E5E3 feat: send warnings on LLM prediction partial failures by GarrettWu · Pull Request #216 · googleapis/python-bigquery-dataframes · GitHub
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feat: send warnings on LLM prediction partial failures #216

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Merged
gcf-merge-on-green merged 2 commits into from
Nov 16, 2023
Merged

feat: send warnings on LLM prediction partial failures #216

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6abbefd
feat: send warnings on LLM prediction partial failures
GarrettWu Nov 16, 2023
a333158
Merge branch 'main' into garrettwu-llm-fail
GarrettWu Nov 16, 2023
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78 changes: 49 additions & 29 deletions bigframes/ml/llm.py
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Original file line number Diff line number Diff line change
Expand Up @@ -17,22 +17,30 @@
from __future__ import annotations

from typing import cast, Literal, Optional, Union
import warnings

import bigframes
from bigframes import clients, constants
from bigframes.core import blocks
from bigframes.ml import base, core, globals, utils
import bigframes.pandas as bpd

_REMOTE_TEXT_GENERATOR_MODEL_ENDPOINT = "text-bison"
_REMOTE_TEXT_GENERATOR_32K_MODEL_ENDPOINT = "text-bison-32k"
_TEXT_GENERATE_RESULT_COLUMN = "ml_generate_text_llm_result"
_TEXT_GENERATOR_BISON_ENDPOINT = "text-bison"
_TEXT_GENERATOR_BISON_32K_ENDPOINT = "text-bison-32k"
_TEXT_GENERATOR_ENDPOINTS = (
_TEXT_GENERATOR_BISON_ENDPOINT,
_TEXT_GENERATOR_BISON_32K_ENDPOINT,
)

_REMOTE_EMBEDDING_GENERATOR_MODEL_ENDPOINT = "textembedding-gecko"
_REMOTE_EMBEDDING_GENERATOR_MUlTILINGUAL_MODEL_ENDPOINT = (
"textembedding-gecko-multilingual"
_EMBEDDING_GENERATOR_GECKO_ENDPOINT = "textembedding-gecko"
_EMBEDDING_GENERATOR_GECKO_MULTILINGUAL_ENDPOINT = "textembedding-gecko-multilingual"
_EMBEDDING_GENERATOR_ENDPOINTS = (
_EMBEDDING_GENERATOR_GECKO_ENDPOINT,
_EMBEDDING_GENERATOR_GECKO_MULTILINGUAL_ENDPOINT,
)
_EMBED_TEXT_RESULT_COLUMN = "text_embedding"

_ML_GENERATE_TEXT_STATUS = "ml_generate_text_status"
_ML_EMBED_TEXT_STATUS = "ml_embed_text_status"


class PaLM2TextGenerator(base.Predictor):
Expand Down Expand Up @@ -90,18 +98,16 @@ def _create_bqml_model(self):
connection_id=connection_name_parts[2],
iam_role="aiplatform.user",
)
if self.model_name == _REMOTE_TEXT_GENERATOR_MODEL_ENDPOINT:
options = {
"endpoint": _REMOTE_TEXT_GENERATOR_MODEL_ENDPOINT,
}
elif self.model_name == _REMOTE_TEXT_GENERATOR_32K_MODEL_ENDPOINT:
options = {
"endpoint": _REMOTE_TEXT_GENERATOR_32K_MODEL_ENDPOINT,
}
else:

if self.model_name not in _TEXT_GENERATOR_ENDPOINTS:
raise ValueError(
f"Model name {self.model_name} is not supported. We only support {_REMOTE_TEXT_GENERATOR_MODEL_ENDPOINT} and {_REMOTE_TEXT_GENERATOR_32K_MODEL_ENDPOINT}."
f"Model name {self.model_name} is not supported. We only support {', '.join(_TEXT_GENERATOR_ENDPOINTS)}."
)

options = {
"endpoint": self.model_name,
}

return self._bqml_model_factory.create_remote_model(
session=self.session, connection_name=self.connection_name, options=options
)
Expand Down Expand Up @@ -182,7 +188,16 @@ def predict(
"top_p": top_p,
"flatten_json_output": True,
}
return self._bqml_model.generate_text(X, options)

df = self._bqml_model.generate_text(X, options)

if (df[_ML_GENERATE_TEXT_STATUS] != "").any():
warnings.warn(
f"Some predictions failed. Check column {_ML_GENERATE_TEXT_STATUS} for detailed status. You may want to filter the failed rows and retry.",
RuntimeWarning,
)

return df


class PaLM2TextEmbeddingGenerator(base.Predictor):
Expand Down Expand Up @@ -241,19 +256,15 @@ def _create_bqml_model(self):
connection_id=connection_name_parts[2],
iam_role="aiplatform.user",
)
if self.model_name == "textembedding-gecko":
options = {
"endpoint": _REMOTE_EMBEDDING_GENERATOR_MODEL_ENDPOINT,
}
elif self.model_name == _REMOTE_EMBEDDING_GENERATOR_MUlTILINGUAL_MODEL_ENDPOINT:
options = {
"endpoint": _REMOTE_EMBEDDING_GENERATOR_MUlTILINGUAL_MODEL_ENDPOINT,
}
else:

if self.model_name not in _EMBEDDING_GENERATOR_ENDPOINTS:
raise ValueError(
f"Model name {self.model_name} is not supported. We only support {_REMOTE_EMBEDDING_GENERATOR_MODEL_ENDPOINT} and {_REMOTE_EMBEDDING_GENERATOR_MUlTILINGUAL_MODEL_ENDPOINT}."
f"Model name {self.model_name} is not supported. We only support {', '.join(_EMBEDDING_GENERATOR_ENDPOINTS)}."
)

options = {
"endpoint": self.model_name,
}
return self._bqml_model_factory.create_remote_model(
session=self.session, connection_name=self.connection_name, options=options
)
Expand Down Expand Up @@ -284,4 +295,13 @@ def predict(self, X: Union[bpd.DataFrame, bpd.Series]) -> bpd.DataFrame:
options = {
"flatten_json_output": True,
}
return self._bqml_model.generate_text_embedding(X, options)

df = self._bqml_model.generate_text_embedding(X, options)

if (df[_ML_EMBED_TEXT_STATUS] != "").any():
warnings.warn(
f"Some predictions failed. Check column {_ML_EMBED_TEXT_STATUS} for detailed status. You may want to filter the failed rows and retry.",
RuntimeWarning,
)

return df
0