# What does this PR do?
In our OpenAI API verification tests, some providers were still calling
tools even when `tool_choice="none"` was passed in the chat completion
requests. Because they aren't all respecting `tool_choice` properly,
this adjusts our routing implementation to remove the `tools` and
`tool_choice` from the request if `tool_choice="none"` is passed in so
that it does not attempt to call any of those tools. Adjusting this in
the router fixes this across all providers.
This also cleans up the non-streaming together.ai responses for tools,
ensuring it returns `None` instead of an empty list when there are no
tool calls, to exactly match the OpenAI API responses in that case.
## Test Plan
I observed existing failures in our OpenAI API verification suite - see
https://github.com/bbrowning/llama-stack-tests/blob/main/openai-api-verification/2025-04-27.md#together-llama-stack
for the failing `test_chat_*_tool_choice_none` tests. All streaming and
non-streaming variants were failing across all 3 tested models.
After this change, all of those 6 failing tests are now passing with no
regression in the other tests.
I verified this via:
```
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=together-llama-stack
```
The entire verification suite is not 100% on together.ai yet, but it's
getting closer.
This also increased the pass rate for fireworks.ai, and did not regress
the groq or openai tests at all.
Signed-off-by: Ben Browning <bbrownin@redhat.com>
# What does this PR do?
switch sambanova inference adaptor to LiteLLM usage to simplify
integration and solve issues with current adaptor when streaming and
tool calling, models and templates updated
## Test Plan
pytest -s -v tests/integration/inference/test_text_inference.py
--stack-config=sambanova
--text-model=sambanova/Meta-Llama-3.3-70B-Instruct
pytest -s -v tests/integration/inference/test_vision_inference.py
--stack-config=sambanova
--vision-model=sambanova/Llama-3.2-11B-Vision-Instruct
# What does this PR do?
Checks for RAGDocument of type InterleavedContent
I noticed when stepping through the code that the supported types for
`RAGDocument` included `InterleavedContent` as a content type. This type
is not checked against before putting the `doc.content` is regex matched
against. This would cause a runtime error. This change adds an explicit
check for type.
The only other part that I'm unclear on is how to handle the
`ImageContent` type since this would always just return `<image>` which
seems like an undesired behavior. Should the `InterleavedContent` type
be removed from `RAGDocument` and replaced with `URI | str`?
## Test Plan
[//]: # (## Documentation)
---------
Signed-off-by: Kevin <kpostlet@redhat.com>
# What does this PR do?
Mainly tried to cover the entire llama_stack/apis directory, we only
have one left. Some excludes were just noop.
Signed-off-by: Sébastien Han <seb@redhat.com>
# What does this PR do?
Revert a change that by mistake forced efficiency_config on torchtune
provider
users.
```
fix: Don't require efficiency_config for torchtune
It was enforced by mistake when
0751a960a5 merged.
Other asserts made sense in that the code was written, potentially, to
always expect a non-None value. But not efficiency_config.
```
Signed-off-by: Ihar Hrachyshka <ihar.hrachyshka@gmail.com>
# What does this PR do?
For the Issue :-
#[2010](https://github.com/meta-llama/llama-stack/issues/2010)
Currently, if we try to connect the Llama stack server to a remote
Milvus instance that has TLS enabled, the connection fails because TLS
support is not implemented in the Llama stack codebase. As a result,
users are unable to use secured Milvus deployments out of the box.
After adding this , the user will be able to connect to remote::Milvus
which is TLS enabled .
if TLS enabled :-
```
vector_io:
- provider_id: milvus
provider_type: remote::milvus
config:
uri: "http://<host>:<port>"
token: "<user>:<password>"
secure: True
server_pem_path: "path/to/server.pem"
```
[//]: # (If resolving an issue, uncomment and update the line below)
[//]: # (Closes #[issue-number])
## Test Plan
I have already tested it by connecting to a Milvus instance which is TLS
enabled and i was able to start llama stack server .
# What does this PR do?
When converting OpenAI message content for the "system" and "assistant"
roles to Llama Stack inference APIs (used for some providers when
dealing with Llama models via OpenAI API requests to get proper prompt /
tool handling), we were not properly converting any non-string content.
I discovered this while running the new Responses AI verification suite
against the Fireworks provider, but instead of fixing it as part of some
ongoing work there split this out into a separate PR.
This fixes that, by using the `openai_content_to_content` helper we used
elsewhere to ensure content parts were mapped properly.
## Test Plan
I added a couple of new tests to `test_openai_compat` to reproduce this
issue and validate its fix. I ran those as below:
```
python -m pytest -s -v tests/unit/providers/utils/inference/test_openai_compat.py
```
Signed-off-by: Ben Browning <bbrownin@redhat.com>
# What does this PR do?
The builtin implementation of code interpreter is not robust and has a
really weak sandboxing shell (the `bubblewrap` container). Given the
availability of better MCP code interpreter servers coming up, we should
use them instead of baking an implementation into the Stack and
expanding the vulnerability surface to the rest of the Stack.
This PR only does the removal. We will add examples with how to
integrate with MCPs in subsequent ones.
## Test Plan
Existing tests.
# What does this PR do?
The goal of this PR is code base modernization.
Schema reflection code needed a minor adjustment to handle UnionTypes
and collections.abc.AsyncIterator. (Both are preferred for latest Python
releases.)
Note to reviewers: almost all changes here are automatically generated
by pyupgrade. Some additional unused imports were cleaned up. The only
change worth of note can be found under `docs/openapi_generator` and
`llama_stack/strong_typing/schema.py` where reflection code was updated
to deal with "newer" types.
Signed-off-by: Ihar Hrachyshka <ihar.hrachyshka@gmail.com>
Nullable param type is not supported, e.g. ['string', 'null'], since it
fails type validation.
Tests:
Run inference with
messages:
- content: You are a helpful assistant that can use tools to get
information.
role: system
- content: What's the temperature in San Francisco in celsius?
role: user
tools:
- function:
description: Get current temperature for a given location.
name: get_weather
parameters:
additionalProperties: false
properties:
location:
description: "City and country e.g. Bogot\xE1, Colombia"
type: string
unit:
description: "Unit of temperature, default to celsius"
type: [string, "null"] # <= nullable type
required:
- location
type: object
type: function
Co-authored-by: Eric Huang <erichuang@fb.com>
# What does this PR do?
Add support for the temperature to the responses API
## Test Plan
Manually tested simple case
unit tests added for simple case and tool calls
Signed-off-by: Derek Higgins <derekh@redhat.com>
# What does this PR do?
When the result of a ToolCall gets passed back into vLLM for the model
to handle the tool call result (as is often the case in agentic
tool-calling workflows), we forgot to handle the case where BuiltinTool
calls are not string values but instead instances of the BuiltinTool
enum. This fixes that, properly converting those enums to string values
before trying to serialize them into an OpenAI chat completion request
to vLLM.
PR #1931 fixed a bug where we weren't passing these tool calling results
back into vLLM, but as a side-effect it created this serialization bug
when using BuiltinTools.
Closes#2070
## Test Plan
I added a new unit test to the openai_compat unit tests to cover this
scenario, ensured the new test failed before this fix, and all the
existing tests there plus the new one passed with this fix.
```
python -m pytest -s -v tests/unit/providers/utils/inference/test_openai_compat.py
```
Signed-off-by: Ben Browning <bbrownin@redhat.com>
# What does this PR do?
Add several new pre-commit hooks to improve code quality and security:
- no-commit-to-branch: prevent direct commits to protected branches like
`main`
- check-yaml: validate YAML files
- detect-private-key: prevent accidental commit of private keys
- requirements-txt-fixer: maintain consistent requirements.txt format
and sorting
- mixed-line-ending: enforce LF line endings to avoid mixed line endings
- check-executables-have-shebangs: ensure executable scripts have
shebangs
- check-json: validate JSON files
- check-shebang-scripts-are-executable: verify shebang scripts are
executable
- check-symlinks: validate symlinks and report broken ones
- check-toml: validate TOML files mainly for pyproject.toml
The respective fixes have been included.
Signed-off-by: Sébastien Han <seb@redhat.com>
# What does this PR do?
Partial revert of fa68ded07c
this commit ensures users know where their new templates are generated
and how to run the newly built distro locally
discussion on Discord:
1351652390
## Test Plan
Did a local run - let me know if we want any unit testing covering this
## Documentation
Updated "Zero to Hero" guide with new output
---------
Signed-off-by: Nathan Weinberg <nweinber@redhat.com>
# What does this PR do?
- Added new Ruff lint rules to detect ambiguous or non-ASCII characters:
- Added per-file ignores where Unicode usage is still required.
- Fixed whatever had to be fixed
Signed-off-by: Sébastien Han <seb@redhat.com>
# What does this PR do?
When running a Llama Stack server and invoking the
`/v1/safety/run-shield` endpoint, the NVIDIA Guardrails endpoint in some
cases errors with a `422: Unprocessable Entity` due to malformed input.
For example, given an request body like:
```
{
"model": "test",
"messages": [
{ "role": "user", "content": "You are stupid." }
]
}
```
`convert_pydantic_to_json_value` converts the message to:
```
{ "role": "user", "content": "You are stupid.", "context": null }
```
Which causes NVIDIA Guardrails to return an error `HTTPError: 422 Client
Error: Unprocessable Entity for url:
http://nemo.test/v1/guardrail/checks`, because `context` shouldn't be
included in the body.
[//]: # (If resolving an issue, uncomment and update the line below)
[//]: # (Closes #[issue-number])
## Test Plan
I ran the Llama Stack server locally and manually verified that the
endpoint now succeeds.
```
message = {"role": "user", "content": "You are stupid."}
response = client.safety.run_shield(messages=[message], shield_id=shield_id, params={})
```
Server logs:
```
14:29:09.656 [START] /v1/safety/run-shield
INFO: 127.0.0.1:54616 - "POST /v1/safety/run-shield HTTP/1.1" 200 OK
14:29:09.918 [END] /v1/safety/run-shield [StatusCode.OK] (262.26ms
```
[//]: # (## Documentation)
Co-authored-by: Jash Gulabrai <jgulabrai@nvidia.com>
# What does this PR do?
Replaced `${env.OTEL_SERVICE_NAME:\u200B}` and similar variants with
properly formatted `${env.OTEL_SERVICE_NAME:}` across all YAML templates
and TelemetryConfig. This prevents silent parsing issues and ensures
consistent environment variable resolution.
Slipped in https://github.com/meta-llama/llama-stack/pull/2058
Signed-off-by: Sébastien Han <seb@redhat.com>
Distribution Template Codegen was broken
# What does this PR do?
[Provide a short summary of what this PR does and why. Link to relevant
issues if applicable.]
[//]: # (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: Derek Higgins <derekh@redhat.com>
# What does this PR do?
The telemetry provider configs is the only one who leverages the env var
`SQLITE_DB_PATH` for pointing to persistent data in the respective
templates, whereas usually `SQLITE_STORE_DIR` is used.
This PR modifies the `sqlite_db_path` in various telemetry configuration
files to use the environment variable `SQLITE_STORE_DIR` instead of
`SQLITE_DB_PATH`. This change ensures that _only_ the SQLITE_STORE_DIR
needs to be set to point to a different persistence location for
providers.
All references to `SQLITE_DB_PATH` have been removed.
Another improvement could be to move `sqlite_db_path` to `db_path` in
the telemetry provider config, to align with the other provider
configurations. That could be done by another PR (if wanted).
# What does this PR do?
In our OpenAI API verification tests, ollama was still calling tools
even when `tool_choice="none"` was passed in its chat completion
requests. Because ollama isn't respecting `tool_choice` properly, this
adjusts our provider implementation to remove the `tools` from the
request if `tool_choice="none"` is passed in so that it does not attempt
to call any of those tools.
## Test Plan
I tested this with a couple of Llama models, using both our OpenAI
completions integration tests and our verification test suites.
### OpenAI Completions / Chat Completions integration tests
These all passed before, and still do.
```
INFERENCE_MODEL="llama3.2:3b-instruct-fp16" \
llama stack build --template ollama --image-type venv --run
```
```
LLAMA_STACK_CONFIG=http://localhost:8321 \
python -m pytest -v \
tests/integration/inference/test_openai_completion.py \
--text-model "llama3.2:3b-instruct-fp16"
```
### OpenAI API Verification test suite
test_chat_*_tool_choice_none OpenAI API verification tests pass now,
when they failed before.
See
https://github.com/bbrowning/llama-stack-tests/blob/main/openai-api-verification/2025-04-27.md#ollama-llama-stack
for an example of these failures from a recent nightly CI run.
```
INFERENCE_MODEL="llama3.3:70b-instruct-q3_K_M" \
llama stack build --template ollama --image-type venv --run
```
```
cat <<-EOF > tests/verifications/conf/ollama-llama-stack.yaml
base_url: http://localhost:8321/v1/openai/v1
api_key_var: OPENAI_API_KEY
models:
- llama3.3:70b-instruct-q3_K_M
model_display_names:
llama3.3:70b-instruct-q3_K_M: Llama-3.3-70B-Instruct
test_exclusions:
llama3.3:70b-instruct-q3_K_M:
- test_chat_non_streaming_image
- test_chat_streaming_image
- test_chat_multi_turn_multiple_images
EOF
```
```
python -m pytest -s -v \
'tests/verifications/openai_api/test_chat_completion.py' \
--provider=ollama-llama-stack
```
Signed-off-by: Ben Browning <bbrownin@redhat.com>
# What does this PR do?
This PR updates how the `AgentType` gets set using the radio button on
the tools page of the playground. This change is needed due to the fact
with its current implementation, the chat interface will resets after
every input, preventing users from having a multi-turn conversation with
the agent.
## Test Plan
Run the Playground without these changes:
```bash
streamlit run llama_stack/distribution/ui/app.py
```
Navigate to the tools page and attempt to have a multi-turn
conversation. You should see the conversation reset after asking a
second question.
Repeat the steps above with these changes and you will see that it works
as expected when asking the agent multiple questions.
Signed-off-by: Michael Clifford <mcliffor@redhat.com>
# What does this PR do?
This provides an initial [OpenAI Responses
API](https://platform.openai.com/docs/api-reference/responses)
implementation. The API is not yet complete, and this is more a
proof-of-concept to show how we can store responses in our key-value
stores and use them to support the Responses API concepts like
`previous_response_id`.
## Test Plan
I've added a new
`tests/integration/openai_responses/test_openai_responses.py` as part of
a test-driven development for this new API. I'm only testing this
locally with the remote-vllm provider for now, but it should work with
any of our inference providers since the only API it requires out of the
inference provider is the `openai_chat_completion` endpoint.
```
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
```
```
LLAMA_STACK_CONFIG="http://localhost:8321" \
python -m pytest -v \
tests/integration/openai_responses/test_openai_responses.py \
--text-model "meta-llama/Llama-3.2-3B-Instruct"
```
---------
Signed-off-by: Ben Browning <bbrownin@redhat.com>
Co-authored-by: Ashwin Bharambe <ashwin.bharambe@gmail.com>
# What does this PR do?
This commit adds a new authentication system to the Llama Stack server
with support for Kubernetes and custom authentication providers. Key
changes include:
- Implemented KubernetesAuthProvider for validating Kubernetes service
account tokens
- Implemented CustomAuthProvider for validating tokens against external
endpoints - this is the same code that was already present.
- Added test for Kubernetes
- Updated server configuration to support authentication settings
- Added documentation for authentication configuration and usage
The authentication system supports:
- Bearer token validation
- Kubernetes service account token validation
- Custom authentication endpoints
## Test Plan
Setup a Kube cluster using Kind or Minikube.
Run a server with:
```
server:
port: 8321
auth:
provider_type: kubernetes
config:
api_server_url: http://url
ca_cert_path: path/to/cert (optional)
```
Run:
```
curl -s -L -H "Authorization: Bearer $(kubectl create token my-user)" http://127.0.0.1:8321/v1/providers
```
Or replace "my-user" with your service account.
Signed-off-by: Sébastien Han <seb@redhat.com>
# What does this PR do?
Implemetation of NeMO Datastore register, unregister API.
Open Issues:
- provider_id gets set to `localfs` in client.datasets.register() as it
is specified in routing_tables.py: DatasetsRoutingTable
see: #1860
Currently I have passed `"provider_id":"nvidia"` in metadata and have
parsed that in `DatasetsRoutingTable`
(Not the best approach, but just a quick workaround to make it work for
now.)
## Test Plan
- Unit test cases: `pytest
tests/unit/providers/nvidia/test_datastore.py`
```bash
========================================================== 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, asyncio-0.26.0, nbval-0.11.0, metadata-3.1.1, html-4.1.1, cov-6.1.0
asyncio: mode=strict, asyncio_default_fixture_loop_scope=None, asyncio_default_test_loop_scope=function
collected 2 items
tests/unit/providers/nvidia/test_datastore.py .. [100%]
============================================================ warnings summary ============================================================
====================================================== 2 passed, 1 warning in 0.84s ======================================================
```
cc: @dglogo, @mattf, @yanxi0830
# What does this PR do?
There are new changes in repo which needs to add some additional
functions to the inference which is fixed. Also need one additional
params to pass some extra arguments to watsonx.ai
[//]: # (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: Sajikumar JS <sajikumar.js@ibm.com>
# 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>
