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50 lines
1.8 KiB
Markdown
50 lines
1.8 KiB
Markdown
# Request Input Tool Sample
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## Overview
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This sample demonstrates how an LLM agent can proactively request clarification or confirmation from the user using the built-in `request_input` tool without losing its context/flow.
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It showcases a highly realistic support assistant that dynamically constructs a JSON schema to only ask for missing details when creating IT support tickets.
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## Sample Inputs
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- `I want to file a technical ticket for a database crash.`
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The agent will analyze the prompt, identify that the `title` and `category` are already provided, and dynamically call `request_input` with a schema requesting only `description` and `priority`.
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- `File a priority HIGH technical ticket titled database crash explained as the MySQL server throwing OOM errors.`
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The agent has all required details and will call `create_support_ticket` immediately without needing clarification.
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## Graph
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```mermaid
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graph TD
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User[User Prompt] --> Agent[Support Assistant Agent]
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Agent -->|Needs Clarification| RequestInput[request_input tool]
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RequestInput -->|User Response| Agent
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Agent -->|All Details Gathered| CreateTicket[create_support_ticket tool]
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```
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## How To
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This sample uses **Pattern B: Standalone Agents** with the `request_input` tool:
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1. **Import `request_input`**:
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```python
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from google.adk.tools import request_input
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```
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1. **Add it to the LLM Agent's tools list**:
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```python
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root_agent = Agent(
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name="support_assistant_agent",
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tools=[create_support_ticket, request_input],
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...
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)
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```
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When the LLM decides it needs clarification, it calls `request_input` with a question and a dynamic `response_schema`. The ADK framework automatically intercepts this, yields a long-running interrupt to the client, and injects the user's reply back as a `FunctionResponse` into the LLM's chat history.
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