104 lines
3.3 KiB
Python
104 lines
3.3 KiB
Python
import argparse
|
|
import base64
|
|
import json
|
|
|
|
from pyspark.sql import SparkSession, types as T
|
|
|
|
|
|
def d(s: str) -> str:
|
|
if not s:
|
|
return ""
|
|
return base64.b64decode(s.encode("ascii")).decode("utf-8")
|
|
|
|
|
|
def main() -> None:
|
|
p = argparse.ArgumentParser(description="Write assistant feedback row via Spark DataFrame")
|
|
p.add_argument("--table", required=True)
|
|
p.add_argument("--feedback-id", required=True)
|
|
p.add_argument("--created-at-utc", required=True)
|
|
p.add_argument("--outcome", required=True)
|
|
p.add_argument("--task-type", required=True)
|
|
p.add_argument("--release-name", default="")
|
|
p.add_argument("--confidence", type=float, default=0.0)
|
|
p.add_argument("--needs-review", default="true")
|
|
p.add_argument("--goal-b64", default="")
|
|
p.add_argument("--draft-b64", default="")
|
|
p.add_argument("--final-b64", default="")
|
|
p.add_argument("--sources-b64", default="")
|
|
p.add_argument("--notes-b64", default="")
|
|
args = p.parse_args()
|
|
|
|
needs_review = str(args.needs_review).lower() == "true"
|
|
goal = d(args.goal_b64)
|
|
draft_text = d(args.draft_b64)
|
|
final_text = d(args.final_b64)
|
|
sources_json = d(args.sources_b64)
|
|
notes = d(args.notes_b64)
|
|
if not sources_json:
|
|
sources_json = "[]"
|
|
# Validate JSON shape but keep raw string in table.
|
|
try:
|
|
json.loads(sources_json)
|
|
except Exception:
|
|
sources_json = "[]"
|
|
|
|
spark = SparkSession.builder.appName("write-assistant-feedback").getOrCreate()
|
|
spark.sql(
|
|
f"""
|
|
CREATE TABLE IF NOT EXISTS {args.table} (
|
|
feedback_id STRING,
|
|
created_at_utc STRING,
|
|
outcome STRING,
|
|
task_type STRING,
|
|
release_name STRING,
|
|
confidence DOUBLE,
|
|
needs_review BOOLEAN,
|
|
goal STRING,
|
|
draft_text STRING,
|
|
final_text STRING,
|
|
sources_json STRING,
|
|
notes STRING
|
|
) USING iceberg
|
|
"""
|
|
)
|
|
|
|
schema = T.StructType(
|
|
[
|
|
T.StructField("feedback_id", T.StringType(), False),
|
|
T.StructField("created_at_utc", T.StringType(), False),
|
|
T.StructField("outcome", T.StringType(), False),
|
|
T.StructField("task_type", T.StringType(), False),
|
|
T.StructField("release_name", T.StringType(), True),
|
|
T.StructField("confidence", T.DoubleType(), True),
|
|
T.StructField("needs_review", T.BooleanType(), False),
|
|
T.StructField("goal", T.StringType(), True),
|
|
T.StructField("draft_text", T.StringType(), True),
|
|
T.StructField("final_text", T.StringType(), True),
|
|
T.StructField("sources_json", T.StringType(), True),
|
|
T.StructField("notes", T.StringType(), True),
|
|
]
|
|
)
|
|
row = [
|
|
(
|
|
args.feedback_id,
|
|
args.created_at_utc,
|
|
args.outcome,
|
|
args.task_type,
|
|
args.release_name or "",
|
|
float(args.confidence),
|
|
needs_review,
|
|
goal,
|
|
draft_text,
|
|
final_text,
|
|
sources_json,
|
|
notes,
|
|
)
|
|
]
|
|
df = spark.createDataFrame(row, schema=schema)
|
|
df.writeTo(args.table).append()
|
|
print(f"[DONE] Recorded assistant feedback {args.feedback_id} into {args.table}")
|
|
|
|
|
|
if __name__ == "__main__":
|
|
main()
|