This commit refactors the Batches protocol to use Pydantic request
models for both create_batch and list_batches methods, improving
consistency, readability, and maintainability.
- create_batch now accepts a single CreateBatchRequest parameter instead
of individual arguments. This aligns the protocol with FastAPI’s
request model pattern, allowing the router to pass the request object
directly without unpacking parameters. Provider implementations now
access fields via request.input_file_id, request.endpoint, etc.
- list_batches now accepts a single ListBatchesRequest parameter,
replacing individual query parameters. The model includes after and
limit fields with proper OpenAPI descriptions. FastAPI automatically
parses query parameters into the model for GET requests, keeping
router code clean. Provider implementations access fields via
request.after and request.limit.
Signed-off-by: Sébastien Han <seb@redhat.com>
This commit introduces a new FastAPI router-based system for defining
API endpoints, enabling a migration path away from the legacy @webmethod
decorator system. The implementation includes router infrastructure,
migration of the Batches API as the first example, and updates
to server, OpenAPI generation, and inspection systems to
support both routing approaches.
The router infrastructure consists of a router registry system
that allows APIs to register FastAPI router factories, which
are then automatically discovered and included in the server
application. Standard error responses are centralized in
router_utils to ensure consistent OpenAPI specification
generation with proper $ref references to component responses.
The Batches API has been migrated to demonstrate the new
pattern. The protocol definition and models remain in
llama_stack_api/batches, maintaining clear separation between
API contracts and server implementation. The FastAPI router
implementation lives in
llama_stack/core/server/routers/batches, following the
established pattern where API contracts are defined in
llama_stack_api and server routing logic lives in
llama_stack/core/server.
The server now checks for registered routers before falling
back to the legacy webmethod-based route discovery, ensuring
backward compatibility during the migration period. The
OpenAPI generator has been updated to handle both router-based
and webmethod-based routes, correctly extracting metadata from
FastAPI route decorators and Pydantic Field descriptions. The
inspect endpoint now includes routes from both systems, with
proper filtering for deprecated routes and API levels.
Response descriptions are now explicitly defined in router decorators,
ensuring the generated OpenAPI specification matches the
previous format. Error responses use $ref references to
component responses (BadRequest400, TooManyRequests429, etc.)
as required by the specification. This is neat and will allow us to
remove a lot of boiler plate code from our generator once the
migration is done.
This implementation provides a foundation for incrementally migrating
other APIs to the router system while maintaining full backward
compatibility with existing webmethod-based APIs.
Closes: https://github.com/llamastack/llama-stack/issues/4188
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?
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?
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?
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>
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.* -->
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>
# 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?
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>
# 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?
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.
We'd like to remove the dependence of `llama-stack` on
`llama-stack-client`. This is a necessary step.
A few small cleanups
- Enables `embeddings` now also
- Remove ModelRegistryHelper dependency (unused)
- Consolidate to auth_credential field via RemoteInferenceProviderConfig
- Implement list_models() to fetch from downstream /v1/models
## Test Plan
Tested using this script
https://gist.github.com/ashwinb/6356463d10f989c0682ab3bff8589581
Output:
```
Listing models from downstream server...
Available models: ['passthrough/ollama/nomic-embed-text:latest', 'passthrough/ollama/all-minilm:l6-v2', 'passthrough/ollama/llama3.2-vision:11b', 'passthrough/ollama/llama3.2-vision:latest', 'passthrough/ollama/llama-guard3:1b', 'passthrough/o
llama/llama3.2:1b', 'passthrough/ollama/all-minilm:latest', 'passthrough/ollama/llama3.2:3b', 'passthrough/ollama/llama3.2:3b-instruct-fp16', 'passthrough/bedrock/meta.llama3-1-8b-instruct-v1:0', 'passthrough/bedrock/meta.llama3-1-70b-instruct
-v1:0', 'passthrough/bedrock/meta.llama3-1-405b-instruct-v1:0', 'passthrough/sentence-transformers/nomic-ai/nomic-embed-text-v1.5']
Using LLM model: passthrough/ollama/llama3.2-vision:11b
Making inference request...
Response: 4.
--- Testing streaming ---
Streamed response: ChatCompletionChunk(id='chatcmpl-64', choices=[Choice(delta=ChoiceDelta(content='1', reasoning_content=None, refusal=None, role='assistant', tool_calls=None), finish_reason='', index=0, logprobs=None)], created=1762381674, m
odel='passthrough/ollama/llama3.2-vision:11b', object='chat.completion.chunk', usage=None)
...
5ChatCompletionChunk(id='chatcmpl-64', choices=[Choice(delta=ChoiceDelta(content='', reasoning_content=None, refusal=None, role='assistant', tool_calls=None), finish_reason='stop', index=0, logprobs=None)], created=1762381674, model='passthrou
gh/ollama/llama3.2-vision:11b', object='chat.completion.chunk', usage=None)
```
## What does this PR do?
The starter distribution now comes with all the required packages to
support persistent stores—like the agent store, metadata, and
inference—using PostgreSQL. Users can enable PostgreSQL support by
setting the `ENABLE_POSTGRES_STORE=1` environment variable.
This PR consolidates the functionality from the removed `postgres-demo`
distribution into the starter distribution, reducing maintenance
overhead.
**Closes: #2619**
**Supersedes: #2851** (rebased and updated)
## Changes Made
1. **Added PostgreSQL support to starter distribution**
- New `run-with-postgres-store.yaml` configuration
- Automatic config switching via `ENABLE_POSTGRES_STORE` environment
variable
- Removed separate `postgres-demo` distribution
2. **Updated to new build system**
- Integrated postgres switching logic into Containerfile entrypoint
- Uses new `storage_backends` and `storage_stores` API
- Properly configured both PostgreSQL KV store and SQL store
3. **Updated dependencies**
- Added `psycopg2-binary` and `asyncpg` to starter distribution
- All postgres-related dependencies automatically included
## How to Use
### With Docker (PostgreSQL):
```bash
docker run \
-e ENABLE_POSTGRES_STORE=1 \
-e POSTGRES_HOST=your_postgres_host \
-e POSTGRES_PORT=5432 \
-e POSTGRES_DB=llamastack \
-e POSTGRES_USER=llamastack \
-e POSTGRES_PASSWORD=llamastack \
-e OPENAI_API_KEY=your_key \
llamastack/distribution-starter
```
### PostgreSQL environment variables:
- `POSTGRES_HOST`: Postgres host (default: `localhost`)
- `POSTGRES_PORT`: Postgres port (default: `5432`)
- `POSTGRES_DB`: Postgres database name (default: `llamastack`)
- `POSTGRES_USER`: Postgres username (default: `llamastack`)
- `POSTGRES_PASSWORD`: Postgres password (default: `llamastack`)
## Test Plan
All pre-commit hooks pass (mypy, ruff, distro-codegen)
`llama stack list-deps starter` confirms psycopg2-binary is included
Storage configuration correctly uses PostgreSQL backends
Container builds successfully with postgres support
## Credits
Original work by @leseb in #2851. Rebased and updated by @r-bit-rry to
work with latest main.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Sébastien Han @leseb
---------
Signed-off-by: Sébastien Han <seb@redhat.com>
Co-authored-by: Sébastien Han <seb@redhat.com>
RAG aka file search is implemented via the Responses API by specifying
the file-search tool. The backend implementation remains unchanged. This
PR merely removes the directly exposed API surface which allowed users
to directly perform searches from the client.
This facility is now available via the `client.vector_store.search()`
OpenAI compatible API.
- 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
This PR removes all routes which we had marked deprecated for the 0.3.0
release.
This includes:
- all the `/v1/openai/v1/` routes (the corresponding /v1 routes still
exist of course)
- the /agents API (which is superseded completely by Responses +
Conversations)
- several alpha routes which had a "v1" route to aide transitioning to
"v1alpha"
This is the corresponding client-python change:
https://github.com/llamastack/llama-stack-client-python/pull/294
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.**
# What does this PR do?
This API hasn't received any traction and close to zero interest from
the community. Let's revisit in the future if things change.
Signed-off-by: Sébastien Han <seb@redhat.com>
Co-authored-by: Ashwin Bharambe <ashwin.bharambe@gmail.com>
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
# 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>
