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evo-ai/.venv/lib/python3.10/site-packages/google/adk/cli/utils/evals.py
2025-04-25 15:30:54 -03:00

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Python

# Copyright 2025 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import Any
from ...sessions.session import Session
def convert_session_to_eval_format(session: Session) -> list[dict[str, Any]]:
"""Converts a session data into eval format.
Args:
session: The session that should be converted.
Returns:
list: A single evaluation dataset in the required format.
"""
eval_case = []
events = session.events if session and session.events else []
for event in events:
if event.author == 'user':
if not event.content or not event.content.parts:
continue
# Extract user query
content = event.content
parts = content.parts
query = parts[0].text or ''
# Find the corresponding tool usage or response for the query
expected_tool_use = []
intermediate_agent_responses = []
# Check subsequent events to extract tool uses or responses for this turn.
for subsequent_event in events[events.index(event) + 1 :]:
event_author = subsequent_event.author or 'agent'
if event_author == 'user':
# We found an event where the author was the user. This means that a
# new turn has started. So close this turn here.
break
if not subsequent_event.content or not subsequent_event.content.parts:
continue
for subsequent_part in subsequent_event.content.parts:
# Some events have both function call and reference
if subsequent_part.function_call:
tool_name = subsequent_part.function_call.name or ''
tool_input = subsequent_part.function_call.args or {}
expected_tool_use.append({
'tool_name': tool_name,
'tool_input': tool_input,
})
elif subsequent_part.text:
# Also keep track of all the natural language responses that
# agent (or sub agents) generated.
intermediate_agent_responses.append(
{'author': event_author, 'text': subsequent_part.text}
)
# If we are here then either we are done reading all the events or we
# encountered an event that had content authored by the end-user.
# This, basically means an end of turn.
# We assume that the last natural language intermediate response is the
# final response from the agent/model. We treat that as a reference.
eval_case.append({
'query': query,
'expected_tool_use': expected_tool_use,
'expected_intermediate_agent_responses': intermediate_agent_responses[
:-1
],
'reference': (
intermediate_agent_responses[-1]['text']
if intermediate_agent_responses
else ''
),
})
return eval_case