mirror of
https://github.com/EvolutionAPI/adk-python.git
synced 2025-07-14 01:41:25 -06:00
73 lines
2.2 KiB
Python
73 lines
2.2 KiB
Python
# Copyright 2025 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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from __future__ import annotations
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from datetime import datetime
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from typing import TYPE_CHECKING
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from typing_extensions import override
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from .base_tool import BaseTool
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from .tool_context import ToolContext
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if TYPE_CHECKING:
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from ..models import LlmRequest
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class PreloadMemoryTool(BaseTool):
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"""A tool that preloads the memory for the current user."""
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def __init__(self):
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# Name and description are not used because this tool only
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# changes llm_request.
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super().__init__(name='preload_memory', description='preload_memory')
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@override
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async def process_llm_request(
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self,
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*,
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tool_context: ToolContext,
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llm_request: LlmRequest,
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) -> None:
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parts = tool_context.user_content.parts
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if not parts or not parts[0].text:
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return
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query = parts[0].text
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response = await tool_context.search_memory(query)
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if not response.memories:
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return
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memory_text = ''
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for memory in response.memories:
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time_str = datetime.fromtimestamp(memory.events[0].timestamp).isoformat()
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memory_text += f'Time: {time_str}\n'
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for event in memory.events:
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# TODO: support multi-part content.
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if (
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event.content
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and event.content.parts
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and event.content.parts[0].text
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):
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memory_text += f'{event.author}: {event.content.parts[0].text}\n'
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si = f"""The following content is from your previous conversations with the user.
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They may be useful for answering the user's current query.
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<PAST_CONVERSATIONS>
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{memory_text}
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</PAST_CONVERSATIONS>
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"""
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llm_request.append_instructions([si])
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preload_memory_tool = PreloadMemoryTool()
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