# What does this PR do?
- Use BackgroundLogger when logging metric events.
- Reuse event loop in BackgroundLogger
## Test Plan
```
cd /docs/source/distributions/k8s-benchmark
# start mock server
python openai-mock-server.py --port 8000
# start stack server
LLAMA_STACK_LOGGING="all=WARNING" uv run --with llama-stack python -m llama_stack.core.server.server docs/source/distributions/k8s-benchmark/stack_run_config.yaml
# run benchmark script
uv run python3 benchmark.py --duration 120 --concurrent 50 --base-url=http://localhost:8321/v1/openai/v1 --model=vllm-inference/meta-llama/Llama-3.2-3B-Instruct
```
### RPS from 57 -> 62
# What does this PR do?
Fix fireworks chat completion broken due to telemetry expecting
response.usage
Closes https://github.com/llamastack/llama-stack/issues/3391
## Test Plan
1. `uv run --with llama-stack llama stack build --distro starter
--image-type venv --run`
Try
```
curl -X POST http://0.0.0.0:8321/v1/openai/v1/chat/completions \
-H "Content-Type: application/json" \
-d '{
"model": "fireworks/accounts/fireworks/models/llama-v3p1-8b-instruct",
"messages": [{"role": "user", "content": "Hello!"}]
}'
```
```
{"id":"chatcmpl-ee922a08-0df0-4974-b0d3-b322113e8bc0","choices":[{"message":{"role":"assistant","content":"Hello! How can I assist you today?","name":null,"tool_calls":null},"finish_reason":"stop","index":0,"logprobs":null}],"object":"chat.completion","created":1757456375,"model":"fireworks/accounts/fireworks/models/llama-v3p1-8b-instruct"}%
```
Without fix fails as mentioned in
https://github.com/llamastack/llama-stack/issues/3391
Co-authored-by: Francisco Arceo <arceofrancisco@gmail.com>
# What does this PR do?
update VertexAI inference provider to use openai-python for
openai-compat functions
## Test Plan
```
$ VERTEX_AI_PROJECT=... uv run llama stack build --image-type venv --providers inference=remote::vertexai --run
...
$ LLAMA_STACK_CONFIG=http://localhost:8321 uv run --group test pytest -v -ra --text-model vertexai/vertex_ai/gemini-2.5-flash tests/integration/inference/test_openai_completion.py
...
```
i don't have an account to test this. `get_api_key` may also need to be
updated per
https://cloud.google.com/vertex-ai/generative-ai/docs/start/openai
---------
Signed-off-by: Sébastien Han <seb@redhat.com>
Co-authored-by: Sébastien Han <seb@redhat.com>
# What does this PR do?
When running RAG in a multi vector DB setting, it can be difficult to
trace where retrieved chunks originate from. This PR adds the
`vector_db_id` into each chunk’s metadata, making it easier to
understand which database a given chunk came from. This is helpful for
debugging and for analyzing retrieval behavior of multiple DBs.
Relevant code:
```python
for vector_db_id, result in zip(vector_db_ids, results):
for chunk, score in zip(result.chunks, result.scores):
if not hasattr(chunk, "metadata") or chunk.metadata is None:
chunk.metadata = {}
chunk.metadata["vector_db_id"] = vector_db_id
chunks.append(chunk)
scores.append(score)
```
## Test Plan
* Ran Llama Stack in debug mode.
* Verified that `vector_db_id` was added to each chunk’s metadata.
* Confirmed that the metadata was printed in the console when using the
RAG tool.
---------
Co-authored-by: are-ces <cpompeia@redhat.com>
Co-authored-by: Francisco Arceo <arceofrancisco@gmail.com>
# What does this PR do?
enables completions storage when using `llama stack build --providers` -
- GET /v1/chat/completions
- GET /v1/chat/completions/{id}
todo: llama stack build and distro codegen should use the same code
paths
## Test Plan
ci
# What does this PR do?
This PR adds support for OpenAI Prompts API.
Note, OpenAI does not explicitly expose the Prompts API but instead
makes it available in the Responses API and in the [Prompts
Dashboard](https://platform.openai.com/docs/guides/prompting#create-a-prompt).
I have added the following APIs:
- CREATE
- GET
- LIST
- UPDATE
- Set Default Version
The Set Default Version API is made available only in the Prompts
Dashboard and configures which prompt version is returned in the GET
(the latest version is the default).
Overall, the expected functionality in Responses will look like this:
```python
from openai import OpenAI
client = OpenAI()
response = client.responses.create(
prompt={
"id": "pmpt_68b0c29740048196bd3a6e6ac3c4d0e20ed9a13f0d15bf5e",
"version": "2",
"variables": {
"city": "San Francisco",
"age": 30,
}
}
)
```
### Resolves https://github.com/llamastack/llama-stack/issues/3276
## Test Plan
Unit tests added. Integration tests can be added after client
generation.
## Next Steps
1. Update Responses API to support Prompt API
2. I'll enhance the UI to implement the Prompt Dashboard.
3. Add cache for lower latency
---------
Signed-off-by: Francisco Javier Arceo <farceo@redhat.com>
# What does this PR do?
Add Kubernetes authentication provider support
- Add KubernetesAuthProvider class for token validation using Kubernetes
SelfSubjectReview API
- Add KubernetesAuthProviderConfig with configurable API server URL, TLS
settings, and claims mapping
- Implement authentication via POST requests to
/apis/authentication.k8s.io/v1/selfsubjectreviews endpoint
- Add support for parsing Kubernetes SelfSubjectReview response format
to extract user information
- Add KUBERNETES provider type to AuthProviderType enum
- Update create_auth_provider factory function to handle 'kubernetes'
provider type
- Add comprehensive unit tests for KubernetesAuthProvider functionality
- Add documentation with configuration examples and usage instructions
The provider validates tokens by sending SelfSubjectReview requests to
the Kubernetes API server and extracts user information from the
userInfo structure in the response.
<!-- 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.* -->
What This Verifies:
Authentication header validation
Token validation with Kubernetes SelfSubjectReview and kubernetes server
API endpoint
Error handling for invalid tokens and HTTP errors
Request payload structure and headers
```
python -m pytest tests/unit/server/test_auth.py -k "kubernetes" -v
```
Signed-off-by: Akram Ben Aissi <akram.benaissi@gmail.com>
Bumps
[@radix-ui/react-dropdown-menu](https://github.com/radix-ui/primitives)
from 2.1.14 to 2.1.16.
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# What does this PR do?
update the Anthropic inference provider to use openai-python for the
openai-compat endpoints
## Test Plan
ci
Co-authored-by: raghotham <rsm@meta.com>
# What does this PR do?
update Groq inference provider to use OpenAIMixin for openai-compat
endpoints
changes on api.groq.com -
- json_schema is now supported for specific models, see
https://console.groq.com/docs/structured-outputs#supported-models
- response_format with streaming is now supported for models that
support response_format
- groq no longer returns a 400 error if tools are provided and
tool_choice is not "required"
## Test Plan
```
$ GROQ_API_KEY=... uv run llama stack build --image-type venv --providers inference=remote::groq --run
...
$ LLAMA_STACK_CONFIG=http://localhost:8321 uv run --group test pytest -v -ra --text-model groq/llama-3.3-70b-versatile tests/integration/inference/test_openai_completion.py -k 'not store'
...
SKIPPED [3] tests/integration/inference/test_openai_completion.py:44: Model groq/llama-3.3-70b-versatile hosted by remote::groq doesn't support OpenAI completions.
SKIPPED [3] tests/integration/inference/test_openai_completion.py:94: Model groq/llama-3.3-70b-versatile hosted by remote::groq doesn't support vllm extra_body parameters.
SKIPPED [4] tests/integration/inference/test_openai_completion.py:73: Model groq/llama-3.3-70b-versatile hosted by remote::groq doesn't support n param.
SKIPPED [1] tests/integration/inference/test_openai_completion.py💯 Model groq/llama-3.3-70b-versatile hosted by remote::groq doesn't support chat completion calls with base64 encoded files.
======================= 8 passed, 11 skipped, 8 deselected, 2 warnings in 5.13s ========================
```
---------
Co-authored-by: raghotham <rsm@meta.com>
# What does this PR do?
update SambaNova inference provider to use OpenAIMixin for openai-compat
endpoints
## Test Plan
```
$ SAMBANOVA_API_KEY=... uv run llama stack build --image-type venv --providers inference=remote::sambanova --run
...
$ LLAMA_STACK_CONFIG=http://localhost:8321 uv run --group test pytest -v -ra --text-model sambanova/Meta-Llama-3.3-70B-Instruct tests/integration/inference -k 'not store'
...
FAILED tests/integration/inference/test_text_inference.py::test_text_chat_completion_tool_calling_tools_not_in_request[txt=sambanova/Meta-Llama-3.3-70B-Instruct-inference:chat_completion:tool_calling_tools_absent-True] - AttributeError: 'NoneType' object has no attribute 'delta'
FAILED tests/integration/inference/test_text_inference.py::test_text_chat_completion_tool_calling_tools_not_in_request[txt=sambanova/Meta-Llama-3.3-70B-Instruct-inference:chat_completion:tool_calling_tools_absent-False] - llama_stack_client.InternalServerError: Error code: 500 - {'detail': 'Internal server error: An une...
=========== 2 failed, 16 passed, 68 skipped, 8 deselected, 3 xfailed, 13 warnings in 15.85s ============
```
the two failures also exist before this change. they are part of the
deprecated inference.chat_completion tests that flow through litellm.
they can be resolved later.
# What does this PR do?
update the Gemini inference provider to use openai-python for the
openai-compat endpoints
partially addresses #3349, does not address /inference/completion or
/inference/chat-completion
## Test Plan
ci
# What does this PR do?
This change migrates the VectorDB id generation to Vector Stores.
This is a breaking change for **_some users_** that may have application
code using the `vector_db_id` parameter in the request of the VectorDB
protocol instead of the `VectorDB.identifier` in the response.
By default we will now create a Vector Store every time we register a
VectorDB. The caveat with this approach is that this maps the
`vector_db_id` → `vector_store.name`. This is a reasonable tradeoff to
transition users towards OpenAI Vector Stores.
As an added benefit, registering VectorDBs will result in them appearing
in the VectorStores admin UI.
### Why?
This PR makes the `POST` API call to `/v1/vector-dbs` swap the
`vector_db_id` parameter in the **request body** into the VectorStore's
name field and sets the `vector_db_id` to the generated vector store id
(e.g., `vs_038247dd-4bbb-4dbb-a6be-d5ecfd46cfdb`).
That means that users would have to do something like follows in their
application code:
```python
res = client.vector_dbs.register(
vector_db_id='my-vector-db-id',
embedding_model='ollama/all-minilm:l6-v2',
embedding_dimension=384,
)
vector_db_id = res.identifier
```
And then the rest of their code would behave, including `VectorIO`'s
insert protocol using `vector_db_id` in the request.
An alternative implementation would be to just delete the `vector_db_id`
parameter in `VectorDB` but the end result would still require users
having to write `vector_db_id = res.identifier` since
`VectorStores.create()` generates the ID for you.
So this approach felt the easiest way to migrate users towards
VectorStores (subsequent PRs will be added to trigger `files.create()`
and `vector_stores.files.create()`).
## Test Plan
Unit tests and integration tests have been added.
Signed-off-by: Francisco Javier Arceo <farceo@redhat.com>
# What does this PR do?
Improved bedrock provider config to read from environment variables like
AWS_ACCESS_KEY_ID. Updated all
fields to use default_factory with lambda patterns like the nvidia
provider does.
Now the environment variables work as documented.
Closes#3305
## Test Plan
Ran the new bedrock config tests:
```bash
python -m pytest tests/unit/providers/inference/bedrock/test_config.py
-v
Verified existing provider tests still work:
python -m pytest tests/unit/providers/test_configs.py -v
What does this PR do?
Fixes error handling when MCP server connections fail. Instead of
returning generic 500 errors, now provides
descriptive error messages with proper HTTP status codes.
Closes#3107
Test Plan
Before fix:
curl -X GET
"http://localhost:8321/v1/tool-runtime/list-tools?tool_group_id=bad-mcp-server"
Returns: {"detail": "Internal server error: An unexpected error
occurred."} (500)
After fix:
curl -X GET
"http://localhost:8321/v1/tool-runtime/list-tools?tool_group_id=bad-mcp-server"
Returns: {"error": {"detail": "Failed to connect to MCP server at
http://localhost:9999/sse: Connection
refused"}} (502)
Tests:
- Added unit test for ConnectionError → 502 translation
- Manually tested with unreachable MCP servers (connection refused)
# What does this PR do?
Noticed the test
https://github.com/llamastack/llama-stack-ops/actions/workflows/test-maybe-cut.yaml
are still failing randomly.
Earlier fixed this with 0.18.0 of fireworks here
https://github.com/llamastack/llama-stack/pull/3267, the local testing
may have inadvertently picked a lower version with `<=` which I assumed
picks latest version.
Now tested with `==` to find the version where it broke and pinning to
version(`<=`) where it was passing.
## Test Plan
Tested locally with the following commands to start a container
Build container
`llama stack build --distro starter --image-type container`
start container `docker run -d -p 8321:8321 --name llama-stack-test
distribution-starter:0.2.20`
check health `http://localhost:8321/v1/health`
Above steps fails without the fix
Tested with `==` to ensure the same version is picked in local testing
instead of anything lower.
Following here for the fix from `fireworks-ai`
1410674695https://github.com/llamastack/llama-stack/issues/3273
- Wrap model loading with asyncio.to_thread() to prevent blocking during
model download/initialization
- Wrap encoding operations with asyncio.to_thread() to run in background
thread
- Convert _load_sentence_transformer_model() to async method
This ensures the async event loop remains responsive during embedding
operations.
Closes: #3332
Signed-off-by: Derek Higgins <derekh@redhat.com>
Co-authored-by: Francisco Arceo <arceofrancisco@gmail.com>
# What does this PR do?
<!-- Provide a short summary of what this PR does and why. Link to
relevant issues if applicable. -->
This PR is eliminating hardcoded status codes: `409` CONFLICT and `404`
NOT_FOUND in `server.py` using `httpx` built-in constants. This
implementation will follow the existing structure to improve
readability, extensibility and developer experience. This is already was
implemented in #3131
<!-- 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`
# What does this PR do?
Sometimes the stream don't have chunks with finish_reason, e.g. canceled
stream, which throws a pydantic error as OpenAIChoice.finish_reason: str
## Test Plan
observe no more such error when benchmarking
One needed to specify record-replay related environment variables for
running integration tests. We could not use defaults because integration
tests could be run against Ollama instances which could be running
different models. For example, text vs vision tests needed separate
instances of Ollama because a single instance typically cannot serve
both of these models if you assume the standard CI worker configuration
on Github. As a result, `client.list()` as returned by the Ollama client
would be different between these runs and we'd end up overwriting
responses.
This PR "solves" it by adding a small amount of complexity -- we store
model list responses specially, keyed by the hashes of the models they
return. At replay time, we merge all of them and pretend that we have
the union of all models available.
## Test Plan
Re-recorded all the tests using `scripts/integration-tests.sh
--inference-mode record`, including the vision tests.
Generated with CC:
Replace cryptic KeyError with clear, actionable error message that
shows:
- Which API the failing provider belongs to
- The provider ID and type that's failing
- Which dependency is missing
- Clear instructions on how to fix the issue
## Test plan
Use a run config with Agents API and no safety provider
Before: KeyError: <Api.safety: 'safety'>
After: Failed to resolve 'agents' provider 'meta-reference' of type
'inline::meta-reference': required dependency 'safety' is not available.
Please add a 'safety' provider to your configuration or check if the
provider is properly configured.
# What does this PR do?
This PR updates the Watsonx provider dependencies from
`ibm_watson_machine_learning` to `ibm_watsonx_ai`.
The old package `ibm_watson_machine_learning` is in **deprecation mode**
([[PyPI
link](https://pypi.org/project/ibm-watson-machine-learning/)](https://pypi.org/project/ibm-watson-machine-learning/))
and relies on older versions of dependencies such as `pandas`. Updating
to `ibm_watsonx_ai` ensures compatibility with current dependency
versions and ongoing support.
## Test Plan
I verified the update by running an inference using a model provided by
Watsonx. The model ran successfully, confirming that the new dependency
works as expected.
Co-authored-by: are-ces <cpompeia@redhat.com>
# What does this PR do?
<!-- Provide a short summary of what this PR does and why. Link to
relevant issues if applicable. -->
The purpose of this PR is to refactor `SQLiteVecIndex` to eliminate
redundant code and simplify the code using generic
`WeightedInMemoryAggregator` that can be used for any vector db
provider. This pattern is already implemented for `PGVectorIndex` in
#3064
CC: @franciscojavierarceo
<!-- 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.* -->
1. `./scripts/unit-tests.sh`
2. Integration tests in CI Workflow
Bumps [@radix-ui/react-tooltip](https://github.com/radix-ui/primitives)
from 1.2.6 to 1.2.8.
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<h2>[12.23.5] 2025-07-11</h2>
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# What does this PR do?
BFCL scoring function is not supported, removing it.
Also minor fixes as the llama stack run is broken for open-benchmark for
test plan verification
1. Correct the model paths for supported models
2. Fix another issue as there is no `provider_id` for DatasetInput but
logger assumes it exists.
```
File "/Users/swapna942/llama-stack/llama_stack/core/stack.py", line 332, in construct_stack
await register_resources(run_config, impls)
File "/Users/swapna942/llama-stack/llama_stack/core/stack.py", line 108, in register_resources
logger.debug(f"registering {rsrc.capitalize()} {obj} for provider {obj.provider_id}")
^^^^^^^^^^^^^^^
File "/Users/swapna942/llama-stack/.venv/lib/python3.13/site-packages/pydantic/main.py", line 991, in __getattr__
raise AttributeError(f'{type(self).__name__!r} object has no attribute {item!r}')
AttributeError: 'DatasetInput' object has no attribute 'provider_id'
```
## Test Plan
```llama stack build --distro open-benchmark --image-type venv``` and run the server succeeds
Issue Link: https://github.com/llamastack/llama-stack/issues/3282
# What does this PR do?
add the ability to use inequalities in the where clause of the sqlstore.
this is infrastructure for files expiration.
## Test Plan
unit tests
# What does this PR do?
1725364988
Fixes the issue with open ai package incompatibilty introduced through
new dependency of fireworks-ai==0.19.18->reward-kit by pinning to
fireworks older version that doesnt pull in reward-kit
## Test Plan
Tested locally with the following commands to start a container
1. Build container
`llama stack build --distro starter --image-type container`
2. start container `docker run -d -p 8321:8321 --name llama-stack-test
distribution-starter:0.2.19`
3. check health http://localhost:8321/v1/health
Above steps fails without the fix
# What does this PR do?
During env var replacement, we're implicitly converting all config types
to their apparent types (e.g., "true" to True, "123" to 123). This may
be arguably useful for when doing an env var substitution, as those are
always strings, but we should definitely avoid touching config values
that have explicit types and are uninvolved in env var substitution.
## Test Plan
Unit
