# What does this PR do?
since llama_stack_api is meant to be _just_ the API definitions of LLS,
we should have pre-commit check that prohibits anyone from accidentally
importing `from llama_stack` or adding `llama_stack` as a dependency
into `llama_stack_api`s pyproject.
## Test Plan
pre-commit should pass.
Signed-off-by: Charlie Doern <cdoern@redhat.com>
Generate the Stainless client config directly from code so we can
validate the config before we ever write the YAML.
This change enforces allowed HTTP verbs/paths, detects duplicate routes
across resources, and ensures README example endpoints exist and match
the OpenAPI spec. The generator now fails fast when config entries
drift, keeping the published config (hopefully) more current with the
spec. I think more validation can be done but this is a good start.
# What does this PR do?
This PR improves type hint cleanup in auto-generated provider
documentation by adding regex logic.
**Issues Fixed:**
- Type hints with missing closing brackets (e.g., `list[str` instead of
`list[str]`)
- Types showing as `<class 'bool'>`, `<class 'str'>` instead of `bool`,
`str`
- The multi-line YAML frontmatter in index documentation files wasn't
ideal, so we now add the proper `|` character.
**Changes:**
1. Replaced string replacement (`.replace`) with regex-based type
cleaning to preserve the trailing bracket in case of `list` and `dict`.
2. Adds the `|` character for multi-line YAML descriptions.
3. I have regenerated the docs. However, let me know if that's not
needed.
## Test Plan
1. Ran uv run python scripts/provider_codegen.py - successfully
regenerated all docs
2. We can see that the updated docs handle correctly type hint cleanup
and multi-line yaml descriptions have now the `|` character.
### Note to the reviewer(s)
This is my first contribution to your lovely repo! Initially I was going
thourgh docs (wanted to use `remote::gemini` as provider) and realized
the issue. I've read the
[CONTRIBUTING.md](https://github.com/llamastack/llama-stack/blob/main/CONTRIBUTING.md)
and decided to open the PR. Let me know if there's anything I did wrong
and I'll update my PR!
---------
Signed-off-by: thepetk <thepetk@gmail.com>
Co-authored-by: Ashwin Bharambe <ashwin.bharambe@gmail.com>
# What does this PR do?
- Remove backward compatibility for authorization in mcp_headers
- Enforce authorization must use dedicated parameter
- Add validation error if Authorization found in provider_data headers
- Update test_mcp.py to use authorization parameter
- Update test_mcp_json_schema.py to use authorization parameter
- Update test_tools_with_schemas.py to use authorization parameter
- Update documentation to show the change in the authorization approach
Breaking Change:
- Authorization can no longer be passed via mcp_headers in provider_data
- Users must use the dedicated 'authorization' parameter instead
- Clear error message guides users to the new approach"
## Test Plan
CI
---------
Co-authored-by: Omar Abdelwahab <omara@fb.com>
Co-authored-by: Ashwin Bharambe <ashwin.bharambe@gmail.com>
FastAPI generator now only unwraps body params explicitly marked with
Body(embed=False) so the /eval run_eval schema once again exposes
RunEvalRequest, matching our integration tests and the server's request
parsing.
Regenerated the OpenAPI specs to capture the restored wrapper.
CI on the Stainless preview builds should be green.
# What does this PR do?
<!-- Provide a short summary of what this PR does and why. Link to
relevant issues if applicable. -->
<!-- If resolving an issue, uncomment and update the line below -->
<!-- Closes #[issue-number] -->
## Test Plan
<!-- Describe the tests you ran to verify your changes with result
summaries. *Provide clear instructions so the plan can be easily
re-executed.* -->
# What does this PR do?
It was referencing strong_typing which was removed in
https://github.com/llamastack/llama-stack/pull/3944
## Test Plan
New CI build test.
Signed-off-by: Sébastien Han <seb@redhat.com>
# What does this PR do?
This replaces the legacy "pyopenapi + strong_typing" pipeline with a
FastAPI-backed generator that has an explicit schema registry inside
`llama_stack_api`. The key changes:
1. **New generator architecture.** FastAPI now builds the OpenAPI schema
directly from the real routes, while helper modules
(`schema_collection`, `endpoints`, `schema_transforms`, etc.)
post-process the result. The old pyopenapi stack and its strong_typing
helpers are removed entirely, so we no longer rely on fragile AST
analysis or top-level import side effects.
2. **Schema registry in `llama_stack_api`.** `schema_utils.py` keeps a
`SchemaInfo` record for every `@json_schema_type`, `register_schema`,
and dynamically created request model. The OpenAPI generator and other
tooling query this registry instead of scanning the package tree,
producing deterministic names (e.g., `{MethodName}Request`), capturing
all optional/nullable fields, and making schema discovery testable. A
new unit test covers the registry behavior.
3. **Regenerated specs + CI alignment.** All docs/Stainless specs are
regenerated from the new pipeline, so optional/nullable fields now match
reality (expect the API Conformance workflow to report breaking
changes—this PR establishes the new baseline). The workflow itself is
back to the stock oasdiff invocation so future regressions surface
normally.
*Conformance will be RED on this PR; we choose to accept the
deviations.*
## Test Plan
- `uv run pytest tests/unit/server/test_schema_registry.py`
- `uv run python -m scripts.openapi_generator.main docs/static`
---------
Signed-off-by: Sébastien Han <seb@redhat.com>
Co-authored-by: Ashwin Bharambe <ashwin.bharambe@gmail.com>
# What does this PR do?
Restores the responses unit tests that were inadvertently deleted in PR
[#4055 ](https://github.com/llamastack/llama-stack/pull/4055)
## Test Plan
I ran the unit tests that I restored. They all passed with one
exception:
tests/unit/providers/agents/meta_reference/test_openai_responses.py::test_reuse_mcp_tool_list
AttributeError: module 'llama_stack.providers.utils.tools' has no
attribute 'mcp'
It's coming from this line:
@patch("llama_stack.providers.utils.tools.mcp.list_mcp_tools")
The mcp.py module (and \_\_init\_\_.py) exists under tools. There are
some 'from mcp ....' imports (mcp package in this case) within it that
python may be interpreting as circular imports (or maybe I'm overlooking
something).
# What does this PR do?
For Runtime Exception the error is not propagated to the user and can be
opaque.
Before fix:
`ERROR - Error processing message: Error code: 500 - {'detail':
'Internal server error: An unexpected error occurred.'}
`
After fix:
`[ERROR] Error code: 404 - {'detail': "Model
'claude-sonnet-4-5-20250929' not found. Use 'client.models.list()' to
list available Models."}
`
(Ran into this few times, while working with OCI + LLAMAStack and Sabre:
Agentic framework integrations with LLAMAStack)
## Test Plan
CI
# What does this PR do?
Adding a user-facing `authorization ` parameter to MCP tool definitions
that allows users to explicitly configure credentials per MCP server,
addressing GitHub Issue #4034 in a secure manner.
## Test Plan
tests/integration/responses/test_mcp_authentication.py
---------
Co-authored-by: Omar Abdelwahab <omara@fb.com>
Co-authored-by: Ashwin Bharambe <ashwin.bharambe@gmail.com>
# What does this PR do?
Require at least 0.49.1 which fixes a security vulnerability in the
parsing logic of the Range header in FileResponse. Release note:
https://github.com/Kludex/starlette/releases/tag/0.49.1
Signed-off-by: Sébastien Han <seb@redhat.com>
# What does this PR do?
the directory structure was src/llama-stack-api/llama_stack_api
instead it should just be src/llama_stack_api to match the other
packages.
update the structure and pyproject/linting config
---------
Signed-off-by: Charlie Doern <cdoern@redhat.com>
Co-authored-by: Ashwin Bharambe <ashwin.bharambe@gmail.com>
# What does this PR do?
Without this we get below in server logs
```
RuntimeError: OpenAI response failed: InferenceRouter._construct_metrics() got an unexpected keyword argument
'model_id'
```
Seems the method signature got update but this callsite was not updated
## Test Plan
CI and test with Sabre (Agent framework integration)
# What does this PR do?
Error out when creating vector store with unknown embedding model
Closes https://github.com/llamastack/llama-stack/issues/4047
## Test Plan
Added tests
Signed-off-by: Francisco Javier Arceo <farceo@redhat.com>
# What does this PR do?
Extract API definitions and provider specifications into a standalone
llama-stack-api package that can be published to PyPI independently of
the main llama-stack server.
see: https://github.com/llamastack/llama-stack/pull/2978 and
https://github.com/llamastack/llama-stack/pull/2978#issuecomment-3145115942
Motivation
External providers currently import from llama-stack, which overrides
the installed version and causes dependency conflicts. This separation
allows external providers to:
- Install only the type definitions they need without server
dependencies
- Avoid version conflicts with the installed llama-stack package
- Be versioned and released independently
This enables us to re-enable external provider module tests that were
previously blocked by these import conflicts.
Changes
- Created llama-stack-api package with minimal dependencies (pydantic,
jsonschema)
- Moved APIs, providers datatypes, strong_typing, and schema_utils
- Updated all imports from llama_stack.* to llama_stack_api.*
- Configured local editable install for development workflow
- Updated linting and type-checking configuration for both packages
Next Steps
- Publish llama-stack-api to PyPI
- Update external provider dependencies
- Re-enable external provider module tests
Pre-cursor PRs to this one:
- #4093
- #3954
- #4064
These PRs moved key pieces _out_ of the Api pkg, limiting the scope of
change here.
relates to #3237
## Test Plan
Package builds successfully and can be imported independently. All
pre-commit hooks pass with expected exclusions maintained.
---------
Signed-off-by: Charlie Doern <cdoern@redhat.com>
# What does this PR do?
- force a min precommit version
- pin to >= 4.3.0 when installing
---------
Signed-off-by: Sébastien Han <seb@redhat.com>
Co-authored-by: Ashwin Bharambe <ashwin.bharambe@gmail.com>
# What does this PR do?
Building/Deploying docs is failing here:
5530320962 (step):8:49
Needs the playground file. Updated it to reflect current admin status.
<!-- 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>
Fixed bug where models with No provider_model_id were incorrectly
filtered from the startup config display. The function was checking
multiple fields when it should only filter items with explicitly
disabled provider_id.
Changes:
o Modified remove_disabled_providers to only check provider_id field o
Changed condition from checking multiple fields with None to only
checking provider_id for "__disabled__", None or empty string
o Added comprehensive unit tests
Closes: #4131
Signed-off-by: Derek Higgins <derekh@redhat.com>
We would like to run all OpenAI compatibility tests using only the
openai-client library. This is most friendly for contributors since they
can run tests without needing to update the client-sdks (which is
getting easier but still a long pole.)
This is the first step in enabling that -- no using "library client" for
any of the Responses tests. This seems like a reasonable trade-off since
the usage of an embeddeble library client for Responses (or any
OpenAI-compatible) behavior seems to be not very common. To do this, we
needed to enable MCP tests (which only worked in library client mode)
for server mode.
docs: Add comprehensive Files API and Vector Store integration
documentation
- Add Files API documentation with OpenAI-compatible endpoints
- Create comprehensive guide for OpenAI-compatible file operations
- Reorganize documentation structure: move file operations to files/
directory
- Add vector store provider documentation for Milvus, SQLite-vec, FAISS
- Clean up redundant files and improve navigation
- Update cross-references and eliminate documentation duplication
- Support for release 0.2.14 FileResponse and Vector Store API features
# What does this PR do?
<!-- Provide a short summary of what this PR does and why. Link to
relevant issues if applicable. -->
<!-- If resolving an issue, uncomment and update the line below -->
<!-- Closes #[issue-number] -->
## 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.* -->
A few changes to the storage layer to ensure we reduce unnecessary
contention arising out of our design choices (and letting the database
layer do its correct thing):
- SQL stores now share a single `SqlAlchemySqlStoreImpl` per backend,
and `kvstore_impl` caches instances per `(backend, namespace)`. This
avoids spawning multiple SQLite connections for the same file, reducing
lock contention and aligning the cache story for all backends.
- Added an async upsert API (with SQLite/Postgres dialect inserts) and
routed it through `AuthorizedSqlStore`, then switched conversations and
responses to call it. Using native `ON CONFLICT DO UPDATE` eliminates
the insert-then-update retry window that previously caused long WAL lock
retries.
### Test Plan
Existing tests, added a unit test for `upsert()`
Fixes issues in the storage system by guaranteeing immediate durability
for responses and ensuring background writers stay alive. Three related
fixes:
* Responses to the OpenAI-compatible API now write directly to
Postgres/SQLite inside the request instead of detouring through an async
queue that might never drain; this restores the expected
read-after-write behavior and removes the "response not found" races
reported by users.
* The access-control shim was stamping owner_principal/access_attributes
as SQL NULL, which Postgres interprets as non-public rows; fixing it to
use the empty-string/JSON-null pattern means conversations and responses
stored without an authenticated user stay queryable (matching SQLite).
* The inference-store queue remains for batching, but its worker tasks
now start lazily on the live event loop so server startup doesn't cancel
them—writes keep flowing even when the stack is launched via llama stack
run.
Closes#4115
### Test Plan
Added a matrix entry to test our "base" suite against Postgres as the
store.
Updated documentation to accurately reflect current behavior where
models are identified as provider_id/provider_model_id in the system.
Changes:
o Clarify that model_id is for configuration purposes only o Explain
models are accessed as provider_id/provider_model_id o Remove outdated
aliasing example that suggested model_id could be used
as a custom identifier
This corrects the documentation which previously suggested model_id
could be used to create friendly aliases, which is not how the code
actually works.
Signed-off-by: Derek Higgins <derekh@redhat.com>
Help users find the comprehensive integration testing docs by linking to
the record-replay documentation. This clarifies that the technical
README complements the main docs.
# What does this PR do?
- Updates `/vector_stores/{vector_store_id}/files/{file_id}/content` to
allow returning `embeddings` and `metadata` using the `extra_query`
- Updates the UI accordingly to display them.
- Update UI to support CRUD operations in the Vector Stores section and
adds a new modal exposing the functionality.
- Updates Vector Store update to fail if a user tries to update Provider
ID (which doesn't make sense to allow)
```python
In [1]: client.vector_stores.files.content(
vector_store_id=vector_store.id,
file_id=file.id,
extra_query={"include_embeddings": True, "include_metadata": True}
)
Out [1]: FileContentResponse(attributes={}, content=[Content(text='This is a test document to check if embeddings are generated properly.\n', type='text', embedding=[0.33760684728622437, ...,], chunk_metadata={'chunk_id': '62a63ae0-c202-f060-1b86-0a688995b8d3', 'document_id': 'file-27291dbc679642ac94ffac6d2810c339', 'source': None, 'created_timestamp': 1762053437, 'updated_timestamp': 1762053437, 'chunk_window': '0-13', 'chunk_tokenizer': 'DEFAULT_TIKTOKEN_TOKENIZER', 'chunk_embedding_model': 'sentence-transformers/nomic
-ai/nomic-embed-text-v1.5', 'chunk_embedding_dimension': 768, 'content_token_count': 13, 'metadata_token_count': 9}, metadata={'filename': 'test-embedding.txt', 'chunk_id': '62a63ae0-c202-f060-1b86-0a688995b8d3', 'document_id': 'file-27291dbc679642ac94ffac6d2810c339', 'token_count': 13, 'metadata_token_count': 9})], file_id='file-27291dbc679642ac94ffac6d2810c339', filename='test-embedding.txt')
```
Screenshots of UI are displayed below:
### List Vector Store with Added "Create New Vector Store"
<img width="1912" height="491" alt="Screenshot 2025-11-06 at 10 47
25 PM"
src="https://github.com/user-attachments/assets/a3a3ddd9-758d-4005-ac9c-5047f03916f3"
/>
### Create New Vector Store
<img width="1918" height="1048" alt="Screenshot 2025-11-06 at 10 47
49 PM"
src="https://github.com/user-attachments/assets/b4dc0d31-696f-4e68-b109-27915090f158"
/>
### Edit Vector Store
<img width="1916" height="1355" alt="Screenshot 2025-11-06 at 10 48
32 PM"
src="https://github.com/user-attachments/assets/ec879c63-4cf7-489f-bb1e-57ccc7931414"
/>
### Vector Store Files Contents page (with Embeddings)
<img width="1914" height="849" alt="Screenshot 2025-11-06 at 11 54
32 PM"
src="https://github.com/user-attachments/assets/3095520d-0e90-41f7-83bd-652f6c3fbf27"
/>
### Vector Store Files Contents Details page (with Embeddings)
<img width="1916" height="1221" alt="Screenshot 2025-11-06 at 11 55
00 PM"
src="https://github.com/user-attachments/assets/e71dbdc5-5b49-472b-a43a-5785f58d196c"
/>
<!-- If resolving an issue, uncomment and update the line below -->
<!-- Closes #[issue-number] -->
## Test Plan
Tests added for Middleware extension and Provider failures.
---------
Signed-off-by: Francisco Javier Arceo <farceo@redhat.com>
# What does this PR do?
Add explicit connection cleanup and shorter timeouts to OpenAI client
fixtures. Fixes CI deadlock after 25+ tests due to connection pool
exhaustion. Also adds 60s timeout to test_conversation_context_loading
as safety net.
## Test Plan
tests pass
Signed-off-by: Charlie Doern <cdoern@redhat.com>
# What does this PR do?
<!-- Provide a short summary of what this PR does and why. Link to
relevant issues if applicable. -->
<!-- If resolving an issue, uncomment and update the line below -->
<!-- Closes #[issue-number] -->
This PR adds Stainless config to specify the Meta copyright file header
for generated files.
Doing it via config instead of custom code will reduce the probability
of git conflict.
## 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.* -->
- review preview builds
Update pypdf dependency to address vulnerabilities causing potential
denial of service through infinite loops or excessive memory usage when
handling malicious PDFs. The update remains fully backward compatible,
with no changes to the PdfReader API.
# What does this PR do?
<!-- Provide a short summary of what this PR does and why. Link to
relevant issues if applicable. -->
Fixes#4120
<!-- 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.* -->
Co-authored-by: Francisco Arceo <arceofrancisco@gmail.com>
# What does this PR do?
In the **Detailed Tutorial**, at **Step 3**, the **Install with venv**
option creates a new virtual environment `client`, activates it then
attempts to install the llama-stack-client using pip.
```
uv venv client --python 3.12
source client/bin/activate
pip install llama-stack-client <- this is the problematic line
```
However, the pip command will likely fail because the `uv venv` command
doesn't, by default, include adding the pip command to the virtual
environment that is created. The pip command will error either because
pip doesn't exist at all, or, if the pip command does exist outside of
the virtual environment, return a different error message. The latter
may be unclear to the user why it is failing.
This PR changes 'pip' to 'uv pip', allowing the install action to
function in the virtual environment as intended, and without the need
for pip to be installed.
<!-- If resolving an issue, uncomment and update the line below -->
<!-- Closes #[issue-number] -->
## Test Plan
1. Use linux or WSL (virtual environments on Windows use `Scripts`
folder instead of `bin` [virtualenv
#993ba13](993ba1316a)
which doesn't align with the tutorial)
2. Clone the `llama-stack` repo
3. Run the following and verify success:
```
uv venv client --python 3.12
source client/bin/activate
```
5. Run the updated command:
```
uv pip install llama-stack-client
```
6. Observe the console output confirms that the virtual environment
`client` was used:
> Using Python 3.12.3 environment at: **client**
# What does this PR do?
the inspect API lacked any mechanism to get all
non-deprecated APIs (v1, v1alpha, v1beta)
change default to this behavior
'v1' filter can be used for user' wanting a list
of stable APIs
## Test Plan
1. pull the PR
2. launch a LLS server
3. run `curl http://beanlab3.bss.redhat.com:8321/v1/inspect/routes`
4. note there are APIs for `v1`, `v1alpha`, and `v1beta` but no
deprecated APIs
Signed-off-by: Nathan Weinberg <nweinber@redhat.com>
# What does this PR do?
Delete ~2,000 lines of dead code from the old bespoke inference API that
was replaced by OpenAI-only API. This includes removing unused type
conversion functions, dead provider methods, and event_logger.py.
Clean up imports across the codebase to remove references to deleted
types. This eliminates unnecessary
code and dependencies, helping isolate the API package as a
self-contained module.
This is the last interdependency between the .api package and "exterior"
packages, meaning that now every other package in llama stack imports
the API, not the other way around.
## Test Plan
this is a structural change, no tests needed.
---------
Signed-off-by: Charlie Doern <cdoern@redhat.com>
# Problem
Responses API uses max_tool_calls parameter to limit the number of tool
calls that can be generated in a response. Currently, LLS implementation
of the Responses API does not support this parameter.
# What does this PR do?
This pull request adds the max_tool_calls field to the response object
definition and updates the inline provider. it also ensures that:
- the total number of calls to built-in and mcp tools do not exceed
max_tool_calls
- an error is thrown if max_tool_calls < 1 (behavior seen with the
OpenAI Responses API, but we can change this if needed)
Closes #[3563](https://github.com/llamastack/llama-stack/issues/3563)
## Test Plan
- Tested manually for change in model response w.r.t supplied
max_tool_calls field.
- Added integration tests to test invalid max_tool_calls parameter.
- Added integration tests to check max_tool_calls parameter with
built-in and function tools.
- Added integration tests to check max_tool_calls parameter in the
returned response object.
- Recorded OpenAI Responses API behavior using a sample script:
https://github.com/s-akhtar-baig/llama-stack-examples/blob/main/responses/src/max_tool_calls.py
Co-authored-by: Ashwin Bharambe <ashwin.bharambe@gmail.com>
# What does this PR do?
Adds OCI GenAI PaaS models for openai chat completion endpoints.
## Test Plan
In an OCI tenancy with access to GenAI PaaS, perform the following
steps:
1. Ensure you have IAM policies in place to use service (check docs
included in this PR)
2. For local development, [setup OCI
cli](https://docs.oracle.com/en-us/iaas/Content/API/SDKDocs/cliinstall.htm)
and configure the CLI with your region, tenancy, and auth
[here](https://docs.oracle.com/en-us/iaas/Content/API/SDKDocs/cliconfigure.htm)
3. Once configured, go through llama-stack setup and run llama-stack
(uses config based auth) like:
```bash
OCI_AUTH_TYPE=config_file \
OCI_CLI_PROFILE=CHICAGO \
OCI_REGION=us-chicago-1 \
OCI_COMPARTMENT_OCID=ocid1.compartment.oc1..aaaaaaaa5...5a \
llama stack run oci
```
4. Hit the `models` endpoint to list models after server is running:
```bash
curl http://localhost:8321/v1/models | jq
...
{
"identifier": "meta.llama-4-scout-17b-16e-instruct",
"provider_resource_id": "ocid1.generativeaimodel.oc1.us-chicago-1.am...q",
"provider_id": "oci",
"type": "model",
"metadata": {
"display_name": "meta.llama-4-scout-17b-16e-instruct",
"capabilities": [
"CHAT"
],
"oci_model_id": "ocid1.generativeaimodel.oc1.us-chicago-1.a...q"
},
"model_type": "llm"
},
...
```
5. Use the "display_name" field to use the model in a
`/chat/completions` request:
```bash
# Streaming result
curl -X POST http://localhost:8321/v1/chat/completions -H "Content-Type: application/json" -d '{
"model": "meta.llama-4-scout-17b-16e-instruct",
"stream": true,
"temperature": 0.9,
"messages": [
{
"role": "system",
"content": "You are a funny comedian. You can be crass."
},
{
"role": "user",
"content": "Tell me a funny joke about programming."
}
]
}'
# Non-streaming result
curl -X POST http://localhost:8321/v1/chat/completions -H "Content-Type: application/json" -d '{
"model": "meta.llama-4-scout-17b-16e-instruct",
"stream": false,
"temperature": 0.9,
"messages": [
{
"role": "system",
"content": "You are a funny comedian. You can be crass."
},
{
"role": "user",
"content": "Tell me a funny joke about programming."
}
]
}'
```
6. Try out other models from the `/models` endpoint.
Mark all register_* / unregister_* APIs as deprecated across models,
shields, tool groups, datasets, benchmarks, and scoring functions. This
is the first step toward moving resource mutations to an `/admin`
namespace as outlined in
https://github.com/llamastack/llama-stack/issues/3809#issuecomment-3492931585.
The deprecation flag will be reflected in the OpenAPI schema to warn API
users that these endpoints are being phased out. Next step will be
implementing the `/admin` route namespace for these resource management
operations.
- `register_model` / `unregister_model`
- `register_shield` / `unregister_shield`
- `register_tool_group` / `unregister_toolgroup`
- `register_dataset` / `unregister_dataset`
- `register_benchmark` / `unregister_benchmark`
- `register_scoring_function` / `unregister_scoring_function`
# What does this PR do?
<!-- Provide a short summary of what this PR does and why. Link to
relevant issues if applicable. -->
Add documentation for llama-stack-k8s-operator under kubernetes
deployment guide.
Signed-off-by: Vaishnavi Hire <vhire@redhat.com>
# What does this PR do?
This PR fixes a bug in LlamaStack 0.3.0 where vector stores created via
the OpenAI-compatible API (`POST /v1/vector_stores`) would fail with
`VectorStoreNotFoundError` after server restart when attempting
operations like `vector_io.insert()` or `vector_io.query()`.
The bug affected **6 vector IO providers**: `pgvector`, `sqlite_vec`,
`chroma`, `milvus`, `qdrant`, and `weaviate`.
Created with the assistance of: claude-4.5-sonnet
## Root Cause
All affected providers had a broken
`_get_and_cache_vector_store_index()` method that:
1. Did not load existing vector stores from persistent storage during
initialization
2. Attempted to use `vector_store_table` (which was either `None` or a
`KVStore` without the required `get_vector_store()` method)
3. Could not reload vector stores after server restart or cache miss
## Solution
This PR implements a consistent pattern across all 6 providers:
1. **Load vector stores during initialization** - Pre-populate the cache
from KV store on startup
2. **Fix lazy loading** - Modified `_get_and_cache_vector_store_index()`
to load directly from KV store instead of relying on
`vector_store_table`
3. **Remove broken dependency** - Eliminated reliance on the
`vector_store_table` pattern
## Testing steps
### 1.1 Configure the stack
Create or use an existing configuration with a vector IO provider.
**Example `run.yaml`:**
```yaml
vector_io_store:
- provider_id: pgvector
provider_type: remote::pgvector
config:
host: localhost
port: 5432
db: llamastack
user: llamastack
password: llamastack
inference:
- provider_id: sentence-transformers
provider_type: inline::sentence-transformers
config:
model: sentence-transformers/all-MiniLM-L6-v2
```
### 1.2 Start the server
```bash
llama stack run run.yaml --port 5000
```
Wait for the server to fully start. You should see:
```
INFO: Started server process
INFO: Application startup complete
```
---
## Step 2: Create a Vector Store
### 2.1 Create via API
```bash
curl -X POST http://localhost:5000/v1/vector_stores \
-H "Content-Type: application/json" \
-d '{
"name": "test-persistence-store",
"extra_body": {
"embedding_model": "sentence-transformers/all-MiniLM-L6-v2",
"embedding_dimension": 384,
"provider_id": "pgvector"
}
}' | jq
```
### 2.2 Expected Response
```json
{
"id": "vs_a1b2c3d4-e5f6-4a7b-8c9d-0e1f2a3b4c5d",
"object": "vector_store",
"name": "test-persistence-store",
"status": "completed",
"created_at": 1730304000,
"file_counts": {
"total": 0,
"completed": 0,
"in_progress": 0,
"failed": 0,
"cancelled": 0
},
"usage_bytes": 0
}
```
**Save the `id` field** (e.g.,
`vs_a1b2c3d4-e5f6-4a7b-8c9d-0e1f2a3b4c5d`) — you’ll need it for the next
steps.
---
## Step 3: Insert Data (Before Restart)
### 3.1 Insert chunks into the vector store
```bash
export VS_ID="vs_a1b2c3d4-e5f6-4a7b-8c9d-0e1f2a3b4c5d"
curl -X POST http://localhost:5000/vector-io/insert \
-H "Content-Type: application/json" \
-d "{
\"vector_store_id\": \"$VS_ID\",
\"chunks\": [
{
\"content\": \"Python is a high-level programming language known for its readability.\",
\"metadata\": {\"source\": \"doc1\", \"page\": 1}
},
{
\"content\": \"Machine learning enables computers to learn from data without explicit programming.\",
\"metadata\": {\"source\": \"doc2\", \"page\": 1}
},
{
\"content\": \"Neural networks are inspired by biological neurons in the brain.\",
\"metadata\": {\"source\": \"doc3\", \"page\": 1}
}
]
}"
```
### 3.2 Expected Response
Status: **200 OK**
Response: *Empty or success confirmation*
---
## Step 4: Query Data (Before Restart – Baseline)
### 4.1 Query the vector store
```bash
curl -X POST http://localhost:5000/vector-io/query \
-H "Content-Type: application/json" \
-d "{
\"vector_store_id\": \"$VS_ID\",
\"query\": \"What is machine learning?\"
}" | jq
```
### 4.2 Expected Response
```json
{
"chunks": [
{
"content": "Machine learning enables computers to learn from data without explicit programming.",
"metadata": {"source": "doc2", "page": 1}
},
{
"content": "Neural networks are inspired by biological neurons in the brain.",
"metadata": {"source": "doc3", "page": 1}
}
],
"scores": [0.85, 0.72]
}
```
**Checkpoint:** Works correctly before restart.
---
## Step 5: Restart the Server (Critical Test)
### 5.1 Stop the server
In the terminal where it’s running:
```
Ctrl + C
```
Wait for:
```
Shutting down...
```
### 5.2 Restart the server
```bash
llama stack run run.yaml --port 5000
```
Wait for:
```
INFO: Started server process
INFO: Application startup complete
```
The vector store cache is now empty, but data should persist.
---
## Step 6: Verify Vector Store Exists (After Restart)
### 6.1 List vector stores
```bash
curl http://localhost:5000/v1/vector_stores | jq
```
### 6.2 Expected Response
```json
{
"object": "list",
"data": [
{
"id": "vs_a1b2c3d4-e5f6-4a7b-8c9d-0e1f2a3b4c5d",
"name": "test-persistence-store",
"status": "completed"
}
]
}
```
**Checkpoint:** Vector store should be listed.
---
## Step 7: Insert Data (After Restart – THE BUG TEST)
### 7.1 Insert new chunks
```bash
curl -X POST http://localhost:5000/vector-io/insert \
-H "Content-Type: application/json" \
-d "{
\"vector_store_id\": \"$VS_ID\",
\"chunks\": [
{
\"content\": \"This chunk was inserted AFTER the server restart.\",
\"metadata\": {\"source\": \"post-restart\", \"test\": true}
}
]
}"
```
### 7.2 Expected Results
**With Fix (Correct):**
```
Status: 200 OK
Response: Success
```
**Without Fix (Bug):**
```json
{
"detail": "VectorStoreNotFoundError: Vector Store 'vs_a1b2c3d4-e5f6-4a7b-8c9d-0e1f2a3b4c5d' not found."
}
```
**Critical Test:** If insertion succeeds, the fix works.
---
## Step 8: Query Data (After Restart – Verification)
### 8.1 Query all data
```bash
curl -X POST http://localhost:5000/vector-io/query \
-H "Content-Type: application/json" \
-d "{
\"vector_store_id\": \"$VS_ID\",
\"query\": \"restart\"
}" | jq
```
### 8.2 Expected Response
```json
{
"chunks": [
{
"content": "This chunk was inserted AFTER the server restart.",
"metadata": {"source": "post-restart", "test": true}
}
],
"scores": [0.95]
}
```
**Checkpoint:** Both old and new data are queryable.
---
## Step 9: Multiple Restart Test (Extra Verification)
### 9.1 Restart again
```bash
Ctrl + C
llama stack run run.yaml --port 5000
```
### 9.2 Query after restart
```bash
curl -X POST http://localhost:5000/vector-io/query \
-H "Content-Type: application/json" \
-d "{
\"vector_store_id\": \"$VS_ID\",
\"query\": \"programming\"
}" | jq
```
**Expected:** Works correctly across multiple restarts.
---------
Co-authored-by: Francisco Arceo <arceofrancisco@gmail.com>
# What does this PR do?
<!-- Provide a short summary of what this PR does and why. Link to
relevant issues if applicable. -->
This pull request adds a new workflow that does 2 things:
1. generate [SDK preview
builds](https://www.stainless.com/docs/guides/automate-updates#set-up-automatic-preview-builds)
whenever the OpenAPI spec file is modified in a PR
2. on PR merge, generate SDK builds that will be pushed to the different
SDK repos (i.e start the release process)
> [!NOTE]
> No repo secret `STAINLESS_API_KEY` is needed, the authentication is
done automatically via GitHub OIDC.
<!-- 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.* -->
I tested in my fork: https://github.com/stainless-api/llama-stack/pull/3
# What does this PR do?
Resolves#4102
1. Added `web_search_2025_08_26` to the `WebSearchToolTypes` list and
the `OpenAIResponseInputToolWebSearch.type` Literal union
2. No changes needed to tool execution logic - all `web_search` types
map to the same underlying tool
3. Backward compatibility is maintained - existing `web_search`,
`web_search_preview`, and `web_search_preview_2025_03_11` types continue
to work
4. Added an integration test case using {"type":
"web_search_2025_08_26"} to verify it works correctly
5. Updated `docs/docs/providers/openai_responses_limitations.mdx` to
reflect that `web_search_2025_08_26` is now supported.
6. Removed incorrect references to `MOD1/MOD2/MOD3` (which don't exist
in the codebase)
<!-- 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: Aakanksha Duggal <aduggal@redhat.com>
Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com>
This dependency has been bothering folks for a long time (cc @leseb). We
really needed it due to "library client" which is primarily used for our
tests and is not a part of the Stack server. Anyone who needs to use the
library client can certainly install `llama-stack-client` in their
environment to make that work.
Updated the notebook references to install `llama-stack-client`
additionally when setting things up.
