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Sandheep P
68 lines
2.5 KiB
Python
68 lines
2.5 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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# Shows how to call all the sub-agents using the LLM's reasoning ability. Run this with "adk run" or "adk web"
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from google.adk.agents import LlmAgent
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from google.adk.tools import google_search
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from google.adk.tools.agent_tool import AgentTool
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from .util import load_instruction_from_file
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# --- Sub Agent 1: Scriptwriter ---
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scriptwriter_agent = LlmAgent(
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name="ShortsScriptwriter",
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model="gemini-2.0-flash-001",
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instruction=load_instruction_from_file("scriptwriter_instruction.txt"),
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tools=[google_search],
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output_key="generated_script", # Save result to state
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)
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# --- Sub Agent 2: Visualizer ---
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visualizer_agent = LlmAgent(
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name="ShortsVisualizer",
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model="gemini-2.0-flash-001",
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instruction=load_instruction_from_file("visualizer_instruction.txt"),
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description="Generates visual concepts based on a provided script.",
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output_key="visual_concepts", # Save result to state
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)
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# --- Sub Agent 3: Formatter ---
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# This agent would read both state keys and combine into the final Markdown
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formatter_agent = LlmAgent(
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name="ConceptFormatter",
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model="gemini-2.0-flash-001",
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instruction="""Combine the script from state['generated_script'] and the visual concepts from state['visual_concepts'] into the final Markdown format requested previously (Hook, Script & Visuals table, Visual Notes, CTA).""",
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description="Formats the final Short concept.",
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output_key="final_short_concept",
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)
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# --- Llm Agent Workflow ---
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youtube_shorts_agent = LlmAgent(
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name="youtube_shorts_agent",
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model="gemini-2.0-flash-001",
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instruction=load_instruction_from_file("shorts_agent_instruction.txt"),
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description="You are an agent that can write scripts, visuals and format youtube short videos. You have subagents that can do this",
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tools=[
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AgentTool(scriptwriter_agent),
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AgentTool(visualizer_agent),
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AgentTool(formatter_agent),
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],
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)
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# --- Root Agent for the Runner ---
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# The runner will now execute the workflow
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root_agent = youtube_shorts_agent
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