO: distro_codegen needs to
actually check that the build template contains all providers mentioned
for the run.yaml file)
## Test Plan
First run `ollama rm all-minilm:latest`.
Run `llama stack build --template ollama && llama stack run ollama --env
INFERENCE_MODEL=llama3.2:3b-instruct-fp16`. See that it outputs a
"Pulling embedding model `all-minilm:latest`" output and the stack
starts up correctly. Verify that `ollama list` shows the model is
correctly downloaded.
# What does this PR do?
- Updates ImageContentItemImageURL import
- fixes `embedding_dimensions` metadata param
## Test Plan
- Ran pytest locally, verified embedding tests pass with new types
cc: @dglogo @sumitb
See Issue #922
The change is slightly backwards incompatible but no callsite (in our
client codebases or stack-apps) every passes a depth-2
`List[List[InterleavedContentItem]]` (which is now disallowed.)
## Test Plan
```bash
$ cd llama_stack/providers/tests/inference
$ pytest -s -v -k fireworks test_embeddings.py \
--inference-model nomic-ai/nomic-embed-text-v1.5 --env EMBEDDING_DIMENSION=784
$ pytest -s -v -k together test_embeddings.py \
--inference-model togethercomputer/m2-bert-80M-8k-retrieval --env EMBEDDING_DIMENSION=784
$ pytest -s -v -k ollama test_embeddings.py \
--inference-model all-minilm:latest --env EMBEDDING_DIMENSION=784
```
Also ran `tests/client-sdk/inference/test_embeddings.py`
# What does this PR do?
The `tool_name` attribute of `ToolDefinition` instances can either be a
str or a BuiltinTool enum type. This fixes the remote vLLM provider to
use the value of those BuiltinTool enums when serializing to JSON
instead of attempting to serialize the actual enum to JSON.
Reference of how this is handled in some other areas, since I followed
that same pattern for the remote vLLM provider here:
- [remote nvidia
provider](https://github.com/meta-llama/llama-stack/blob/v0.1.3/llama_stack/providers/remote/inference/nvidia/openai_utils.py#L137-L140)
- [meta reference
provider](https://github.com/meta-llama/llama-stack/blob/v0.1.3/llama_stack/providers/inline/agents/meta_reference/agent_instance.py#L635-L636)
There is opportunity to potentially reconcile the remove nvidia and
remote vllm bits where they are both translating Llama Stack Inference
APIs to OpenAI client requests, but that's a can of worms I didn't want
to open for this bug fix.
This explicitly fixes this error when using the remote vLLM provider and
the agent tests:
```
TypeError: Object of type BuiltinTool is not JSON serializable
```
So, this is related to #1144 and addresses the immediate issue raised
there. With this fix,
`tests/client-sdk/agents/test_agents.py::test_builtin_tool_web_search`
now gets past the JSON serialization error when using the remote vLLM
provider and actually attempts to call the web search tool. I don't have
any API keys setup for the actual web search providers yet, so I cannot
verify everything works after that point.
## Test Plan
I ran the `test_builtin_tool_web_search` locally with the remote vLLM
provider like:
```
VLLM_URL="http://localhost:8000/v1" INFERENCE_MODEL="meta-llama/Llama-3.2-3B-Instruct" LLAMA_STACK_CONFIG=remote-vllm python -m pytest -v tests/client-sdk/agents/test_agents.py::test_builtin_tool_web_search --inference-model "meta-llama/Llama-3.2-3B-Instruct"
```
Before my change, that reproduced the `TypeError: Object of type
BuiltinTool is not JSON serializable` error. After my change, that error
is gone and the test actually attempts the web search. That failed for
me locally, due to lack of API key, but it gets past the JSON
serialization error.
Signed-off-by: Ben Browning <bbrownin@redhat.com>
# What does this PR do?
add /v1/inference/embeddings implementation to NVIDIA provider
**open topics** -
- *asymmetric models*. NeMo Retriever includes asymmetric models, which
are models that embed differently depending on if the input is destined
for storage or lookup against storage. the /v1/inference/embeddings api
does not allow the user to indicate the type of embedding to perform.
see https://github.com/meta-llama/llama-stack/issues/934
- *truncation*. embedding models typically have a limited context
window, e.g. 1024 tokens is common though newer models have 8k windows.
when the input is larger than this window the endpoint cannot perform
its designed function. two options: 0. return an error so the user can
reduce the input size and retry; 1. perform truncation for the user and
proceed (common strategies are left or right truncation). many users
encounter context window size limits and will struggle to write reliable
programs. this struggle is especially acute without access to the
model's tokenizer. the /v1/inference/embeddings api does not allow the
user to delegate truncation policy. see
https://github.com/meta-llama/llama-stack/issues/933
- *dimensions*. "Matryoshka" embedding models are available. they allow
users to control the number of embedding dimensions the model produces.
this is a critical feature for managing storage constraints. embeddings
of 1024 dimensions what achieve 95% recall for an application may not be
worth the storage cost if a 512 dimensions can achieve 93% recall.
controlling embedding dimensions allows applications to determine their
recall and storage tradeoffs. the /v1/inference/embeddings api does not
allow the user to control the output dimensions. see
https://github.com/meta-llama/llama-stack/issues/932
## Test Plan
- `llama stack run llama_stack/templates/nvidia/run.yaml`
- `LLAMA_STACK_BASE_URL=http://localhost:8321 pytest -v
tests/client-sdk/inference/test_embedding.py --embedding-model
baai/bge-m3`
## Sources
Please link relevant resources if necessary.
## Before submitting
- [ ] This PR fixes a typo or improves the docs (you can dismiss the
other checks if that's the case).
- [x] Ran pre-commit to handle lint / formatting issues.
- [x] Read the [contributor
guideline](https://github.com/meta-llama/llama-stack/blob/main/CONTRIBUTING.md),
Pull Request section?
- [ ] Updated relevant documentation.
- [x] Wrote necessary unit or integration tests.
---------
Co-authored-by: Ashwin Bharambe <ashwin.bharambe@gmail.com>
# What does this PR do?
We have support for embeddings in our Inference providers, but so far we
haven't done the final step of actually registering the known embedding
models and making sure they are extremely easy to use. This is one step
towards that.
## Test Plan
Run existing inference tests.
```bash
$ cd llama_stack/providers/tests/inference
$ pytest -s -v -k fireworks test_embeddings.py \
--inference-model nomic-ai/nomic-embed-text-v1.5 --env EMBEDDING_DIMENSION=784
$ pytest -s -v -k together test_embeddings.py \
--inference-model togethercomputer/m2-bert-80M-8k-retrieval --env EMBEDDING_DIMENSION=784
$ pytest -s -v -k ollama test_embeddings.py \
--inference-model all-minilm:latest --env EMBEDDING_DIMENSION=784
```
The value of the EMBEDDING_DIMENSION isn't actually used in these tests,
it is merely used by the test fixtures to check if the model is an LLM
or Embedding.
# What does this PR do?
We have several places running tests for different purposes.
- oss llama stack
- provider tests
- e2e tests
- provider llama stack
- unit tests
- e2e tests
It would be nice if they can *share the same set of test data*, so we
maintain the consistency between spec and implementation. This is what
this diff is about, isolating test data from test coding, so that we can
reuse the same data at different places by writing different test
coding.
## Test Plan
== Set up Ollama local server
== Run a provider test
conda activate stack
OLLAMA_URL="http://localhost:8321" \
pytest -v -s -k "ollama" --inference-model="llama3.2:3b-instruct-fp16" \
llama_stack/providers/tests/inference/test_text_inference.py::TestInference::test_completion_structured_output
// test_structured_output should also work
== Run an e2e test
conda activate sherpa
with-proxy pip install llama-stack
export INFERENCE_MODEL=llama3.2:3b-instruct-fp16
export LLAMA_STACK_PORT=8322
with-proxy llama stack build --template ollama
with-proxy llama stack run --env OLLAMA_URL=http://localhost:8321 ollama
- Run test client,
LLAMA_STACK_PORT=8322 LLAMA_STACK_BASE_URL="http://localhost:8322" \
pytest -v -s --inference-model="llama3.2:3b-instruct-fp16" \
tests/client-sdk/inference/test_text_inference.py::test_text_completion_structured_output
// test_text_chat_completion_structured_output should also work
## Notes
- This PR was automatically generated by oss_sync
- Please refer to D69478008 for more details.
# What does this PR do?
This fixes the following issue on the server side when the tool call
response contains empty args. This happens when running
`examples.agents.e2e_loop_with_client_tools` but `get_ticker_data`
returns `[]`:
```
Traceback (most recent call last):
File "/home/yutang/repos/llama-stack/llama_stack/distribution/server/server.py", line 208, in sse_generator
async for item in event_gen:
File "/home/yutang/repos/llama-stack/llama_stack/providers/inline/agents/meta_reference/agents.py", line 169, in _create_agent_turn_streaming
async for event in agent.create_and_execute_turn(request):
File "/home/yutang/repos/llama-stack/llama_stack/providers/inline/agents/meta_reference/agent_instance.py", line 189, in create_and_execute_turn
async for chunk in self.run(
File "/home/yutang/repos/llama-stack/llama_stack/providers/inline/agents/meta_reference/agent_instance.py", line 258, in run
async for res in self._run(
File "/home/yutang/repos/llama-stack/llama_stack/providers/inline/agents/meta_reference/agent_instance.py", line 499, in _run
async for chunk in await self.inference_api.chat_completion(
File "/home/yutang/repos/llama-stack/llama_stack/distribution/routers/routers.py", line 182, in <genexpr>
return (chunk async for chunk in await provider.chat_completion(**params))
File "/home/yutang/repos/llama-stack/llama_stack/providers/remote/inference/vllm/vllm.py", line 296, in _stream_chat_completion
async for chunk in res:
File "/home/yutang/repos/llama-stack/llama_stack/providers/remote/inference/vllm/vllm.py", line 162, in _process_vllm_chat_completion_stream_response
arguments=json.loads(tool_call_buf.arguments),
File "/home/yutang/.conda/envs/distribution-myenv/lib/python3.10/json/__init__.py", line 346, in loads
return _default_decoder.decode(s)
File "/home/yutang/.conda/envs/distribution-myenv/lib/python3.10/json/decoder.py", line 337, in decode
obj, end = self.raw_decode(s, idx=_w(s, 0).end())
File "/home/yutang/.conda/envs/distribution-myenv/lib/python3.10/json/decoder.py", line 355, in raw_decode
raise JSONDecodeError("Expecting value", s, err.value) from None
json.decoder.JSONDecodeError: Expecting value: line 1 column 1 (char 0)
```
## Test Plan
All existing tests in
`tests/client-sdk/inference/test_text_inference.py` passed.
[//]: # (## Documentation)
---------
Signed-off-by: Yuan Tang <terrytangyuan@gmail.com>
## What does this PR do?
In this PR, we implement a passthrough inference provider that works for
any endpoints that respect llama stack inference API definition.
## Test Plan
config some endpoint that respect llama stack inference API definition
and got the inference results successfully
<img width="1268" alt="Screenshot 2025-02-19 at 8 52 51 PM"
src="https://github.com/user-attachments/assets/447816e4-ea7a-4365-b90c-386dc7dcf4a1"
/>
There should be a choke-point for llama3.api imports -- this is the
prompt adapter. Creating a ChatFormat() object on demand is inexpensive.
The underlying Tokenizer is a singleton anyway.
# What does this PR do?
Before this change, `distro_codegen.py` would only work if the user
manually installed multiple provider-specific dependencies (see #1122).
Now, users can run `distro_codegen.py` without any provider-specific
dependencies because we avoid importing the entire provider
implementations just to get the config needed to build the provider
template.
Concretely, this mostly means moving the
MODEL_ALIASES (and related variants) definitions to a new models.py
class within the provider implementation for those providers that
require additional dependencies. It also meant moving a couple of
imports from top-level imports to inside `get_adapter_impl` for some
providers, which follows the pattern used by multiple existing
providers.
To ensure we don't regress and accidentally add new imports that cause
distro_codegen.py to fail, the stubbed-in pre-commit hook for
distro_codegen.py was uncommented and slightly tweaked to run via `uv
run python ...` to ensure it runs with only the project's default
dependencies and to run automatically instead of manually.
Lastly, this updates distro_codegen.py itself to keep track of paths it
might have changed and to only `git diff` those specific paths when
checking for changed files instead of doing a diff on the entire working
tree. The latter was overly broad and would require a user have no other
unstaged changes in their working tree, even if those unstaged changes
were unrelated to generated code. Now it only flags uncommitted changes
for paths distro_codegen.py actually writes to.
Our generated code was also out-of-date, presumably because of these
issues, so this commit also has some updates to the generated code
purely because it was out of sync, and the pre-commit hook now enforces
things to be updated.
(Closes#1122)
## Test Plan
I manually tested distro_codegen.py and the pre-commit hook to verify
those work as expected, flagging any uncommited changes and catching any
imports that attempt to pull in provider-specific dependencies.
However, I do not have valid api keys to the impacted provider
implementations, and am unable to easily run the inference tests against
each changed provider. There are no functional changes to the provider
implementations here, but I'd appreciate a second set of eyes on the
changed import statements and moving of MODEL_ALIASES type code to a
separate models.py to ensure I didn't make any obvious errors.
---------
Signed-off-by: Ben Browning <bbrownin@redhat.com>
Co-authored-by: Ashwin Bharambe <ashwin.bharambe@gmail.com>
llama-models should have extremely minimal cruft. Its sole purpose
should be didactic -- show the simplest implementation of the llama
models and document the prompt formats, etc.
This PR is the complement to
https://github.com/meta-llama/llama-models/pull/279
## Test Plan
Ensure all `llama` CLI `model` sub-commands work:
```bash
llama model list
llama model download --model-id ...
llama model prompt-format -m ...
```
Ran tests:
```bash
cd tests/client-sdk
LLAMA_STACK_CONFIG=fireworks pytest -s -v inference/
LLAMA_STACK_CONFIG=fireworks pytest -s -v vector_io/
LLAMA_STACK_CONFIG=fireworks pytest -s -v agents/
```
Create a fresh venv `uv venv && source .venv/bin/activate` and run
`llama stack build --template fireworks --image-type venv` followed by
`llama stack run together --image-type venv` <-- the server runs
Also checked that the OpenAPI generator can run and there is no change
in the generated files as a result.
```bash
cd docs/openapi_generator
sh run_openapi_generator.sh
```
# What does this PR do?
- Remove hardcoded configurations from pre-commit.
- Allow configuration to be set via pyproject.toml.
- Merge .ruff.toml settings into pyproject.toml.
- Ensure the linter and formatter use the defined configuration instead
of being overridden by pre-commit.
Signed-off-by: Sébastien Han <seb@redhat.com>
[//]: # (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.*]
[//]: # (## Documentation)
Signed-off-by: Sébastien Han <seb@redhat.com>
This was missed in https://github.com/meta-llama/llama-stack/pull/1023.
```
Traceback (most recent call last):
File "/home/yutang/.conda/envs/distribution-myenv/lib/python3.10/runpy.py", line 196, in _run_module_as_main
return _run_code(code, main_globals, None,
File "/home/yutang/.conda/envs/distribution-myenv/lib/python3.10/runpy.py", line 86, in _run_code
exec(code, run_globals)
File "/home/yutang/repos/llama-stack/llama_stack/distribution/server/server.py", line 488, in <module>
main()
File "/home/yutang/repos/llama-stack/llama_stack/distribution/server/server.py", line 389, in main
impls = asyncio.run(construct_stack(config))
File "/home/yutang/.conda/envs/distribution-myenv/lib/python3.10/asyncio/runners.py", line 44, in run
return loop.run_until_complete(main)
File "/home/yutang/.conda/envs/distribution-myenv/lib/python3.10/asyncio/base_events.py", line 649, in run_until_complete
return future.result()
File "/home/yutang/repos/llama-stack/llama_stack/distribution/stack.py", line 202, in construct_stack
impls = await resolve_impls(run_config, provider_registry or get_provider_registry(), dist_registry)
File "/home/yutang/repos/llama-stack/llama_stack/distribution/resolver.py", line 230, in resolve_impls
impl = await instantiate_provider(
File "/home/yutang/repos/llama-stack/llama_stack/distribution/resolver.py", line 312, in instantiate_provider
config_type = instantiate_class_type(provider_spec.config_class)
File "/home/yutang/repos/llama-stack/llama_stack/distribution/utils/dynamic.py", line 13, in instantiate_class_type
return getattr(module, class_name)
AttributeError: module 'llama_stack.providers.inline.vector_io.faiss' has no attribute 'FaissImplConfig'
```
---------
Signed-off-by: Yuan Tang <terrytangyuan@gmail.com>
# What does this PR do?
This changes all VectorIO providers classes to follow the pattern
`<ProviderName>VectorIOConfig` and `<ProviderName>VectorIOAdapter`. All
API endpoints for VectorIOs are currently consistent with `/vector-io`.
Note that API endpoint for VectorDB stay unchanged as `/vector-dbs`.
## Test Plan
I don't have a way to test all providers. This is a simple renaming so
things should work as expected.
---------
Signed-off-by: Yuan Tang <terrytangyuan@gmail.com>
# What does this PR do?
- Configured ruff linter to automatically fix import sorting issues.
- Set --exit-non-zero-on-fix to ensure non-zero exit code when fixes are
applied.
- Enabled the 'I' selection to focus on import-related linting rules.
- Ran the linter, and formatted all codebase imports accordingly.
- Removed the black dep from the "dev" group since we use ruff
Signed-off-by: Sébastien Han <seb@redhat.com>
[//]: # (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.*]
[//]: # (## Documentation)
[//]: # (- [ ] Added a Changelog entry if the change is significant)
Signed-off-by: Sébastien Han <seb@redhat.com>
# What does this PR do?
The remote-vllm provider was not passing logprobs options from
CompletionRequest or ChatCompletionRequests through to the OpenAI client
parameters. I manually verified this, as well as observed this provider
failing `TestInference::test_completion_logprobs`. This was filed as
issue #1073.
This fixes that by passing the `logprobs.top_k` value through to the
parameters we pass into the OpenAI client.
Additionally, this fixes a bug in `test_text_inference.py` where it
mistakenly assumed chunk.delta were of type `ContentDelta` for
completion requests. The deltas are of type `ContentDelta` for chat
completion requests, but for basic completion requests the deltas are of
type string. This test was likely failing for other providers that did
properly support logprobs because of this latter issue in the test,
which was hit while fixing the above issue with the remote-vllm
provider.
(Closes#1073)
## Test Plan
First, you need a vllm running. I ran one locally like this:
```
vllm serve meta-llama/Llama-3.2-3B-Instruct --port 8001 --enable-auto-tool-choice --tool-call-parser llama3_json
```
Next, run test_text_inference.py against this vllm using the remote vllm
provider like this:
```
VLLM_URL="http://localhost:8001/v1" python -m pytest -s -v llama_stack/providers/tests/inference/test_text_inference.py --providers "inference=vllm_remote"
```
Before my change, the test failed with this error:
```
llama_stack/providers/tests/inference/test_text_inference.py:155: in test_completion_logprobs
assert 1 <= len(response.logprobs) <= 5
E TypeError: object of type 'NoneType' has no len()
```
After my change, the test passes.
[//]: # (## Documentation)
Signed-off-by: Ben Browning <bbrownin@redhat.com>
# What does this PR do?
[Provide a short summary of what this PR does and why. Link to relevant
issues if applicable.]
Closes https://github.com/meta-llama/llama-stack/issues/1046.
## 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.*]
```
LLAMA_STACK_BASE_URL=http://localhost:5002 pytest -v tests/client-sdk/inference/test_text_inference.py
================================================================= test session starts =================================================================
platform linux -- Python 3.10.16, pytest-8.3.4, pluggy-1.5.0 -- /home/yutang/.conda/envs/distribution-myenv/bin/python3.10
cachedir: .pytest_cache
rootdir: /home/yutang/repos/llama-stack
configfile: pyproject.toml
plugins: anyio-4.8.0
collected 14 items
tests/client-sdk/inference/test_text_inference.py::test_text_completion_non_streaming[meta-llama/Llama-3.1-8B-Instruct] PASSED [ 7%]
tests/client-sdk/inference/test_text_inference.py::test_text_completion_streaming[meta-llama/Llama-3.1-8B-Instruct] PASSED [ 14%]
tests/client-sdk/inference/test_text_inference.py::test_completion_log_probs_non_streaming[meta-llama/Llama-3.1-8B-Instruct] XFAIL (remote:...) [ 21%]
tests/client-sdk/inference/test_text_inference.py::test_completion_log_probs_streaming[meta-llama/Llama-3.1-8B-Instruct] XFAIL (remote::vll...) [ 28%]
tests/client-sdk/inference/test_text_inference.py::test_text_completion_structured_output[meta-llama/Llama-3.1-8B-Instruct] PASSED [ 35%]
tests/client-sdk/inference/test_text_inference.py::test_text_chat_completion_non_streaming[meta-llama/Llama-3.1-8B-Instruct-Which planet do humans live on?-Earth] PASSED [ 42%]
tests/client-sdk/inference/test_text_inference.py::test_text_chat_completion_non_streaming[meta-llama/Llama-3.1-8B-Instruct-Which planet has rings around it with a name starting with letter S?-Saturn] PASSED [ 50%]
tests/client-sdk/inference/test_text_inference.py::test_text_chat_completion_streaming[meta-llama/Llama-3.1-8B-Instruct-What's the name of the Sun in latin?-Sol] PASSED [ 57%]
tests/client-sdk/inference/test_text_inference.py::test_text_chat_completion_streaming[meta-llama/Llama-3.1-8B-Instruct-What is the name of the US captial?-Washington] PASSED [ 64%]
tests/client-sdk/inference/test_text_inference.py::test_text_chat_completion_with_tool_calling_and_non_streaming[meta-llama/Llama-3.1-8B-Instruct] PASSED [ 71%]
tests/client-sdk/inference/test_text_inference.py::test_text_chat_completion_with_tool_calling_and_streaming[meta-llama/Llama-3.1-8B-Instruct] PASSED [ 78%]
tests/client-sdk/inference/test_text_inference.py::test_text_chat_completion_structured_output[meta-llama/Llama-3.1-8B-Instruct] PASSED [ 85%]
tests/client-sdk/inference/test_text_inference.py::test_text_chat_completion_tool_calling_tools_not_in_request[meta-llama/Llama-3.1-8B-Instruct-True] PASSED [ 92%]
tests/client-sdk/inference/test_text_inference.py::test_text_chat_completion_tool_calling_tools_not_in_request[meta-llama/Llama-3.1-8B-Instruct-False] PASSED [100%]
=============================================== 12 passed, 2 xfailed, 1 warning in 366.56s (0:06:06) ================================================
```
---------
Signed-off-by: Yuan Tang <terrytangyuan@gmail.com>
# What does this PR do?
This PR adds support for tool calling for non-streaming chat completion.
Prior to this, tool calls were not passed to chat completion requests
and the tools object needs to be restructured properly to be compatible
with vLLM provider.
## Test Plan
```
LLAMA_STACK_BASE_URL=http://localhost:5002 pytest -v tests/client-sdk/inference/test_text_inference.py
================================================================= test session starts =================================================================
platform linux -- Python 3.10.16, pytest-8.3.4, pluggy-1.5.0 -- /home/yutang/.conda/envs/distribution-myenv/bin/python3.10
cachedir: .pytest_cache
rootdir: /home/yutang/repos/llama-stack
configfile: pyproject.toml
plugins: anyio-4.8.0
collected 12 items
tests/client-sdk/inference/test_text_inference.py::test_text_completion_non_streaming[meta-llama/Llama-3.1-8B-Instruct] PASSED [ 8%]
tests/client-sdk/inference/test_text_inference.py::test_text_completion_streaming[meta-llama/Llama-3.1-8B-Instruct] PASSED [ 16%]
tests/client-sdk/inference/test_text_inference.py::test_completion_log_probs_non_streaming[meta-llama/Llama-3.1-8B-Instruct] XFAIL (remote:...) [ 25%]
tests/client-sdk/inference/test_text_inference.py::test_completion_log_probs_streaming[meta-llama/Llama-3.1-8B-Instruct] XFAIL (remote::vll...) [ 33%]
tests/client-sdk/inference/test_text_inference.py::test_text_completion_structured_output[meta-llama/Llama-3.1-8B-Instruct] PASSED [ 41%]
tests/client-sdk/inference/test_text_inference.py::test_text_chat_completion_non_streaming[meta-llama/Llama-3.1-8B-Instruct-Which planet do humans live on?-Earth] PASSED [ 50%]
tests/client-sdk/inference/test_text_inference.py::test_text_chat_completion_non_streaming[meta-llama/Llama-3.1-8B-Instruct-Which planet has rings around it with a name starting with letter S?-Saturn] PASSED [ 58%]
tests/client-sdk/inference/test_text_inference.py::test_text_chat_completion_streaming[meta-llama/Llama-3.1-8B-Instruct-What's the name of the Sun in latin?-Sol] PASSED [ 66%]
tests/client-sdk/inference/test_text_inference.py::test_text_chat_completion_streaming[meta-llama/Llama-3.1-8B-Instruct-What is the name of the US captial?-Washington] PASSED [ 75%]
tests/client-sdk/inference/test_text_inference.py::test_text_chat_completion_with_tool_calling_and_non_streaming[meta-llama/Llama-3.1-8B-Instruct] PASSED [ 83%]
tests/client-sdk/inference/test_text_inference.py::test_text_chat_completion_with_tool_calling_and_streaming[meta-llama/Llama-3.1-8B-Instruct] FAILED [ 91%]
tests/client-sdk/inference/test_text_inference.py::test_text_chat_completion_structured_output[meta-llama/Llama-3.1-8B-Instruct] PASSED [100%]
```
---------
Signed-off-by: Yuan Tang <terrytangyuan@gmail.com>
# What does this PR do?
`tool_config` is missing from the signature but is used in
`ChatCompletionRequest()`.
## Test Plan
This is a small fix. I don't have SambaNova to test the change but I
doubt that this is currently working.
Signed-off-by: Yuan Tang <terrytangyuan@gmail.com>
# What does this PR do?
I tried running the Qdrant provider and found some bugs. See #1021 for
details. @terrytangyuan wrote there:
> Please feel free to submit your changes in a PR. I fixed similar
issues for pgvector provider. This might be an issue introduced from a
refactoring.
So I am submitting this PR.
Closes#1021
## Test Plan
Here are the highlights for what I did to test this:
References:
-
https://llama-stack.readthedocs.io/en/latest/getting_started/index.html
-
https://github.com/meta-llama/llama-stack-apps/blob/main/examples/agents/rag_with_vector_db.py
-
https://github.com/meta-llama/llama-stack/blob/main/docs/zero_to_hero_guide/README.md#build-configure-and-run-llama-stack
Install and run Qdrant server:
```
podman pull qdrant/qdrant
mkdir qdrant-data
podman run -p 6333:6333 -v $(pwd)/qdrant-data:/qdrant/storage qdrant/qdrant
```
Install and run Llama Stack from the venv-support PR (mainly because I
didn't want to install conda):
```
brew install cmake # Should just need this once
git clone https://github.com/meta-llama/llama-models.git
gh repo clone cdoern/llama-stack
cd llama-stack
gh pr checkout 1018 # This is the checkout that introduces venv support for build/run. Otherwise you have to use conda. Eventually this wil be part of main, hopefully.
uv sync --extra dev
uv pip install -e .
source .venv/bin/activate
uv pip install qdrant_client
LLAMA_STACK_DIR=$(pwd) LLAMA_MODELS_DIR=../llama-models llama stack build --template ollama --image-type venv
```
```
edit llama_stack/templates/ollama/run.yaml
```
in that editor under:
```
vector_io:
```
add:
```
- provider_id: qdrant
provider_type: remote::qdrant
config: {}
```
see
https://github.com/meta-llama/llama-stack/blob/main/llama_stack/providers/remote/vector_io/qdrant/config.py#L14
for config options (but I didn't need any)
```
LLAMA_STACK_DIR=$(pwd) LLAMA_MODELS_DIR=../llama-models llama stack run ollama --image-type venv \
--port $LLAMA_STACK_PORT \
--env INFERENCE_MODEL=$INFERENCE_MODEL \
--env SAFETY_MODEL=$SAFETY_MODEL \
--env OLLAMA_URL=$OLLAMA_URL
```
Then I tested it out in a notebook. Key highlights included:
```
qdrant_provider = None
for provider in client.providers.list():
if provider.api == "vector_io" and provider.provider_id == "qdrant":
qdrant_provider = provider
qdrant_provider
assert qdrant_provider is not None, "QDrant is not a provider. You need to edit the run yaml file you use in your `llama stack run` call"
vector_db_id = f"test-vector-db-{uuid.uuid4().hex}"
client.vector_dbs.register(
vector_db_id=vector_db_id,
embedding_model="all-MiniLM-L6-v2",
embedding_dimension=384,
provider_id=qdrant_provider.provider_id,
)
```
Other than that, I just followed what was in
https://llama-stack.readthedocs.io/en/latest/getting_started/index.html
It would be good to have automated tests for this in the future, but
that would be a big undertaking.
Signed-off-by: Bill Murdock <bmurdock@redhat.com>
# What does this PR do?
Moved model availability check logic into a dedicated
check_model_availability function. Eliminated redundant code by reusing
the helper function in both embedding and non-embedding model
registration.
Signed-off-by: Sébastien Han <seb@redhat.com>
## Test Plan
Run Ollama and serve 2 models to get most the unit test pass:
```
ollama run llama3.2:3b-instruct-fp16 --keepalive 2m &
ollama run llama3.1:8b --keepalive 2m &
```
Run the unit test:
```
uv run pytest -v -k "ollama" --inference-model=llama3.2:3b-instruct-fp16 llama_stack/providers/tests/inference/test_model_registration.py
/Users/leseb/Documents/AI/llama-stack/.venv/lib/python3.13/site-packages/pytest_asyncio/plugin.py:207: 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 3.13.1, pytest-8.3.4, pluggy-1.5.0 -- /Users/leseb/Documents/AI/llama-stack/.venv/bin/python3
cachedir: .pytest_cache
metadata: {'Python': '3.13.1', 'Platform': 'macOS-15.3-arm64-arm-64bit-Mach-O', 'Packages': {'pytest': '8.3.4', 'pluggy': '1.5.0'}, 'Plugins': {'html': '4.1.1', 'metadata': '3.1.1', 'asyncio': '0.25.3', 'anyio': '4.8.0', 'nbval': '0.11.0'}}
rootdir: /Users/leseb/Documents/AI/llama-stack
configfile: pyproject.toml
plugins: html-4.1.1, metadata-3.1.1, asyncio-0.25.3, anyio-4.8.0, nbval-0.11.0
asyncio: mode=Mode.STRICT, asyncio_default_fixture_loop_scope=None
collected 65 items / 60 deselected / 5 selected
llama_stack/providers/tests/inference/test_model_registration.py::TestModelRegistration::test_register_unsupported_model[-ollama] PASSED [ 20%]
llama_stack/providers/tests/inference/test_model_registration.py::TestModelRegistration::test_register_nonexistent_model[-ollama] PASSED [ 40%]
llama_stack/providers/tests/inference/test_model_registration.py::TestModelRegistration::test_register_with_llama_model[-ollama] FAILED [ 60%]
llama_stack/providers/tests/inference/test_model_registration.py::TestModelRegistration::test_initialize_model_during_registering[-ollama] FAILED [ 80%]
llama_stack/providers/tests/inference/test_model_registration.py::TestModelRegistration::test_register_with_invalid_llama_model[-ollama] PASSED [100%]
================================================== FAILURES ==================================================
_______________________ TestModelRegistration.test_register_with_llama_model[-ollama] ________________________
llama_stack/providers/tests/inference/test_model_registration.py:54: in test_register_with_llama_model
_ = await models_impl.register_model(
llama_stack/providers/utils/telemetry/trace_protocol.py:91: in async_wrapper
result = await method(self, *args, **kwargs)
llama_stack/distribution/routers/routing_tables.py:245: in register_model
registered_model = await self.register_object(model)
llama_stack/distribution/routers/routing_tables.py:192: in register_object
registered_obj = await register_object_with_provider(obj, p)
llama_stack/distribution/routers/routing_tables.py:53: in register_object_with_provider
return await p.register_model(obj)
llama_stack/providers/utils/telemetry/trace_protocol.py:91: in async_wrapper
result = await method(self, *args, **kwargs)
llama_stack/providers/remote/inference/ollama/ollama.py:368: in register_model
await check_model_availability(model.provider_resource_id)
llama_stack/providers/remote/inference/ollama/ollama.py:359: in check_model_availability
raise ValueError(
E ValueError: Model 'custom-model' is not available in Ollama. Available models: llama3.1:8b, llama3.2:3b-instruct-fp16
__________________ TestModelRegistration.test_initialize_model_during_registering[-ollama] ___________________
llama_stack/providers/tests/inference/test_model_registration.py:85: in test_initialize_model_during_registering
mock_load_model.assert_called_once()
/opt/homebrew/Cellar/python@3.13/3.13.1/Frameworks/Python.framework/Versions/3.13/lib/python3.13/unittest/mock.py:956: in assert_called_once
raise AssertionError(msg)
E AssertionError: Expected 'load_model' to have been called once. Called 0 times.
-------------------------------------------- Captured stderr call --------------------------------------------
W0207 11:55:26.777000 90854 .venv/lib/python3.13/site-packages/torch/distributed/elastic/multiprocessing/redirects.py:29] NOTE: Redirects are currently not supported in Windows or MacOs.
========================================== short test summary info ===========================================
FAILED llama_stack/providers/tests/inference/test_model_registration.py::TestModelRegistration::test_register_with_llama_model[-ollama] - ValueError: Model 'custom-model' is not available in Ollama. Available models: llama3.1:8b, llama3.2:3b-i...
FAILED llama_stack/providers/tests/inference/test_model_registration.py::TestModelRegistration::test_initialize_model_during_registering[-ollama] - AssertionError: Expected 'load_model' to have been called once. Called 0 times.
=========================== 2 failed, 3 passed, 60 deselected, 2 warnings in 1.84s ===========================
```
We only "care" about the `test_register_nonexistent_model` for this
code.
## Sources
Please link relevant resources if necessary.
## Before submitting
- [ ] This PR fixes a typo or improves the docs (you can dismiss the
other checks if that's the case).
- [ ] Ran pre-commit to handle lint / formatting issues.
- [ ] Read the [contributor
guideline](https://github.com/meta-llama/llama-stack/blob/main/CONTRIBUTING.md),
Pull Request section?
- [ ] Updated relevant documentation.
- [ ] Wrote necessary unit or integration tests.
Signed-off-by: Sébastien Han <seb@redhat.com>
# What does this PR do?
Imported `ToolConfig` from the `llama_stack.apis.inference` module to
resolve missing reference and ensure proper functionality within the
`groq.py` file.
Signed-off-by: Sébastien Han <seb@redhat.com>
## Test Plan
Without the change, pytest will run with the following error:
```
uv run pytest -v -s -k "ollama" llama_stack/providers/tests/
/Users/leseb/Documents/AI/llama-stack/.venv/lib/python3.13/site-packages/pytest_asyncio/plugin.py:207: 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 3.13.1, pytest-8.3.4, pluggy-1.5.0 -- /Users/leseb/Documents/AI/llama-stack/.venv/bin/python3
cachedir: .pytest_cache
metadata: {'Python': '3.13.1', 'Platform': 'macOS-15.3-arm64-arm-64bit-Mach-O', 'Packages': {'pytest': '8.3.4', 'pluggy': '1.5.0'}, 'Plugins': {'html': '4.1.1', 'metadata': '3.1.1', 'asyncio': '0.25.3', 'anyio': '4.8.0', 'nbval': '0.11.0'}}
rootdir: /Users/leseb/Documents/AI/llama-stack
configfile: pyproject.toml
plugins: html-4.1.1, metadata-3.1.1, asyncio-0.25.3, anyio-4.8.0, nbval-0.11.0
asyncio: mode=Mode.STRICT, asyncio_default_fixture_loop_scope=None
collected 379 items / 1 error / 349 deselected / 30 selected
=================================================== ERRORS ===================================================
__________________ ERROR collecting llama_stack/providers/tests/inference/groq/test_init.py __________________
llama_stack/providers/tests/inference/groq/test_init.py:11: in <module>
from llama_stack.providers.remote.inference.groq.groq import GroqInferenceAdapter
llama_stack/providers/remote/inference/groq/groq.py:72: in <module>
class GroqInferenceAdapter(Inference, ModelRegistryHelper, NeedsRequestProviderData):
llama_stack/providers/remote/inference/groq/groq.py:102: in GroqInferenceAdapter
tool_config: Optional[ToolConfig] = None,
E NameError: name 'ToolConfig' is not defined
========================================== short test summary info ===========================================
ERROR llama_stack/providers/tests/inference/groq/test_init.py - NameError: name 'ToolConfig' is not defined
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! Interrupted: 1 error during collection !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
=============================== 349 deselected, 22 warnings, 1 error in 0.28s ================================
```
With the change the test continues to run and fails with a different
error:
```
uv run pytest -v -s llama_stack/providers/tests/
/Users/leseb/Documents/AI/llama-stack/.venv/lib/python3.13/site-packages/pytest_asyncio/plugin.py:207: 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 3.13.1, pytest-8.3.4, pluggy-1.5.0 -- /Users/leseb/Documents/AI/llama-stack/.venv/bin/python3
cachedir: .pytest_cache
metadata: {'Python': '3.13.1', 'Platform': 'macOS-15.3-arm64-arm-64bit-Mach-O', 'Packages': {'pytest': '8.3.4', 'pluggy': '1.5.0'}, 'Plugins': {'html': '4.1.1', 'metadata': '3.1.1', 'asyncio': '0.25.3', 'anyio': '4.8.0', 'nbval': '0.11.0'}}
rootdir: /Users/leseb/Documents/AI/llama-stack
configfile: pyproject.toml
plugins: html-4.1.1, metadata-3.1.1, asyncio-0.25.3, anyio-4.8.0, nbval-0.11.0
asyncio: mode=Mode.STRICT, asyncio_default_fixture_loop_scope=None
collected 342 items / 1 error
=================================================== ERRORS ===================================================
______________ ERROR collecting llama_stack/providers/tests/inference/test_vision_inference.py _______________
llama_stack/providers/tests/inference/test_vision_inference.py:29: in <module>
class TestVisionModelInference:
llama_stack/providers/tests/inference/test_vision_inference.py:35: in TestVisionModelInference
ImageContentItem(image=dict(data=PASTA_IMAGE)),
E pydantic_core._pydantic_core.ValidationError: 1 validation error for ImageContentItem
E image.data
E Input should be a valid string, unable to parse raw data as a unicode string [type=string_unicode, input_value=b'\xff\xd8\xff\xe0\x00\x1...0\xe6\x9f5\xb5?\xff\xd9', input_type=bytes]
E For further information visit https://errors.pydantic.dev/2.10/v/string_unicode
========================================== short test summary info ===========================================
ERROR llama_stack/providers/tests/inference/test_vision_inference.py - pydantic_core._pydantic_core.ValidationError: 1 validation error for ImageContentItem
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! Interrupted: 1 error during collection !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
======================================= 22 warnings, 1 error in 0.25s ========================================
```
Which is fixed in https://github.com/meta-llama/llama-stack/pull/1003.
## Sources
Please link relevant resources if necessary.
## Before submitting
- [ ] This PR fixes a typo or improves the docs (you can dismiss the
other checks if that's the case).
- [ ] Ran pre-commit to handle lint / formatting issues.
- [ ] Read the [contributor
guideline](https://github.com/meta-llama/llama-stack/blob/main/CONTRIBUTING.md),
Pull Request section?
- [ ] Updated relevant documentation.
- [ ] Wrote necessary unit or integration tests.
Signed-off-by: Sébastien Han <seb@redhat.com>
