mirror of
https://github.com/EvolutionAPI/adk-python.git
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84 lines
2.3 KiB
Python
84 lines
2.3 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 typing import TYPE_CHECKING
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from google.genai import types
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from typing_extensions import override
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from .function_tool import FunctionTool
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from .tool_context import ToolContext
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if TYPE_CHECKING:
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from ..memory.base_memory_service import MemoryResult
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from ..models import LlmRequest
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async def load_memory(
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query: str, tool_context: ToolContext
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) -> 'list[MemoryResult]':
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"""Loads the memory for the current user.
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Args:
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query: The query to load the memory for.
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Returns:
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A list of memory results.
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"""
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response = await tool_context.search_memory(query)
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return response.memories
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class LoadMemoryTool(FunctionTool):
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"""A tool that loads the memory for the current user."""
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def __init__(self):
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super().__init__(load_memory)
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@override
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def _get_declaration(self) -> types.FunctionDeclaration | None:
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return types.FunctionDeclaration(
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name=self.name,
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description=self.description,
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parameters=types.Schema(
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type=types.Type.OBJECT,
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properties={
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'query': types.Schema(
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type=types.Type.STRING,
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)
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},
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),
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)
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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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await super().process_llm_request(
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tool_context=tool_context, llm_request=llm_request
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)
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# Tell the model about the memory.
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llm_request.append_instructions(["""
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You have memory. You can use it to answer questions. If any questions need
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you to look up the memory, you should call load_memory function with a query.
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"""])
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load_memory_tool = LoadMemoryTool()
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