# What does this PR do?
This seems to be an ancient artifact when we were using readthedocs? Now
docusaurus read the specs directly.
---------
Signed-off-by: Sébastien Han <seb@redhat.com>
# What does this PR do?
Allow filtering for v1alpha, v1beta, deprecated and v1. Backward
incompatible change since by default it only returns v1 apis now.
## Test Plan
added unit test
# What does this PR do?
Add rerank API for NVIDIA Inference Provider.
<!-- If resolving an issue, uncomment and update the line below -->
Closes#3278
## Test Plan
Unit test:
```
pytest tests/unit/providers/nvidia/test_rerank_inference.py
```
Integration test:
```
pytest -s -v tests/integration/inference/test_rerank.py --stack-config="inference=nvidia" --rerank-model=nvidia/nvidia/nv-rerankqa-mistral-4b-v3 --env NVIDIA_API_KEY="" --env NVIDIA_BASE_URL="https://integrate.api.nvidia.com"
```
# What does this PR do?
chunk_id in the Chunk class executes actual logic to compute a chunk ID.
This sort of logic should not live in the API spec.
Instead, the providers should be in charge of calling generate_chunk_id,
and pass it to `Chunk`.
this removes the incorrect dependency between Provider impl and API impl
Signed-off-by: Charlie Doern <cdoern@redhat.com>
# What does this PR do?
- Adds OpenAI files provider
- Note that file content retrieval is pretty limited by `purpose`
https://community.openai.com/t/file-uploads-error-why-can-t-i-download-files-with-purpose-user-data/1357013?utm_source=chatgpt.com
## Test Plan
Modify run yaml to use openai files provider:
```
files:
- provider_id: openai
provider_type: remote::openai
config:
api_key: ${env.OPENAI_API_KEY:=}
metadata_store:
backend: sql_default
table_name: openai_files_metadata
# Then run files tests
❯ uv run --no-sync ./scripts/integration-tests.sh --stack-config server:ci-tests --inference-mode replay --setup ollama --suite base --pattern test_files
```
# 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 responsible for making changes to Responses API scheme to
introduce OpenAI compatible prompts there. Change to the API only,
therefore currently no implementation at all. However, the follow up PR
with actual implementation will be submitted after current PR lands.
The need of this functionality was initiated in #3514.
> Note, #3514 is divided on three separate PRs. Current PR is the second
of three.
<!-- 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.* -->
CI
# What does this PR do?
Introduces two main fixes to enhance the stability of Responses API when
dealing with tool calling responses and structured outputs.
### Changes Made
1. It added OpenAIResponseOutputMessageMCPCall and ListTools to
OpenAIResponseInput but
https://github.com/llamastack/llama-stack/pull/3810 got merge that did
the same in a different way. Still this PR does it in a way that keep
the sync between OpenAIResponsesOutput and the allowed objects in
OpenAIResponseInput.
2. Add protection in case self.ctx.response_format does not have type
attribute
BREAKING CHANGE: OpenAIResponseInput now uses OpenAIResponseOutput union
type.
This is semantically equivalent - all previously accepted types are
still supported
via the OpenAIResponseOutput union. This improves type consistency and
maintainability.
This patch ensures if max tokens is not defined, then is set to None
instead of 0 when calling openai_chat_completion. This way some
providers (like gemini) that cannot handle the `max_tokens = 0` will not
fail
Issue: #3666
# What does this PR do?
Add static file import system for docs
- Use `remark-code-import` plugin to embed code at build time
- Support importing Python code with syntax highlighting using
`raw-loader` + `ReactMarkdown`
One caveat is that currently when embedding markdown with code used the
syntax highlighting isn't behaving but I'll investigate that in a follow
up.
## Test Plan
Python Example:
<img width="1372" height="995" alt="Screenshot 2025-10-23 at 9 22 18 PM"
src="https://github.com/user-attachments/assets/656d2c78-4d9b-45a4-bd5e-3f8490352b85"
/>
Markdown example:
<img width="1496" height="1070" alt="Screenshot 2025-10-23 at 9 22
38 PM"
src="https://github.com/user-attachments/assets/6c0a07ec-ff7c-45aa-b05f-8c46acd4445c"
/>
---------
Signed-off-by: Francisco Javier Arceo <farceo@redhat.com>
# What does this PR do?
https://platform.openai.com/docs/api-reference/moderations supports
optional model parameter.
This PR adds support for using moderations API with model=None if a
default shield id is provided via safety config.
## Test Plan
added tests
manual test:
```
> SAFETY_MODEL='together/meta-llama/Llama-Guard-4-12B' uv run llama stack run starter
> curl http://localhost:8321/v1/moderations \
-H "Content-Type: application/json" \
-d '{
"input": [
"hello"
]
}'
```
Migrates k8s run configs to match the updated run configs
- Replace storage.references with storage.stores
- Wrap resources under registered_resources section
- Update provider configs to use persistence with namespace/backend
- Add telemetry and vector_stores top-level sections
- Simplify agent/files metadata store configuration
Bumps [openai](https://github.com/openai/openai-python) from 1.107.0 to
2.5.0.
<details>
<summary>Release notes</summary>
<p><em>Sourced from <a
href="https://github.com/openai/openai-python/releases">openai's
releases</a>.</em></p>
<blockquote>
<h2>v2.5.0</h2>
<h2>2.5.0 (2025-10-17)</h2>
<p>Full Changelog: <a
href="https://github.com/openai/openai-python/compare/v2.4.0...v2.5.0">v2.4.0...v2.5.0</a></p>
<h3>Features</h3>
<ul>
<li><strong>api:</strong> api update (<a
href="8b280d57d6">8b280d5</a>)</li>
</ul>
<h3>Chores</h3>
<ul>
<li>bump <code>httpx-aiohttp</code> version to 0.1.9 (<a
href="67f2f0afe5">67f2f0a</a>)</li>
</ul>
<h2>v2.4.0</h2>
<h2>2.4.0 (2025-10-16)</h2>
<p>Full Changelog: <a
href="https://github.com/openai/openai-python/compare/v2.3.0...v2.4.0">v2.3.0...v2.4.0</a></p>
<h3>Features</h3>
<ul>
<li><strong>api:</strong> Add support for gpt-4o-transcribe-diarize on
audio/transcriptions endpoint (<a
href="bdbe9b8f44">bdbe9b8</a>)</li>
</ul>
<h3>Chores</h3>
<ul>
<li>fix dangling comment (<a
href="da14e99606">da14e99</a>)</li>
<li><strong>internal:</strong> detect missing future annotations with
ruff (<a
href="2672b8f072">2672b8f</a>)</li>
</ul>
<h2>v2.3.0</h2>
<h2>2.3.0 (2025-10-10)</h2>
<p>Full Changelog: <a
href="https://github.com/openai/openai-python/compare/v2.2.0...v2.3.0">v2.2.0...v2.3.0</a></p>
<h3>Features</h3>
<ul>
<li><strong>api:</strong> comparison filter in/not in (<a
href="aa49f626a6">aa49f62</a>)</li>
</ul>
<h3>Chores</h3>
<ul>
<li><strong>package:</strong> bump jiter to >=0.10.0 to support
Python 3.14 (<a
href="https://redirect.github.com/openai/openai-python/issues/2618">#2618</a>)
(<a
href="aa445cab5c">aa445ca</a>)</li>
</ul>
<h2>v2.2.0</h2>
<h2>2.2.0 (2025-10-06)</h2>
<p>Full Changelog: <a
href="https://github.com/openai/openai-python/compare/v2.1.0...v2.2.0">v2.1.0...v2.2.0</a></p>
<h3>Features</h3>
<!-- raw HTML omitted -->
</blockquote>
<p>... (truncated)</p>
</details>
<details>
<summary>Changelog</summary>
<p><em>Sourced from <a
href="https://github.com/openai/openai-python/blob/main/CHANGELOG.md">openai's
changelog</a>.</em></p>
<blockquote>
<h2>2.5.0 (2025-10-17)</h2>
<p>Full Changelog: <a
href="https://github.com/openai/openai-python/compare/v2.4.0...v2.5.0">v2.4.0...v2.5.0</a></p>
<h3>Features</h3>
<ul>
<li><strong>api:</strong> api update (<a
href="8b280d57d6">8b280d5</a>)</li>
</ul>
<h3>Chores</h3>
<ul>
<li>bump <code>httpx-aiohttp</code> version to 0.1.9 (<a
href="67f2f0afe5">67f2f0a</a>)</li>
</ul>
<h2>2.4.0 (2025-10-16)</h2>
<p>Full Changelog: <a
href="https://github.com/openai/openai-python/compare/v2.3.0...v2.4.0">v2.3.0...v2.4.0</a></p>
<h3>Features</h3>
<ul>
<li><strong>api:</strong> Add support for gpt-4o-transcribe-diarize on
audio/transcriptions endpoint (<a
href="bdbe9b8f44">bdbe9b8</a>)</li>
</ul>
<h3>Chores</h3>
<ul>
<li>fix dangling comment (<a
href="da14e99606">da14e99</a>)</li>
<li><strong>internal:</strong> detect missing future annotations with
ruff (<a
href="2672b8f072">2672b8f</a>)</li>
</ul>
<h2>2.3.0 (2025-10-10)</h2>
<p>Full Changelog: <a
href="https://github.com/openai/openai-python/compare/v2.2.0...v2.3.0">v2.2.0...v2.3.0</a></p>
<h3>Features</h3>
<ul>
<li><strong>api:</strong> comparison filter in/not in (<a
href="aa49f626a6">aa49f62</a>)</li>
</ul>
<h3>Chores</h3>
<ul>
<li><strong>package:</strong> bump jiter to >=0.10.0 to support
Python 3.14 (<a
href="https://redirect.github.com/openai/openai-python/issues/2618">#2618</a>)
(<a
href="aa445cab5c">aa445ca</a>)</li>
</ul>
<h2>2.2.0 (2025-10-06)</h2>
<p>Full Changelog: <a
href="https://github.com/openai/openai-python/compare/v2.1.0...v2.2.0">v2.1.0...v2.2.0</a></p>
<h3>Features</h3>
<ul>
<li><strong>api:</strong> dev day 2025 launches (<a
href="38ac0093eb">38ac009</a>)</li>
</ul>
<h3>Bug Fixes</h3>
<!-- raw HTML omitted -->
</blockquote>
<p>... (truncated)</p>
</details>
<details>
<summary>Commits</summary>
<ul>
<li><a
href="513ae76253"><code>513ae76</code></a>
release: 2.5.0 (<a
href="https://redirect.github.com/openai/openai-python/issues/2694">#2694</a>)</li>
<li><a
href="ebf32212f7"><code>ebf3221</code></a>
release: 2.4.0</li>
<li><a
href="e043d7b164"><code>e043d7b</code></a>
chore: fix dangling comment</li>
<li><a
href="25cbb74f83"><code>25cbb74</code></a>
feat(api): Add support for gpt-4o-transcribe-diarize on
audio/transcriptions ...</li>
<li><a
href="8cdfd0650e"><code>8cdfd06</code></a>
codegen metadata</li>
<li><a
href="d5c64434b7"><code>d5c6443</code></a>
codegen metadata</li>
<li><a
href="b20a9e7b81"><code>b20a9e7</code></a>
chore(internal): detect missing future annotations with ruff</li>
<li><a
href="e5f93f5dae"><code>e5f93f5</code></a>
release: 2.3.0</li>
<li><a
href="044878859c"><code>0448788</code></a>
feat(api): comparison filter in/not in</li>
<li><a
href="85a91ade61"><code>85a91ad</code></a>
chore(package): bump jiter to >=0.10.0 to support Python 3.14 (<a
href="https://redirect.github.com/openai/openai-python/issues/2618">#2618</a>)</li>
<li>Additional commits viewable in <a
href="https://github.com/openai/openai-python/compare/v1.107.0...v2.5.0">compare
view</a></li>
</ul>
</details>
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Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com>
Co-authored-by: Ashwin Bharambe <ashwin.bharambe@gmail.com>
# What does this PR do?
<!-- Provide a short summary of what this PR does and why. Link to
relevant issues if applicable. -->
- Extend the model type to include rerank models.
- Implement `rerank()` method in inference router.
- Add `rerank_model_list` to `OpenAIMixin` to enable providers to
register and identify rerank models
- Update documentation.
<!-- 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.* -->
```
pytest tests/unit/providers/utils/inference/test_openai_mixin.py
```
# What does this PR do?
Updated quickstart `demo_script.py` to use OpenAI APIs, which is simply:
```python
import io, requests
from openai import OpenAI
url="https://www.paulgraham.com/greatwork.html"
client = OpenAI(base_url="http://localhost:8321/v1/", api_key="none")
vs = client.vector_stores.create()
response = requests.get(url)
pseudo_file = io.BytesIO(str(response.content).encode('utf-8'))
uploaded_file = client.files.create(file=(url, pseudo_file, "text/html"), purpose="assistants")
client.vector_stores.files.create(vector_store_id=vs.id, file_id=uploaded_file.id)
resp = client.responses.create(
model="openai/gpt-4o",
input="How do you do great work? Use the existing knowledge_search tool.",
tools=[{"type": "file_search", "vector_store_ids": [vs.id]}],
include=["file_search_call.results"],
)
print(resp)
```
<!-- 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.* -->
---------
Signed-off-by: Francisco Javier Arceo <farceo@redhat.com>
Kill the `builtin::rag` tool group completely since it is no longer
targeted. We use the Responses implementation for knowledge_search which
uses the `openai_vector_stores` pathway.
---------
Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com>
# What does this PR do?
Refactor setting default vector store provider and embedding model to
use an optional `vector_stores` config in the `StackRunConfig` and clean
up code to do so (had to add back in some pieces of VectorDB). Also
added remote Qdrant and Weaviate to starter distro (based on other PR
where inference providers were added for UX).
New config is simply (default for Starter distro):
```yaml
vector_stores:
default_provider_id: faiss
default_embedding_model:
provider_id: sentence-transformers
model_id: nomic-ai/nomic-embed-text-v1.5
```
## Test Plan
CI and Unit tests.
---------
Signed-off-by: Francisco Javier Arceo <farceo@redhat.com>
Co-authored-by: Ashwin Bharambe <ashwin.bharambe@gmail.com>
**This PR changes configurations in a backward incompatible way.**
Run configs today repeat full SQLite/Postgres snippets everywhere a
store is needed, which means duplicated credentials, extra connection
pools, and lots of drift between files. This PR introduces named storage
backends so the stack and providers can share a single catalog and
reference those backends by name.
## Key Changes
- Add `storage.backends` to `StackRunConfig`, register each KV/SQL
backend once at startup, and validate that references point to the right
family.
- Move server stores under `storage.stores` with lightweight references
(backend + namespace/table) instead of full configs.
- Update every provider/config/doc to use the new reference style;
docs/codegen now surface the simplified YAML.
## Migration
Before:
```yaml
metadata_store:
type: sqlite
db_path: ~/.llama/distributions/foo/registry.db
inference_store:
type: postgres
host: ${env.POSTGRES_HOST}
port: ${env.POSTGRES_PORT}
db: ${env.POSTGRES_DB}
user: ${env.POSTGRES_USER}
password: ${env.POSTGRES_PASSWORD}
conversations_store:
type: postgres
host: ${env.POSTGRES_HOST}
port: ${env.POSTGRES_PORT}
db: ${env.POSTGRES_DB}
user: ${env.POSTGRES_USER}
password: ${env.POSTGRES_PASSWORD}
```
After:
```yaml
storage:
backends:
kv_default:
type: kv_sqlite
db_path: ~/.llama/distributions/foo/kvstore.db
sql_default:
type: sql_postgres
host: ${env.POSTGRES_HOST}
port: ${env.POSTGRES_PORT}
db: ${env.POSTGRES_DB}
user: ${env.POSTGRES_USER}
password: ${env.POSTGRES_PASSWORD}
stores:
metadata:
backend: kv_default
namespace: registry
inference:
backend: sql_default
table_name: inference_store
max_write_queue_size: 10000
num_writers: 4
conversations:
backend: sql_default
table_name: openai_conversations
```
Provider configs follow the same pattern—for example, a Chroma vector
adapter switches from:
```yaml
providers:
vector_io:
- provider_id: chromadb
provider_type: remote::chromadb
config:
url: ${env.CHROMADB_URL}
kvstore:
type: sqlite
db_path: ~/.llama/distributions/foo/chroma.db
```
to:
```yaml
providers:
vector_io:
- provider_id: chromadb
provider_type: remote::chromadb
config:
url: ${env.CHROMADB_URL}
persistence:
backend: kv_default
namespace: vector_io::chroma_remote
```
Once the backends are declared, everything else just points at them, so
rotating credentials or swapping to Postgres happens in one place and
the stack reuses a single connection pool.
# Problem
The current inline provider appends the user provided instructions to
messages as a system prompt, but the returned response object does not
contain the instructions field (as specified in the OpenAI responses
spec).
# What does this PR do?
This pull request adds the instruction field to the response object
definition and updates the inline provider. It also ensures that
instructions from previous response is not carried over to the next
response (as specified in the openAI spec).
Closes #[3566](https://github.com/llamastack/llama-stack/issues/3566)
## Test Plan
- Tested manually for change in model response w.r.t supplied
instructions field.
- Added unit test to check that the instructions from previous response
is not carried over to the next response.
- Added integration tests to check instructions parameter in the
returned response object.
- Added new recordings for the integration tests.
---------
Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com>
# What does this PR do?
relates to #2878
We introduce a Containerfile which is used to replaced the `llama stack
build` command (removal in a separate PR).
```
llama stack build --distro starter --image-type venv --run
```
is replaced by
```
llama stack list-deps starter | xargs -L1 uv pip install
llama stack run starter
```
- See the updated workflow files for e2e workflow.
## Test Plan
CI
```
❯ docker build . -f docker/Dockerfile --build-arg DISTRO_NAME=starter --build-arg INSTALL_MODE=editable --tag test_starter
❯ docker run -p 8321:8321 test_starter
❯ curl http://localhost:8321/v1/chat/completions \
-H "Content-Type: application/json" \
-d '{
"model": "gpt-4o-mini",
"messages": [
{
"role": "user",
"content": "Hello!"
}
]
}'
```
---
[//]: # (BEGIN SAPLING FOOTER)
Stack created with [Sapling](https://sapling-scm.com). Best reviewed
with
[ReviewStack](https://reviewstack.dev/llamastack/llama-stack/pull/3839).
* #3855
* __->__ #3839
# What does this PR do?
the sidebar currently has an extra `ii. Run the Script` because its
incorrectly put into the doc as an H3 not an H4 (like the other ones)
<img width="239" height="218" alt="Screenshot 2025-10-20 at 1 04 54 PM"
src="https://github.com/user-attachments/assets/eb8cb26e-7ea9-4b61-9101-d64965b39647"
/>
Fix this which will update the sidebar
Signed-off-by: Charlie Doern <cdoern@redhat.com>
# What does this PR do?
remove telemetry as a providable API from the codebase. This includes
removing it from generated distributions but also the provider registry,
the router, etc
since `setup_logger` is tied pretty strictly to `Api.telemetry` being in
impls we still need an "instantiated provider" in our implementations.
However it should not be auto-routed or provided. So in
validate_and_prepare_providers (called from resolve_impls) I made it so
that if run_config.telemetry.enabled, we set up the meta-reference
"provider" internally to be used so that log_event will work when
called.
This is the neatest way I think we can remove telemetry from the
provider configs but also not need to rip apart the whole "telemetry is
a provider" logic just yet, but we can do it internally later without
disrupting users.
so telemetry is removed from the registry such that if a user puts
`telemetry:` as an API in their build/run config it will err out, but
can still be used by us internally as we go through this transition.
relates to #3806
Signed-off-by: Charlie Doern <cdoern@redhat.com>
# What does this PR do?
Adds a subpage of the OpenAI compatibility page in the documentation.
This subpage documents known limitations of the Responses API.
<!-- If resolving an issue, uncomment and update the line below -->
Closes#3575
---------
Signed-off-by: Bill Murdock <bmurdock@redhat.com>
# What does this PR do?
Have closed the previous PR due to merge conflicts with multiple PRs
Addressed all comments from
https://github.com/llamastack/llama-stack/pull/3768 (sorry for carrying
over to this one)
## Test Plan
Added UTs and integration tests
This PR updates the Conversation item related types and improves a
couple critical parts of the implemenation:
- it creates a streaming output item for the final assistant message
output by
the model. until now we only added content parts and included that
message in the final response.
- rewrites the conversation update code completely to account for items
other than messages (tool calls, outputs, etc.)
## Test Plan
Used the test script from
https://github.com/llamastack/llama-stack-client-python/pull/281 for
this
```
TEST_API_BASE_URL=http://localhost:8321/v1 \
pytest tests/integration/test_agent_turn_step_events.py::test_client_side_function_tool -xvs
```
# What does this PR do?
Enables automatic embedding model detection for vector stores and by
using a `default_configured` boolean that can be defined in the
`run.yaml`.
<!-- If resolving an issue, uncomment and update the line below -->
<!-- Closes #[issue-number] -->
## Test Plan
- Unit tests
- Integration tests
- Simple example below:
Spin up the stack:
```bash
uv run llama stack build --distro starter --image-type venv --run
```
Then test with OpenAI's client:
```python
from openai import OpenAI
client = OpenAI(base_url="http://localhost:8321/v1/", api_key="none")
vs = client.vector_stores.create()
```
Previously you needed:
```python
vs = client.vector_stores.create(
extra_body={
"embedding_model": "sentence-transformers/all-MiniLM-L6-v2",
"embedding_dimension": 384,
}
)
```
The `extra_body` is now unnecessary.
---------
Signed-off-by: Francisco Javier Arceo <farceo@redhat.com>
# What does this PR do?
As discussed on discord, we do not need to reinvent the wheel for
telemetry. Instead we'll lean into the canonical OTEL stack.
Logs/traces/metrics will still be sent via OTEL - they just won't be
stored on, queried through Stack.
This is the first of many PRs to remove telemetry API from Stack.
1) removed webmethod decorators to remove from API spec
2) removed tests as @iamemilio is adding them on otel directly.
## Test Plan
# 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 replace the Llama Stack's default embedding
model by nomic-embed-text-v1.5.
These are the key reasons why Llama Stack community decided to switch
from all-MiniLM-L6-v2 to nomic-embed-text-v1.5:
1. The training data for
[all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2#training-data)
includes a lot of data sets with various licensing terms, so it is
tricky to know when/whether it is appropriate to use this model for
commercial applications.
2. The model is not particularly competitive on major benchmarks. For
example, if you look at the [MTEB
Leaderboard](https://huggingface.co/spaces/mteb/leaderboard) and click
on Miscellaneous/BEIR to see English information retrieval accuracy, you
see that the top of the leaderboard is dominated by enormous models but
also that there are many, many models of relatively modest size whith
much higher Retrieval scores. If you want to look closely at the data, I
recommend clicking "Download Table" because it is easier to browse that
way.
More discussion info can be founded
[here](https://github.com/llamastack/llama-stack/issues/2418)
<!-- If resolving an issue, uncomment and update the line below -->
<!-- Closes #[issue-number] -->
Closes#2418
## 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. Run `./scripts/unit-tests.sh`
2. Integration tests via CI wokrflow
---------
Signed-off-by: Sébastien Han <seb@redhat.com>
Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com>
Co-authored-by: Francisco Arceo <arceofrancisco@gmail.com>
Co-authored-by: Sébastien Han <seb@redhat.com>
Applies the same pattern from
https://github.com/llamastack/llama-stack/pull/3777 to embeddings and
vector_stores.create() endpoints.
This should _not_ be a breaking change since (a) our tests were already
using the `extra_body` parameter when passing in to the backend (b) but
the backend probably wasn't extracting the parameters correctly. This PR
will fix that.
Updated APIs: `openai_embeddings(), openai_create_vector_store(),
openai_create_vector_store_file_batch()`
