# What does this PR do?
Update README for supported DBs
<!-- 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>
# What does this PR do?
Adds support to Vector store Open AI APIs in Qdrant.
<!-- If resolving an issue, uncomment and update the line below -->
Closes#2463
## 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: Varsha Prasad Narsing <varshaprasad96@gmail.com>
Co-authored-by: ehhuang <ehhuang@users.noreply.github.com>
Co-authored-by: Francisco Arceo <arceofrancisco@gmail.com>
# What does this PR do?
This should be more robust as sometimes its run without running build
first.
## Test Plan
OLLAMA_URL=http://localhost:11434 LLAMA_STACK_TEST_INFERENCE_MODE=replay
LLAMA_STACK_TEST_RECORDING_DIR=tests/integration/recordings
LLAMA_STACK_CONFIG=server:starter uv run --with pytest-repeat pytest
tests/integration/telemetry
--text-model="ollama/llama3.2:3b-instruct-fp16" -vvs
# What does this PR do?
This PR (1) enables the files API for Weaviate and (2) enables
integration tests for Weaviate, which adds a docker container to the
github action.
This PR also handles a couple of edge cases for in creating the
collection and ensuring the tests all pass.
## Test Plan
CI enabled
---------
Signed-off-by: Francisco Javier Arceo <farceo@redhat.com>
We are going to split record and replay workflows completely to simplify
the concurrency key design.
We can add vision tests by just adding to our matrix.
# What does this PR do?
Improve user experience by providing specific guidance when no API key
is available, showing both provider data header and config options with
the correct field name for each provider.
Also adds comprehensive test coverage for API key resolution scenarios.
addresses #2990 for providers using litellm openai mixin
## Test Plan
`./scripts/unit-tests.sh
tests/unit/providers/inference/test_litellm_openai_mixin.py`
This PR significantly refactors the Integration Tests workflow. The main
goal behind the PR was to enable recording of vision tests which were
never run as part of our CI ever before. During debugging, I ended up
making several other changes refactoring and hopefully increasing the
robustness of the workflow.
After doing the experiments, I have updated the trigger event to be
`pull_request_target` so this workflow can get write permissions by
default but it will run with source code from the base (main) branch in
the source repository only. If you do change the workflow, you'd need to
experiment using the `workflow_dispatch` triggers. This should not be
news to anyone using Github Actions (except me!)
It is likely to be a little rocky though while I learn more about GitHub
Actions, etc. Please be patient :)
---------
Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com>
# What does this PR do?
I realized that when a new PR is opened, the integration tests aren't
triggering (or aren't always?) since the replay logic was introduced
amend the concurrency logic a bit to trigger on opened PRs
---------
Signed-off-by: Charlie Doern <cdoern@redhat.com>
Co-authored-by: Ashwin Bharambe <ashwin.bharambe@gmail.com>
get_vector_db() will raise an exception if a vector store won't be
returned
client handling is redundant
Signed-off-by: Nathan Weinberg <nweinber@redhat.com>
# What does this PR do?
<!-- Provide a short summary of what this PR does and why. Link to
relevant issues if applicable. -->
It looks like the coverage badge is still present in the README. This PR
removes it.
For more context: https://github.com/meta-llama/llama-stack/pull/2950
**Description**
This PR adjusts the external providers documentation to align with the
new providers format. Splits up sections into the existing external
providers and how to create them as well.
<img width="1049" height="478" alt="Screenshot 2025-07-31 at 9 48 26 AM"
src="https://github.com/user-attachments/assets/f13599cb-2fd1-4e57-8ca9-27b067264e33"
/>
Open to feedback and adjusting titles
What does this PR do?
This PR adds support for Direct Preference Optimization (DPO) training
via the existing HuggingFace inline provider. It introduces a new DPO
training recipe, config schema updates, dataset integration, and
end-to-end testing to support preference-based fine-tuning with TRL.
Test Plan
Added integration test:
tests/integration/post_training/test_post_training.py::TestPostTraining::test_preference_optimize
Ran tests on both CPU and CUDA environments
---------
Co-authored-by: Ubuntu <ubuntu@ip-172-31-43-83.ec2.internal>
Co-authored-by: Ashwin Bharambe <ashwin.bharambe@gmail.com>
# What does this PR do?
I've been tinkering a little with a simple chat playground in the UI, so
I'm opening the PR with what's kind of a WIP.
If you look at the first commit, that includes the big part of the
changes. The rest of the files changed come from adding installing the
`shadcn` components.
Note this is missing a lot; e.g.,
- sessions
- document upload
- audio (the shadcn components install these by default from
https://shadcn-chatbot-kit.vercel.app/docs/components/chat)
I still need to wire up a lot more to make it actually fully functional
but it does basic chat using the LS Typescript Client.
Basic demo:
<img width="1329" height="1430" alt="Image"
src="https://github.com/user-attachments/assets/917a2096-36d4-4925-b83b-f1f2cda98698"
/>
<img width="1319" height="1424" alt="Image"
src="https://github.com/user-attachments/assets/fab1583b-1c72-4bf3-baf2-405aee13c6bb"
/>
<!-- 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>
This PR focuses on improving the developer experience by adding
comprehensive docstrings to the API data models across the Llama Stack.
These docstrings provide detailed explanations for each model and its
fields, making the API easier to understand and use.
**Key changes:**
- **Added Docstrings:** Added reST formatted docstrings to Pydantic
models in the `llama_stack/apis/` directory. This includes models for:
- Agents (`agents.py`)
- Benchmarks (`benchmarks.py`)
- Datasets (`datasets.py`)
- Inference (`inference.py`)
- And many other API modules.
- **OpenAPI Spec Update:** Regenerated the OpenAPI specification
(`docs/_static/llama-stack-spec.yaml` and
`docs/_static/llama-stack-spec.html`) to include the new docstrings.
This will be reflected in the API documentation, providing richer
information to users.
**Impact:**
- Developers using the Llama Stack API will have a better understanding
of the data structures.
- The auto-generated API documentation is now more informative.
---------
Co-authored-by: Ashwin Bharambe <ashwin.bharambe@gmail.com>
# What does this PR do?
1. Creates a new `VectorStoreNotFoundError` class
2. Implements the new class where appropriate
Relates to #2379
Signed-off-by: Nathan Weinberg <nweinber@redhat.com>
# What does this PR do?
1. Adds a broad schema for custom exception classes in the Llama Stack
project
2. Creates a new `DatasetNotFoundError` class
3. Implements the new class where appropriate
Relates to #2379
Signed-off-by: Nathan Weinberg <nweinber@redhat.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 following error in unit test that was running on up to
date main branch:
```
FAILED tests/unit/distribution/test_inference_recordings.py::TestInferenceRecording::test_recording_mode - ModuleNotFoundError: No module named 'ollama'
FAILED tests/unit/distribution/test_inference_recordings.py::TestInferenceRecording::test_replay_mode - ModuleNotFoundError: No module named 'ollama'
FAILED tests/unit/distribution/test_inference_recordings.py::TestInferenceRecording::test_replay_missing_recording - ModuleNotFoundError: No module named 'ollama'
FAILED tests/unit/distribution/test_inference_recordings.py::TestInferenceRecording::test_embeddings_recording - ModuleNotFoundError: No module named 'ollama'
=============================== 4 failed, 499 passed, 198 warnings in 34.50s ================================
```
<!-- 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.* -->
Run `./scripts/unit-tests.sh`
# What does this PR do?
1. Creates a new `ModelNotFoundError` class
2. Implements the new class where appropriate
Relates to #2379
Signed-off-by: Nathan Weinberg <nweinber@redhat.com>
We want to avoid re-triggering the workflow when random other labels are
added (e.g., `meta-cla`, etc.) Also no point restarting the workflow
when someone _unlabels_.
**Description**
This PR removes some of the warnings when uv builds the docs
- Errors appear when generating docs about .md files not appearing in
toctree. ~~Adding content to the `providers-gen.py ` file that adds `---
orphan: true ---` to to each file.~~. Added a toctree generator to the
`providers-gen.py` file, this gets rid of the errors in the builds.
- Deletes the `_openai_compat` files, extension of PR #2849
- Adds the `files` APIs section to the `providers` toctree on the index
page
- Manually adds the `--- orphan: true ---` to the advanced apis. Ill try
to find a way to modify the providers code gen so it automatically adds
it, but this fixes the errors.
- Adds the `testing.md` to the `contributing` toctree
- Adds `starting_llama_stack_server.md` to `distributions` toctree
There are some other warnings im still looking at but this PR gets rid
of most of the toctree errors
Theres also an issue with the actual distribution-codegen that I can
investigate in another PR. Opened a bug for it here #2873
We tried to always keep Ollama enabled. However doing so makes the
provider implementation half-assed -- should it error when it cannot
connect to Ollama or not? What happens during periodic model refresh?
Etc. Instead do the same thing we do for vLLM -- use the `OLLAMA_URL` to
conditionally enable the provider.
## Test Plan
Run `uv run llama stack build --template starter --image-type venv
--run` with and without `OLLAMA_URL` set. Verify using
`llama-stack-client provider list` that ollama is correctly enabled.
# What does this PR do?
- Initialize route_impls to None in constructor to prevent
AttributeError
- Consolidate initialization checks to single point in request() method
- Improve error message to be more helpful ("Please call initialize()
first")
- Add comprehensive test suite to prevent regressions
The library client now has better error handling when users forget to
call initialize(), showing a clear ValueError instead of confusing
AttributeError. All initialization validation is now centralized in the
request() method, with internal methods (_call_non_streaming,
_call_streaming, _convert_body) relying on this single check for
cleaner, more maintainable code.
closes#2943
## Test Plan
`./scripts/unit-tests.sh`
A couple of important updates:
- When recording tests, we cannot be generating a matrix because all the
independent recordings will conflict.
- In fact, we just don't need a matrix on test types any more because
things are very fast and the overhead of `llama stack build` and setting
up `uv` etc. is much more.
- Refactored the running of tests into an independent action
This PR makes setting up Ollama optional for CI. By default, we use
`replay` mode for inference requests and use the stored results from the
`tests/integration/recordings/` directory.
Every so often, users will update tests which will need us to re-record.
To do this, we check for the existence of a label `re-record-tests` on
the PR. If detected,
- ollama is spun up
- inference mode is set to record
- after the tests are done, if any new changes are detected, they are
pushed back to the PR
## Test Plan
This is GitHub CI. Gotta test it live.
Continuing with https://github.com/meta-llama/llama-stack/pull/2952
This also includes a "fix" to inference store related tests so that we
pull a large number of inference responses from the DB so as to always
find the one we just wrote.
Post training tests need _much_ better thinking before we can re-enable
them to be run on every single PR. Running periodically should be
approached only when it is shown that the tests are reliable and as
light-weight as can be; otherwise, it is just kicking the can down the
road.
Continue to build on top of
https://github.com/meta-llama/llama-stack/pull/2941
## Test Plan
Run server with `LLAMA_STACK_TEST_INFERENCE_MODE=record` and then run
the integration tests with `--stack-config=server:starter`. Then restart
the server with `LLAMA_STACK_TEST_INFERENCE_MODE=replay` and re-run the
tests. Verify that no request hit Ollama at any point.
# What does this PR do?
when --image-name is not provided the build script default to the
image_name in the config, this makes sure the same is done for the run
script
## Test Plan
llama stack build w/o --image-name
At the moment, the code coverage action has just been failing. It's
misleading when interpreting the status badge on the main branch.
https://github.com/meta-llama/llama-stack/actions/workflows/coverage-badge.yml
# 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.* -->
---------
Signed-off-by: Francisco Javier Arceo <farceo@redhat.com>
Implements a comprehensive recording and replay system for inference API
calls that eliminates dependency on online inference providers during
testing. The system treats inference as deterministic by recording real
API responses and replaying them in subsequent test runs. Applies to
OpenAI clients (which should cover many inference requests) as well as
Ollama AsyncClient.
For storing, we use a hybrid system: Sqlite for fast lookups and JSON
files for easy greppability / debuggability.
As expected, tests become much much faster (more than 3x in just
inference testing.)
```bash
LLAMA_STACK_TEST_INFERENCE_MODE=record LLAMA_STACK_TEST_RECORDING_DIR=<...> \
uv run pytest -s -v tests/integration/inference \
--stack-config=starter \
-k "not( builtin_tool or safety_with_image or code_interpreter or test_rag )" \
--text-model="ollama/llama3.2:3b-instruct-fp16" \
--embedding-model=sentence-transformers/all-MiniLM-L6-v2
```
```bash
LLAMA_STACK_TEST_INFERENCE_MODE=replay LLAMA_STACK_TEST_RECORDING_DIR=<...> \
uv run pytest -s -v tests/integration/inference \
--stack-config=starter \
-k "not( builtin_tool or safety_with_image or code_interpreter or test_rag )" \
--text-model="ollama/llama3.2:3b-instruct-fp16" \
--embedding-model=sentence-transformers/all-MiniLM-L6-v2
```
- `LLAMA_STACK_TEST_INFERENCE_MODE`: `live` (default), `record`, or
`replay`
- `LLAMA_STACK_TEST_RECORDING_DIR`: Storage location (must be specified
for record or replay modes)
# What does this PR do?
- Change max_seq_length to max_length in SFTConfig constructor
- TRL deprecated max_seq_length in Feb 2024 and removed it in v0.20.0
- Reference: https://github.com/huggingface/trl/pull/2895
This resolves the SFT training failure in CI tests
