structure saas with tools
This commit is contained in:
Davidson Gomes
407
.venv/lib/python3.10/site-packages/vertexai/evaluation/utils.py
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407
.venv/lib/python3.10/site-packages/vertexai/evaluation/utils.py
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# -*- coding: utf-8 -*-
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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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#
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import functools
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import io
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import json
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import os
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import tempfile
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import threading
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import time
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from typing import Any, Callable, Dict, List, Literal, Optional, TYPE_CHECKING, Union
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from google.cloud import bigquery
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from google.cloud import storage
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from google.cloud.aiplatform import base
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from google.cloud.aiplatform import compat
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from google.cloud.aiplatform import initializer
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from google.cloud.aiplatform import utils
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from google.cloud.aiplatform.utils import _ipython_utils
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from google.cloud.aiplatform_v1.services import (
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evaluation_service as gapic_evaluation_services,
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)
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from vertexai.evaluation import _base as eval_base
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from vertexai.evaluation.metrics import _base as metrics_base
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from vertexai.evaluation.metrics import (
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metric_prompt_template as metric_prompt_template_base,
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)
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if TYPE_CHECKING:
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import pandas as pd
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_BQ_PREFIX = "bq://"
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_GCS_PREFIX = "gs://"
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_LOGGER = base.Logger(__name__)
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class _EvaluationServiceClientWithOverride(utils.ClientWithOverride):
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_is_temporary = False
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_default_version = compat.V1
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_version_map = (
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(
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compat.V1,
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gapic_evaluation_services.EvaluationServiceClient,
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),
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)
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class RateLimiter:
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"""Helper class for rate-limiting requests to Vertex AI to improve QoS.
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Attributes:
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seconds_per_event: The time interval (in seconds) between events to
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maintain the desired rate.
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last: The timestamp of the last event.
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_lock: A lock to ensure thread safety.
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"""
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def __init__(self, rate: Optional[float] = None):
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"""Initializes the rate limiter.
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A simple rate limiter for controlling the frequency of API calls. This class
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implements a token bucket algorithm to limit the rate at which events
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can occur. It's designed for cases where the batch size (number of events
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per call) is always 1 for traffic shaping and rate limiting.
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Args:
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rate: The number of queries allowed per second.
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Raises:
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ValueError: If the rate is not positive.
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"""
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if not rate or rate <= 0:
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raise ValueError("Rate must be a positive number")
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self.seconds_per_event = 1.0 / rate
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self.last = time.time() - self.seconds_per_event
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self._lock = threading.Lock()
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def _admit(self) -> float:
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"""Checks if an event can be admitted or calculates the remaining delay."""
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now = time.time()
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time_since_last = now - self.last
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if time_since_last >= self.seconds_per_event:
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self.last = now
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return 0
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else:
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return self.seconds_per_event - time_since_last
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def sleep_and_advance(self):
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"""Blocks the current thread until the next event can be admitted."""
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with self._lock:
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delay = self._admit()
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if delay > 0:
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time.sleep(delay)
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self.last = time.time()
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def rate_limit(rate: Optional[float] = None) -> Callable[[Any], Any]:
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"""Decorator version of rate limiter."""
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def _rate_limit(method):
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limiter = RateLimiter(rate)
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@functools.wraps(method)
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def wrapper(*args, **kwargs):
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limiter.sleep_and_advance()
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return method(*args, **kwargs)
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return wrapper
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return _rate_limit
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def create_evaluation_service_client(
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api_base_path_override: Optional[str] = None,
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) -> _EvaluationServiceClientWithOverride:
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"""Creates a client for the evaluation service.
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Args:
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api_base_path_override: Optional. Override default api base path.
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Returns:
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Instantiated Vertex AI EvaluationServiceClient with optional
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overrides.
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"""
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return initializer.global_config.create_client(
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client_class=_EvaluationServiceClientWithOverride,
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location_override=initializer.global_config.location,
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api_base_path_override=api_base_path_override,
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)
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def load_dataset(
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source: Union[str, "pd.DataFrame", Dict[str, Any]],
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) -> "pd.DataFrame":
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"""Loads dataset from various sources into a DataFrame.
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Args:
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source: The dataset source. Supports the following dataset formats:
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* pandas.DataFrame: Used directly for evaluation.
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* Dict: Converted to a pandas DataFrame before evaluation.
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* str: Interpreted as a file path or URI. Supported formats include:
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* Local JSONL or CSV files: Loaded from the local filesystem.
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* GCS JSONL or CSV files: Loaded from Google Cloud Storage
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(e.g., 'gs://bucket/data.csv').
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* BigQuery table URI: Loaded from Google Cloud BigQuery
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(e.g., 'bq://project-id.dataset.table_name').
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Returns:
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The dataset in pandas DataFrame format.
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"""
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try:
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import pandas as pd
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except ImportError:
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raise ImportError(
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'Pandas is not installed. Please install the SDK using "pip install'
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' google-cloud-aiplatform[evaluation]"'
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)
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if isinstance(source, pd.DataFrame):
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return source.copy()
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elif isinstance(source, dict):
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return pd.DataFrame(source)
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elif isinstance(source, str):
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if source.startswith(_BQ_PREFIX):
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return _load_bigquery(source[len(_BQ_PREFIX) :])
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_, extension = os.path.splitext(source)
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file_type = extension.lower()[1:]
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if file_type == "jsonl":
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return _load_jsonl(source)
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elif file_type == "csv":
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return _load_csv(source)
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else:
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raise ValueError(
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f"Unsupported file type: {file_type} from {source}. Please"
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" provide a valid GCS path with `jsonl` or `csv` suffix or a valid"
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" BigQuery table URI."
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)
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else:
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raise TypeError(
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"Unsupported dataset type. Must be a `pd.DataFrame`, Python dictionary,"
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" valid GCS path with `jsonl` or `csv` suffix or a valid BigQuery table URI."
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)
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def _load_jsonl(filepath: str) -> "pd.DataFrame":
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"""Loads data from a JSONL file into a DataFrame."""
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try:
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import pandas as pd
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except ImportError:
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raise ImportError(
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'Pandas is not installed. Please install the SDK using "pip install'
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' google-cloud-aiplatform[evaluation]"'
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)
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if filepath.startswith(_GCS_PREFIX):
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file_contents = _read_gcs_file_contents(filepath)
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return pd.read_json(file_contents, lines=True)
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else:
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with open(filepath, "r") as f:
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return pd.read_json(f, lines=True)
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def _load_csv(filepath: str) -> "pd.DataFrame":
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"""Loads data from a CSV file into a DataFrame."""
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try:
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import pandas as pd
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except ImportError:
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raise ImportError(
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'Pandas is not installed. Please install the SDK using "pip install'
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' google-cloud-aiplatform[evaluation]"'
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)
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if filepath.startswith(_GCS_PREFIX):
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file_contents = _read_gcs_file_contents(filepath)
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return pd.read_csv(io.StringIO(file_contents), encoding="utf-8")
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else:
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return pd.read_csv(filepath, encoding="utf-8")
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def _load_bigquery(table_id: str) -> "pd.DataFrame":
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"""Loads data from a BigQuery table into a DataFrame."""
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bigquery_client = bigquery.Client(project=initializer.global_config.project)
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table = bigquery_client.get_table(table_id)
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return bigquery_client.list_rows(table).to_dataframe()
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def _read_gcs_file_contents(filepath: str) -> str:
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"""Reads the contents of a file from Google Cloud Storage.
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Args:
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filepath: The GCS file path (e.g., 'gs://bucket_name/file.csv')
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Returns:
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str: The contents of the file.
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"""
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storage_client = storage.Client(
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project=initializer.global_config.project,
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credentials=initializer.global_config.credentials,
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)
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bucket_name, blob_path = filepath[len(_GCS_PREFIX) :].split("/", 1)
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bucket = storage_client.get_bucket(bucket_name)
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blob = bucket.blob(blob_path)
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return blob.download_as_string().decode("utf-8")
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def _upload_pandas_df_to_gcs(
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df: "pd.DataFrame", upload_gcs_path: str, file_type: Literal["csv", "jsonl"]
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) -> None:
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"""Uploads the provided Pandas DataFrame to a GCS bucket.
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Args:
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df: The Pandas DataFrame to upload.
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upload_gcs_path: The GCS path to upload the data file.
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file_type: The file type of the data file.
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"""
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with tempfile.TemporaryDirectory() as temp_dir:
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if file_type == "csv":
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local_dataset_path = os.path.join(temp_dir, "metrics_table.csv")
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df.to_csv(path_or_buf=local_dataset_path)
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elif file_type == "jsonl":
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local_dataset_path = os.path.join(temp_dir, "metrics_table.jsonl")
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df.to_json(path_or_buf=local_dataset_path, orient="records", lines=True)
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else:
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raise ValueError(
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f"Unsupported file type: {file_type} from {upload_gcs_path}."
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" Please provide a valid GCS path with `jsonl` or `csv` suffix."
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)
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_upload_file_to_gcs(upload_gcs_path, local_dataset_path)
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def _upload_evaluation_summary_to_gcs(
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summary_metrics: Dict[str, float],
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upload_gcs_path: str,
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candidate_model_name: Optional[str] = None,
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baseline_model_name: Optional[str] = None,
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dataset_uri: Optional[str] = None,
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metrics: Optional[List[Union[str, metrics_base._Metric]]] = None,
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) -> None:
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"""Uploads the evaluation summary to a GCS bucket."""
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summary = {
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"summary_metrics": summary_metrics,
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}
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if candidate_model_name:
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summary["candidate_model_name"] = candidate_model_name
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if baseline_model_name:
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summary["baseline_model_name"] = baseline_model_name
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if dataset_uri:
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summary["dataset_uri"] = dataset_uri
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if metrics:
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metric_descriptions = {}
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for metric in metrics:
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if isinstance(metric, metrics_base._ModelBasedMetric) and isinstance(
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metric._raw_metric_prompt_template,
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metric_prompt_template_base._MetricPromptTemplate,
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):
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metric_descriptions[metric.metric_name] = {
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"criteria": metric._raw_metric_prompt_template._criteria,
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"rating_rubric": metric._raw_metric_prompt_template._rating_rubric,
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}
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summary["metric_descriptions"] = metric_descriptions
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with tempfile.TemporaryDirectory() as temp_dir:
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local_summary_path = os.path.join(temp_dir, "summary_metrics.json")
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json.dump(summary, open(local_summary_path, "w"))
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_upload_file_to_gcs(upload_gcs_path, local_summary_path)
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def _upload_file_to_gcs(upload_gcs_path: str, filename: str) -> None:
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storage_client = storage.Client(
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project=initializer.global_config.project,
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credentials=initializer.global_config.credentials,
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)
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storage.Blob.from_string(
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uri=upload_gcs_path, client=storage_client
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).upload_from_filename(filename)
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def upload_evaluation_results(
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eval_result: eval_base.EvalResult,
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destination_uri_prefix: str,
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file_name: Optional[str] = None,
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candidate_model_name: Optional[str] = None,
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baseline_model_name: Optional[str] = None,
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dataset_uri: Optional[str] = None,
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metrics: Optional[List[Union[str, metrics_base._Metric]]] = None,
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) -> None:
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"""Uploads eval results to GCS destination.
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Args:
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eval_result: Eval results to upload.
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destination_uri_prefix: GCS folder to store the data.
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file_name: Optional. File name to store the metrics table.
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candidate_model_name: Optional. Candidate model name.
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baseline_model_name: Optional. Baseline model name.
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dataset_uri: Optional. URI pointing to the dataset.
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metrics: Optional. List of metrics used for evaluation.
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"""
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if not destination_uri_prefix:
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_ipython_utils.display_gen_ai_evaluation_results_button()
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return
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if eval_result.metrics_table is None:
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return
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if destination_uri_prefix.startswith(_GCS_PREFIX):
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if file_name:
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base_name, extension = os.path.splitext(file_name)
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file_type = extension.lower()[1:]
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output_folder = destination_uri_prefix + "/" + base_name
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metrics_table_path = output_folder + "/" + file_name
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_upload_pandas_df_to_gcs(
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eval_result.metrics_table, metrics_table_path, file_type
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)
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_upload_evaluation_summary_to_gcs(
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eval_result.summary_metrics,
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output_folder + "/summary_metrics.json",
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candidate_model_name,
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baseline_model_name,
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dataset_uri,
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metrics,
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)
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_ipython_utils.display_gen_ai_evaluation_results_button(
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metrics_table_path.split(_GCS_PREFIX)[1]
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)
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else:
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raise ValueError(
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f"Unsupported destination URI: {destination_uri_prefix}."
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f" Please provide a valid GCS bucket URI prefix starting with"
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f" {_GCS_PREFIX}."
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)
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def initialize_metric_column_mapping(
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metric_column_mapping: Optional[Dict[str, str]], dataset: "pd.DataFrame"
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):
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"""Initializes metric column mapping with dataset columns."""
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initialized_metric_column_mapping = {}
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for column in dataset.columns:
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initialized_metric_column_mapping[column] = column
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if metric_column_mapping:
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for key, value in metric_column_mapping.items():
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if key in initialized_metric_column_mapping:
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_LOGGER.warning(
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f"Cannot override `{key}` column with `{key}:{value}` mapping"
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f" because `{key}` column is present in the evaluation"
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" dataset. `metric_column_mapping` cannot override keys"
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" that are already in evaluation dataset columns."
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)
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else:
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initialized_metric_column_mapping[key] = value
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return initialized_metric_column_mapping
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