feat(api)!: support extra_body to embeddings and vector_stores APIs (#3794)
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Applies the same pattern from
https://github.com/llamastack/llama-stack/pull/3777 to embeddings and
vector_stores.create() endpoints.

This should _not_ be a breaking change since (a) our tests were already
using the `extra_body` parameter when passing in to the backend (b) but
the backend probably wasn't extracting the parameters correctly. This PR
will fix that.

Updated APIs: `openai_embeddings(), openai_create_vector_store(),
openai_create_vector_store_file_batch()`
This commit is contained in:
Ashwin Bharambe 2025-10-12 19:01:52 -07:00 committed by GitHub
parent 3bb6ef351b
commit ecc8a554d2
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26 changed files with 451 additions and 426 deletions

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@ -513,6 +513,14 @@ class AsyncLlamaStackAsLibraryClient(AsyncLlamaStackClient):
# Strip NOT_GIVENs to use the defaults in signature
body = {k: v for k, v in body.items() if v is not NOT_GIVEN}
# Check if there's an unwrapped body parameter among multiple parameters
# (e.g., path param + body param like: vector_store_id: str, params: Annotated[Model, Body(...)])
unwrapped_body_param = None
for param in params_list:
if is_unwrapped_body_param(param.annotation):
unwrapped_body_param = param
break
# Convert parameters to Pydantic models where needed
converted_body = {}
for param_name, param in sig.parameters.items():
@ -522,5 +530,11 @@ class AsyncLlamaStackAsLibraryClient(AsyncLlamaStackClient):
converted_body[param_name] = value
else:
converted_body[param_name] = convert_to_pydantic(param.annotation, value)
elif unwrapped_body_param and param.name == unwrapped_body_param.name:
# This is the unwrapped body param - construct it from remaining body keys
base_type = get_args(param.annotation)[0]
# Extract only the keys that aren't already used by other params
remaining_keys = {k: v for k, v in body.items() if k not in converted_body}
converted_body[param.name] = base_type(**remaining_keys)
return converted_body