forked from phoenix-oss/llama-stack-mirror
b42eb1ccbc
# What does this PR do? In the Responses API, we convert incoming response requests to chat completion requests. When streaming the resulting chunks of those chat completion requests, inference providers that use OpenAI clients will often return a `type=None` value in the tool call parts of the response. This causes issues when we try to dump and load that response into our pydantic model, because type cannot be None in the Responses API model we're loading these into. So, strip the "type" field, if present, off those chat completion tool call results before dumping and loading them as our typed pydantic models, which will apply our default value for that type field. ## Test Plan This was found via manual testing of the Responses API with codex, where I was getting errors in some tool call situations. I added a unit test to simulate this scenario and verify the fix, as well as manual codex testing to verify the fix. Signed-off-by: Ben Browning <bbrownin@redhat.com> |
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| .. | ||
| client-sdk/post_training | ||
| external-provider/llama-stack-provider-ollama | ||
| integration | ||
| unit | ||
| verifications | ||
| __init__.py | ||
| README.md | ||
Llama Stack Tests
Llama Stack has multiple layers of testing done to ensure continuous functionality and prevent regressions to the codebase.
| Testing Type | Details |
|---|---|
| Unit | unit/README.md |
| Integration | integration/README.md |
| Verification | verifications/README.md |