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chore: Add semantics.filter, guarded by an experiment flag. (#1040)
* feat: Add , guarded by an experiment flag. * fix test index * remove redundant line * Move semantic operators into a separate Semantics class * move test file location * check column references and update tests * check column references and update tests * 🦉 Updates from OwlBot post-processor See https://github.com/googleapis/repo-automation-bots/blob/main/packages/owl-bot/README.md * Clean up further * fix model name --------- Co-authored-by: Owl Bot <gcf-owl-bot[bot]@users.noreply.github.com>
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‎bigframes/dataframe.py

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import bigframes.operations.aggregations
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import bigframes.operations.aggregations as agg_ops
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import bigframes.operations.plotting as plotting
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import bigframes.operations.semantics
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import bigframes.operations.structs
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import bigframes.series
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import bigframes.series as bf_series
@@ -3875,3 +3876,7 @@ def _throw_if_null_index(self, opname: str):
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raise bigframes.exceptions.NullIndexError(
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f"DataFrame cannot perform {opname} as it has no index. Set an index using set_index."
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)
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@property
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def semantics(self):
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return bigframes.operations.semantics.Semantics(self)

‎bigframes/operations/semantics.py

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# Copyright 2024 Google LLC
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import re
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import typing
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import bigframes
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class Semantics:
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def __init__(self, df) -> None:
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if not bigframes.options.experiments.semantic_operators:
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raise NotImplementedError()
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self._df = df
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def filter(self, instruction: str, model):
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"""
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Filters the DataFrame with the semantics of the user instruction.
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Args:
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instruction:
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An instruction on how to filter the data. This value must contain
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column references by name, which should be wrapped in a pair of braces.
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For example, if you have a column "food", you can refer to this column
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in the instructions like:
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"The {food} is healthy."
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model:
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A LLM model provided by Bigframes ML package.
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Returns:
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DataFrame filtered by the instruction.
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Raises:
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NotImplementedError: when the semantic operator experiment is off.
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ValueError: when the instruction refers to a non-existing column, or when no
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columns are referred to.
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"""
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# Validate column references
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columns = re.findall(r"(?<!{)\{(?!{)(.*?)\}(?!\})", instruction)
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if not columns:
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raise ValueError("No column references.")
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for column in columns:
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if column not in self._df.columns:
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raise ValueError(f"Column {column} not found.")
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# Replace column references with names.
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instruction = instruction.format(**{col: col for col in columns})
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prompt_df = self._df.copy()
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# Combine context from multiple columns.
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for idx, col in enumerate(columns):
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if idx == 0:
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prompt_df["context"] = f"{col} is `" + prompt_df[col] + "`\n"
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else:
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prompt_df["context"] += f"{col} is `" + prompt_df[col] + "`\n"
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prompt_df["prompt"] = (
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"Decide the folowing claim by only True and False: "
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+ instruction
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+ "\nContext:"
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+ prompt_df["context"]
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)
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import bigframes.dataframe
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results = typing.cast(
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bigframes.dataframe.DataFrame, model.predict(prompt_df["prompt"])
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)
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return self._df[
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results["ml_generate_text_llm_result"].str.lower().str.contains("true")
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]

‎notebooks/experimental/semantic_operators.ipynb

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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Preparation"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"import bigframes\n",
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"import bigframes.pandas as bpd"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Enable the semantic operator experiment"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"/usr/local/google/home/sycai/src/python-bigquery-dataframes/bigframes/_config/experiment_options.py:33: UserWarning: Semantic operators are still under experiments, and are subject to change in the future.\n",
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" warnings.warn(\n"
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]
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}
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],
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"source": [
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"bigframes.options.experiments.semantic_operators = True"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Prepare the LLM model. Here we are going to use Gemini 1.5 Flash."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"/usr/local/google/home/sycai/src/python-bigquery-dataframes/bigframes/pandas/__init__.py:559: DefaultLocationWarning: No explicit location is set, so using location US for the session.\n",
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" return global_session.get_global_session()\n"
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]
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},
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{
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"data": {
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"text/html": [
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"Query job 05cef003-6ac9-4cfc-b21c-3d6aed5d5b78 is DONE. 0 Bytes processed. <a target=\"_blank\" href=\"https://console.cloud.google.com/bigquery?project=bigframes-dev&j=bq:US:05cef003-6ac9-4cfc-b21c-3d6aed5d5b78&page=queryresults\">Open Job</a>"
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],
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"text/plain": [
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"<IPython.core.display.HTML object>"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"import bigframes.ml.llm as llm\n",
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"model = llm.GeminiTextGenerator(model_name=llm._GEMINI_1P5_FLASH_001_ENDPOINT)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Semantic Filtering"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"Query job f9439f7e-13cd-4990-847b-d318f223af02 is DONE. 0 Bytes processed. <a target=\"_blank\" href=\"https://console.cloud.google.com/bigquery?project=bigframes-dev&j=bq:US:f9439f7e-13cd-4990-847b-d318f223af02&page=queryresults\">Open Job</a>"
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],
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"<IPython.core.display.HTML object>"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"/usr/local/google/home/sycai/src/python-bigquery-dataframes/bigframes/core/__init__.py:112: PreviewWarning: Interpreting JSON column(s) as StringDtype. This behavior may change in future versions.\n",
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" warnings.warn(\n"
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]
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},
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{
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"data": {
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],
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"data": {
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],
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},
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"data": {
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"data": {
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>country</th>\n",
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" <th>city</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>1</th>\n",
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" <td>Germany</td>\n",
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" <td>Berlin</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"<p>1 rows × 2 columns</p>\n",
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"</div>[1 rows x 2 columns in total]"
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],
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"text/plain": [
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" country city\n",
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"1 Germany Berlin\n",
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"\n",
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"[1 rows x 2 columns]"
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]
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},
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"execution_count": 5,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"df = bpd.DataFrame({'country': ['USA', 'Germany'], 'city': ['Seattle', 'Berlin']})\n",
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"df.semantics.filter(\"{city} is the capital of {country}\", model)"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "venv",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.11.9"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}

‎tests/system/small/operations/conftest.py

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# Copyright 2024 Google LLC
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import pytest
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import bigframes.ml.llm as llm
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@pytest.fixture(scope="session")
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def gemini_flash_model(session, bq_connection) -> llm.GeminiTextGenerator:
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return llm.GeminiTextGenerator(
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session=session,
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connection_name=bq_connection,
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model_name="gemini-1.5-flash-001",
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)

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