adk-python/src/google/adk/tools/preload_memory_tool.py
Shangjie Chen bcf1deb582 ADK changes
PiperOrigin-RevId: 754131080
2025-05-02 14:19:18 -07:00

73 lines
2.2 KiB
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 __future__ import annotations
from datetime import datetime
from typing import TYPE_CHECKING
from typing_extensions import override
from .base_tool import BaseTool
from .tool_context import ToolContext
if TYPE_CHECKING:
from ..models import LlmRequest
class PreloadMemoryTool(BaseTool):
"""A tool that preloads the memory for the current user."""
def __init__(self):
# Name and description are not used because this tool only
# changes llm_request.
super().__init__(name='preload_memory', description='preload_memory')
@override
async def process_llm_request(
self,
*,
tool_context: ToolContext,
llm_request: LlmRequest,
) -> None:
parts = tool_context.user_content.parts
if not parts or not parts[0].text:
return
query = parts[0].text
response = await tool_context.search_memory(query)
if not response.memories:
return
memory_text = ''
for memory in response.memories:
time_str = datetime.fromtimestamp(memory.events[0].timestamp).isoformat()
memory_text += f'Time: {time_str}\n'
for event in memory.events:
# TODO: support multi-part content.
if (
event.content
and event.content.parts
and event.content.parts[0].text
):
memory_text += f'{event.author}: {event.content.parts[0].text}\n'
si = f"""The following content is from your previous conversations with the user.
They may be useful for answering the user's current query.
<PAST_CONVERSATIONS>
{memory_text}
</PAST_CONVERSATIONS>
"""
llm_request.append_instructions([si])
preload_memory_tool = PreloadMemoryTool()