currently providers have a `pip_package` list. Rather than make our own form of python dependency management, we should use `pyproject.toml` files in each provider declaring the dependencies in a more trackable manner.
Each provider can then be installed using the already in place `module` field in the ProviderSpec, pointing to the directory the provider lives in
we can then simply `uv pip install` this directory as opposed to installing the dependencies one by one
Signed-off-by: Charlie Doern <cdoern@redhat.com>
# What does this PR do?
Context: https://github.com/meta-llama/llama-stack/issues/2937
The API design is inspired by existing offerings, but not exactly the
same:
* `top_n` as the parameter to control number of results, instead of
`top_k`, since `n` is conventional to control number
* `truncation` bool instead of `max_token_per_doc`, since we should just
handle the truncation automatically depending on model capability,
instead of user setting the context length manually.
* `data` field in the response, to be consistent with other OpenAI APIs
(though they don't have a rerank API). Also, it is one less name to
learn in the API.
## Test Plan
Co-authored-by: Ashwin Bharambe <ashwin.bharambe@gmail.com>
# What does this PR do?
<!-- Provide a short summary of what this PR does and why. Link to
relevant issues if applicable. -->
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?
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>
# What does this PR do?
add an `OpenAIMixin` for use by inference providers who remote endpoints
support an OpenAI compatible API.
use is demonstrated by refactoring
- OpenAIInferenceAdapter
- NVIDIAInferenceAdapter (adds embedding support)
- LlamaCompatInferenceAdapter
## Test Plan
existing unit and integration tests
# What does this PR do?
This PR introduces APIs to retrieve past chat completion requests, which
will be used in the LS UI.
Our current `Telemetry` is ill-suited for this purpose as it's untyped
so we'd need to filter by obscure attribute names, making it brittle.
Since these APIs are 'provided by stack' and don't need to be
implemented by inference providers, we introduce a new InferenceProvider
class, containing the existing inference protocol, which is implemented
by inference providers.
The APIs are OpenAI-compliant, with an additional `input_messages`
field.
## Test Plan
This PR just adds the API and marks them provided_by_stack. S
tart stack server -> doesn't crash
# What does this PR do?
The goal of this PR is code base modernization.
Schema reflection code needed a minor adjustment to handle UnionTypes
and collections.abc.AsyncIterator. (Both are preferred for latest Python
releases.)
Note to reviewers: almost all changes here are automatically generated
by pyupgrade. Some additional unused imports were cleaned up. The only
change worth of note can be found under `docs/openapi_generator` and
`llama_stack/strong_typing/schema.py` where reflection code was updated
to deal with "newer" types.
Signed-off-by: Ihar Hrachyshka <ihar.hrachyshka@gmail.com>
