mirror of
https://github.com/EvolutionAPI/adk-python.git
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114 lines
3.6 KiB
Python
114 lines
3.6 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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import json
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from typing import Any
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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 .base_tool import BaseTool
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if TYPE_CHECKING:
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from ..models.llm_request import LlmRequest
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from .tool_context import ToolContext
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class LoadArtifactsTool(BaseTool):
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"""A tool that loads the artifacts and adds them to the session."""
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def __init__(self):
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super().__init__(
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name='load_artifacts',
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description='Loads the artifacts and adds them to the session.',
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)
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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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'artifact_names': types.Schema(
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type=types.Type.ARRAY,
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items=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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)
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@override
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async def run_async(
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self, *, args: dict[str, Any], tool_context: ToolContext
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) -> Any:
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artifact_names: list[str] = args.get('artifact_names', [])
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return {'artifact_names': artifact_names}
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@override
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async def process_llm_request(
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self, *, tool_context: ToolContext, 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,
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llm_request=llm_request,
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)
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await self._append_artifacts_to_llm_request(
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tool_context=tool_context, llm_request=llm_request
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)
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async def _append_artifacts_to_llm_request(
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self, *, tool_context: ToolContext, llm_request: LlmRequest
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):
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artifact_names = await tool_context.list_artifacts()
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if not artifact_names:
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return
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# Tell the model about the available artifacts.
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llm_request.append_instructions([f"""You have a list of artifacts:
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{json.dumps(artifact_names)}
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When the user asks questions about any of the artifacts, you should call the
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`load_artifacts` function to load the artifact. Do not generate any text other
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than the function call.
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"""])
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# Attach the content of the artifacts if the model requests them.
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# This only adds the content to the model request, instead of the session.
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if llm_request.contents and llm_request.contents[-1].parts:
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function_response = llm_request.contents[-1].parts[0].function_response
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if function_response and function_response.name == 'load_artifacts':
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artifact_names = function_response.response['artifact_names']
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for artifact_name in artifact_names:
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artifact = await tool_context.load_artifact(artifact_name)
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llm_request.contents.append(
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types.Content(
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role='user',
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parts=[
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types.Part.from_text(
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text=f'Artifact {artifact_name} is:'
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),
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artifact,
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],
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)
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)
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load_artifacts_tool = LoadArtifactsTool()
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