# What does this PR do?
This adds some initial content documenting our OpenAI compatible APIs
- Responses, Chat Completions, Completions, and Models - along with
instructions on how to use them via OpenAI or Llama Stack clients and
some simple examples for each.
It's not a lot of content, but it's a start so that users have some idea
how to get going as we continue to work on these APIs.
## Test Plan
I generated the docs site locally and verified things render properly. I
also ran each code example to ensure it works as expected. And, I asked
my AI code assistant to do a quick spell-check and review of the docs
and it didn't flag any obvious errors.
---------
Signed-off-by: Ben Browning <bbrownin@redhat.com>
Co-authored-by: Francisco Arceo <farceo@redhat.com>
This allows a set of rules to be defined for determining access to
resources. The rules are (loosely) based on the cedar policy format.
A rule defines a list of action either to permit or to forbid. It may
specify a principal or a resource that must match for the rule to take
effect. It may also specify a condition, either a 'when' or an 'unless',
with additional constraints as to where the rule applies.
A list of rules is held for each type to be protected and tried in order
to find a match. If a match is found, the request is permitted or
forbidden depening on the type of rule. If no match is found, the
request is denied. If no rules are specified for a given type, a rule
that allows any action as long as the resource attributes match the user
attributes is added (i.e. the previous behaviour is the default.
Some examples in yaml:
```
model:
- permit:
principal: user-1
actions: [create, read, delete]
comment: user-1 has full access to all models
- permit:
principal: user-2
actions: [read]
resource: model-1
comment: user-2 has read access to model-1 only
- permit:
actions: [read]
when:
user_in: resource.namespaces
comment: any user has read access to models with matching attributes
vector_db:
- forbid:
actions: [create, read, delete]
unless:
user_in: role::admin
comment: only user with admin role can use vector_db resources
```
---------
Signed-off-by: Gordon Sim <gsim@redhat.com>
# What does this PR do?
This adds the missing `text` parameter to the Responses API that is how
users control structured outputs. All we do with that parameter is map
it to the corresponding chat completion response_format.
## Test Plan
The new unit tests exercise the various permutations allowed for this
property, while a couple of new verification tests actually use it for
real to verify the model outputs are following the format as expected.
Unit tests:
`python -m pytest -s -v
tests/unit/providers/agents/meta_reference/test_openai_responses.py`
Verification tests:
```
llama stack run llama_stack/templates/together/run.yaml
pytest -s -vv 'tests/verifications/openai_api/test_responses.py' \
--base-url=http://localhost:8321/v1/openai/v1 \
--model meta-llama/Llama-4-Scout-17B-16E-Instruct
```
Note that the verification tests can only be run with a real Llama Stack
server (as opposed to using the library client via
`--provider=stack:together`) because the Llama Stack python client is
not yet updated to accept this text field.
Signed-off-by: Ben Browning <bbrownin@redhat.com>
# What does this PR do?
<!-- Provide a short summary of what this PR does and why. Link to
relevant issues if applicable. -->
This PR fixes a bug where running a known template by name using:
`llama stack run ollama`
would fail with the following error:
`ValueError: Config file ollama does not exist`
<!-- If resolving an issue, uncomment and update the line below -->
Closes#2291
## Test Plan
<!-- Describe the tests you ran to verify your changes with result
summaries. *Provide clear instructions so the plan can be easily
re-executed.* -->
`llama stack run ollama` should work
# What does this PR do?
TSIA
Added Files provider to the fireworks template. Might want to add to all
templates as a follow-up.
## Test Plan
llama-stack pytest tests/unit/files/test_files.py
llama-stack llama stack build --template fireworks --image-type conda
--run
LLAMA_STACK_CONFIG=http://localhost:8321 pytest -s -v
tests/integration/files/
# What does this PR do?
The chat completion ids generated by Ollama are not unique enough to use
with stored chat completions as they rely on only 3 numbers of
randomness to give unique values - ie `chatcmpl-373`. This causes
frequent collisions in id values of chat completions in Ollama, which
creates issues in our SQL storage of chat completions by id where it
expects ids to actually be unique.
So, this adjusts Ollama responses to use uuids as unique ids. This does
mean we're replacing the ids generated natively by Ollama. If we don't
wish to do this, we'll either need to relax the unique constraint on our
chat completions id field in the inference storage or convince Ollama
upstream to use something closer to uuid values here.
Closes#2315
## Test Plan
I tested by running the openai completion / chat completion integration
tests in a loop. Without this change, I regularly get unique id
collisions. With this change, I do not. We sometimes see flakes from
these unique id collisions in our CI tests, and this will resolve those.
```
INFERENCE_MODEL="meta-llama/Llama-3.2-3B-Instruct" \
llama stack run llama_stack/templates/ollama/run.yaml
while true; do; \
INFERENCE_MODEL="meta-llama/Llama-3.2-3B-Instruct" \
pytest -s -v \
tests/integration/inference/test_openai_completion.py \
--stack-config=http://localhost:8321 \
--text-model="meta-llama/Llama-3.2-3B-Instruct"; \
done
```
Signed-off-by: Ben Browning <bbrownin@redhat.com>
I think the implementation needs more simplification. Spent way too much
time trying to get the tests pass with models not co-operating :(
Finally had to switch claude-sonnet to get things to pass reliably.
### Test Plan
```
export TAVILY_SEARCH_API_KEY=...
export OPENAI_API_KEY=...
uv run pytest -p no:warnings \
-s -v tests/verifications/openai_api/test_responses.py \
--provider=stack:starter \
--model openai/gpt-4o
```
# What does this PR do?
1. remove openai dep
2. temporarily update llama-stack-client to stainless sync'd branch as
the responses/inputitems API wasn't included in the last push. This will
automatically be updated to the next version in the release.
## Test Plan
npm run dev
go to any responses details page
fixes provider to use stream var correctly
Before
```
curl --request POST \
--url http://localhost:8321/v1/openai/v1/chat/completions \
--header 'content-type: application/json' \
--data '{
"model": "meta-llama/Llama-4-Scout-17B-16E-Instruct",
"messages": [
{
"role": "user",
"content": "Who are you?"
}
]
}'
{"detail":"Internal server error: An unexpected error occurred."}
```
After
```
llama-stack % curl --request POST \
--url http://localhost:8321/v1/openai/v1/chat/completions \
--header 'content-type: application/json' \
--data '{
"model": "accounts/fireworks/models/llama4-scout-instruct-basic",
"messages": [
{
"role": "user",
"content": "Who are you?"
}
]
}'
{"id":"chatcmpl-97978538-271d-4c73-8d4d-c509bfb6c87e","choices":[{"message":{"role":"assistant","content":"I'm an AI assistant designed by Meta. I'm here to answer your questions, share interesting ideas and maybe even surprise you with a fresh perspective. What's on your mind?","name":null,"tool_calls":null},"finish_reason":"stop","index":0,"logprobs":null}],"object":"chat.completion","created":1748896403,"model":"accounts/fireworks/models/llama4-scout-instruct-basic"}%
```
This Kubernetes cluster has:
- vLLM for serving an inference model
- vLLM for serving a safety model
- Postgres DB (for metadata and other state for the Llama Stack distro)
- Chroma DB for Vector IO (memory)
Perhaps most importantly, this was me trying to learn Kubernetes for the
first time.
## Test Plan
Run `sh apply.sh` against an EKS cluster, then after `kubectl
port-forward service/llama-stack-service 8321:8321` and after many
attempts, we have finally:
<img width="1589" alt="image"
src="https://github.com/user-attachments/assets/c69f242d-6aaa-4def-9f7c-172113b8bfc1"
/>
<img width="1978" alt="image"
src="https://github.com/user-attachments/assets/cf678404-f551-4fa5-9077-bebe3e8e8ae8"
/>
# What does this PR do?
The remote-vllm `test_chat_completion_doesnt_block_event_loop` unit test
was often failing for me on a Mac with a `httpx.ReadError`. I traced
this back to the swap to the `AsyncOpenAI` client in the remote-vllm
provider as where this started, and it looks like the async client needs
a bit more accurate HTTP request handling from our mock server.
So, this fixes that unit test to send proper Content-Type and
Content-Length headers which makes the `AsyncOpenAI` client happier on
Macs.
## Test Plan
All the test_remote_vllm.py unit tests consistently pass for me on a Mac
now, without any flaking in the event loop one.
`pytest -s -v tests/unit/providers/inference/test_remote_vllm.py`
Signed-off-by: Ben Browning <bbrownin@redhat.com>
# What does this PR do?
[Provide a short summary of what this PR does and why. Link to relevant
issues if applicable.]
Removes the ability to run llama stack container images through the
llama stack CLI
Closes#2110
## 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.*]
Run:
```
llama stack run /path/to/run.yaml --image-type container
```
Expected outcome:
```
llama stack run: error: argument --image-type: invalid choice: 'container' (choose from 'conda', 'venv')
```
[//]: # (## Documentation)
# What does this PR do?
Adds a new endpoint that is compatible with OpenAI for embeddings api.
`/openai/v1/embeddings`
Added providers for OpenAI, LiteLLM and SentenceTransformer.
## Test Plan
```
LLAMA_STACK_CONFIG=http://localhost:8321 pytest -sv tests/integration/inference/test_openai_embeddings.py --embedding-model all-MiniLM-L6-v2,text-embedding-3-small,gemini/text-embedding-004
```
# What does this PR do?
This adds a check to ensure we don't attempt to concatenate `None + str`
or `str + None` when building up our arguments for streaming tool calls
in the Responses API.
## Test Plan
All existing tests pass with this change.
Unit tests:
```
python -m pytest -s -v \
tests/unit/providers/agents/meta_reference/test_openai_responses.py
```
Integration tests:
```
llama stack run llama_stack/templates/together/run.yaml
LLAMA_STACK_CONFIG=http://localhost:8321 \
python -m pytest -s -v \
tests/integration/agents/test_openai_responses.py \
--text-model meta-llama/Llama-4-Scout-17B-16E-Instruct
```
Verification tests:
```
llama stack run llama_stack/templates/together/run.yaml
pytest -s -v 'tests/verifications/openai_api/test_responses.py' \
--base-url=http://localhost:8321/v1/openai/v1 \
--model meta-llama/Llama-4-Scout-17B-16E-Instruct
```
Additionally, the manual example using Codex CLI from #2325 now succeeds
instead of throwing a 500 error.
Closes#2325
Signed-off-by: Ben Browning <bbrownin@redhat.com>
# What does this PR do?
* Added support postgresql inference store
* Added 'oracle' template that demos how to config postgresql stores
(except for telemetry, which is not supported currently)
## Test Plan
llama stack build --template oracle --image-type conda --run
LLAMA_STACK_CONFIG=http://localhost:8321 pytest -s -v tests/integration/
--text-model accounts/fireworks/models/llama-v3p3-70b-instruct -k
'inference_store'
# What does this PR do?
Updates sambanova inference to use strict as false in json_schema
structured output
## Test Plan
pytest -s -v tests/integration/inference/test_text_inference.py
--stack-config=sambanova
--text-model=sambanova/Meta-Llama-3.3-70B-Instruct
# What does this PR do?
<!-- Provide a short summary of what this PR does and why. Link to
relevant issues if applicable. -->
Adds an import for all of the template modules before the executor to
prevent deadlock
<!-- If resolving an issue, uncomment and update the line below -->
Closes#2278
## 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.* -->
```
# Run the pre-commit multiple times and verify the deadlock doesn't occur
for i in {1..10}; do pre-commit run --all-files; done
```
We must store the full (re-hydrated) input not just the original input
in the Response object. Of course, this is not very space efficient and
we should likely find a better storage scheme so that we can only store
unique entries in the database and then re-hydrate them efficiently
later. But that can be done safely later.
Closes https://github.com/meta-llama/llama-stack/issues/2299
## Test Plan
Unit test
# What does this PR do?
Previously prompt guard was hard coded to require cuda which prevented
it from being used on an instance without a cuda support.
This PR allows prompt guard to be configured to use either cpu or cuda.
[//]: # (If resolving an issue, uncomment and update the line below)
Closes [#2133](https://github.com/meta-llama/llama-stack/issues/2133)
## Test Plan (Edited after incorporating suggestion)
1) started stack configured with prompt guard as follows on a system
without a GPU
and validated prompt guard could be used through the APIs
2) validated on a system with a gpu (but without llama stack) that the
python selecting between cpu and cuda support returned the right value
when a cuda device was available.
3) ran the unit tests as per -
https://github.com/meta-llama/llama-stack/blob/main/tests/unit/README.md
[//]: # (## Documentation)
---------
Signed-off-by: Michael Dawson <mdawson@devrus.com>
# What does this PR do?
Use a more common pattern and known terminology from the ecosystem,
where Route is more approved than Endpoint.
Signed-off-by: Sébastien Han <seb@redhat.com>
# What does this PR do?
Followup of https://github.com/meta-llama/llama-stack/pull/2287. We must
use `--group` when running commands with uv.
<!-- 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] -->
Signed-off-by: Sébastien Han <seb@redhat.com>
# What does this PR do?
the providers list is missing post_training. Add that column and
`HuggingFace`, `TorchTune`, and `NVIDIA NEMO` as supported providers.
also point to these providers in docs/source/providers/index.md, and
describe basic functionality
There are other missing provider types here as well, but starting with
this
Signed-off-by: Charlie Doern <cdoern@redhat.com>
Co-authored-by: Francisco Arceo <arceofrancisco@gmail.com>
# What does this PR do?
Fix a bug in openai_compat where choices are not indexed correctly.
## Test Plan
Added a new test.
Rerun the failed inference_store tests:
llama stack run fireworks --image-type conda
pytest -s -v tests/integration/ --stack-config http://localhost:8321 -k
'test_inference_store' --text-model meta-llama/Llama-3.3-70B-Instruct
--count 10
# What does this PR do?
Changed the test to not require tool_call in output, but still keeping
the tools params there as a smoke test.
## Test Plan
Used llama3.3 from fireworks (same as CI)
<img width="1433" alt="image"
src="https://github.com/user-attachments/assets/1e5fca98-9b4f-402e-a0bc-d9f910f2c207"
/>
Run with ollama distro and 3b model.
# What does this PR do?
The previous `[project.optional-dependencies]` was misrepresenting what
the packages were. They were NOT optional dependencies to the project
but development dependencies. Unlike optional dependencies, development
dependencies are local-only and will not be included in the project
requirements when published to PyPI or other indexes. As such,
development dependencies are not included in the [project] table.
Additionally, the dev group is synced by default.
Source:
https://docs.astral.sh/uv/concepts/projects/dependencies/#development-dependencies
Signed-off-by: Sébastien Han <seb@redhat.com>
This adds initial streaming support to the Responses API.
This PR makes sure that the _first_ inference call made to chat
completions streams out.
There's more to be done:
- tool call output tokens need to stream out when possible
- we need to loop through multiple rounds of inference and they all need
to stream out.
## Test Plan
Added a test. Executed as:
```
FIREWORKS_API_KEY=... \
pytest -s -v 'tests/verifications/openai_api/test_responses.py' \
--provider=stack:fireworks --model meta-llama/Llama-4-Scout-17B-16E-Instruct
```
Then, started a llama stack fireworks distro and tested against it like
this:
```
OPENAI_API_KEY=blah \
pytest -s -v 'tests/verifications/openai_api/test_responses.py' \
--base-url http://localhost:8321/v1/openai/v1 \
--model meta-llama/Llama-4-Scout-17B-16E-Instruct
```
# What does this PR do?
Handles the case where the vllm config `tls_verify` is set to `false` or
`true`.
Closes: https://github.com/meta-llama/llama-stack/issues/2283
Signed-off-by: Sébastien Han <seb@redhat.com>
# What does this PR do?
It's not used anywhere in the build process. Ancient artifact from an
old attempt of using sub packages to build distros.
## 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.* -->
N/A
Signed-off-by: Sébastien Han <seb@redhat.com>
