o Introduces vLLM provider support to the record/replay testing
framework
o Enabling both recording and replay of vLLM API interactions alongside
existing Ollama support.
The changes enable testing of vLLM functionality. vLLM tests focus on
inference capabilities, while Ollama continues to exercise the full API
surface
including vision features.
--
This is an alternative to #3128 , using qwen3 instead of llama 3.2 1B
appears to be more capable at structure output and tool calls.
---------
Signed-off-by: Derek Higgins <derekh@redhat.com>
Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com>
# What does this PR do?
- when create vector store is called without chunk strategy, we actually
the strategy used so that the value is persisted instead of
strategy='None'
## Test Plan
updated tests
# What does this PR do?
1. Make telemetry tests as easy as possible for users by expanding the
`SpanStub` data class and creating the `MetricStub` dataclass as a way
to consistently marshal telemetry data in test fixtures and unmarshal
and handle it in tests.
2. Structure server and client tests to always follow the same standards
for consistent testing experience by using the `SpanStub` and
`MetricStub` data class objects.
3. Enable Metrics Testing for completions endpoint
4. Correct token metrics to use histograms instead of counts to capture
tokens per request rather than a cumulative count of tokens over the
lifecycle of the server.
## Test Plan
These are tests
# What does this PR do?
Fixes issue #3922 where `llama stack list` only showed distributions
after they were run. This PR makes the command show all available
distributions immediately on a fresh install.
Closes#3922
## Changes
- **Updated `_get_distribution_dirs()`** to discover both built-in and
built distributions:
- Built-in distributions from `src/llama_stack/distributions/` (e.g.,
starter, nvidia, dell)
- Built distributions from `~/.llama/distributions`
- **Added a "Source" column** to distinguish between "built-in" and
"built" distributions
- **Built distributions override built-in ones** with the same name
(expected behavior)
- **Updated config file detection logic** to handle both naming
conventions:
- Built-in: `build.yaml` and `run.yaml`
- Built: `{name}-build.yaml` and `{name}-run.yaml`
## Test Plan
### Unit Tests
Added comprehensive unit tests in
`tests/unit/distribution/test_stack_list.py`:
```bash
uv run pytest tests/unit/distribution/test_stack_list.py -v
```
**Result**: ✅ All 8 tests pass
- `test_builtin_distros_shown_without_running` - Verifies the core fix
for issue #3922
- `test_builtin_and_built_distros_shown_together` - Ensures both types
are shown
- `test_built_distribution_overrides_builtin` - Tests override behavior
- `test_empty_distributions` - Edge case handling
- `test_config_files_detection_builtin` - Config file detection for
built-in distros
- `test_config_files_detection_built` - Config file detection for built
distros
- `test_llamastack_prefix_stripped` - Name normalization
- `test_hidden_directories_ignored` - Filters hidden directories
### Manual Testing
**Before the fix** (simulated with empty `~/.llama/distributions`):
```bash
$ llama stack list
No stacks found in ~/.llama/distributions
```
**After the fix**:
```bash
$ llama stack list
┏━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━┓
┃ Stack Name ┃ Source ┃ Path ┃ Build Config ┃ Run Config ┃
┡━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━┩
│ ci-tests │ built-in │ /path/to/src/... │ Yes │ Yes │
│ dell │ built-in │ /path/to/src/... │ Yes │ Yes │
│ meta-reference-g… │ built-in │ /path/to/src/... │ Yes │ Yes │
│ nvidia │ built-in │ /path/to/src/... │ Yes │ Yes │
│ open-benchmark │ built-in │ /path/to/src/... │ Yes │ Yes │
│ postgres-demo │ built-in │ /path/to/src/... │ Yes │ Yes │
│ starter │ built-in │ /path/to/src/... │ Yes │ Yes │
│ starter-gpu │ built-in │ /path/to/src/... │ Yes │ Yes │
│ watsonx │ built-in │ /path/to/src/... │ Yes │ Yes │
└───────────────────┴──────────┴───────────────────┴──────────────┴────────────┘
```
**After running a distribution**:
```bash
$ llama stack run starter # Creates ~/.llama/distributions/starter
$ llama stack list
┏━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━┓
┃ Stack Name ┃ Source ┃ Path ┃ Build Config ┃ Run Config ┃
┡━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━┩
│ ... │ built-in │ ... │ Yes │ Yes │
│ starter │ built │ ~/.llama/distri… │ No │ No │
│ ... │ built-in │ ... │ Yes │ Yes │
└───────────────────┴──────────┴───────────────────┴──────────────┴────────────┘
```
Note how `starter` now shows as "built" and points to
`~/.llama/distributions`, overriding the built-in version.
## Breaking Changes
**No breaking changes** - This is a bug fix that improves user
experience with minimal risk:
- No programmatic parsing of output found in the codebase
- Table format is clearly for human consumption
- The new "Source" column helps users understand where distributions
come from
- The behavior change is exactly what users expect (seeing all available
distributions)
---------
Co-authored-by: Claude <noreply@anthropic.com>
Added a script to cleanup recordings. While doing this, moved the CI
matrix generation to a separate script so there is a single source of
truth for the matrix.
Ran the cleanup script as:
```
PYTHONPATH=. python scripts/cleanup_recordings.py
```
Also added this as part of the pre-commit workflow to ensure that the
recordings are always up to date and that no stale recordings are left
in the repo.
# What does this PR do?
These were maybe be included in the webmethod?
The unit test was pointless too since the request was never used
anywhere?
This shouldn't be in the API definition, if we never consume it.
## Test Plan
CI with pre-commit on OpenAPI spec generation.
Signed-off-by: Sébastien Han <seb@redhat.com>
- Removes the deprecated agents (sessions and turns) API that was marked
alpha in 0.3.0
- Cleans up unused imports and orphaned types after the API removal
- Removes `SessionNotFoundError` and `AgentTurnInputType` which are no
longer needed
The agents API is completely superseded by the Responses + Conversations
APIs, and the client SDK Agent class already uses those implementations.
Corresponding client-side PR:
https://github.com/llamastack/llama-stack-client-python/pull/295
# What does this PR do?
<!-- Provide a short summary of what this PR does and why. Link to
relevant issues if applicable. -->
This PR migrates `unittest` to `pytest` in
`tests/unit/providers/nvidia/test_eval.py`.
<!-- If resolving an issue, uncomment and update the line below -->
<!-- Closes #[issue-number] -->
Part of https://github.com/llamastack/llama-stack/issues/2680
Supersedes https://github.com/llamastack/llama-stack/pull/2791
Signed-off-by: Mustafa Elbehery <melbeher@redhat.com>
The llama-stack-client now uses /`v1/openai/v1/models` which returns
OpenAI-compatible model objects with 'id' and 'custom_metadata' fields
instead of the Resource-style 'identifier' field. Updated api_recorder
to handle the new endpoint and modified tests to access model metadata
appropriately. Deleted stale model recordings for re-recording.
**NOTE: CI will be red on this one since it is dependent on
https://github.com/llamastack/llama-stack-client-python/pull/291/files
landing. I verified locally that it is green.**
We need to remove `/v1/openai/v1` paths shortly. There is one trouble --
our current `/v1/openai/v1/models` endpoint provides different data than
`/v1/models`. Unfortunately our tests target the latter (llama-stack
customized) behavior. We need to get to true OpenAI compatibility.
This is step 1: adding `custom_metadata` field to `OpenAIModel` that
includes all the extra stuff we add in the native `/v1/models` response.
This can be extracted on the consumer end by look at
`__pydantic_extra__` or other similar fields.
This PR:
- Adds `custom_metadata` field to `OpenAIModel` class in
`src/llama_stack/apis/models/models.py`
- Modified `openai_list_models()` in
`src/llama_stack/core/routing_tables/models.py` to populate
custom_metadata
Next Steps
1. Update stainless client to use `/v1/openai/v1/models` instead of
`/v1/models`
2. Migrate tests to read from `custom_metadata`
3. Remove `/v1/openai/v1/` prefix entirely and consolidate to single
`/v1/models` endpoint
Without this hint Qwen3-0.6B tends to reply with the full name
and sometimes doesn't reply with the correct drafted year.
---------
Signed-off-by: Derek Higgins <derekh@redhat.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?
llama stack run --providers takes a list of providers in the format of
api1=provider1,api2=provider2
this allows users to run with a simple list of providers.
given the architecture of `create_app`, this run config needs to be
written to disk. use ~/.llama/distribution/providers-run/run.yaml each
time for consistency
resolves#3956
## Test Plan
new unit tests to ensure --providers.
Signed-off-by: Charlie Doern <cdoern@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?
<!-- Provide a short summary of what this PR does and why. Link to
relevant issues if applicable. -->
This PR fixes the handling of the external_providers_dir configuration
field to align with its ongoing deprecation, in favor of the provider
`module` specification approach.
It addresses the issue in #3950, where using the default provided
run.yaml config resulted in the `external_providers_dir` parameter being
set to the literal string `None`, and crashing the llama-stack server
when starting.
<!-- If resolving an issue, uncomment and update the line below -->
Closes#3950
## 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.* -->
- Built a new container image from `podman build . -f
containers/Containerfile --build-arg DISTRO_NAME=starter --tag
llama-stack:starter`
- Tested it locally with `podman run -it localhost/llama-stack:starter`
- Tested it on an OpenShift 4.19 cluster, deployed via the
llama-stack-k8s-operator.
Signed-off-by: Doug Edgar <dedgar@redhat.com>
… case variations
The ollama/llama3.2:3b-instruct-fp16 model returns string values with
trailing whitespace in structured JSON output. Updated test assertions
to use case-insensitive substring matching instead of exact equality.
Use .lower() for case-insensitive comparison
Check if expected value is contained in actual value (handles
whitespace)
Closes: #3996
Signed-off-by: Derek Higgins <derekh@redhat.com>
This should be "remote::vllm". This causes some log probs tests to be
skipped with remote vllm. (They
fail if run).
Signed-off-by: Derek Higgins <derekh@redhat.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>
## Summary
Fixes all mypy type errors in `providers/inline/agents/meta_reference/`
and removes exclusions from pyproject.toml.
## Changes
- Fix type annotations for Safety API message parameters
(OpenAIMessageParam)
- Add Action enum usage in access control checks
- Correct method signatures to match API supertype (parameter ordering)
- Handle optional return types with proper None checks
- Remove 3 meta_reference exclusions from mypy config
**Files fixed:** 25 errors across 3 files (safety.py, persistence.py,
agents.py)
This adds automated backward compatibility testing for `run.yaml` files.
As we evolve `StackRunConfig`, changes can inadvertently break existing
user configurations. This workflow catches those breaks before merge.
We test old run.yaml files (from main and the latest release) against
the PR's new code. If configs that worked before now fail, the PR is
blocked unless explicitly acknowledged as a breaking change.
**Two test layers:**
- Schema validation: Quick pytest checks that configs parse without
errors
- Integration tests: Full test suite execution to catch runtime semantic
issues (cross-field validations, provider initialization, etc.)
**What we test against:**
- main branch: Breaking changes here block the PR (this is the gate)
- Latest release: Informational only - shows if we've drifted from what
users have
If tests fail, the PR author must acknowledge the breaking change by
adding `!:` to the PR title (e.g., `feat!: change xyz`) or including
`BREAKING CHANGE:` in a commit message. Once acknowledged, the check
passes with a warning.
These jobs are run:
1. `check-main-compatibility` - Schema validation of all distribution
run.yaml files from main
2. `test-integration-main` - Full integration test suite using main's
ci-tests run.yaml
3. `test-integration-release` - Integration tests with latest release
config (informational)
4. `check-schema-release-compatibility` - Schema checks against release
(informational)
The integration tests catch issues that schema validation alone would
miss, like assertion failures in
`StackRunConfig.validate_server_stores()` or provider-specific runtime
logic.
Resolves#3311
Related to #3237
# 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
```
This PR enables routing of fully qualified model IDs of the form
`provider_id/model_id` even when the models are not registered with the
Stack.
Here's the situation: assume a remote inference provider which works
only when users provide their own API keys via
`X-LlamaStack-Provider-Data` header. By definition, we cannot list
models and hence update our routing registry. But because we _require_ a
provider ID in the models now, we can identify which provider to route
to and let that provider decide.
Note that we still try to look up our registry since it may have a
pre-registered alias. Just that we don't outright fail when we are not
able to look it up.
Also, updated inference router so that the responses have the _exact_
model that the request had.
## Test Plan
Added an integration test
Closes#3929
---------
Co-authored-by: ehhuang <ehhuang@users.noreply.github.com>
## Summary
- `preserve_contexts_async_generator` left `PROVIDER_DATA_VAR` (and
other context vars) populated after a streaming generator completed on
HEAD~1, so the asyncio context for request N+1 started with request N's
provider payload.
- FastAPI dependencies and middleware execute before
`request_provider_data_context` rebinds the header data, meaning
auth/logging hooks could observe a prior tenant's credentials or treat
them as authenticated. Traces and any background work that inspects the
context outside the `with` block leak as well—this is a real security
regression, not just a CLI artifact.
- The wrapper now restores each tracked `ContextVar` to the value it
held before the iteration (falling back to clearing when necessary)
after every yield and when the generator terminates, so provider data is
wiped while callers that set their own defaults keep them.
## Test Plan
- `uv run pytest tests/unit/core/test_provider_data_context.py -q`
- `uv run pytest tests/unit/distribution/test_context.py -q`
Both suites fail on HEAD~1 and pass with this change.
# What does this PR do?
add provider-data key passing support to Cerebras, Databricks, NVIDIA
and RunPod
also, added missing tests for Fireworks, Anthropic, Gemini, SambaNova,
and vLLM
addresses #3517
## Test Plan
ci w/ new tests
---------
Co-authored-by: Ashwin Bharambe <ashwin.bharambe@gmail.com>
# 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
The vector_provider_wrapper was only limiting providers to
faiss/sqlite-vec for replay mode, but CI tests also run in record mode
with the same limited set of providers. This caused test failures when
trying to test against milvus, chromadb, pgvector, weaviate, and qdrant
which aren't configured in the record job.
# What does this PR do?
Clean up telemetry code since the telemetry API has been remove.
- moved telemetry files out of providers to core
- removed from Api
## Test Plan
❯ OTEL_SERVICE_NAME=llama_stack
OTEL_EXPORTER_OTLP_ENDPOINT=http://localhost:4318 uv run llama stack run
starter
❯ curl http://localhost:8321/v1/chat/completions \
-H "Content-Type: application/json" \
-d '{
"model": "openai/gpt-4o-mini",
"messages": [
{
"role": "user",
"content": "Hello!"
}
]
}'
-> verify traces in Grafana
CI
# 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"
]
}'
```
Let us enable responses suite in CI now.
Also a minor fix: MCP tool tests intentionally trigger authentication
failures to verify error handling, but the resulting error logs clutter
test output.
Our unit test outputs are filled with all kinds of obscene logs. This
makes it really hard to spot real issues quickly. The problem is that
these logs are necessary to output at the given logging level when the
server is operating normally. It's just that we don't want to see some
of them (especially the noisy ones) during tests.
This PR begins the cleanup. We pytest's caplog fixture to for
suppression.
Move conversation sync logic before yield to ensure it executes even
when
streaming consumers break early after receiving response.completed
event.
## Test Plan
```
OLLAMA_URL=http://localhost:11434 \
pytest -sv tests/integration/responses/ \
--stack-config server:ci-tests \
--text-model ollama/llama3.2:3b-instruct-fp16 \
--inference-mode live \
-k conversation_multi
```
This test now passes.
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: 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?
metadata is conflicting with the default embedding model set on server
side via extra body, removing the check and just letting metadata take
precedence over extra body
`ValueError: Embedding model inconsistent between metadata
('text-embedding-3-small') and extra_body
('sentence-transformers/nomic-ai/nomic-embed-text-v1.5')`
## Test Plan
CI
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.
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