1.45.0
New Features
- Feature Store:
FeatureView now supports an initialization_warehouse that is used for the initial build and any
subsequent reinitializations of the backing dynamic table (a full scan of the source data), while warehouse
continues to drive the lighter incremental refreshes. This mirrors the dynamic table INITIALIZATION_WAREHOUSE
knob, lets you pair a larger warehouse for initialization with a smaller one for steady-state refresh, and is also
used for the one-time backfill of streaming feature views. It can be set at registration, changed via
update_feature_view(initialization_warehouse=...), and is surfaced by list_feature_views(verbose=True).
draft_fv = FeatureView(
name="F_TRIP",
entities=[entity],
feature_df=feature_df,
refresh_freq="1d",
warehouse="SMALL_WH", # incremental refreshes
initialization_warehouse="LARGE_WH", # initial build / reinitialization
)
fv = fs.register_feature_view(draft_fv, version="1.0")
- Registry: LLM models deployed with the OpenAI chat signatures now support structured outputs
through an optional response_format param matching the OpenAI Chat Completions API
({"type": "json_schema", "json_schema": {"name": "...", "schema": {...}}}), letting callers
constrain model output to a JSON Schema.
from pydantic import BaseModel
import pandas as pd
class CityCountry(BaseModel):
city: str
country: str
response_format = {
"type": "json_schema",
"json_schema": {
"name": "city_country",
"schema": CityCountry.model_json_schema(),
},
}
x_df = pd.DataFrame.from_records(
[
{
"messages": [
{
"role": "user",
"content": [
{"type": "text", "text": "What is the capital of France?"},
],
},
],
}
]
)
mv.run(
X=x_df,
params={"response_format": response_format},
service_name=...,
)Bug Fixes
Behavior Changes
Deprecations
