mirror of
https://github.com/google/adk-docs.git
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Megan O'Keefe
71 lines
2.7 KiB
Python
71 lines
2.7 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 google.adk.tools import ToolContext, FunctionTool
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from google.genai import types
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def process_document(
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document_name: str, analysis_query: str, tool_context: ToolContext
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) -> dict:
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"""Analyzes a document using context from memory."""
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# 1. Load the artifact
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print(f"Tool: Attempting to load artifact: {document_name}")
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document_part = tool_context.load_artifact(document_name)
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if not document_part:
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return {"status": "error", "message": f"Document '{document_name}' not found."}
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document_text = document_part.text # Assuming it's text for simplicity
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print(f"Tool: Loaded document '{document_name}' ({len(document_text)} chars).")
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# 2. Search memory for related context
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print(f"Tool: Searching memory for context related to: '{analysis_query}'")
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memory_response = tool_context.search_memory(
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f"Context for analyzing document about {analysis_query}"
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)
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memory_context = "\n".join(
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[
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m.events[0].content.parts[0].text
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for m in memory_response.memories
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if m.events and m.events[0].content
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]
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) # Simplified extraction
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print(f"Tool: Found memory context: {memory_context[:100]}...")
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# 3. Perform analysis (placeholder)
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analysis_result = f"Analysis of '{document_name}' regarding '{analysis_query}' using memory context: [Placeholder Analysis Result]"
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print("Tool: Performed analysis.")
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# 4. Save the analysis result as a new artifact
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analysis_part = types.Part.from_text(text=analysis_result)
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new_artifact_name = f"analysis_{document_name}"
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version = await tool_context.save_artifact(new_artifact_name, analysis_part)
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print(f"Tool: Saved analysis result as '{new_artifact_name}' version {version}.")
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return {
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"status": "success",
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"analysis_artifact": new_artifact_name,
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"version": version,
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}
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doc_analysis_tool = FunctionTool(func=process_document)
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# In an Agent:
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# Assume artifact 'report.txt' was previously saved.
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# Assume memory service is configured and has relevant past data.
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# my_agent = Agent(..., tools=[doc_analysis_tool], artifact_service=..., memory_service=...)
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