# What does this PR do?
remove unused chat_completion implementations
vllm features ported -
- requires max_tokens be set, use config value
- set tool_choice to none if no tools provided
## Test Plan
ci
# What does this PR do?
<!-- Provide a short summary of what this PR does and why. Link to
relevant issues if applicable. -->
- This PR implements keyword and hybrid search for Weaviate DB based on
its inbuilt functions.
- Added fixtures to conftest.py for Weaviate.
- Enabled integration tests for remote Weaviate on all 3 search modes.
<!-- If resolving an issue, uncomment and update the line below -->
<!-- Closes #[issue-number] -->
Closes#3010
## 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.* -->
Unit tests and integration tests should pass on this PR.
# What does this PR do?
add ModelsProtocolPrivate methods to OpenAIMixin
this will allow providers using OpenAIMixin to use a common interface
## Test Plan
ci w/ new tests
# What does this PR do?
closes#3268closes#3498
When resuming from previous response ID, currently we attempt to convert
from the stored responses input to chat completion messages, which is
not always possible, e.g. for tool calls where some data is lost once
converted from chat completion message to repsonses input format.
This PR stores the chat completion messages that correspond to the
_last_ call to chat completion, which is sufficient to be resumed from
in the next responses API call, where we load these saved messages and
skip conversion entirely.
Separate issue to optimize storage:
https://github.com/llamastack/llama-stack/issues/3646
## Test Plan
existing CI tests
This is a sweeping change to clean up some gunk around our "Tool"
definitions.
First, we had two types `Tool` and `ToolDef`. The first of these was a
"Resource" type for the registry but we had stopped registering tools
inside the Registry long back (and only registered ToolGroups.) The
latter was for specifying tools for the Agents API. This PR removes the
former and adds an optional `toolgroup_id` field to the latter.
Secondly, as pointed out by @bbrowning in
https://github.com/llamastack/llama-stack/pull/3003#issuecomment-3245270132,
we were doing a lossy conversion from a full JSON schema from the MCP
tool specification into our ToolDefinition to send it to the model.
There is no necessity to do this -- we ourselves aren't doing any
execution at all but merely passing it to the chat completions API which
supports this. By doing this (and by doing it poorly), we encountered
limitations like not supporting array items, or not resolving $refs,
etc.
To fix this, we replaced the `parameters` field by `{ input_schema,
output_schema }` which can be full blown JSON schemas.
Finally, there were some types in our llama-related chat format
conversion which needed some cleanup. We are taking this opportunity to
clean those up.
This PR is a substantial breaking change to the API. However, given our
window for introducing breaking changes, this suits us just fine. I will
be landing a concurrent `llama-stack-client` change as well since API
shapes are changing.
# What does this PR do?
the LiteLLMOpenAIMixin provides support for reading key from provider
data (headers users send).
this adds the same functionality to the OpenAIMixin.
this is infrastructure for migrating providers.
## Test Plan
ci w/ new tests
https://github.com/llamastack/llama-stack/pull/3604 broke multipart form
data field parsing for the Files API since it changed its shape -- so as
to match the API exactly to the OpenAI spec even in the generated client
code.
The underlying reason is that multipart/form-data cannot transport
structured nested fields. Each field must be str-serialized. The client
(specifically the OpenAI client whose behavior we must match),
transports sub-fields as `expires_after[anchor]` and
`expires_after[seconds]`, etc. We must be able to handle these fields
somehow on the server without compromising the shape of the YAML spec.
This PR "fixes" this by adding a dependency to convert the data. The
main trade-off here is that we must add this `Depends()` annotation on
every provider implementation for Files. This is a headache, but a much
more reasonable one (in my opinion) given the alternatives.
## Test Plan
Tests as shown in
https://github.com/llamastack/llama-stack/pull/3604#issuecomment-3351090653
pass.
# What does this PR do?
Refs: https://github.com/llamastack/llama-stack/issues/3420
When telemetry is enabled the router uncondionally expects the usage
attribute to be availble and fails if it is not present.
Usage is not currently being requested by litellm_openai_mixin.py for
streaming requests when using the responses API which means that
providers like vertexai fail if telemetry is enabled and streaming is
used.
This is part of the required fix. Other part is in liteLLM, will plan to
submit PR for that soon.
## Test Plan
I applied this change along with the change for litellm in a llama stack
deployment and validated that I could make streaming requests through
the responses API to a gemini model and they would succeed instead of
failing due to the missing usage attribute when telemetry is enabled.
Signed-off-by: Michael Dawson <midawson@redhat.com>
# What does this PR do?
Mirroring the same changes that was used for inference_store:
https://github.com/llamastack/llama-stack/pull/3383
Will follow up with a shared internal API for managing these write
queues.
## Test Plan
existing tests
# What does this PR do?
<!-- Provide a short summary of what this PR does and why. Link to
relevant issues if applicable. -->
Add items and title to ToolParameter/ToolParamDefinition. Adding items
will resolve the issue that occurs with Gemini LLM when an MCP tool has
array-type properties.
<!-- 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.* -->
Unite test cases will be added.
---------
Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com>
Co-authored-by: Kai Wu <kaiwu@meta.com>
Co-authored-by: Ashwin Bharambe <ashwin.bharambe@gmail.com>
# What does this PR do?
unpublish (make unavailable to users) the following apis -
- `/v1/inference/completion`, replaced by `/v1/openai/v1/completions`
- `/v1/inference/chat-completion`, replaced by
`/v1/openai/v1/chat/completions`
- `/v1/inference/embeddings`, replaced by `/v1/openai/v1/embeddings`
- `/v1/inference/batch-completion`, replaced by `/v1/openai/v1/batches`
- `/v1/inference/batch-chat-completion`, replaced by
`/v1/openai/v1/batches`
note: the implementations are still available for internal use, e.g.
agents uses chat-completion.
# What does this PR do?
simplify Ollama inference adapter by -
- moving image_url download code to OpenAIMixin
- being a ModelRegistryHelper instead of having one (mypy blocks
check_model_availability method assignment)
## Test Plan
- add unit tests for new download feature
- add integration tests for openai_chat_completion w/ image_url (close
test gap)
# What does this PR do?
Switches from `random.getrandbits` to `secrets.randbits` in the
telemetry module.
<!-- If resolving an issue, uncomment and update the line below -->
Closes#3553
## Test Plan
Unit tests from scripts/unit-tests.sh were ran to verify the tests still
pass.
Signed-off-by: Doug Edgar <dedgar@redhat.com>
# What does this PR do?
- remove auto-download of ollama embedding models
- add embedding model metadata to dynamic listing w/ unit test
- add support and tests for allowed_models
- removed inference provider models.py files where dynamic listing is
enabled
- store embedding metadata in embedding_model_metadata field on
inference providers
- make model_entries optional on ModelRegistryHelper and
LiteLLMOpenAIMixin
- make OpenAIMixin a ModelRegistryHelper
- skip base64 embedding test for remote::ollama, always returns floats
- only use OpenAI client for ollama model listing
- remove unused build_model_entry function
- remove unused get_huggingface_repo function
## Test Plan
ci w/ new tests
# What does this PR do?
the openai_embeddings method on OpenAIMixin was returning the provider's
model id instead of the llama stack name
## Test Plan
before -
```
$ ./scripts/integration-tests.sh --stack-config server:ci-tests --setup gpt --subdirs inference --inference-mode live --pattern test_openai_embeddings_single_string
...
FAILED tests/integration/inference/test_openai_embeddings.py::test_openai_embeddings_single_string[openai_client-emb=openai/text-embedding-3-small] - AssertionError: assert 'text-embedding-3-small' == 'openai/text-...dding-3-small'
FAILED tests/integration/inference/test_openai_embeddings.py::test_openai_embeddings_single_string[llama_stack_client-emb=openai/text-embedding-3-small] - AssertionError: assert 'text-embedding-3-small' == 'openai/text-...dding-3-small'
========================================== 2 failed, 95 deselected, 4 warnings in 3.87s ===========================================
```
after -
```
$ ./scripts/integration-tests.sh --stack-config server:ci-tests --setup gpt --subdirs inference --inference-mode live --pattern test_openai_embeddings_single_string ...
========================================== 2 passed, 95 deselected, 4 warnings in 2.12s ===========================================
```
# What does this PR do?
<!-- Provide a short summary of what this PR does and why. Link to
relevant issues if applicable. -->
change ModelRegistryHelper to use ProviderModelEntry instead of
hardcoded ModelType.llm which fixed issue #3330.
<!-- If resolving an issue, uncomment and update the line below -->
<!-- Closes #[3330] -->
## Test Plan
<!-- Describe the tests you ran to verify your changes with result
summaries. *Provide clear instructions so the plan can be easily
re-executed.* -->
1. open llama-stack server
```
uv sync --python 3.12
source .venv/bin/activate
uv run llama stack build --distro starter --image-type venv --run
```
2.Used following script to test
```
from llama_stack_client import LlamaStackClient
import os
def test_openai_embedding_type():
client = LlamaStackClient(
base_url=os.environ.get("LLAMA_STACK_ENDPOINT", "http://localhost:8321"),
provider_data={
"openai_api_key": os.environ.get("OPENAI_API_KEY", ""),
},
)
model = client.models.retrieve("openai/text-embedding-3-small")
print(model)
assert model.identifier == "openai/text-embedding-3-small"
assert model.model_type == "embedding"
test_openai_embedding_type()
```
logs:
```
python test_openai.py
INFO:httpx:HTTP Request: GET http://localhost:8321/v1/models/openai/text-embedding-3-small "HTTP/1.1 200 OK"
Model(identifier='openai/text-embedding-3-small', metadata={'embedding_dimension': 1536.0, 'context_length': 8192.0}, api_model_type='embedding', provider_id='openai', type='model', provider_resource_id='text-embedding-3-small', owner=None, source='listed_from_provider', model_type='embedding')
```
# What does this PR do?
This PR is generated with AI and reviewed by me.
Refactors the AuthorizedSqlStore class to store the access policy as an
instance variable rather than passing it as a parameter to each method
call. This simplifies the API.
# Test Plan
existing tests
# What does this PR do?
this replaces the static model listing for any provider using
OpenAIMixin
currently -
- anthropic
- azure openai
- gemini
- groq
- llama-api
- nvidia
- openai
- sambanova
- tgi
- vertexai
- vllm
- not changed: together has its own impl
## Test Plan
- new unit tests
- manual for llama-api, openai, groq, gemini
```
for provider in llama-openai-compat openai groq gemini; do
uv run llama stack build --image-type venv --providers inference=remote::provider --run &
uv run --with llama-stack-client llama-stack-client models list | grep Total
```
results (17 sep 2025):
- llama-api: 4
- openai: 86
- groq: 21
- gemini: 66
closes#3467
# What does this PR do?
adds dynamic model support to TGI
add new overwrite_completion_id feature to OpenAIMixin to deal with TGI
always returning id=""
## Test Plan
tgi: `docker run --gpus all --shm-size 1g -p 8080:80 -v /data:/data
ghcr.io/huggingface/text-generation-inference --model-id
Qwen/Qwen3-0.6B`
stack: `TGI_URL=http://localhost:8080 uv run llama stack build
--image-type venv --distro ci-tests --run`
test: `./scripts/integration-tests.sh --stack-config
http://localhost:8321 --setup tgi --subdirs inference --pattern openai`
# What does this PR do?
Migrates MD5 and SHA-1 hash algorithms to SHA-256.
In particular, replaces:
- MD5 in chunk ID generation.
- MD5 in file verification.
- SHA-1 in model identifier digests.
And updates all related test expectations.
Original discussion:
https://github.com/llamastack/llama-stack/discussions/3413
<!-- If resolving an issue, uncomment and update the line below -->
Closes#3424.
## Test Plan
Unit tests from scripts/unit-tests.sh were updated to match the new hash
output, and ran to verify the tests pass.
Signed-off-by: Doug Edgar <dedgar@redhat.com>
# What does this PR do?
update vLLM inference provider to use OpenAIMixin for openai-compat
functions
inference recordings from Qwen3-0.6B and vLLM 0.8.3 -
```
docker run --gpus all -v ~/.cache/huggingface:/root/.cache/huggingface -p 8000:8000 --ipc=host \
vllm/vllm-openai:latest \
--model Qwen/Qwen3-0.6B --enable-auto-tool-choice --tool-call-parser hermes
```
## Test Plan
```
./scripts/integration-tests.sh --stack-config server:ci-tests --setup vllm --subdirs inference
```
# What does this PR do?
Duplicate chat completion IDs can be generated during tests especially
if they are replaying recorded responses across different tests. No need
to warn or error under those circumstances. In the wild, this is not
likely to happen at all (no evidence) so we aren't really hiding any
problem.
# What does this PR do?
Adds a write worker queue for writes to inference store. This avoids
overwhelming request processing with slow inference writes.
## Test Plan
Benchmark:
```
cd /docs/source/distributions/k8s-benchmark
# start mock server
python openai-mock-server.py --port 8000
# start stack server
LLAMA_STACK_LOGGING="all=WARNING" uv run --with llama-stack python -m llama_stack.core.server.server docs/source/distributions/k8s-benchmark/stack_run_config.yaml
# run benchmark script
uv run python3 benchmark.py --duration 120 --concurrent 50 --base-url=http://localhost:8321/v1/openai/v1 --model=vllm-inference/meta-llama/Llama-3.2-3B-Instruct
```
## RPS from 21 -> 57
# What does this PR do?
- Use BackgroundLogger when logging metric events.
- Reuse event loop in BackgroundLogger
## Test Plan
```
cd /docs/source/distributions/k8s-benchmark
# start mock server
python openai-mock-server.py --port 8000
# start stack server
LLAMA_STACK_LOGGING="all=WARNING" uv run --with llama-stack python -m llama_stack.core.server.server docs/source/distributions/k8s-benchmark/stack_run_config.yaml
# run benchmark script
uv run python3 benchmark.py --duration 120 --concurrent 50 --base-url=http://localhost:8321/v1/openai/v1 --model=vllm-inference/meta-llama/Llama-3.2-3B-Instruct
```
### RPS from 57 -> 62
# What does this PR do?
Improved bedrock provider config to read from environment variables like
AWS_ACCESS_KEY_ID. Updated all
fields to use default_factory with lambda patterns like the nvidia
provider does.
Now the environment variables work as documented.
Closes#3305
## Test Plan
Ran the new bedrock config tests:
```bash
python -m pytest tests/unit/providers/inference/bedrock/test_config.py
-v
Verified existing provider tests still work:
python -m pytest tests/unit/providers/test_configs.py -v
What does this PR do?
Fixes error handling when MCP server connections fail. Instead of
returning generic 500 errors, now provides
descriptive error messages with proper HTTP status codes.
Closes#3107
Test Plan
Before fix:
curl -X GET
"http://localhost:8321/v1/tool-runtime/list-tools?tool_group_id=bad-mcp-server"
Returns: {"detail": "Internal server error: An unexpected error
occurred."} (500)
After fix:
curl -X GET
"http://localhost:8321/v1/tool-runtime/list-tools?tool_group_id=bad-mcp-server"
Returns: {"error": {"detail": "Failed to connect to MCP server at
http://localhost:9999/sse: Connection
refused"}} (502)
Tests:
- Added unit test for ConnectionError → 502 translation
- Manually tested with unreachable MCP servers (connection refused)
- Wrap model loading with asyncio.to_thread() to prevent blocking during
model download/initialization
- Wrap encoding operations with asyncio.to_thread() to run in background
thread
- Convert _load_sentence_transformer_model() to async method
This ensures the async event loop remains responsive during embedding
operations.
Closes: #3332
Signed-off-by: Derek Higgins <derekh@redhat.com>
Co-authored-by: Francisco Arceo <arceofrancisco@gmail.com>
# What does this PR do?
add the ability to use inequalities in the where clause of the sqlstore.
this is infrastructure for files expiration.
## Test Plan
unit tests
# What does this PR do?
closes https://github.com/llamastack/llama-stack/issues/3236
mypy considered our default implementations (raise NotImplementedError)
to be trivial. the result was we implemented the same stubs in
providers.
this change puts enough into the default impls so mypy considers them
non-trivial. this allows us to remove the duplicate implementations.
# What does this PR do?
<!-- Provide a short summary of what this PR does and why. Link to
relevant issues if applicable. -->
This PR renames categories of llama_stack loggers.
This PR aligns logging categories as per the package name, as well as
reviews from initial
https://github.com/meta-llama/llama-stack/pull/2868. This is a follow up
to #3061.
<!-- If resolving an issue, uncomment and update the line below -->
<!-- Closes #[issue-number] -->
Replaces https://github.com/meta-llama/llama-stack/pull/2868
Part of https://github.com/meta-llama/llama-stack/issues/2865
cc @leseb @rhuss
Signed-off-by: Mustafa Elbehery <melbeher@redhat.com>
# What does this PR do?
We noticed that when llama-stack is running for a long time, we would
run into database errors when trying to run messages through the agent
(which we configured to persist against postgres), seemingly due to the
database connections being stale or disconnected. This commit adds
`pool_pre_ping=True` to the SQLAlchemy engine creation to help mitigate
this issue by checking the connection before using it, and
re-establishing it if necessary.
More information in:
https://docs.sqlalchemy.org/en/20/core/pooling.html#dealing-with-disconnects
We're also open to other suggestions on how to handle this issue, this
PR is just a suggestion.
## Test Plan
We have not tested it yet (we're in the process of doing that) and we're
hoping it's going to resolve our issue.
# What does this PR do?
This PR adds a step in pre-commit to enforce using `llama_stack` logger.
Currently, various parts of the code base uses different loggers. As a
custom `llama_stack` logger exist and used in the codebase, it is better
to standardize its utilization.
Signed-off-by: Mustafa Elbehery <melbeher@redhat.com>
Co-authored-by: Matthew Farrellee <matt@cs.wisc.edu>
This OpenAI client release
0843a11164
ends up breaking litellm
169a17400f/litellm/types/llms/openai.py (L40)
Update the dependency pin. Also make the imports a bit more defensive
anyhow if something else during `llama stack build` ends up moving
openai to a previous version.
## Test Plan
Run pre-release script integration tests.
The OpenAI compatibility layer was incorrectly importing
ChatCompletionMessageToolCallParam instead of the
ChatCompletionMessageFunctionToolCall class. This caused "Cannot
instantiate typing.Union" errors when processing agent requests with
tool calls.
Closes: #3141
Signed-off-by: Derek Higgins <derekh@redhat.com>
Some fixes to MCP tests. And a bunch of fixes for Vector providers.
I also enabled a bunch of Vector IO tests to be used with
`LlamaStackLibraryClient`
## Test Plan
Run Responses tests with llama stack library client:
```
pytest -s -v tests/integration/non_ci/responses/ --stack-config=server:starter \
--text-model openai/gpt-4o \
--embedding-model=sentence-transformers/all-MiniLM-L6-v2 \
-k "client_with_models"
```
Do the same with `-k openai_client`
The rest should be taken care of by CI.
# What does this PR do?
This PR implements hybrid search for Milvus DB based on the inbuilt
milvus support.
To test:
```
pytest tests/unit/providers/vector_io/remote/test_milvus.py -v -s
--tb=long --disable-warnings --asyncio-mode=auto
```
Signed-off-by: Varsha Prasad Narsing <varshaprasad96@gmail.com>
