The OpenAI completion API supports strings, array of strings, array of
tokens, or array of token arrays. So, expand our type hinting to
support all of these types.
Signed-off-by: Ben Browning <bbrownin@redhat.com>
The OpenAI completion prompt field can be a string or an array, so
update things to use and pass that properly.
This also stubs in a basic conversion of OpenAI non-streaming
completion requests to Llama Stack completion calls, for those
providers that don't actually have an OpenAI backend to allow them to
still accept requests via the OpenAI APIs.
Signed-off-by: Ben Browning <bbrownin@redhat.com>
# What does this PR do?
current passthrough impl returns chatcompletion_message.content as a
TextItem() , not a straight string. so it's not compatible with other
providers, and causes parsing error downstream.
change away from the generic pydantic conversion, and explicitly parse
out content.text
## Test Plan
setup llama server with passthrough
```
llama-stack-client eval run-benchmark "MMMU_Pro_standard" --model-id meta-llama/Llama-3-8B --output-dir /tmp/ --num-examples 20
```
works without parsing error
## What does this PR do?
We noticed that the passthrough inference provider doesn't work agent
due to the type mis-match between client and server. We manually cast
the llama stack client type to llama stack server type to fix the issue.
## test
run `python -m examples.agents.hello localhost 8321` within
llama-stack-apps
<img width="1073" alt="Screenshot 2025-03-11 at 8 43 44 PM"
src="https://github.com/user-attachments/assets/bd1bdd31-606a-420c-a249-95f6184cc0b1"
/>
fix https://github.com/meta-llama/llama-stack/issues/1560
# What does this PR do?
The commit addresses the Ruff warning B008 by refactoring the code to
avoid calling SamplingParams() directly in function argument defaults.
Instead, it either uses Field(default_factory=SamplingParams) for
Pydantic models or sets the default to None and instantiates
SamplingParams inside the function body when the argument is None.
Signed-off-by: Sébastien Han <seb@redhat.com>
## What does this PR do?
In this PR, we implement a passthrough inference provider that works for
any endpoints that respect llama stack inference API definition.
## Test Plan
config some endpoint that respect llama stack inference API definition
and got the inference results successfully
<img width="1268" alt="Screenshot 2025-02-19 at 8 52 51 PM"
src="https://github.com/user-attachments/assets/447816e4-ea7a-4365-b90c-386dc7dcf4a1"
/>
