# What does this PR do?
Add support for "instructions" to the responses API. Instructions
provide a way to swap out system (or developer) messages in new
responses.
## Test Plan
unit tests added
Signed-off-by: Derek Higgins <derekh@redhat.com>
# What does this PR do?
When launching a fine-tuning job, an upcoming version of NeMo Customizer
will expect the `config` name to be formatted as
`namespace/name@version`. Here, `config` is a reference to a model +
additional metadata. There could be multiple `config`s that reference
the same base model.
This PR updates NVIDIA's `supervised_fine_tune` to simply pass the
`model` param as-is to NeMo Customizer. Currently, it expects a
specific, allowlisted llama model (i.e. `meta/Llama3.1-8B-Instruct`) and
converts it to the provider format (`meta/llama-3.1-8b-instruct`).
[//]: # (If resolving an issue, uncomment and update the line below)
[//]: # (Closes #[issue-number])
## Test Plan
From a notebook, I built an image with my changes:
```
!llama stack build --template nvidia --image-type venv
from llama_stack.distribution.library_client import LlamaStackAsLibraryClient
client = LlamaStackAsLibraryClient("nvidia")
client.initialize()
```
And could successfully launch a job:
```
response = client.post_training.supervised_fine_tune(
job_uuid="",
model="meta/llama-3.2-1b-instruct@v1.0.0+A100", # Model passed as-is to Customimzer
...
)
job_id = response.job_uuid
print(f"Created job with ID: {job_id}")
Output:
Created job with ID: cust-Jm4oGmbwcvoufaLU4XkrRU
```
[//]: # (## Documentation)
---------
Co-authored-by: Jash Gulabrai <jgulabrai@nvidia.com>
This PR adds a notion of `principal` (aka some kind of persistent
identity) to the authentication infrastructure of the Stack. Until now
we only used access attributes ("claims" in the more standard OAuth /
OIDC setup) but we need the notion of a User fundamentally as well.
(Thanks @rhuss for bringing this up.)
This value is not yet _used_ anywhere downstream but will be used to
segregate access to resources.
In addition, the PR introduces a built-in JWT token validator so the
Stack does not need to contact an authentication provider to validating
the authorization and merely check the signed token for the represented
claims. Public keys are refreshed via the configured JWKS server. This
Auth Provider should overwhelmingly be considered the default given the
seamless integration it offers with OAuth setups.
# What does this PR do?
This fixes an issue in how we used the tool_call_buf from streaming tool
calls in the remote-vllm provider where it would end up concatenating
parameters from multiple different tool call results instead of
aggregating the results from each tool call separately.
It also fixes an issue found while digging into that where we were
accidentally mixing the json string form of tool call parameters with
the string representation of the python form, which mean we'd end up
with single quotes in what should be double-quoted json strings.
Closes#1120
## Test Plan
The following tests are now passing 100% for the remote-vllm provider,
where some of the test_text_inference were failing before this change:
```
VLLM_URL="http://localhost:8000/v1" INFERENCE_MODEL="RedHatAI/Llama-4-Scout-17B-16E-Instruct-FP8-dynamic" LLAMA_STACK_CONFIG=remote-vllm python -m pytest -v tests/integration/inference/test_text_inference.py --text-model "RedHatAI/Llama-4-Scout-17B-16E-Instruct-FP8-dynamic"
VLLM_URL="http://localhost:8000/v1" INFERENCE_MODEL="RedHatAI/Llama-4-Scout-17B-16E-Instruct-FP8-dynamic" LLAMA_STACK_CONFIG=remote-vllm python -m pytest -v tests/integration/inference/test_vision_inference.py --vision-model "RedHatAI/Llama-4-Scout-17B-16E-Instruct-FP8-dynamic"
```
All but one of the agent tests are passing (including the multi-tool
one). See the PR at https://github.com/vllm-project/vllm/pull/17917 and
a gist at
https://gist.github.com/bbrowning/4734240ce96b4264340caa9584e47c9e for
changes needed there, which will have to get made upstream in vLLM.
Agent tests:
```
VLLM_URL="http://localhost:8000/v1" INFERENCE_MODEL="RedHatAI/Llama-4-Scout-17B-16E-Instruct-FP8-dynamic" LLAMA_STACK_CONFIG=remote-vllm python -m pytest -v tests/integration/agents/test_agents.py --text-model "RedHatAI/Llama-4-Scout-17B-16E-Instruct-FP8-dynamic"
````
---------
Signed-off-by: Ben Browning <bbrownin@redhat.com>
# What does this PR do?
his PR allows users to customize the template used for chunks when
inserted into the context. Additionally, this enables metadata injection
into the context of an LLM for RAG. This makes a naive and crude
assumption that each chunk should include the metadata, this is
obviously redundant when multiple chunks are returned from the same
document. In order to remove any sort of duplication of chunks, we'd
have to make much more significant changes so this is a reasonable first
step that unblocks users requesting this enhancement in
https://github.com/meta-llama/llama-stack/issues/1767.
In the future, this can be extended to support citations.
List of Changes:
- `llama_stack/apis/tools/rag_tool.py`
- Added `chunk_template` field in `RAGQueryConfig`.
- Added `field_validator` to validate the `chunk_template` field in
`RAGQueryConfig`.
- Ensured the `chunk_template` field includes placeholders `{index}` and
`{chunk.content}`.
- Updated the `query` method to use the `chunk_template` for formatting
chunk text content.
- `llama_stack/providers/inline/tool_runtime/rag/memory.py`
- Modified the `insert` method to pass `doc.metadata` for chunk
creation.
- Enhanced the `query` method to format results using `chunk_template`
and exclude unnecessary metadata fields like `token_count`.
- `llama_stack/providers/utils/memory/vector_store.py`
- Updated `make_overlapped_chunks` to include metadata serialization and
token count for both content and metadata.
- Added error handling for metadata serialization issues.
- `pyproject.toml`
- Added `pydantic.field_validator` as a recognized `classmethod`
decorator in the linting configuration.
- `tests/integration/tool_runtime/test_rag_tool.py`
- Refactored test assertions to separate `assert_valid_chunk_response`
and `assert_valid_text_response`.
- Added integration tests to validate `chunk_template` functionality
with and without metadata inclusion.
- Included a test case to ensure `chunk_template` validation errors are
raised appropriately.
- `tests/unit/rag/test_vector_store.py`
- Added unit tests for `make_overlapped_chunks`, verifying chunk
creation with overlapping tokens and metadata integrity.
- Added tests to handle metadata serialization errors, ensuring proper
exception handling.
- `docs/_static/llama-stack-spec.html`
- Added a new `chunk_template` field of type `string` with a default
template for formatting retrieved chunks in RAGQueryConfig.
- Updated the `required` fields to include `chunk_template`.
- `docs/_static/llama-stack-spec.yaml`
- Introduced `chunk_template` field with a default value for
RAGQueryConfig.
- Updated the required configuration list to include `chunk_template`.
- `docs/source/building_applications/rag.md`
- Documented the `chunk_template` configuration, explaining how to
customize metadata formatting in RAG queries.
- Added examples demonstrating the usage of the `chunk_template` field
in RAG tool queries.
- Highlighted default values for `RAG` agent configurations.
# Resolves https://github.com/meta-llama/llama-stack/issues/1767
## Test Plan
Updated both `test_vector_store.py` and `test_rag_tool.py` and tested
end-to-end with a script.
I also tested the quickstart to enable this and specified this metadata:
```python
document = RAGDocument(
document_id="document_1",
content=source,
mime_type="text/html",
metadata={"author": "Paul Graham", "title": "How to do great work"},
)
```
Which produced the output below:
This highlights the usefulness of the additional metadata. Notice how
the metadata is redundant for different chunks of the same document. I
think we can update that in a subsequent PR.
# Documentation
I've added a brief comment about this in the documentation to outline
this to users and updated the API documentation.
---------
Signed-off-by: Francisco Javier Arceo <farceo@redhat.com>
# What does this PR do?
In the Responses API, we convert incoming response requests to chat
completion requests. When streaming the resulting chunks of those chat
completion requests, inference providers that use OpenAI clients will
often return a `type=None` value in the tool call parts of the response.
This causes issues when we try to dump and load that response into our
pydantic model, because type cannot be None in the Responses API model
we're loading these into.
So, strip the "type" field, if present, off those chat completion tool
call results before dumping and loading them as our typed pydantic
models, which will apply our default value for that type field.
## Test Plan
This was found via manual testing of the Responses API with codex, where
I was getting errors in some tool call situations. I added a unit test
to simulate this scenario and verify the fix, as well as manual codex
testing to verify the fix.
Signed-off-by: Ben Browning <bbrownin@redhat.com>
# What does this PR do?
Closes#2113.
Closes#1783.
Fixes a bug in handling the end of tool execution request stream where
no `finish_reason` is provided by the model.
## Test Plan
1. Ran existing unit tests
2. Added a dedicated test verifying correct behavior in this edge case
3. Ran the code snapshot from #2113
[//]: # (## Documentation)
# What does this PR do?
Closes#2111.
Fixes an error causing Llama Stack to just return `<tool_call>` and
complete the turn without actually executing the tool. See the issue
description for more detail.
## Test Plan
1) Ran existing unit tests
2) Added a dedicated test verifying correct behavior in this edge case
3) Ran the code snapshot from #2111
# What does this PR do?
This is a combination of what was previously 3 separate PRs - #2069,
#2075, and #2083. It turns out all 3 of those are needed to land a
working function calling Responses implementation. The web search
builtin tool was already working, but this wires in support for custom
function calling.
I ended up combining all three into one PR because they all had lots of
merge conflicts, both with each other but also with #1806 that just
landed. And, because landing any of them individually would have only
left a partially working implementation merged.
The new things added here are:
* Storing of input items from previous responses and restoring of those
input items when adding previous responses to the conversation state
* Handling of multiple input item messages roles, not just "user"
messages.
* Support for custom tools passed into the Responses API to enable
function calling outside of just the builtin websearch tool.
Closes#2074Closes#2080
## Test Plan
### Unit Tests
Several new unit tests were added, and they all pass. Ran via:
```
python -m pytest -s -v tests/unit/providers/agents/meta_reference/test_openai_responses.py
```
### Responses API Verification Tests
I ran our verification run.yaml against multiple providers to ensure we
were getting a decent pass rate. Specifically, I ensured the new custom
tool verification test passed across multiple providers and that the
multi-turn examples passed across at least some of the providers (some
providers struggle with the multi-turn workflows still).
Running the stack setup for verification testing:
```
llama stack run --image-type venv tests/verifications/openai-api-verification-run.yaml
```
Together, passing 100% as an example:
```
pytest -s -v 'tests/verifications/openai_api/test_responses.py' --provider=together-llama-stack
```
## Documentation
We will need to start documenting the OpenAI APIs, but for now the
Responses stuff is still rapidly evolving so delaying that.
---------
Signed-off-by: Derek Higgins <derekh@redhat.com>
Signed-off-by: Ben Browning <bbrownin@redhat.com>
Co-authored-by: Derek Higgins <derekh@redhat.com>
Co-authored-by: Ashwin Bharambe <ashwin.bharambe@gmail.com>
# What does this PR do?
reduces duplication and centralizes information to be easier to find for
contributors
Signed-off-by: Nathan Weinberg <nweinber@redhat.com>
# What does this PR do?
This PR fixes the behavior of the `/tool-runtime/rag-tool/query`
endpoint when invoked with an empty `vector_db_ids` parameter.
As of now, it simply returns an empty result, which leads to a
misleading error message from the server and makes it difficult and
time-consuming to detect the problem with the input parameter.
The proposed fix is to return an indicative error message in this case.
## Test Plan
Running the following script:
```
agent = Agent(
client,
model=MODEL_ID,
instructions=SYSTEM_PROMPT,
tools=[
dict(
name="builtin::rag/knowledge_search",
args={
"vector_db_ids": [],
},
)
],
)
response = agent.create_turn(
messages=[
{
"role": "user",
"content": "How to install OpenShift?",
}
],
session_id=agent.create_session(f"rag-session")
)
```
results in the following error message in the non-patched version:
```
{"type": "function", "name": "knowledge_search", "parameters": {"query": "installing OpenShift"}}400: Invalid value: Tool call result (id: 494b8020-90bb-449b-aa76-10960d6b2cc2, name: knowledge_search) does not have any content
```
and in the following one in the patched version:
```
{"type": "function", "name": "knowledge_search", "parameters": {"query": "installing OpenShift"}}400: Invalid value: No vector DBs were provided to the RAG tool. Please provide at least one DB.
```
# 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>
Add fixtures for SqliteKVStore, DiskDistributionRegistry and
CachedDiskDistributionRegistry. And use them in tests that had all been
duplicating similar setups.
## Test Plan
unit tests continue to run
Signed-off-by: Derek Higgins <derekh@redhat.com>
# 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 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>
# 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?
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?
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?
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?
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>
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?
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?
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.
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?
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?
This PR adds unit tests for the NVIDIA Safety provider implementation.
[//]: # (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. Ran `./scripts/unit-tests.sh
tests/unit/providers/nvidia/test_safety.py` from the root of the
project. Verified tests pass.
```
tests/unit/providers/nvidia/test_safety.py::TestNVIDIASafetyAdapter::test_init_nemo_guardrails Initializing NVIDIASafetyAdapter(http://nemo.test)...
PASSED
tests/unit/providers/nvidia/test_safety.py::TestNVIDIASafetyAdapter::test_init_nemo_guardrails_invalid_temperature Initializing NVIDIASafetyAdapter(http://nemo.test)...
PASSED
tests/unit/providers/nvidia/test_safety.py::TestNVIDIASafetyAdapter::test_register_shield_with_valid_id Initializing NVIDIASafetyAdapter(http://nemo.test)...
PASSED
tests/unit/providers/nvidia/test_safety.py::TestNVIDIASafetyAdapter::test_register_shield_without_id Initializing NVIDIASafetyAdapter(http://nemo.test)...
PASSED
tests/unit/providers/nvidia/test_safety.py::TestNVIDIASafetyAdapter::test_run_shield_allowed Initializing NVIDIASafetyAdapter(http://nemo.test)...
PASSED
tests/unit/providers/nvidia/test_safety.py::TestNVIDIASafetyAdapter::test_run_shield_blocked Initializing NVIDIASafetyAdapter(http://nemo.test)...
PASSED
tests/unit/providers/nvidia/test_safety.py::TestNVIDIASafetyAdapter::test_run_shield_http_error Initializing NVIDIASafetyAdapter(http://nemo.test)...
PASSED
tests/unit/providers/nvidia/test_safety.py::TestNVIDIASafetyAdapter::test_run_shield_not_found Initializing NVIDIASafetyAdapter(http://nemo.test)...
PASSED
```
[//]: # (## Documentation)
---------
Co-authored-by: Jash Gulabrai <jgulabrai@nvidia.com>
# What does this PR do?
Providers that live outside of the llama-stack codebase are now
supported.
A new property `external_providers_dir` has been added to the main
config and can be configured as follow:
```
external_providers_dir: /etc/llama-stack/providers.d/
```
Where the expected structure is:
```
providers.d/
inference/
custom_ollama.yaml
vllm.yaml
vector_io/
qdrant.yaml
```
Where `custom_ollama.yaml` is:
```
adapter:
adapter_type: custom_ollama
pip_packages: ["ollama", "aiohttp"]
config_class: llama_stack_ollama_provider.config.OllamaImplConfig
module: llama_stack_ollama_provider
api_dependencies: []
optional_api_dependencies: []
```
Obviously the package must be installed on the system, here is the
`llama_stack_ollama_provider` example:
```
$ uv pip show llama-stack-ollama-provider
Using Python 3.10.16 environment at: /Users/leseb/Documents/AI/llama-stack/.venv
Name: llama-stack-ollama-provider
Version: 0.1.0
Location: /Users/leseb/Documents/AI/llama-stack/.venv/lib/python3.10/site-packages
Editable project location: /private/var/folders/mq/rnm5w_7s2d3fxmtkx02knvhm0000gn/T/tmp.ZBHU5Ezxg4/ollama/llama-stack-ollama-provider
Requires:
Required-by:
```
Closes: https://github.com/meta-llama/llama-stack/issues/658
Signed-off-by: Sébastien Han <seb@redhat.com>
Move the test_context.py under the main tests directory, and fix the
code.
The problem was that the function captures the initial values of the
context variables and then restores those same initial values before
each iteration. This means that any modifications made to the context
variables during iteration are lost when the next iteration starts.
Error was:
```
====================================================== FAILURES =======================================================
______________________________________ test_preserve_contexts_across_event_loops ______________________________________
@pytest.mark.asyncio
async def test_preserve_contexts_across_event_loops():
"""
Test that context variables are preserved across event loop boundaries with nested generators.
This simulates the real-world scenario where:
1. A new event loop is created for each streaming request
2. The async generator runs inside that loop
3. There are multiple levels of nested generators
4. Context needs to be preserved across these boundaries
"""
# Create context variables
request_id = ContextVar("request_id", default=None)
user_id = ContextVar("user_id", default=None)
# Set initial values
# Results container to verify values across thread boundaries
results = []
# Inner-most generator (level 2)
async def inner_generator():
# Should have the context from the outer scope
yield (1, request_id.get(), user_id.get())
# Modify one context variable
user_id.set("user-modified")
# Should reflect the modification
yield (2, request_id.get(), user_id.get())
# Middle generator (level 1)
async def middle_generator():
inner_gen = inner_generator()
# Forward the first yield from inner
item = await inner_gen.__anext__()
yield item
# Forward the second yield from inner
item = await inner_gen.__anext__()
yield item
request_id.set("req-modified")
# Add our own yield with both modified variables
yield (3, request_id.get(), user_id.get())
# Function to run in a separate thread with a new event loop
def run_in_new_loop():
# Create a new event loop for this thread
loop = asyncio.new_event_loop()
asyncio.set_event_loop(loop)
try:
# Outer generator (runs in the new loop)
async def outer_generator():
request_id.set("req-12345")
user_id.set("user-6789")
# Wrap the middle generator
wrapped_gen = preserve_contexts_async_generator(middle_generator(), [request_id, user_id])
# Process all items from the middle generator
async for item in wrapped_gen:
# Store results for verification
results.append(item)
# Run the outer generator in the new loop
loop.run_until_complete(outer_generator())
finally:
loop.close()
# Run the generator chain in a separate thread with a new event loop
with ThreadPoolExecutor(max_workers=1) as executor:
future = executor.submit(run_in_new_loop)
future.result() # Wait for completion
# Verify the results
assert len(results) == 3
# First yield should have original values
assert results[0] == (1, "req-12345", "user-6789")
# Second yield should have modified user_id
assert results[1] == (2, "req-12345", "user-modified")
# Third yield should have both modified values
> assert results[2] == (3, "req-modified", "user-modified")
E AssertionError: assert (3, 'req-modified', 'user-6789') == (3, 'req-modified', 'user-modified')
E
E At index 2 diff: 'user-6789' != 'user-modified'
E
E Full diff:
E (
E 3,
E 'req-modified',
E - 'user-modified',
E + 'user-6789',
E )
tests/unit/distribution/test_context.py:155: AssertionError
-------------------------------------------------- Captured log call --------------------------------------------------
ERROR asyncio:base_events.py:1758 Task was destroyed but it is pending!
task: <Task pending name='Task-7' coro=<<async_generator_athrow without __name__>()>>
================================================== warnings summary ===================================================
.venv/lib/python3.10/site-packages/pydantic/fields.py:1042
/Users/leseb/Documents/AI/llama-stack/.venv/lib/python3.10/site-packages/pydantic/fields.py:1042: 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/2.10/migration/
warn(
-- Docs: https://docs.pytest.org/en/stable/how-to/capture-warnings.html
=============================================== short test summary info ===============================================
FAILED tests/unit/distribution/test_context.py::test_preserve_contexts_across_event_loops - AssertionError: assert (3, 'req-modified', 'user-6789') == (3, 'req-modified', 'user-modified')
At index 2 diff: 'user-6789' != 'user-modified'
Full diff:
(
3,
'req-modified',
- 'user-modified',
+ 'user-6789',
)
```
[//]: # (## Documentation)
Signed-off-by: Sébastien Han <seb@redhat.com>
# What does this PR do?
Re-enable isort enforcement.
It was disabled in 1a73f8305b, probably by
mistake.
Signed-off-by: Ihar Hrachyshka <ihar.hrachyshka@gmail.com>
# What does this PR do?
This is to stay consistent with other APIs.
This change registers files in API, even though there are still no
providers. Removing tests that require a provider existing for a merged
API to enable it in API layer.
Signed-off-by: Ihar Hrachyshka <ihar.hrachyshka@gmail.com>
[//]: # (If resolving an issue, uncomment and update the line below)
[//]: # (Closes #[issue-number])
## Test Plan
[Describe the tests you ran to verify your changes with result
summaries. *Provide clear instructions so the plan can be easily
re-executed.*]
[//]: # (## Documentation)
Signed-off-by: Ihar Hrachyshka <ihar.hrachyshka@gmail.com>
# What does this PR do?
This PR adds support for NVIDIA's NeMo Customizer API to the Llama Stack
post-training module. The integration enables users to fine-tune models
using NVIDIA's cloud-based customization service through a consistent
Llama Stack interface.
[//]: # (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.*]
Yet to be done
Things pending under this PR:
- [x] Integration of fine-tuned model(new checkpoint) for inference with
nvidia llm distribution
- [x] distribution integration of API
- [x] Add test cases for customizer(In Progress)
- [x] Documentation
```
LLAMA_STACK_BASE_URL=http://localhost:5002 pytest -v tests/client-sdk/post_training/test_supervised_fine_tuning.py
============================================================================================================================================================================ test session starts =============================================================================================================================================================================
platform linux -- Python 3.10.0, pytest-8.3.4, pluggy-1.5.0 -- /home/ubuntu/llama-stack/.venv/bin/python
cachedir: .pytest_cache
metadata: {'Python': '3.10.0', 'Platform': 'Linux-6.8.0-1021-gcp-x86_64-with-glibc2.35', 'Packages': {'pytest': '8.3.4', 'pluggy': '1.5.0'}, 'Plugins': {'nbval': '0.11.0', 'metadata': '3.1.1', 'anyio': '4.8.0', 'html': '4.1.1', 'asyncio': '0.25.3'}}
rootdir: /home/ubuntu/llama-stack
configfile: pyproject.toml
plugins: nbval-0.11.0, metadata-3.1.1, anyio-4.8.0, html-4.1.1, asyncio-0.25.3
asyncio: mode=strict, asyncio_default_fixture_loop_scope=None
collected 2 items
tests/client-sdk/post_training/test_supervised_fine_tuning.py::test_post_training_provider_registration[txt=8B] PASSED [ 50%]
tests/client-sdk/post_training/test_supervised_fine_tuning.py::test_list_training_jobs[txt=8B] PASSED [100%]
======================================================================================================================================================================== 2 passed, 1 warning in 0.10s ========================================================================================================================================================================
```
cc: @mattf @dglogo @sumitb
---------
Co-authored-by: Ubuntu <ubuntu@llama-stack-customizer-dev-inst-2tx95fyisatvlic4we8hidx5tfj.us-central1-a.c.brevdevprod.internal>
# What does this PR do?
This PR updates the sqlite-vec database calls to be non-blocking. Note
that each operation creates a new connection, which incurs some
performance overhead but is reasonable given [SQLite's threading and
connections constraints](https://www.sqlite.org/threadsafe.html).
Summary of changes:
- Refactored `SQLiteVecIndex` class to store database path instead of
connection object
- Added `_create_sqlite_connection()` helper function to create
connections on demand
- Ensured proper connection closure in all database operations
- Fixed test fixtures to use a file-based SQLite database for
thread-safety
- Updated the `SQLiteVecVectorIOAdapter` class to handle per-operation
connections
This PR helps chip away at
https://github.com/meta-llama/llama-stack/issues/1489
## Test Plan
sqlite-vec unit tests passed locally as well as a test script using the
client as a library.
## Misc
FYI @varshaprasad96 @kevincogan
Signed-off-by: Francisco Javier Arceo <farceo@redhat.com>
We need to be able to handle stale registry entries gracefully. More
needs to be done when we are deleting important attributes from
resources which could have been persisted. But at the very least, the
server cannot die.
## Test Plan
Added unit tests
# What does this PR do?
closes#1584
This should be a rather innocuous change.
## Test Plan
Verify that there's no more tool call parsing error for example in issue
<img width="1216" alt="image"
src="https://github.com/user-attachments/assets/a5a6f4e8-2093-4ca2-bc06-794b707a0429"
/>
LLAMA_STACK_CONFIG=fireworks pytest -s -v
tests/integration/agents/test_agents.py --safety-shield
meta-llama/Llama-Guard-3-8B --text-model
meta-llama/Llama-3.1-8B-Instruct
This PR introduces a way to implement Attribute Based Access Control
(ABAC) for the Llama Stack server.
The rough design is:
- https://github.com/meta-llama/llama-stack/pull/1626 added a way for
the Llama Stack server to query an authenticator
- We build upon that and expect "access attributes" as part of the
response. These attributes indicate the scopes available for the
request.
- We use these attributes to perform access control for registered
resources as well as for constructing the default access control
policies for newly created resources.
- By default, if you support authentication but don't return access
attributes, we will add a unique namespace pointing to the API_KEY. That
way, all resources by default will be scoped to API_KEYs.
An important aspect of this design is that Llama Stack stays out of the
business of credential management or the CRUD for attributes. How you
manage your namespaces or projects is entirely up to you. The design
only implements access control checks for the metadata / book-keeping
information that the Stack tracks.
### Limitations
- Currently, read vs. write vs. admin permissions aren't made explicit,
but this can be easily extended by adding appropriate attributes to the
`AccessAttributes` data structure.
- This design does not apply to agent instances since they are not
considered resources the Stack knows about. Agent instances are
completely within the scope of the Agents API provider.
### Test Plan
Added unit tests, existing integration tests
# What does this PR do?
DocVQA asks model to look a a picture, then answer a question given in
text, with a text answer by text information in the picture. these
questions often require understanding of relative positions of texts
within the picture.
original dataset is defined in the "Task1" of
https://www.docvqa.org/datasets
## Test Plan
setup llama server with
```
llama stack run ./llama_stack/templates/open-benchmark/run.yaml
```
then send traffic:
```
llama-stack-client eval run-benchmark "meta-reference-docvqa" --model-id meta-llama/Llama-3.3-70B-Instruct --output-dir /tmp/gpqa --num-examples 200
```
### What does this PR do?
Currently, `ToolCall.arguments` is a `Dict[str, RecursiveType]`.
However, on the client SDK side -- the `RecursiveType` gets deserialized
into a number ( both int and float get collapsed ) and hence when params
are `int` they get converted to float which might break client side
tools that might be doing type checking.
Closes: https://github.com/meta-llama/llama-stack/issues/1683
### Test Plan
Stainless changes --
https://github.com/meta-llama/llama-stack-client-python/pull/204
```
pytest -s -v --stack-config=fireworks tests/integration/agents/test_agents.py --text-model meta-llama/Llama-3.1-8B-Instruct
```
This PR adds support (or is a proposal for) for supporting API KEY
authentication on the Llama Stack server end. `llama-stack-client`
already supports accepting an api_key parameter and passes it down
through every request as an `Authentication: ` header.
Currently, Llama Stack does not propose APIs for handling authentication
or authorization for resources of any kind. Given that, and the fact
that any deployment will typically have _some_ authentication system
present, we simply adopt a delegation mechanism: delegate to an HTTPS
endpoint performing key management / authentication.
It is configured via:
```yaml
server:
auth:
endpoint: <...>
```
in the run.yaml configuration.
## How It Works
When authentication is enabled:
1. Every API request must include an `Authorization: Bearer <token>`
header
2. The server will send a _POST_ validation request to the configured
endpoint with the following payload:
```json
{
"api_key": "<token>",
"request": {
"path": "/api/path",
"headers": { "header1": "value1", ... },
"params": { "param1": "value1", ... }
}
}
```
3. If the authentication endpoint returns a 200 status code, the request
is allowed to proceed
4. If the authentication endpoint returns any other status code, a 401
Unauthorized response is returned
## Test Plan
Unit tests
# What does this PR do?
Removed local execution option from the remote Qdrant provider and
introduced an explicit inline provider for the embedded execution.
Updated the ollama template to include this option: this part can be
reverted in case we don't want to have two default `vector_io`
providers.
(Closes#1082)
## Test Plan
Build and run an ollama distro:
```bash
llama stack build --template ollama --image-type conda
llama stack run --image-type conda ollama
```
Run one of the sample ingestionapplicatinos like
[rag_with_vector_db.py](https://github.com/meta-llama/llama-stack-apps/blob/main/examples/agents/rag_with_vector_db.py),
but replace this line:
```py
selected_vector_provider = vector_providers[0]
```
with the following, to use the `qdrant` provider:
```py
selected_vector_provider = vector_providers[1]
```
After running the test code, verify the timestamp of the Qdrant store:
```bash
% ls -ltr ~/.llama/distributions/ollama/qdrant.db/collection/test_vector_db_*
total 784
-rw-r--r--@ 1 dmartino staff 401408 Feb 26 10:07 storage.sqlite
```
[//]: # (## Documentation)
---------
Signed-off-by: Daniele Martinoli <dmartino@redhat.com>
Co-authored-by: Francisco Arceo <farceo@redhat.com>
