# What does this PR do?
This addresses 2 bugs I ran into when launching a fine-tuning job with
the NVIDIA Adapter:
1. Session handling in `_make_request` helper function returns an error.
```
INFO: 127.0.0.1:55831 - "POST /v1/post-training/supervised-fine-tune HTTP/1.1" 500 Internal Server Error
16:11:45.643 [END] /v1/post-training/supervised-fine-tune [StatusCode.OK] (270.44ms)
16:11:45.643 [ERROR] Error executing endpoint route='/v1/post-training/supervised-fine-tune' method='post'
Traceback (most recent call last):
File "/Users/jgulabrai/Projects/forks/llama-stack/llama_stack/distribution/server/server.py", line 201, in endpoint
return await maybe_await(value)
File "/Users/jgulabrai/Projects/forks/llama-stack/llama_stack/distribution/server/server.py", line 161, in maybe_await
return await value
File "/Users/jgulabrai/Projects/forks/llama-stack/llama_stack/providers/remote/post_training/nvidia/post_training.py", line 408, in supervised_fine_tune
response = await self._make_request(
File "/Users/jgulabrai/Projects/forks/llama-stack/llama_stack/providers/remote/post_training/nvidia/post_training.py", line 98, in _make_request
async with self.session.request(method, url, params=params, json=json, **kwargs) as response:
File "/Users/jgulabrai/Projects/forks/llama-stack/.venv/lib/python3.10/site-packages/aiohttp/client.py", line 1425, in __aenter__
self._resp: _RetType = await self._coro
File "/Users/jgulabrai/Projects/forks/llama-stack/.venv/lib/python3.10/site-packages/aiohttp/client.py", line 579, in _request
handle = tm.start()
File "/Users/jgulabrai/Projects/forks/llama-stack/.venv/lib/python3.10/site-packages/aiohttp/helpers.py", line 587, in start
return self._loop.call_at(when, self.__call__)
File "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/asyncio/base_events.py", line 724, in call_at
self._check_closed()
File "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/asyncio/base_events.py", line 510, in _check_closed
raise RuntimeError('Event loop is closed')
RuntimeError: Event loop is closed
```
Note: This only occurred when initializing the client like so:
```
client = LlamaStackClient(
base_url="http://0.0.0.0:8321"
)
response = client.post_training.supervised_fine_tune(...) # Returns error
```
I didn't run into this issue when using the library client:
```
client = LlamaStackAsLibraryClient("nvidia")
client.initialize()
response = client.post_training.supervised_fine_tune(...) # Works fine
```
2. The `algorithm_config` param in `supervised_fine_tune` is parsed as a
`dict` when run from unit tests, but a Pydantic model when invoked using
the Llama Stack client. So, the call fails outside of unit tests:
```
INFO: 127.0.0.1:54024 - "POST /v1/post-training/supervised-fine-tune HTTP/1.1" 500 Internal Server Error
21:14:02.315 [END] /v1/post-training/supervised-fine-tune [StatusCode.OK] (71.18ms)
21:14:02.314 [ERROR] Error executing endpoint route='/v1/post-training/supervised-fine-tune' method='post'
Traceback (most recent call last):
File "/Users/jgulabrai/Projects/forks/llama-stack/llama_stack/distribution/server/server.py", line 205, in endpoint
return await maybe_await(value)
File "/Users/jgulabrai/Projects/forks/llama-stack/llama_stack/distribution/server/server.py", line 164, in maybe_await
return await value
File "/Users/jgulabrai/Projects/forks/llama-stack/llama_stack/providers/remote/post_training/nvidia/post_training.py", line 407, in supervised_fine_tune
"adapter_dim": algorithm_config.get("adapter_dim"),
File "/Users/jgulabrai/Projects/forks/llama-stack/.venv/lib/python3.10/site-packages/pydantic/main.py", line 891, in __getattr__
raise AttributeError(f'{type(self).__name__!r} object has no attribute {item!r}')
AttributeError: 'LoraFinetuningConfig' object has no attribute 'get'
```
The code assumes `algorithm_config` should be `dict`, so I just handle
both cases.
[//]: # (If resolving an issue, uncomment and update the line below)
[//]: # (Closes #[issue-number])
## Test Plan
1. I ran a local Llama Stack server with the necessary env vars:
```
lama stack run llama_stack/templates/nvidia/run.yaml --port 8321 --env ...
```
And invoked `supervised_fine_tune` to confirm neither of the errors
above occur.
```
client = LlamaStackClient(
base_url="http://0.0.0.0:8321"
)
response = client.post_training.supervised_fine_tune(...)
```
2. I confirmed the unit tests still pass: `./scripts/unit-tests.sh
tests/unit/providers/nvidia/test_supervised_fine_tuning.py`
[//]: # (## Documentation)
---------
Co-authored-by: Jash Gulabrai <jgulabrai@nvidia.com>
# What does this PR do?
## Test Plan
LLAMA_STACK_CONFIG=http://localhost:5002 pytest -s -v
tests/integration/inference --safety-shield meta-llama/Llama-Guard-3-8B
--vision-model meta-llama/Llama-4-Scout-17B-16E-Instruct --text-model
meta-llama/Llama-4-Scout-17B-16E-Instruct
adding the --gpu all flag to Docker run commands
for meta-reference-gpu distributions ensures models are loaded into GPU
instead of CPU.
Remove docs for meta-reference-quantized-gpu
The distribution was removed in #1887
but these files were left behind.
Fixes: #1798
# What does this PR do?
Fixes doc to add --gpu all command to docker run
[//]: # (If resolving an issue, uncomment and update the line below)
Closes#1798
## 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.*]
verified in docker documentation but untested
---------
Signed-off-by: Derek Higgins <derekh@redhat.com>
# What does this PR do?
IBM watsonx ai added as the inference [#1741
](https://github.com/meta-llama/llama-stack/issues/1741)
[//]: # (If resolving an issue, uncomment and update the line below)
[//]: # (Closes #[issue-number])
---------
Co-authored-by: Sajikumar JS <sajikumar.js@ibm.com>
# What does this PR do?
Enhances the user experience in the `llama stack build` command by
adding interactive TAB completion for image type selection. This ensures
the UX consistency with other parts of the CLI that already support tab
completion, such as provider selection, providing a more intuitive and
discoverable interface for users.
<img width="1531" alt="image"
src="https://github.com/user-attachments/assets/12161d45-451d-4820-b34d-7ea4decf810f"
/>
# What does this PR do?
This PR improves the Tools page in the LlamaStack Playground UI by
enhancing the readability of the active tool list shown in the sidebar.
- Previously, active tools were displayed in a flat JSON array with
verbose identifiers (e.g., builtin::code_interpreter:code_interpreter).
- This PR updates the logic to group tools by their toolgroup (e.g.,
builtin::websearch) and renders each tool name in a simplified,
human-readable format (e.g., web_search).
- This change improves usability when working with multiple toolgroups,
especially in configurations involving MCP tools or complex tool
identifiers.
Before and After Comparison:
**Before**
**After**
[//]: # (If resolving an issue, uncomment and update the line below)
[//]: # (Closes #[issue-number])
## Test Plan
- Followed the [LlamaStack UI Developer Setup
instructions](https://github.com/meta-llama/llama-stack/tree/main/llama_stack/distribution/ui)
- Ran the Streamlit UI via: `uv run --with "[.ui]" streamlit run
llama_stack/distribution/ui/app.py`
- Selected multiple built-in toolgroups (e.g., code_interpreter,
websearch, wolfram_alpha) from the sidebar.
[//]: # (## Documentation)
# What does this PR do?
Adds custom model registration functionality to NVIDIAInferenceAdapter
which let's the inference happen on:
- post-training model
- non-llama models in API Catalogue(behind
https://integrate.api.nvidia.com and endpoints compatible with
AyncOpenAI)
## Example Usage:
```python
from llama_stack.apis.models import Model, ModelType
from llama_stack.distribution.library_client import LlamaStackAsLibraryClient
client = LlamaStackAsLibraryClient("nvidia")
_ = client.initialize()
client.models.register(
model_id=model_name,
model_type=ModelType.llm,
provider_id="nvidia"
)
response = client.inference.chat_completion(
model_id=model_name,
messages=[{"role":"system","content":"You are a helpful assistant."},{"role":"user","content":"Write a limerick about the wonders of GPU computing."}],
)
```
## Test Plan
```bash
pytest tests/unit/providers/nvidia/test_supervised_fine_tuning.py
========================================================== test session starts ===========================================================
platform linux -- Python 3.10.0, pytest-8.3.5, pluggy-1.5.0
rootdir: /home/ubuntu/llama-stack
configfile: pyproject.toml
plugins: anyio-4.9.0
collected 6 items
tests/unit/providers/nvidia/test_supervised_fine_tuning.py ...... [100%]
============================================================ warnings summary ============================================================
../miniconda/envs/nvidia-1/lib/python3.10/site-packages/pydantic/fields.py:1076
/home/ubuntu/miniconda/envs/nvidia-1/lib/python3.10/site-packages/pydantic/fields.py:1076: PydanticDeprecatedSince20: Using extra keyword arguments on `Field` is deprecated and will be removed. Use `json_schema_extra` instead. (Extra keys: 'contentEncoding'). Deprecated in Pydantic V2.0 to be removed in V3.0. See Pydantic V2 Migration Guide at https://errors.pydantic.dev/2.11/migration/
warn(
-- Docs: https://docs.pytest.org/en/stable/how-to/capture-warnings.html
====================================================== 6 passed, 1 warning in 1.51s ======================================================
```
[//]: # (## Documentation)
Updated Readme.md
cc: @dglogo, @sumitb, @mattf
# What does this PR do?
This PR adds support for NVIDIA's NeMo Evaluator API to the Llama Stack
eval module. The integration enables users to evaluate models via the
Llama Stack interface.
## 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.*]
1. Added unit tests and successfully ran from root of project:
`./scripts/unit-tests.sh tests/unit/providers/nvidia/test_eval.py`
```
tests/unit/providers/nvidia/test_eval.py::TestNVIDIAEvalImpl::test_job_cancel PASSED
tests/unit/providers/nvidia/test_eval.py::TestNVIDIAEvalImpl::test_job_result PASSED
tests/unit/providers/nvidia/test_eval.py::TestNVIDIAEvalImpl::test_job_status PASSED
tests/unit/providers/nvidia/test_eval.py::TestNVIDIAEvalImpl::test_register_benchmark PASSED
tests/unit/providers/nvidia/test_eval.py::TestNVIDIAEvalImpl::test_run_eval PASSED
```
2. Verified I could build the Llama Stack image: `LLAMA_STACK_DIR=$(pwd)
llama stack build --template nvidia --image-type venv`
Documentation added to
`llama_stack/providers/remote/eval/nvidia/README.md`
---------
Co-authored-by: Jash Gulabrai <jgulabrai@nvidia.com>
# What does this PR do?
This expands the `test_sse` test suite and fixes some edge cases with
bugs in our SSE error handling to ensure streaming clients always get a
proper error response.
First, we handle the case where a client disconnects before we actually
start streaming the response back. Previously we only handled the case
where a client disconnected as we were streaming the response, but there
was an edge case where a client disconnecting before we streamed any
response back did not trigger our logic to cleanly handle that
disconnect.
Second, we handle the case where an error is thrown from the server
before the actual async generator gets created from the provider. This
happens in scenarios like the newly merged OpenAI API input validation,
where we eagerly raise validation errors before returning the async
generator object that streams the responses back.
## Test Plan
Tested via:
```
python -m pytest -s -v tests/unit/server/test_sse.py
```
Both test cases failed before, and passed afterwards. The test cases
were written based on me experimenting with actual clients that would do
bad things like randomly disconnect or send invalid input in streaming
mode and I hit these two cases, where things were misbehaving in our
error handling.
Signed-off-by: Ben Browning <bbrownin@redhat.com>
Include the tool call details with the chat when doing Rag with Remote
vllm
Fixes: #1929
With this PR the tool call is included in the chat returned to vllm, the
model (meta-llama/Llama-3.1-8B-Instruct) the returns the answer as
expected.
Signed-off-by: Derek Higgins <derekh@redhat.com>
# What does this PR do?
This PR addresses the content dominance problem that frequently arises
with multiple models when executing queries with the RAG tool. When the
retrieved content is too large, it disproportionately influences the
generation process, causing the model to ignore the original question
and to provide meaningless comments on the retrieved information
instead.
This situation is especially common with agentic RAG, which is the
standard way of doing RAG in Llama Stack, since directly manipulating
the prompt combining the query with the retrieved content is not
possible.
This PR appends a grounding message to the results returned by the
knowledge search tool, reminding the model about the original query and
the purpose of the inference call. This makes the problem significantly
less likely to occur.
## Test Plan
Running the following script before the fix demonstrates the content
dominance problem where the model insists to comment on the retrieved
content and refuses to address the question.
Running the script after the fix results in getting the correct answer.
```
import os
import uuid
from llama_stack_client import Agent, AgentEventLogger, RAGDocument, LlamaStackClient
# the server endpoint
LLAMA_STACK_SERVER_URL = "http://localhost:8321"
# inference settings
MODEL_ID = ""meta-llama/Llama-3.1-8B-Instruct"
SYSTEM_PROMPT = "You are a helpful assistant. "
# RAG settings
VECTOR_DB_EMBEDDING_MODEL = "all-MiniLM-L6-v2"
VECTOR_DB_EMBEDDING_DIMENSION = 384
VECTOR_DB_CHUNK_SIZE = 512
# initialize the server connection
client = LlamaStackClient(base_url=os.environ.get("LLAMA_STACK_ENDPOINT", LLAMA_STACK_SERVER_URL))
# init the RAG retrieval parameters
vector_db_id = f"test_vector_db_{uuid.uuid4()}"
vector_providers = [
provider for provider in client.providers.list() if provider.api == "vector_io"
]
vector_provider_to_use = vector_providers[0]
# define and register the document collection to be used
client.vector_dbs.register(
vector_db_id=vector_db_id,
embedding_model=VECTOR_DB_EMBEDDING_MODEL,
embedding_dimension=VECTOR_DB_EMBEDDING_DIMENSION,
provider_id=vector_provider_to_use.provider_id,
)
# ingest the documents into the newly created document collection
urls = [
("https://www.openshift.guide/openshift-guide-screen.pdf", "application/pdf"),
]
documents = [
RAGDocument(
document_id=f"num-{i}",
content=url,
mime_type=url_type,
metadata={},
)
for i, (url, url_type) in enumerate(urls)
]
client.tool_runtime.rag_tool.insert(
documents=documents,
vector_db_id=vector_db_id,
chunk_size_in_tokens=VECTOR_DB_CHUNK_SIZE,
)
queries = [
"How to install OpenShift?",
]
# initializing the agent
agent = Agent(
client,
model=MODEL_ID,
instructions=SYSTEM_PROMPT,
# we make our agent aware of the RAG tool by including builtin::rag/knowledge_search in the list of tools
tools=[
dict(
name="builtin::rag/knowledge_search",
args={
"vector_db_ids": [vector_db_id], # list of IDs of document collections to consider during retrieval
},
)
],
)
for prompt in queries:
print(f"User> {prompt}")
# create a new turn with a new session ID for each prompt
response = agent.create_turn(
messages=[
{
"role": "user",
"content": prompt,
}
],
session_id=agent.create_session(f"rag-session_{uuid.uuid4()}")
)
# print the response, including tool calls output
for log in AgentEventLogger().log(response):
print(log.content, end='')
```
As part of the build process, we now include the generated run.yaml
(based of the provided build configuration file) into the container. We
updated the entrypoint to use this run configuration as well.
Given this simple distribution configuration:
```
# build.yaml
version: '2'
distribution_spec:
description: Use (an external) Ollama server for running LLM inference
providers:
inference:
- remote::ollama
vector_io:
- inline::faiss
safety:
- inline::llama-guard
agents:
- inline::meta-reference
telemetry:
- inline::meta-reference
eval:
- inline::meta-reference
datasetio:
- remote::huggingface
- inline::localfs
scoring:
- inline::basic
- inline::llm-as-judge
- inline::braintrust
tool_runtime:
- remote::brave-search
- remote::tavily-search
- inline::code-interpreter
- inline::rag-runtime
- remote::model-context-protocol
- remote::wolfram-alpha
container_image: "registry.access.redhat.com/ubi9"
image_type: container
image_name: test
```
Build it:
```
llama stack build --config build.yaml
```
Run it:
```
podman run --rm \
-p 8321:8321 \
-e OLLAMA_URL=http://host.containers.internal:11434 \
--name llama-stack-server \
localhost/leseb-test:0.2.2
```
Signed-off-by: Sébastien Han <seb@redhat.com>
# What does this PR do?
When clients called the Open AI API with invalid input that wasn't
caught by our own Pydantic API validation but instead only caught by the
backend inference provider, that backend inference provider was
returning a HTTP 400 error. However, we were wrapping that into a HTTP
500 error, obfuscating the actual issue from calling clients and
triggering OpenAI client retry logic.
This change adjusts our existing `translate_exception` method in
`server.py` to wrap `openai.BadRequestError` as HTTP 400 errors, passing
through the string representation of the error message to the calling
user so they can see the actual input validation error and correct it. I
tried changing this in a few other places, but ultimately
`translate_exception` was the only real place to handle this for both
streaming and non-streaming requests across all inference providers that
use the OpenAI server APIs.
This also tightens up our validation a bit for the OpenAI chat
completions API, to catch empty `messages` parameters, invalid
`tool_choice` parameters, invalid `tools` items, or passing
`tool_choice` when `tools` isn't given.
Lastly, this extends our OpenAI API chat completions verifications to
also check for consistent input validation across providers. Providers
behind Llama Stack should automatically pass all the new tests due to
the input validation added here, but some of the providers fail this
test when not run behind Llama Stack due to differences in how they
handle input validation and errors.
(Closes#1951)
## Test Plan
To test this, start an OpenAI API verification stack:
```
llama stack run --image-type venv tests/verifications/openai-api-verification-run.yaml
```
Then, run the new verification tests with your provider(s) of choice:
```
python -m pytest -s -v \
tests/verifications/openai_api/test_chat_completion.py \
--provider openai-llama-stack
python -m pytest -s -v \
tests/verifications/openai_api/test_chat_completion.py \
--provider together-llama-stack
```
Signed-off-by: Ben Browning <bbrownin@redhat.com>
# What does this PR do?
This PR adds the name of the tool that is used by the agent on the
"tools" page of the playground. See image below for an example.
## Test Plan
Run the playground and navigate to the tools page. There users can see
that this additional text is present when tools are invoked and absent
when they are not.
```
streamlit run llama_stack/distribution/ui/app.py
```
Signed-off-by: Michael Clifford <mcliffor@redhat.com>
# What does this PR do?
Previously, when a streaming client would disconnect before we were
finished streaming the entire response, an error like the below would
get raised from the `sse_generator` function in
`llama_stack/distribution/server/server.py`:
```
AttributeError: 'coroutine' object has no attribute 'aclose'. Did you mean: 'close'?
```
This was because we were calling `aclose` on a coroutine instead of the
awaited value from that coroutine. This change fixes that, so that we
save off the awaited value and then can call `aclose` on it if we
encounter an `asyncio.CancelledError`, like we see when a client
disconnects before we're finished streaming.
The other changes in here are to add a simple set of tests for the happy
path of our SSE streaming and this client disconnect path.
That unfortunately requires adding one more dependency into our unit
test section of pyproject.toml since `server.py` requires loading some
of the telemetry code for me to test this functionality.
## Test Plan
I wrote the tests in `tests/unit/server/test_sse.py` first, verified the
client disconnected test failed before my change, and that it passed
afterwards.
```
python -m pytest -s -v tests/unit/server/test_sse.py
```
Signed-off-by: Ben Browning <bbrownin@redhat.com>
# What does this PR do?
Closes#1968.
The asynchronous client in `VLLMInferenceAdapter` is now initialized
directly before first use and not in `VLLMInferenceAdapter.initialize`.
This prevents issues arising due to accessing an expired event loop from
a completed `asyncio.run`.
## Test Plan
Ran unit tests, including `test_remote_vllm.py`.
Ran the code snippet mentioned in #1968.
---------
Co-authored-by: Sébastien Han <seb@redhat.com>
# What does this PR do?
Now, tool outputs and retrieved chunks from the vector DB (i.e.,
everything except for the actual model reply) are hidden under an
expander form when presented to the user.
# Test Plan
Navigate to the RAG page in the Playground UI.
# What does this PR do?
The together inference provider was throwing a stack trace every time it
shut down, as it was trying to call a non-existent `close` method on the
AsyncTogether client. While fixing that, I also adjusted its shutdown
logic to close the OpenAI client if we've created one of those, as that
client does have a `close` method.
In testing that, I also realized we were defaulting to treating all
requests as streaming requests instead of defaulting to non-streaming.
So, this flips that default to non-streaming to match how the other
providers work.
## Test Plan
I tested this by ensuring the together inference provider no longer
spits out a long stack trace when shutting it down and by running the
OpenAI API chat completion verification suite to ensure the change in
default streaming logic didn't mess anything else up.
Signed-off-by: Ben Browning <bbrownin@redhat.com>
# What does this PR do?
This PR cleans up the sidebar on the tools page of the playground in the
following ways:
* created a clearer hierarchy of configuration options and tool
selections.
* Removed the `mcp::` or `builtin::` prefixes from the tool selection
buttons.
[//]: # (If resolving an issue, uncomment and update the line below)
[//]: # (Closes #[issue-number])
## Test Plan
Run the playground and see the updated sidebar does not cause any new
errors.
```
streamlit run llama_stack/distribution/ui/app.py
```
[//]: # (## Documentation)
Signed-off-by: Michael Clifford <mcliffor@redhat.com>
37da47ef8e (diff-4d7c51b1efe9043e44439a949dfd92e5827321b34082903477fd04876edb7552)
Pydantic was updated from v1 to v2 in this commit which caused this
breaking change
# What does this PR do?
Part of #1857
This won't fix the Validation error with the example, but it will
correctly supply user with a proper error rather than a 5xx code.
Signed-off-by: Kevin <kpostlet@redhat.com>
# What does this PR do?
We were passing a dict into the compat mixin for OpenAI Completions when
using Llama models with Fireworks, and that was breaking some strong
typing code that was added in openai_compat.py. We shouldn't have been
converting these params to a dict in that case anyway, so this adjusts
things to pass the params in as their actual original types when calling
the OpenAIChatCompletionToLlamaStackMixin.
## Test Plan
All of the fireworks provider verification tests were failing due to
some OpenAI compatibility cleanup in #1962. The changes in that PR were
good to make, and this just cleans up the fireworks provider code to
stop passing in untyped dicts to some of those `openai_compat.py`
methods since we have the original strongly-typed parameters we can pass
in.
```
llama stack run --image-type venv tests/verifications/openai-api-verification-run.yaml
```
```
python -m pytest -s -v tests/verifications/openai_api/test_chat_completion.py --provider=fireworks-llama-stack
```
Before this PR, all of the fireworks OpenAI verification tests were
failing. Now, most of them are passing.
Signed-off-by: Ben Browning <bbrownin@redhat.com>
# What does this PR do?
- Update NVIDIA documentation links to GA docs
- Remove reference to notebooks until merged
[//]: # (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)
Co-authored-by: Jash Gulabrai <jgulabrai@nvidia.com>
# What does this PR do?
This is helpful when debugging issues with vLLM + Llama Stack after this
PR https://github.com/vllm-project/vllm/pull/15593
---------
Signed-off-by: Yuan Tang <terrytangyuan@gmail.com>
# What does this PR do?
NVIDIA Inference provider was using the ModelRegistryHelper to map input
model ids to provider model ids. this updates it to use the model_store.
## Test Plan
`LLAMA_STACK_CONFIG=http://localhost:8321 uv run pytest -v
tests/integration/inference/{test_embedding.py,test_text_inference.py,test_openai_completion.py}
--embedding-model nvidia/llama-3.2-nv-embedqa-1b-v2
--text-model=meta-llama/Llama-3.1-70B-Instruct`
# What does this PR do?
Fixes the UBI 9 container build failure ( `error: command 'gcc' failed`
when installing `polyleven`, `faiss`, etc.) by installing the missing
compiler tool‑chain:
- `python3.11-devel gcc` make added to the UBI 9 `dnf install` line.
### Closes#1970
## Test Plan
- Build a distro with an UBI image
Test plan:
python tests/verifications/generate_report.py --providers
fireworks,together,llama_meta_ref,openai
Co-authored-by: Eric Huang <erichuang@fb.com>
# What does this PR do?
Allow users to name an agent and use the name in telemetry instead of
relying on randomly generated agent_ids. This improves the developer
experience by making it easier to find specific agents in telemetry
logs.
Closes#1832
## Test Plan
- Added tests to verify the agent name is properly stored and retrieved
- Ran `uv run -- pytest -v
tests/integration/telemetry/test_telemetry.py::test_agent_name_filtering`
from the root of the project and made sure the tests pass
- Ran `uv run -- pytest -v
tests/integration/telemetry/test_telemetry.py::test_agent_query_spans`
to verify existing code without agent names still works correctly
## Use Example
```
agent = Agent(
llama_stack_client,
model=text_model_id,
name="CustomerSupportAgent", # New parameter
instructions="You are a helpful customer support assistant"
)
session_id = agent.create_session(f"test-session-{uuid4()}")
```
## Implementation Notes
- Agent names are optional string parameters with no additional
validation
- Names are not required to be unique - multiple agents can have the
same name
- The agent_id remains the unique identifier for an agent
---------
Co-authored-by: raghotham <raghotham@gmail.com>
# What does this PR do?
Some of our multi-turn verification tests were failing because I had
accidentally marked content as a required field in the OpenAI chat
completion request assistant messages, but it's actually optional. It is
required for messages from other roles, but assistant is explicitly
allowed to be optional.
Similarly, the assistant message tool_calls field should default to None
instead of an empty list.
These two changes get the openai-llama-stack verification test back to
100% passing, just like it passes 100% when not behind Llama Stack. They
also increase the pass rate of some of the other providers in the
verification test, but don't get them to 100%.
## Test Plan
I started a Llama Stack server setup to run all the verification tests
(requires OPENAI_API_KEY env variable)
```
llama stack run --image-type venv tests/verifications/openai-api-verification-run.yaml
```
Then, I manually ran the verification tests to see which were failing,
fix them, and ran them again after these changes to ensure they were all
passing.
```
python -m pytest -s -v tests/verifications/openai_api/test_chat_completion.py --provider=openai-llama-stack
```
Signed-off-by: Ben Browning <bbrownin@redhat.com>
# What does this PR do?
Add NVIDIA platform docs that serve as a starting point for Llama Stack
users and explains all supported microservices.
[//]: # (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)
---------
Co-authored-by: Jash Gulabrai <jgulabrai@nvidia.com>
# What does this PR do?
This PR handles the case where a Customization Job's status is
`unknown`. Since we don't map `unknown` to a valid `JobStatus`, the
PostTraining provider throws an exception when fetching/listing a job.
[//]: # (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.*]
`./scripts/unit-tests.sh
tests/unit/providers/nvidia/test_supervised_fine_tuning.py` succeeds
[//]: # (## Documentation)
Co-authored-by: Jash Gulabrai <jgulabrai@nvidia.com>
# What does this PR do?
Fixes a crash that occurred when building a stack as a container image
via the interactive wizard without supplying --template or --config.
- Root cause: template_or_config was None; only the container path
relies on that parameter, which later reaches subprocess.run() and
triggers
`TypeError: expected str, bytes or os.PathLike object, not NoneType.`
- Change: in `_run_stack_build_command_from_build_config` we now fall
back to the freshly‑written build‑spec file whenever both optional
sources are missing. Also adds a spy‑based unit test that asserts a
valid string path is passed to build_image() for container builds.
### Closes#1976
## Test Plan
- New unit test: test_build_path.py. Monkey‑patches build_image,
captures the fourth argument, and verifies it is a real path
- Manual smoke test:
```
llama stack build --image-type container
# answer wizard prompts
```
Build proceeds into Docker without raising the previous TypeError.
## Future Work
Harmonise `build_image` arguments so every image type receives the same
inputs, eliminating this asymmetric special‑case.
# What does this PR do?
Build failures are hard to read, sometimes we get errors like:
```
Error building stack: 'key'
```
Which are difficult to debug without a proper trace.
## Test Plan
If `llama stack build` fails you get a traceback now.
Signed-off-by: Sébastien Han <seb@redhat.com>
# What does this PR do?
This PR lets users select an existing vdb to use with their agent on the
tools page of the playground. The drop down menu that lets users select
a vdb only appears when the rag tool is selected. Without this change,
there is no way for a user to specify which vdb they want their rag tool
to use on the tools page. I have intentionally left the RAG options
sparse here since the full RAG options are exposed on the RAG page.
## Test Plan
Without these changes the RAG tool will throw the following error:
`name: knowledge_search) does not have any content `
With these changes the RAG tool works as expected.
Signed-off-by: Michael Clifford <mcliffor@redhat.com>
# What does this PR do?
Adds `meta/llama-3.2-1b-instruct` to list of models that NeMo Customizer
can fine-tune. This is the model our example notebooks typically use for
fine-tuning.
[//]: # (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)
Co-authored-by: Jash Gulabrai <jgulabrai@nvidia.com>
Fixes: #1955
Since 0.2.0, the vLLM gets an empty list (vs ``None``in 0.1.9 and
before) when there are no tools configured which causes the issue
described in #1955 p. This patch avoids sending the 'tools' param to the
vLLM altogether instead of an empty list.
It also adds a small unit test to avoid regressions.
The OpenAI
[specification](https://platform.openai.com/docs/api-reference/chat/create)
does not explicitly state that the list cannot be empty but I found this
out through experimentation and it might depend on the actual remote
vllm. In any case, as this parameter is Optional, is best to skip it
altogether if there's no tools configured.
Signed-off-by: Daniel Alvarez <dalvarez@redhat.com>
# What does this PR do?
This PR adds a `max_tokens` slider to playground tools page. I have
found that in some instances the llama stack server throws a 500 error
if the max_tokens value is not explicitly set in the agent's
`sampling_params`. This PR, uses the same implementation of the
`max_tokens` slider from the chat page, and includes it on the tools
page.
## Test Plan
1. Attempting to call a tool without these changes results in a `500:
Internal server error: An unexpected error occurred`.
2. Attempting to call a tool with these changes results in the expected
output.
Signed-off-by: Michael Clifford <mcliffor@redhat.com>
# What does this PR do?
PR adds instructions to setup vLLM remote endpoint for vllm-remote llama
stack distribution.
## Test Plan
* Verified with manual tests of the configured vllm-remote against vllm
endpoint running on the system with Intel GPU
* Also verified with ci pytests (see cmdline below). Test passes in the
same capacity as it does on the A10 Nvidia setup (some tests do fail
which seems to be known issues with vllm remote llama stack
distribution)
```
pytest -s -v tests/integration/inference/test_text_inference.py \
--stack-config=http://localhost:5001 \
--text-model=meta-llama/Llama-3.2-3B-Instruct
```
CC: @ashwinb
Signed-off-by: Dmitry Rogozhkin <dmitry.v.rogozhkin@intel.com>
# What does this PR do?
allow users to specify only the providers they want in the llama stack
build command. If a user wants a non-interactive build, but doesn't want
to use a template, `--providers` allows someone to specify something
like `--providers inference=remote::ollama` for a distro with JUST
ollama
## Test Plan
`llama stack build --providers inference=remote::ollama --image-type
venv`
<img width="1084" alt="Screenshot 2025-03-20 at 9 34 14 AM"
src="https://github.com/user-attachments/assets/502b5fa2-edab-4267-a595-4f987204a6a9"
/>
`llama stack run --image-type venv
/Users/charliedoern/projects/Documents/llama-stack/venv-run.yaml`
<img width="1149" alt="Screenshot 2025-03-20 at 9 35 19 AM"
src="https://github.com/user-attachments/assets/433765f3-6b7f-4383-9241-dad085b69228"
/>
---------
Signed-off-by: Charlie Doern <cdoern@redhat.com>
Signed-off-by: Sébastien Han <seb@redhat.com>
Co-authored-by: Sébastien Han <seb@redhat.com>
## What does this PR do?
This PR improves the server's request routing logic by ensuring built-in
FastAPI paths such as `/docs`, `/redoc`, `/openapi.json`,
`/favicon.ico`, and `/static` bypass the custom `TracingMiddleware`.
This prevents unnecessary tracing logic for documentation and static
file requests, ensuring better performance and cleaner logs.
Additionally, it adds proper metadata (`title`, `description`, and
`version`) to the FastAPI application initialization and updates the
requirements document accordingly.
[//]: # (Closes#1822 )
---
## Test Plan
- Ran the server locally with `uvicorn` using the provided `run.yaml`
config
- Verified that:
- FastAPI docs (`/docs`, `/redoc`) load correctly without triggering the
custom tracing middleware
- All other routes still go through the middleware and trace logic
- Application metadata appears as expected in the OpenAPI docs
To reproduce:
1. Start the server with `python server.py --template <template-name>`
2. Navigate to `/docs` and `/redoc`
3. Confirm that no extra trace headers are added for those routes
4. Confirm other API endpoints behave as expected and include
`x-trace-id` in the response headers
[//]: # (## Documentation)
---
Froze the requirements file to include many of the other libraries that
have been added in the past few releases to make install easier.
---------
Co-authored-by: Sébastien Han <seb@redhat.com>
# What does this PR do?
ollama's CLI supports running models via commands such as 'ollama run
llama3.2' this syntax does not work with the INFERENCE_MODEL llamastack
var as currently specifying a tag such as 'latest' is required
this commit will check to see if the 'latest' model is available and use
that model if a user passes a model name without a tag but the 'latest'
is available in ollama
## Test Plan
Behavior pre-code change
```bash
$ INFERENCE_MODEL=llama3.2 llama stack build --template ollama --image-type venv --run
...
INFO 2025-04-08 13:42:42,842 llama_stack.providers.remote.inference.ollama.ollama:80 inference: checking
connectivity to Ollama at `http://beanlab1.bss.redhat.com:11434`...
Traceback (most recent call last):
File "<frozen runpy>", line 198, in _run_module_as_main
File "<frozen runpy>", line 88, in _run_code
File "/home/nathan/ai/llama-stack/repos/llama-stack/llama_stack/distribution/server/server.py", line 502, in <module>
main()
File "/home/nathan/ai/llama-stack/repos/llama-stack/llama_stack/distribution/server/server.py", line 401, in main
impls = asyncio.run(construct_stack(config))
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/lib64/python3.12/asyncio/runners.py", line 195, in run
return runner.run(main)
^^^^^^^^^^^^^^^^
File "/usr/lib64/python3.12/asyncio/runners.py", line 118, in run
return self._loop.run_until_complete(task)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/lib64/python3.12/asyncio/base_events.py", line 691, in run_until_complete
return future.result()
^^^^^^^^^^^^^^^
File "/home/nathan/ai/llama-stack/repos/llama-stack/llama_stack/distribution/stack.py", line 222, in construct_stack
await register_resources(run_config, impls)
File "/home/nathan/ai/llama-stack/repos/llama-stack/llama_stack/distribution/stack.py", line 99, in register_resources
await method(**obj.model_dump())
File "/home/nathan/ai/llama-stack/repos/llama-stack/llama_stack/providers/utils/telemetry/trace_protocol.py", line 102, in async_wrapper
result = await method(self, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/nathan/ai/llama-stack/repos/llama-stack/llama_stack/distribution/routers/routing_tables.py", line 294, in register_model
registered_model = await self.register_object(model)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/nathan/ai/llama-stack/repos/llama-stack/llama_stack/distribution/routers/routing_tables.py", line 228, in register_object
registered_obj = await register_object_with_provider(obj, p)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/nathan/ai/llama-stack/repos/llama-stack/llama_stack/distribution/routers/routing_tables.py", line 77, in register_object_with_provider
return await p.register_model(obj)
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/nathan/ai/llama-stack/repos/llama-stack/llama_stack/providers/utils/telemetry/trace_protocol.py", line 102, in async_wrapper
result = await method(self, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/nathan/ai/llama-stack/repos/llama-stack/llama_stack/providers/remote/inference/ollama/ollama.py", line 315, in register_model
raise ValueError(
ValueError: Model 'llama3.2' is not available in Ollama. Available models: llama3.2:latest
++ error_handler 108
++ echo 'Error occurred in script at line: 108'
Error occurred in script at line: 108
++ exit 1
```
Behavior post-code change
```bash
$ INFERENCE_MODEL=llama3.2 llama stack build --template ollama --image-type venv --run
...
INFO 2025-04-08 13:58:17,365 llama_stack.providers.remote.inference.ollama.ollama:80 inference: checking
connectivity to Ollama at `http://beanlab1.bss.redhat.com:11434`...
WARNING 2025-04-08 13:58:18,190 llama_stack.providers.remote.inference.ollama.ollama:317 inference: Imprecise provider
resource id was used but 'latest' is available in Ollama - using 'llama3.2:latest'
INFO 2025-04-08 13:58:18,191 llama_stack.providers.remote.inference.ollama.ollama:308 inference: Pulling embedding
model `all-minilm:latest` if necessary...
INFO 2025-04-08 13:58:18,799 __main__:478 server: Listening on ['::', '0.0.0.0']:8321
INFO: Started server process [28378]
INFO: Waiting for application startup.
INFO 2025-04-08 13:58:18,803 __main__:148 server: Starting up
INFO: Application startup complete.
INFO: Uvicorn running on http://['::', '0.0.0.0']:8321 (Press CTRL+C to quit)
...
```
## Documentation
Did not document this anywhere but happy to do so if there is an
appropriate place
Signed-off-by: Nathan Weinberg <nweinber@redhat.com>
# What does this PR do?
Now a separate thread is started to execute training jobs. Training
requests now return job ID before the job completes. (Which fixes API
timeouts for any jobs that take longer than a minute.)
Note: the scheduler code is meant to be spun out in the future into a
common provider service that can be reused for different APIs and
providers. It is also expected to back the /jobs API proposed here:
https://github.com/meta-llama/llama-stack/discussions/1238
Hence its somewhat generalized form which is expected to simplify its
adoption elsewhere in the future.
Note: this patch doesn't attempt to implement missing APIs (e.g. cancel
or job removal). This work will belong to follow-up PRs.
[//]: # (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 for the scheduler module. For the API coverage, did
manual testing and was able to run a training cycle on GPU. The initial
call returned job ID before the training completed, as (now) expected.
Artifacts are returned as expected.
```
JobArtifactsResponse(checkpoints=[{'identifier': 'meta-llama/Llama-3.2-3B-Instruct-sft-0', 'created_at': '2025-03-07T22:45:19.892714', 'epoch': 0, 'post_training_job_id': 'test-job2ee77104-2fd3-4a4e-84cf-f83f8b8f1f50', 'path': '/home/ec2-user/.llama/checkpoints/meta-llama/Llama-3.2-3B-Instruct-sft-0', 'training_metrics': None}], job_uuid='test-job2ee77104-2fd3-4a4e-84cf-f83f8b8f1f50')
```
The integration test is currently disabled for the provider. I will look
into how it can be enabled in a different PR / issue context.
[//]: # (## Documentation)
Signed-off-by: Ihar Hrachyshka <ihar.hrachyshka@gmail.com>
# What does this PR do?
TLDR: Changes needed to get 100% passing tests for OpenAI API
verification tests when run against Llama Stack with the `together`,
`fireworks`, and `openai` providers. And `groq` is better than before,
at 88% passing.
This cleans up the OpenAI API support for image message types
(specifically `image_url` types) and handling of the `response_format`
chat completion parameter. Both of these required a few more Pydantic
model definitions in our Inference API, just to move from the
not-quite-right stubs I had in place to something fleshed out to match
the actual OpenAI API specs.
As part of testing this, I also found and fixed a bug in the litellm
implementation of openai_completion and openai_chat_completion, so the
providers based on those should actually be working now.
The method `prepare_openai_completion_params` in
`llama_stack/providers/utils/inference/openai_compat.py` was improved to
actually recursively clean up input parameters, including handling of
lists, dicts, and dumping of Pydantic models to dicts. These changes
were required to get to 100% passing tests on the OpenAI API
verification against the `openai` provider.
With the above, the together.ai provider was passing as well as it is
without Llama Stack. But, since we have Llama Stack in the middle, I
took the opportunity to clean up the together.ai provider so that it now
also passes the OpenAI API spec tests we have at 100%. That means
together.ai is now passing our verification test better when using an
OpenAI client talking to Llama Stack than it is when hitting together.ai
directly, without Llama Stack in the middle.
And, another round of work for Fireworks to improve translation of
incoming OpenAI chat completion requests to Llama Stack chat completion
requests gets the fireworks provider passing at 100%. The server-side
fireworks.ai tool calling support with OpenAI chat completions and Llama
4 models isn't great yet, but by pointing the OpenAI clients at Llama
Stack's API we can clean things up and get everything working as
expected for Llama 4 models.
## Test Plan
### OpenAI API Verification Tests
I ran the OpenAI API verification tests as below and 100% of the tests
passed.
First, start a Llama Stack server that runs the `openai` provider with
the `gpt-4o` and `gpt-4o-mini` models deployed. There's not a template
setup to do this out of the box, so I added a
`tests/verifications/openai-api-verification-run.yaml` to do this.
First, ensure you have the necessary API key environment variables set:
```
export TOGETHER_API_KEY="..."
export FIREWORKS_API_KEY="..."
export OPENAI_API_KEY="..."
```
Then, run a Llama Stack server that serves up all these providers:
```
llama stack run \
--image-type venv \
tests/verifications/openai-api-verification-run.yaml
```
Finally, generate a new verification report against all these providers,
both with and without the Llama Stack server in the middle.
```
python tests/verifications/generate_report.py \
--run-tests \
--provider \
together \
fireworks \
groq \
openai \
together-llama-stack \
fireworks-llama-stack \
groq-llama-stack \
openai-llama-stack
```
You'll see that most of the configurations with Llama Stack in the
middle now pass at 100%, even though some of them do not pass at 100%
when hitting the backend provider's API directly with an OpenAI client.
### OpenAI Completion Integration Tests with vLLM:
I also ran the smaller `test_openai_completion.py` test suite (that's
not yet merged with the verification tests) on multiple of the
providers, since I had to adjust the method signature of
openai_chat_completion a bit and thus had to touch lots of these
providers to match. Here's the tests I ran there, all passing:
```
VLLM_URL="http://localhost:8000/v1" INFERENCE_MODEL="meta-llama/Llama-3.2-3B-Instruct" llama stack build --template remote-vllm --image-type venv --run
```
in another terminal
```
LLAMA_STACK_CONFIG=http://localhost:8321 INFERENCE_MODEL="meta-llama/Llama-3.2-3B-Instruct" python -m pytest -v tests/integration/inference/test_openai_completion.py --text-model "meta-llama/Llama-3.2-3B-Instruct"
```
### OpenAI Completion Integration Tests with ollama
```
INFERENCE_MODEL="llama3.2:3b-instruct-q8_0" llama stack build --template ollama --image-type venv --run
```
in another terminal
```
LLAMA_STACK_CONFIG=http://localhost:8321 INFERENCE_MODEL="llama3.2:3b-instruct-q8_0" python -m pytest -v tests/integration/inference/test_openai_completion.py --text-model "llama3.2:3b-instruct-q8_0"
```
### OpenAI Completion Integration Tests with together.ai
```
INFERENCE_MODEL="meta-llama/Llama-3.2-3B-Instruct-Turbo" llama stack build --template together --image-type venv --run
```
in another terminal
```
LLAMA_STACK_CONFIG=http://localhost:8321 INFERENCE_MODEL="meta-llama/Llama-3.2-3B-Instruct-Turbo" python -m pytest -v tests/integration/inference/test_openai_completion.py --text-model "meta-llama/Llama-3.2-3B-Instruct-Turbo"
```
### OpenAI Completion Integration Tests with fireworks.ai
```
INFERENCE_MODEL="meta-llama/Llama-3.1-8B-Instruct" llama stack build --template fireworks --image-type venv --run
```
in another terminal
```
LLAMA_STACK_CONFIG=http://localhost:8321 INFERENCE_MODEL="meta-llama/Llama-3.1-8B-Instruct" python -m pytest -v tests/integration/inference/test_openai_completion.py --text-model "meta-llama/Llama-3.1-8B-Instruct"
---------
Signed-off-by: Ben Browning <bbrownin@redhat.com>
